Information Forensics: An Overview of the First Decade
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paper_title: Group-Oriented Fingerprinting for Multimedia Forensics

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Digital fingerprinting of multimedia data involves embedding information in the content signal and offers protection to the digital rights of the content by allowing illegitimate usage of the content to be identified by authorized parties. One potential threat to fingerprinting is collusion, whereby a group of adversaries combine their individual copies in an attempt to remove the underlying fingerprints. Former studies indicate that collusion attacks based on a few dozen independent copies can confound a fingerprinting system that employs orthogonal modulation. However, in practice an adversary is more likely to collude with some users than with other users due to geographic or social circumstances. To take advantage of prior knowledge of the collusion pattern, we propose a two-tier group-oriented fingerprinting scheme where users likely to collude with each other are assigned correlated fingerprints. Additionally, we extend our construction to represent the natural social and geographic hierarchical relationships between users by developing a more flexible tree-structure-based fingerprinting system. We also propose a multistage colluder identification scheme by taking advantage of the hierarchial nature of the fingerprints. We evaluate the performance of the proposed fingerprinting scheme by studying the collusion resistance of a fingerprinting system employing Gaussian-distributed fingerprints. Our results show that the group-oriented fingerprinting system provides the superior collusion resistance over a system employing orthogonal modulation when knowledge of the potential collusion pattern is available.
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paper_title: A natural image model approach to splicing detection

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Image splicing detection is of fundamental importance in digital forensics and therefore has attracted increasing attention recently. In this paper, we propose a blind, passive, yet effective splicing detection approach based on a natural image model. This natural image model consists of statistical features extracted from the given test image as well as 2-D arrays generated by applying to the test images multi-size block discrete cosine transform (MBDCT). The statistical features include moments of characteristic functions of wavelet subbands and Markov transition probabilities of difference 2-D arrays. To evaluate the performance of our proposed model, we further present a concrete implementation of this model that has been designed for and applied to the Columbia Image Splicing Detection Evaluation Dataset. Our experimental works have demonstrated that this new splicing detection scheme outperforms the state of the art by a significant margin when applied to the above-mentioned dataset, indicating that the proposed approach possesses promising capability in splicing detection.
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paper_title: Image splicing detection using 2-D phase congruency and statistical moments of characteristic function

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A new approach to efficient blind image splicing detection is proposed in this paper. Image splicing is the process of ::: making a composite picture by cutting and joining two or more photographs. The spliced image may introduce a number ::: of sharp transitions such as lines, edges and corners. Phase congruency has been known as a sensitive measure of these ::: sharp transitions and hence been proposed as features for splicing detection. In addition to the phase information, the ::: magnitude information is also used for splicing detection. Specifically, statistical moments of characteristic functions of ::: wavelet subbands have been examined to catch the difference between the authentic images and spliced images. ::: Consequently, the proposed scheme extracts image features from moments of wavelet characteristic functions and 2-D ::: phase congruency for image splicing detection. The experiments have demonstrated that the proposed approach can ::: achieve a higher detection rate as compared with the state-of-the-art.
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paper_title: Image manipulation detection with Binary Similarity Measures

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Since extremely powerful technologies are now available to generate and process digital images, there is a concomitant need for developing techniques to distinguish the original images from the altered ones, the genuine ones from the doctored ones. In this paper we focus on this problem and propose a method based on the neighbor bit planes of the image. The basic idea is that, the correlation between the bit planes as well the binary texture characteristics within the bit planes will differ between an original and a doctored image. This change in the intrinsic characteristics of the image can be monitored via the quantal-spatial moments of the bit planes. These so-called Binary Similarity Measures are used as features in classifier design. It has been shown that the linear classifiers based on BSM features can detect with satisfactory reliability most of the image doctoring executed via Photoshop tool.
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paper_title: Higher-order Wavelet Statistics and their Application to Digital Forensics

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We describe a statistical model for natural images that is built upon a multi-scale wavelet decomposition. The model consists of first- and higher-order statistics that capture certain statistical regularities of natural images. We show how this model can be useful in several digital forensic applications, specifically in detecting various types of digital tampering.
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paper_title: How realistic is photorealistic?

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Computer graphics rendering software is capable of generating highly photorealistic images that can be impossible to differentiate from photographic images. As a result, the unique stature of photographs as a definitive recording of events is being diminished (the ease with which digital images can be manipulated is, of course, also contributing to this demise). To this end, we describe a method for differentiating between photorealistic and photographic images. Specifically, we show that a statistical model based on first-order and higher order wavelet statistics reveals subtle but significant differences between photorealistic and photographic images.
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paper_title: Blind detection of photomontage using higher order statistics

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We investigate the prospect of using bicoherence features for blind image splicing detection. Image splicing is an essential operation for digital photomontaging, which in turn is a technique for creating image forgery. We examine the properties of bicoherence features on a data set, which contains image blocks of diverse image properties. We then demonstrate the limitation of the baseline bicoherence features for image splicing detection. Our investigation has led to two suggestions for improving the performance of bicoherence features, i.e., estimating the bicoherence features of the authentic counterpart and incorporating features that characterize the variance of the feature performance. The features derived from the suggestions are evaluated with support vector machine (SVM) classification and is shown to improve the image splicing detection accuracy from 62% to about 70%.
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paper_title: Physics-motivated features for distinguishing photographic images and computer graphics

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The increasing photorealism for computer graphics has made computer graphics a convincing form of image forgery. Therefore, classifying photographic images and photorealistic computer graphics has become an important problem for image forgery detection. In this paper, we propose a new geometry-based image model, motivated by the physical image generation process, to tackle the above-mentioned problem. The proposed model reveals certain physical differences between the two image categories, such as the gamma correction in photographic images and the sharp structures in computer graphics. For the problem of image forgery detection, we propose two levels of image authenticity definition, i.e., imaging-process authenticity and scene authenticity, and analyze our technique against these definitions. Such definition is important for making the concept of image authenticity computable. Apart from offering physical insights, our technique with a classification accuracy of 83.5% outperforms those in the prior work, i.e., wavelet features at 80.3% and cartoon features at 71.0%. We also consider a recapturing attack scenario and propose a counter-attack measure. In addition, we constructed a publicly available benchmark dataset with images of diverse content and computer graphics of high photorealism.
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paper_title: Steganography in Digital Media: Principles, Algorithms, and Applications

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Steganography, the art of hiding of information in apparently innocuous objects or images, is a field with a rich heritage, and an area of rapid current development. This clear, self-contained guide shows you how to understand the building blocks of covert communication in digital media files and how to apply the techniques in practice, including those of steganalysis, the detection of steganography. Assuming only a basic knowledge in calculus and statistics, the book blends the various strands of steganography, including information theory, coding, signal estimation and detection, and statistical signal processing. Experiments on real media files demonstrate the performance of the techniques in real life, and most techniques are supplied with pseudo-code, making it easy to implement the algorithms. The book is ideal for students taking courses on steganography and information hiding, and is also a useful reference for engineers and practitioners working in media security and information assurance.
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paper_title: Image manipulation detection

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Techniques and methodologies for validating the authenticity of digital images and testing for the presence of doctoring and manipulation operations on them has recently attracted attention. We review three categories of forensic features and discuss the design of classifiers between doctored and original images. The performance of classifiers with respect to selected controlled manipulations as well as to uncontrolled manipulations is analyzed. The tools for image manipulation detection are treated under feature fusion and decision fusion scenarios.
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paper_title: Exposing digital forgeries through chromatic aberration

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Virtually all optical imaging systems introduce a variety of aberrations into an image. Chromatic aberration, for example, results from the failure of an optical system to perfectly focus light of different wavelengths. Lateral chromatic aberration manifests itself, to a first-order approximation, as an expansion/contraction of color channels with respect to one another. When tampering with an image, this aberration is often disturbed and fails to be consistent across the image. We describe a computational technique for automatically estimating lateral chromatic aberration and show its efficacy in detecting digital tampering.
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paper_title: Exposing image forgery with blind noise estimation

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Noise is unwanted in high quality images, but it can aid image tampering. For example, noise can be intentionally added in image to conceal tampered regions and to create special visual effects. It may also be introduced unnoticed during camera imaging process, which makes the noise levels inconsistent in splicing images. In this paper, we propose a method to expose such image forgeries by detecting the noise variance differences between original and tampered parts of an image. The noise variance of local image blocks is estimated using a recently developed technique, where no prior information about the imaging device or original image is required. The tampered region is segmented from the original image by a two-phase coarse-to-fine clustering of image blocks. Our experimental results demonstrate that the proposed method can effectively detect image forgeries with high detection accuracy and low false positive rate both quantitatively and qualitatively.
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paper_title: Using noise inconsistencies for blind image forensics

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A commonly used tool to conceal the traces of tampering is the addition of locally random noise to the altered image regions. The noise degradation is the main cause of failure of many active or passive image forgery detection methods. Typically, the amount of noise is uniform across the entire authentic image. Adding locally random noise may cause inconsistencies in the image's noise. Therefore, the detection of various noise levels in an image may signify tampering. In this paper, we propose a novel method capable of dividing an investigated image into various partitions with homogenous noise levels. In other words, we introduce a segmentation method detecting changes in noise level. We assume the additive white Gaussian noise. Several examples are shown to demonstrate the proposed method's output. An extensive quantitative measure of the efficiency of the noise estimation part as a function of different noise standard deviations, region sizes and various JPEG compression qualities is proposed as well.
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paper_title: Exposing image splicing with inconsistent local noise variances

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Image splicing is a simple and common image tampering operation, where a selected region from an image is pasted into another image with the aim to change its content. In this paper, based on the fact that images from different origins tend to have different amount of noise introduced by the sensors or post-processing steps, we describe an effective method to expose image splicing by detecting inconsistencies in local noise variances. Our method estimates local noise variances based on an observation that kurtosis values of natural images in band-pass filtered domains tend to concentrate around a constant value, and is accelerated by the use of integral image. We demonstrate the efficacy and robustness of our method based on several sets of forged images generated with image splicing.
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paper_title: Detecting Image Splicing using Geometry Invariants and Camera Characteristics Consistency

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Recent advances in computer technology have made digital image tampering more and more common. In this paper, we propose an authentic vs. spliced image classification method making use of geometry invariants in a semi-automatic manner. For a given image, we identify suspicious splicing areas, compute the geometry invariants from the pixels within each region, and then estimate the camera response function (CRF) from these geometry invariants. The cross-fitting errors are fed into a statistical classifier. Experiments show a very promising accuracy, 87%, over a large data set of 363 natural and spliced images. To the best of our knowledge, this is the first work detecting image splicing by verifying camera characteristic consistency from a single-channel image.
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paper_title: Camera Response Functions for Image Forensics: An Automatic Algorithm for Splicing Detection

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We present a fully automatic method to detect doctored digital images. Our method is based on a rigorous consistency checking principle of physical characteristics among different arbitrarily shaped image regions. In this paper, we specifically study the camera response function (CRF), a fundamental property in cameras mapping input irradiance to output image intensity. A test image is first automatically segmented into distinct arbitrarily shaped regions. One CRF is estimated from each region using geometric invariants from locally planar irradiance points (LPIPs). To classify a boundary segment between two regions as authentic or spliced, CRF-based cross fitting and local image features are computed and fed to statistical classifiers. Such segment level scores are further fused to infer the image level authenticity. Tests on two data sets reach performance levels of 70% precision and 70% recall, showing promising potential for real-world applications. Moreover, we examine individual features and discover the key factor in splicing detection. Our experiments show that the anomaly introduced around splicing boundaries plays the major role in detecting splicing. Such finding is important for designing effective and efficient solutions to image splicing detection.
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paper_title: Statistical Tools for Digital Forensics

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A digitally altered photograph, often leaving no visual clues of having been tampered with, can be indistinguishable from an authentic photograph. As a result, photographs no longer hold the unique stature as a definitive recording of events. We describe several statistical techniques for detecting traces of digital tampering in the absence of any digital watermark or signature. In particular, we quantify statistical correlations that result from specific forms of digital tampering, and devise detection schemes to reveal these correlations.
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paper_title: Detecting doctored images using camera response normality and consistency

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The advance in image/video editing techniques has facilitated people in synthesizing realistic images/videos that may hard to be distinguished from real ones by visual examination. This poses a problem: how to differentiate real images/videos from doctored ones? This is a serious problem because some legal issues may occur if there is no reliable way for doctored image/video detection when human inspection fails. Digital watermarking cannot solve this problem completely. We propose an approach that computes the response functions of the camera by selecting appropriate patches in different ways. An image may be doctored if the response functions are abnormal or inconsistent to each other. The normality of the response functions is classified by a trained support vector machine (SVM). Experiments show that our method is effective for high-contrast images with many textureless edges.
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paper_title: Forensic detection of image manipulation using statistical intrinsic fingerprints

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As the use of digital images has increased, so has the means and the incentive to create digital image forgeries. Accordingly, there is a great need for digital image forensic techniques capable of detecting image alterations and forged images. A number of image processing operations, such as histogram equalization or gamma correction, are equivalent to pixel value mappings. In this paper, we show that pixel value mappings leave behind statistical traces, which we shall refer to as a mapping's intrinsic fingerprint, in an image's pixel value histogram. We then propose forensic methods for detecting general forms globally and locally applied contrast enhancement as well as a method for identifying the use of histogram equalization by searching for the identifying features of each operation's intrinsic fingerprint. Additionally, we propose a method to detect the global addition of noise to a previously JPEG-compressed image by observing that the intrinsic fingerprint of a specific mapping will be altered if it is applied to an image's pixel values after the addition of noise. Through a number of simulations, we test the efficacy of each proposed forensic technique. Our simulation results show that aside from exceptional cases, all of our detection methods are able to correctly detect the use of their designated image processing operation with a probability of 99% given a false alarm probability of 7% or less.
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paper_title: Multimedia Data Hiding

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Introduction.- Preliminaries.- Classification and capacity of embedding.- Handling uneven embedding capacity.- Data hiding in binary images.- Multilevel data hiding for image & video.- Data hiding for image authentication.- Data hiding for video communication.- Attacks on known data-hiding algorithms.- Attacks on unknown data-hiding algorithms.- Conclusions and perspectives.
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paper_title: A SIFT-Based Forensic Method for Copy–Move Attack Detection and Transformation Recovery

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One of the principal problems in image forensics is determining if a particular image is authentic or not. This can be a crucial task when images are used as basic evidence to influence judgment like, for example, in a court of law. To carry out such forensic analysis, various technological instruments have been developed in the literature. In this paper, the problem of detecting if an image has been forged is investigated; in particular, attention has been paid to the case in which an area of an image is copied and then pasted onto another zone to create a duplication or to cancel something that was awkward. Generally, to adapt the image patch to the new context a geometric transformation is needed. To detect such modifications, a novel methodology based on scale invariant features transform (SIFT) is proposed. Such a method allows us to both understand if a copy-move attack has occurred and, furthermore, to recover the geometric transformation used to perform cloning. Extensive experimental results are presented to confirm that the technique is able to precisely individuate the altered area and, in addition, to estimate the geometric transformation parameters with high reliability. The method also deals with multiple cloning.
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paper_title: Detection of copy-move forgery using a method based on blur moment invariants.

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In our society digital images are a powerful and widely used communication medium. They have an important impact on our life. In recent years, due to the advent of high-performance commodity hardware and improved human-computer interfaces, it has become relatively easy to create fake images. Modern, easy to use image processing software enables forgeries that are undetectable by the naked eye. In this work we propose a method to automatically detect and localize duplicated regions in digital images. The presence of duplicated regions in an image may signify a common type of forgery called copy-move forgery. The method is based on blur moment invariants, which allows successful detection of copy-move forgery, even when blur degradation, additional noise, or arbitrary contrast changes are present in the duplicated regions. These modifications are commonly used techniques to conceal traces of copy-move forgery. Our method works equally well for lossy format such as JPEG. We demonstrate our method on several images affected by copy-move forgery.
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paper_title: Detection of Copy-Move Forgery in Digital Images

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An algorithm called exact match is proposed to solve the problem of copy-move attack in digital images. The method may successfully detect the forged part even when the copied area is enhanced/retouched to merge it with the background and when the forged image is saved in a lossy format, such as JPEG. The result is much more efficient than traditional methods.
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paper_title: Distinctive Image Features from Scale-Invariant Keypoints

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The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images. These features can then be used to reliably match objects in diering images. The algorithm was rst proposed by Lowe [12] and further developed to increase performance resulting in the classic paper [13] that served as foundation for SIFT which has played an important role in robotic and machine vision in the past decade.
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paper_title: Region Duplication Detection Using Image Feature Matching

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Region duplication is a simple and effective operation to create digital image forgeries, where a continuous portion of pixels in an image, after possible geometrical and illumination adjustments, are copied and pasted to a different location in the same image. Most existing region duplication detection methods are based on directly matching blocks of image pixels or transform coefficients, and are not effective when the duplicated regions have geometrical or illumination distortions. In this work, we describe a new region duplication detection method that is robust to distortions of the duplicated regions. Our method starts by estimating the transform between matched scale invariant feature transform (SIFT) keypoints, which are insensitive to geometrical and illumination distortions, and then finds all pixels within the duplicated regions after discounting the estimated transforms. The proposed method shows effective detection on an automatically synthesized forgery image database with duplicated and distorted regions. We further demonstrate its practical performance with several challenging forgery images created with state-of-the-art tools.
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paper_title: A Sorted Neighborhood Approach for Detecting Duplicated Regions in Image Forgeries Based on DWT and SVD

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The presence of duplicated regions in the image can be considered as a tell-tale sign for image forgery, which belongs to the research field of digital image forensics. In this paper, a blind forensics approach based on DWT (discrete wavelet transform) and SVD (singular value decomposition) is proposed to detect the specific artifact. Firstly, DWT is applied to the image, and SVD is used on fixed-size blocks of low-frequency component in wavelet sub-band to yield a reduced dimension representation. Then the SV vectors are then lexicographically sorted and duplicated image blocks will be close in the sorted list, and therefore will be compared during the detection steps. The experimental results demonstrate that the proposed approach can not only decrease computational complexity, but also localize the duplicated regions accurately even when the image was highly compressed or edge processed.
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paper_title: Detection of Copy-Move Forgery in Digital Images Using SIFT Algorithm

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As result of powerful image processing tools, digital image forgeries have already become a serious social problem. In this paper we describe an effective method to detect copy-move forgery in digital images. This method works by first extracting SIFT descriptors of an image, which are invariant to changes in illumination, rotation, scaling etc. Owing to the similarity between pasted region and copied region, descriptors are then matched between each other to seek for any possible forgery in images. Experiments have been performed to demonstrate the efficiency of this method on different forgeries and quantify its robustness and sensitivity to post image processing, such as additive noise and lossy JPEG compression etc, or even compound processing.
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paper_title: Robust Detection of Region-Duplication Forgery in Digital Image

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Region duplication forgery, in which a part of a digital image is copied and then pasted to another portion of the same image in order to conceal an important object in the scene, is one of the common image forgery techniques. In this paper, we describe an efficient and robust algorithm for detecting and localizing this type of malicious tampering. We present experimental results which show that our method is robust and can successfully detect this type of tampering for images that have been subjected to various forms of post region duplication image processing, including blurring, noise contamination, severe lossy compression, and a mixture of these processing operations.
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paper_title: Detection of linear and cubic interpolation in JPEG compressed images

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A novel algorithm is introduced that can detect the presence of interpolation in images prior to compression as well as estimate the interpolation factor. The interpolation detection algorithm exploits a periodicity in the second derivative signal of interpolated images. The algorithm performs well for a wide variety of interpolation factors, both integer factors and non-integer factors. The algorithm performance is noted with respect to a digital camera's "digital zoom" feature. Overall the algorithm has demonstrated robust results and might prove to be useful for situations where an original resolution of the image determines the action of an image processing chain.
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paper_title: Blind Authentication Using Periodic Properties of Interpolation

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In this paper, we analyze and analytically describe the specific statistical changes brought into the covariance structure of signal by the interpolation process. We show that interpolated signals and their derivatives contain specific detectable periodic properties. Based on this, we propose a blind, efficient, and automatic method capable of finding traces of resampling and interpolation. The proposed method can be very useful in many areas, especially in image security and authentication. For instance, when two or more images are spliced together, to create high quality and consistent image forgeries, almost always geometric transformations, such as scaling, rotation, or skewing are needed. These procedures are typically based on a resampling and interpolation step. By having a method capable of detecting the traces of resampling, we can significantly reduce the successful usage of such forgeries. Among other points, the presented method is also very useful in estimation of the geometric transformations factors.
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paper_title: Fast and reliable resampling detection by spectral analysis of fixed linear predictor residue

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This paper revisits the state-of-the-art resampling detector, which is based on periodic artifacts in the residue of a local linear predictor. Inspired by recent findings from the literature, we take a closer look at the complex detection procedure and model the detected artifacts in the spatial and frequency domain by means of the variance of the prediction residue. We give an exact formulation on how transformation parameters influence the appearance of periodic artifacts and analytically derive the expected position of characteristic resampling peaks. We present an equivalent accelerated and simplified detector, which is orders of magnitudes faster than the conventional scheme and experimentally shown to be comparably reliable.
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paper_title: Statistical Tools for Digital Forensics

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A digitally altered photograph, often leaving no visual clues of having been tampered with, can be indistinguishable from an authentic photograph. As a result, photographs no longer hold the unique stature as a definitive recording of events. We describe several statistical techniques for detecting traces of digital tampering in the absence of any digital watermark or signature. In particular, we quantify statistical correlations that result from specific forms of digital tampering, and devise detection schemes to reveal these correlations.
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paper_title: On resampling detection in re-compressed images

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Resampling detection has become a standard tool in digital image forensics. This paper investigates the important case of resampling detection in re-compressed JPEG images. We show how blocking artifacts of the previous compression step can help to increase the otherwise drastically reduced detection performance in JPEG compressed images. We give a formulation on how affine transformations of JPEG compressed images affect state-of-the-art resampling detectors and derive a new efficient detection variant, which better suits this relevant detection scenario. The principal appropriateness of using JPEG pre-compression artifacts for the detection of resampling in re-compressed images is backed with experimental evidence on a large image set and for a variety of different JPEG qualities.
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paper_title: Exposing digital forgeries by detecting traces of resampling

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The unique stature of photographs as a definitive recording of events is being diminished due, in part, to the ease with which digital images can be manipulated and altered. Although good forgeries may leave no visual clues of having been tampered with, they may, nevertheless, alter the underlying statistics of an image. For example, we describe how resampling (e.g., scaling or rotating) introduces specific statistical correlations, and describe how these correlations can be automatically detected in any portion of an image. This technique works in the absence of any digital watermark or signature. We show the efficacy of this approach on uncompressed TIFF images, and JPEG and GIF images with minimal compression. We expect this technique to be among the first of many tools that will be needed to expose digital forgeries.
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paper_title: Forensic estimation and reconstruction of a contrast enhancement mapping

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Due to the ease with which convincing digital image forgeries can be created, a need has arisen for digital forensic techniques capable of detecting image manipulation. Once image alterations have been identified, the next logical forensic task is to recover as much information as possible about the unaltered version of image and the operation used to modify it. Previous work has dealt with the forensic detection of contrast enhancement in digital images. In this paper we propose an iterative algorithm to jointly estimate any arbitrary contrast enhancement mapping used to modify an image as well as the pixel value histogram of the image before contrast enhancement. To do this, we use a probabilistic model of an image's pixel value histogram to determine which histogram entries are most likely to correspond to contrast enhancement artifacts. Experimental results are presented to demonstrate the effectiveness of our proposed method.
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paper_title: Forensic detection of image manipulation using statistical intrinsic fingerprints

paper_content:

As the use of digital images has increased, so has the means and the incentive to create digital image forgeries. Accordingly, there is a great need for digital image forensic techniques capable of detecting image alterations and forged images. A number of image processing operations, such as histogram equalization or gamma correction, are equivalent to pixel value mappings. In this paper, we show that pixel value mappings leave behind statistical traces, which we shall refer to as a mapping's intrinsic fingerprint, in an image's pixel value histogram. We then propose forensic methods for detecting general forms globally and locally applied contrast enhancement as well as a method for identifying the use of histogram equalization by searching for the identifying features of each operation's intrinsic fingerprint. Additionally, we propose a method to detect the global addition of noise to a previously JPEG-compressed image by observing that the intrinsic fingerprint of a specific mapping will be altered if it is applied to an image's pixel values after the addition of noise. Through a number of simulations, we test the efficacy of each proposed forensic technique. Our simulation results show that aside from exceptional cases, all of our detection methods are able to correctly detect the use of their designated image processing operation with a probability of 99% given a false alarm probability of 7% or less.
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paper_title: Blind forensics of contrast enhancement in digital images

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Digital images have seen increased use in applications where their authenticity is of prime importance. This proves to be problematic due to the widespread availability of digital image editing software. As a result, there is a need for the development of reliable techniques for verifying an image's authenticity. In this paper, a blind forensic algorithm is proposed for detecting the use of global contrast enhancement operations to modify digital images. Furthermore, a separate algorithm is proposed to identify the use of histogram equalization, a commonly implemented contrast enhancement operation. Both algorithms perform detection by seeking out unique artifacts introduced into an image's histogram as a result of the particular operation examined. Additionally, results are presented showing the effectiveness of both proposed algorithms.
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paper_title: Blind Forensics of Median Filtering in Digital Images

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Exposing the processing history of a digital image is an important problem for forensic analyzers and steganalyzers. As the median filter is a popular nonlinear denoising operator, the blind forensics of median filtering is particularly interesting. This paper proposes a novel approach for detecting median filtering in digital images, which can 1) accurately detect median filtering in arbitrary images, even reliably detect median filtering in low-resolution and JPEG compressed images; and 2) reliably detect tampering when part of a median-filtered image is inserted into a nonmedian-filtered image, or vice versa. The effectiveness of the proposed approach is exhaustively evaluated in five different image databases.
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paper_title: Robust median filtering forensics based on the autoregressive model of median filtered residual

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One important aspect of multimedia forensics is exposing an image's processing history. Median filtering is a popular noise removal and image enhancement tool. It is also an effective tool in anti-forensics recently. An image is usually saved in a compressed format such as the JPEG format. The forensic detection of median filtering from a JPEG compressed image remains challenging, because typical filter characteristics are suppressed by JPEG quantization and blocking artifacts. In this paper, we introduce a robust median filtering detection scheme based on the autoregressive model of median filtered residual. Median filtering is first applied on a test image and the difference between the initial image and the filtered output image is called the median filtered residual (MFR). The MFR is used as the forensic fingerprint. Thus, the interference from the image edge and texture, which is regarded as a limitation of the existing forensic methods, can be reduced. Because the overlapped window filtering introduces correlation among the pixels of MFR, an autoregressive (AR) model of the MFR is calculated and the AR coefficients are used by a support vector machine (SVM) for classification. Experimental results show that the proposed median filtering detection method is very robust to JPEG post-compression with a quality factor as low as 30. It distinguishes well between median filtering and other manipulations, such as Gaussian filtering, average filtering, and rescaling and performs well on low-resolution images of size 32 × 32. The proposed method achieves not only much better performance than the existing state-of-the-art methods, but also has very small dimension of feature, i.e., 10-D.
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paper_title: Forensic detection of median filtering in digital images

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In digital image forensics, prior works are prone to the detection of malicious tampering. However, there is also a need for developing techniques to identify general content-preserved manipulations, which are employed to conceal tampering trails frequently. In this paper, we propose a blind forensic algorithm to detect median filtering (MF), which is applied extensively for signal denoising and digital image enhancement. The probability of zero values on the first order difference map in texture regions can serve as MF statistical fingerprint, which distinguishes MF from other operations. Since anti-forensic techniques enjoy utilizing MF to attack the linearity assumption of existing forensics algorithms, blind detection of the non-linear MF becomes especially significant. Both theoretically reasoning and experimental results verify the effectiveness of our proposed MF forensics scheme.
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paper_title: Steganalysis by Subtractive Pixel Adjacency Matrix

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This paper presents a method for detection of steganographic methods that embed in the spatial domain by adding a low-amplitude independent stego signal, an example of which is least significant bit (LSB) matching. First, arguments are provided for modeling the differences between adjacent pixels using first-order and second-order Markov chains. Subsets of sample transition probability matrices are then used as features for a steganalyzer implemented by support vector machines. The major part of experiments, performed on four diverse image databases, focuses on evaluation of detection of LSB matching. The comparison to prior art reveals that the presented feature set offers superior accuracy in detecting LSB matching. Even though the feature set was developed specifically for spatial domain steganalysis, by constructing steganalyzers for ten algorithms for JPEG images, it is demonstrated that the features detect steganography in the transform domain as well.
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paper_title: Streaking in median filtered images

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This paper presents a probabilistic analysis of the streaking or blotching effect commonly observed in median filtered signals in both one and two dimensions. The effcts are identified as runs of equal or nearly equal values which create visual impressions that have no visual correlate. For one-dimensional discrete iid random signals with continuous input probability densities, the probability of a streak of length L occurring is computed and shown to be independent of the input probability distribution. Expressions for the first and second moments of the streak length are also derived, and certain asymptotic results are given. As the analysis and definition of the analogous effect in two dimensions is less tractable, the probability that medians taken from distinct overlapping windows will take the same value is derived for various filter geometries. The analytic results are supported by examples using both one- and two-dimensional signals.
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paper_title: On detection of median filtering in digital images

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In digital image forensics, it is generally accepted that intentional manipulations of the image content are ::: most critical and hence numerous forensic methods focus on the detection of such 'malicious' post-processing. ::: However, it is also beneficial to know as much as possible about the general processing history of an image, ::: including content-preserving operations, since they can affect the reliability of forensic methods in various ways. ::: In this paper, we present a simple yet effective technique to detect median filtering in digital images-a widely ::: used denoising and smoothing operator. As a great variety of forensic methods relies on some kind of a linearity ::: assumption, a detection of non-linear median filtering is of particular interest. The effectiveness of our method ::: is backed with experimental evidence on a large image database.
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paper_title: Identification of bitmap compression history: JPEG detection and quantizer estimation

paper_content:

Sometimes image processing units inherit images in raster bitmap format only, so that processing is to be carried without knowledge of past operations that may compromise image quality (e.g., compression). To carry further processing, it is useful to not only know whether the image has been previously JPEG compressed, but to learn what quantization table was used. This is the case, for example, if one wants to remove JPEG artifacts or for JPEG re-compression. In this paper, a fast and efficient method is provided to determine whether an image has been previously JPEG compressed. After detecting a compression signature, we estimate compression parameters. Specifically, we developed a method for the maximum likelihood estimation of JPEG quantization steps. The quantizer estimation method is very robust so that only sporadically an estimated quantizer step size is off, and when so, it is by one value.
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paper_title: Digital image source coder forensics via intrinsic fingerprints

paper_content:

Recent development in multimedia processing and network technologies has facilitated the distribution and sharing of multimedia through networks, and increased the security demands of multimedia contents. Traditional image content protection schemes use extrinsic approaches, such as watermarking or fingerprinting. However, under many circumstances, extrinsic content protection is not possible. Therefore, there is great interest in developing forensic tools via intrinsic fingerprints to solve these problems. Source coding is a common step of natural image acquisition, so in this paper, we focus on the fundamental research on digital image source coder forensics via intrinsic fingerprints. First, we investigate the unique intrinsic fingerprint of many popular image source encoders, including transform-based coding (both discrete cosine transform and discrete wavelet transform based), subband coding, differential image coding, and also block processing as the traces of evidence. Based on the intrinsic fingerprint of image source encoders, we construct an image source coding forensic detector that identifies which source encoder is applied, what the coding parameters are along with confidence measures of the result. Our simulation results show that the proposed system provides trustworthy performance: for most test cases, the probability of detecting the correct source encoder is over 90%.
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paper_title: Transform Coder Classification for Digital Image Forensics

paper_content:

The area of non-intrusive forensic analysis has found many applications in the area of digital imaging. One unexplored area is the identification of source coding in digital images. In other words, given a digital image, can we identify which compression scheme was used, if any? This paper focuses on the aspect of transform coder classification, where we wish to determine which transform was used during compression. This scheme analyzes the histograms of coefficient subbands to determine the nature of the transform method. By obtaining the distance between the obtained histogram and the estimate of the original histogram, we can determine if the image was compressed using the transform tested. Results show that this method can successfully classify compression by transform as well as detect whether any compression has occurred at all in an image.
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paper_title: Block Size Forensic Analysis in Digital Images

paper_content:

In non-intrusive forensic analysis, we wish to find information and properties about a piece of data without any reference to the original data prior to processing. An important first step to forensic analysis is the detection and estimation of block processing. Most existing work in block measurement uses strong assumptions on the data related to the block size or the method of compression. In this paper, we propose a new method to estimate the block size in digital images in a blind manner for use in a forensic context. We make no assumptions on the block size or the nature of any previous processing. Our scheme can accurately estimate block sizes in images up to a PSNR of 42 dB where block artifacts are perceptually invisible. We also offer a measure of detection accuracy which correctly classifies an image as block-processed with a probability of 95.0% while keeping the probability of false alarm at 7.4%.
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paper_title: Exposing Digital Forgeries From JPEG Ghosts

paper_content:

When creating a digital forgery, it is often necessary to combine several images, for example, when compositing one person's head onto another person's body. If these images were originally of different JPEG compression quality, then the digital composite may contain a trace of the original compression qualities. To this end, we describe a technique to detect whether the part of an image was initially compressed at a lower quality than the rest of the image. This approach is applicable to images of high and low quality as well as resolution.
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paper_title: A generalized Benford's law for JPEG coefficients and its applications in image forensics

paper_content:

In this paper, a novel statistical model based on Benford's law for the probability distributions of the first digits of the ::: block-DCT and quantized JPEG coefficients is presented. A parametric logarithmic law, i.e., the generalized Benford's ::: law, is formulated. Furthermore, some potential applications of this model in image forensics are discussed in this paper, ::: which include the detection of JPEG compression for images in bitmap format, the estimation of JPEG compression Qfactor ::: for JPEG compressed bitmap image, and the detection of double compressed JPEG image. The results of our ::: extensive experiments demonstrate the effectiveness of the proposed statistical model.
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paper_title: Detection of Double-Compression in JPEG Images for Applications in Steganography

paper_content:

This paper presents a method for the detection of double JPEG compression and a maximum-likelihood estimator of the primary quality factor. These methods are essential for construction of accurate targeted and blind steganalysis methods for JPEG images. The proposed methods use support vector machine classifiers with feature vectors formed by histograms of low-frequency discrete cosine transformation coefficients. The performance of the algorithms is compared to selected prior art.
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paper_title: Detecting doubly compressed JPEG images by using Mode Based First Digit Features

paper_content:

In this paper, we utilize the probabilities of the first digits of quantized DCT (discrete cosine transform) coefficients from individual AC (alternate current) modes to detect doubly compressed JPEG images. Our proposed features, named by mode based first digit features (MBFDF), have been shown to outperform all previous methods on discriminating doubly compressed JPEG images from singly compressed JPEG images. Furthermore, combining the MBFDF with a multi-class classification strategy can be exploited to identify the quality factor in the primary JPEG compression, thus successfully revealing the double JPEG compression history of a given JPEG image.
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paper_title: Estimation of primary quantization matrix in double compressed jpeg images

paper_content:

In this report, we present a method for estimation of primary quantization matrix from a double compressed JPEG image. We first identify characteristic features that occur in DCT histograms of individual coefficients due to double compression. Then, we present 3 different approaches that estimate the original quantization matrix from double compressed images. Finally, most successful of them Neural Network classifier is discussed and its performance and reliability is evaluated in a series of experiments on various databases of double compressed images. It is also explained in this paper, how double compression detection techniques and primary quantization matrix estimators can be used in steganalysis of JPEG files and in digital forensic analysis for detection of digital forgeries.
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paper_title: Detecting doctored JPEG images via DCT coefficient analysis

paper_content:

The steady improvement in image/video editing techniques has enabled people to synthesize realistic images/videos conveniently. Some legal issues may occur when a doctored image cannot be distinguished from a real one by visual examination. Realizing that it might be impossible to develop a method that is universal for all kinds of images and JPEG is the most frequently used image format, we propose an approach that can detect doctored JPEG images and further locate the doctored parts, by examining the double quantization effect hidden among the DCT coefficients. Up to date, this approach is the only one that can locate the doctored part automatically. And it has several other advantages: the ability to detect images doctored by different kinds of synthesizing methods (such as alpha matting and inpainting, besides simple image cut/paste), the ability to work without fully decompressing the JPEG images, and the fast speed. Experiments show that our method is effective for JPEG images, especially when the compression quality is high.
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paper_title: Detecting Double JPEG Compression With the Same Quantization Matrix

paper_content:

Detection of double joint photographic experts group (JPEG) compression is of great significance in the field of digital forensics. Some successful approaches have been presented for detecting double JPEG compression when the primary compression and the secondary compression have different quantization matrixes. However, when the primary compression and the secondary compression have the same quantization matrix, no detection method has been reported yet. In this paper, we present a method which can detect double JPEG compression with the same quantization matrix. Our algorithm is based on the observation that in the process of recompressing a JPEG image with the same quantization matrix over and over again, the number of different JPEG coefficients, i.e., the quantized discrete cosine transform coefficients between the sequential two versions will monotonically decrease in general. For example, the number of different JPEG coefficients between the singly and doubly compressed images is generally larger than the number of different JPEG coefficients between the corresponding doubly and triply compressed images. Via a novel random perturbation strategy implemented on the JPEG coefficients of the recompressed test image, we can find a “proper” randomly perturbed ratio. For different images, this universal “proper” ratio will generate a dynamically changed threshold, which can be utilized to discriminate the singly compressed image and doubly compressed image. Furthermore, our method has the potential to detect triple JPEG compression, four times JPEG compression, etc.
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paper_title: Statistical Tools for Digital Forensics

paper_content:

A digitally altered photograph, often leaving no visual clues of having been tampered with, can be indistinguishable from an authentic photograph. As a result, photographs no longer hold the unique stature as a definitive recording of events. We describe several statistical techniques for detecting traces of digital tampering in the absence of any digital watermark or signature. In particular, we quantify statistical correlations that result from specific forms of digital tampering, and devise detection schemes to reveal these correlations.
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paper_title: Passive detection of doctored JPEG image via block artifact grid extraction

paper_content:

It has been noticed that the block artifact grids (BAG), caused by the blocking processing during JPEG compression, are usually mismatched when interpolating or concealing objects by copy-paste operations. In this paper, the BAGs are extracted blindly with a new extraction algorithm, and then abnormal BAGs can be detected with a marking procedure. Then the phenomenon of grid mismatch or grid blank can be taken as a trail of such forensics. Experimental results show that our method can mark these trails efficiently.
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paper_title: Temporal Forensics and Anti-Forensics for Motion Compensated Video

paper_content:

Due to the ease with which digital information can be altered, many digital forensic techniques have been developed to authenticate multimedia content. Similarly, a number of anti-forensic operations have recently been designed to make digital forgeries undetectable by forensic techniques. However, like the digital manipulations they are designed to hide, many anti-forensic operations leave behind their own forensically detectable traces. As a result, a digital forger must balance the trade-off between completely erasing evidence of their forgery and introducing new evidence of anti-forensic manipulation. Because a forensic investigator is typically bound by a constraint on their probability of false alarm (P_fa), they must also balance a trade-off between the accuracy with which they detect forgeries and the accuracy with which they detect the use of anti-forensics. In this paper, we analyze the interaction between a forger and a forensic investigator by examining the problem of authenticating digital videos. Specifically, we study the problem of adding or deleting a sequence of frames from a digital video. We begin by developing a theoretical model of the forensically detectable fingerprints that frame deletion or addition leaves behind, then use this model to improve upon the video frame deletion or addition detection technique proposed by Wang and Farid. Next, we propose an anti-forensic technique designed to fool video forensic techniques and develop a method for detecting the use of anti-forensics. We introduce a new set of techniques for evaluating the performance of anti-forensic operations and develop a game theoretic framework for analyzing the interplay between a forensic investigator and a forger. We use these new techniques to evaluate the performance of each of our proposed forensic and anti-forensic techniques, and identify the optimal actions of both the forger and forensic investigator.
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paper_title: Exposing digital forgeries in video by detecting double MPEG compression

paper_content:

With the advent of sophisticated and low-cost video editing software, it is becoming increasingly easier to tamper with digital video. In addition,an ever-growing number of video surveillance cameras is giving rise to an enormous amount of video data. The ability to ensure the integrity and authenticity of this data poses considerable challenges. Here we begin to explore techniques for detecting traces of tampering in digital video. Specifically, we show how a doublycompressed MPEG video sequence introduces specific static and temporal statistical perturbations whose presence can be used as evidence of tampering.
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paper_title: Exposing digital forgeries in video by detecting double quantization

paper_content:

We describe a technique for detecting double quantization in digital video that results from double MPEG compression or from combining two videos of different qualities (e.g., green-screening). We describe how double quantization can introduce statistical artifacts that while not visible, can be quantified, measured, and used to detect tampering. This technique can detect highly localized tampering in regions as small as 16 x 16 pixels.
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paper_title: Exposing digital forgeries by detecting inconsistencies in lighting

paper_content:

When creating a digital composite of, for example, two people standing side-by-side, it is often difficult to match the lighting conditions from the individual photographs. Lighting inconsistencies can therefore be a useful tool for revealing traces of digital tampering. Borrowing and extending tools from the field of computer vision, we describe how the direction of a point light source can be estimated from only a single image. We show the efficacy of this approach in real-world settings.
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paper_title: Exposing Digital Forgeries in Ballistic Motion

paper_content:

We describe a geometric technique to detect physically implausible trajectories of objects in video sequences. This technique explicitly models the three-dimensional ballistic motion of objects in free-flight and the two-dimensional projection of the trajectory into the image plane of a static or moving camera. Deviations from this model provide evidence of manipulation. The technique assumes that the object's trajectory is substantially influenced only by gravity, that the image of the object's center of mass can be determined from the images, and requires that any camera motion can be estimated from background elements. The computational requirements of the algorithm are modest, and any detected inconsistencies can be illustrated in an intuitive, geometric fashion. We demonstrate the efficacy of this analysis on videos of our own creation and on videos obtained from video-sharing websites.
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paper_title: Exposing digital forgeries through specular highlights on the eye

paper_content:

When creating a digital composite of two people, it is difficult to exactly match the lighting conditions under which each individual was originally photographed. In many situations, the light source in a scene gives rise to a specular highlight on the eyes. We show how the direction to a light source can be estimated from this highlight. Inconsistencies in lighting across an image are then used to reveal traces of digital tampering.
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paper_title: Exposing Digital Forgeries in Complex Lighting Environments

paper_content:

The availability of sophisticated digital imaging technology has given rise to digital forgeries that are increasing in sophistication and frequency. We describe a technique for exposing such fakes by detecting inconsistencies in lighting. We show how to approximate complex lighting environments with a low-dimensional model and, further, how to estimate the model's parameters from a single image. Inconsistencies in the lighting model are th.en used as evidence of tampering.
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paper_title: Nonintrusive component forensics of visual sensors using output images

paper_content:

Rapid technology development and the widespread use of visual sensors have led to a number of new problems related to protecting intellectual property rights, handling patent infringements, authenticating acquisition sources, and identifying content manipulations. This paper introduces nonintrusive component forensics as a new methodology for the forensic analysis of visual sensing information, aiming to identify the algorithms and parameters employed inside various processing modules of a digital device by only using the device output data without breaking the device apart. We propose techniques to estimate the algorithms and parameters employed by important camera components, such as color filter array and color interpolation modules. The estimated interpolation coefficients provide useful features to construct an efficient camera identifier to determine the brand and model from which an image was captured. The results obtained from such component analysis are also useful to examine the similarities between the technologies employed by different camera models to identify potential infringement/licensing and to facilitate studies on technology evolution
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paper_title: Accurate Detection of Demosaicing Regularity for Digital Image Forensics

paper_content:

In this paper, we propose a novel accurate detection framework of demosaicing regularity from different source images. The proposed framework first reversely classifies the demosaiced samples into several categories and then estimates the underlying demosaicing formulas for each category based on partial second-order derivative correlation models, which detect both the intrachannel and the cross-channel demosaicing correlation. An expectation-maximization reverse classification scheme is used to iteratively resolve the ambiguous demosaicing axes in order to best reveal the implicit grouping adopted by the underlying demosaicing algorithm. Comparison results based on syntactic images show that our proposed formulation significantly improves the accuracy of the regenerated demosaiced samples from the sensor samples for a large number of diversified demosaicing algorithms. By running sequential forward feature selection, our reduced feature sets used in conjunction with the probabilistic support vector machine classifier achieve superior performance in identifying 16 demosaicing algorithms in the presence of common camera post demosaicing processing. When applied to real applications, including camera model and RAW-tool identification, our selected features achieve nearly perfect classification performances based on large sets of cropped image blocks.
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paper_title: Non-Intrusive Forensic Analysis of Visual Sensors Using Output Images

paper_content:

This paper considers the problem of non-intrusive forensic analysis of the individual components in visual sensors and its implementation. As a new addition to the emerging area of forensic engineering, we present a framework for analyzing technologies employed inside digital cameras based on output images, and develop a set of forensic signal processing algorithms for visual sensors based on color array sensor and interpolation methods. We show through simulations that the proposed method is robust against compression and noise, and can help identify various processing components inside the camera. Such a non-intrusive forensic framework would provide useful evidence for analyzing technology infringement and evolution for visual sensors.
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paper_title: Exposing digital forgeries in color filter array interpolated images

paper_content:

With the advent of low-cost and high-resolution digital cameras, and sophisticated photo editing software, digital images can be easily manipulated and altered. Although good forgeries may leave no visual clues of having been tampered with, they may, nevertheless, alter the underlying statistics of an image. Most digital cameras, for example, employ a single sensor in conjunction with a color filter array (CFA), and then interpolate the missing color samples to obtain a three channel color image. This interpolation introduces specific correlations which are likely to be destroyed when tampering with an image. We quantify the specific correlations introduced by CFA interpolation, and describe how these correlations, or lack thereof, can be automatically detected in any portion of an image. We show the efficacy of this approach in revealing traces of digital tampering in lossless and lossy compressed color images interpolated with several different CFA algorithms.
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paper_title: Source camera identification based on CFA interpolation

paper_content:

In this work, we focus our interest on blind source camera identification problem by extending our results in the direction of M. Kharrazi et al. (2004). The interpolation in the color surface of an image due to the use of a color filter array (CFA) forms the basis of the paper. We propose to identify the source camera of an image based on traces of the proprietary interpolation algorithm deployed by a digital camera. For this purpose, a set of image characteristics are defined and then used in conjunction with a support vector machine based multi-class classifier to determine the originating digital camera. We also provide initial results on identifying source among two and three digital cameras.
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paper_title: Component Forensics of Digital Cameras: A Non-Intrusive Approach

paper_content:

This paper considers the problem of component forensics and proposes a methodology to identify the algorithms and parameters employed by various processing modules inside a digital camera. The proposed analysis techniques are non-intrusive, using only sample output images collected from the camera to find the color filter array pattern; and the algorithm and parameters of color interpolation employed in cameras. As demonstrated by various case studies in the paper, the features obtained from component forensic analysis provide useful evidence for such applications as detecting technology infringement, protecting intellectual property rights, determining camera source, and identifying image tampering.
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paper_title: Capturing images with digital still cameras

paper_content:

We face multiple technical tasks when designing and developing digital cameras for consumer use. As would be expected, image quality is a top concern. Digital still cameras fall into two categories. One uses a black box and a camera lens with a CCD (charge-coupled device) back unit that can acquire more than 3 million pixels. Designed for professional and business use, this camera normally costs more than $5,000. The second type of digital camera is designed for consumers and employs a CCD capable of acquiring 350,000 to over 1 million pixels (Mpixels). Because it is easy to handle and carries a reasonable price for the picture quality, its market has grown dramatically in last three years. Here, I discuss the image-capturing system for a consumer-use digital still camera after briefly explaining its history.
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paper_title: Interactions between color plane interpolation and other image processing functions in electronic photography

paper_content:

Electronic cameras using a single CCD detector acquire scene color by subsampling in three, color planes and subsequently interpolating the information to reconstruct three, full-resolution color planes. The nature and size of the interpolation errors are a function of the algorithm used. When interpolation errors are propagated through the rest of the imaging chain, it becomes evident that synergistic effects among image processing operations must be considered when selecting and tuning an interpolation algorithm. This presentation demonstrates and comments on these image processing interactions.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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paper_title: IMPROVEMENTS ON SOURCE CAMERA-MODEL IDENTIFICATION BASED ON CFA INTERPOLATION

paper_content:

The idea of using traces of interpolation algorithms, deployed by a digital camera, as an identifier in the source camera-model identification problem has been initially studied in [2]. In this work, we improve our previous approach by incorporating methods to better detect the interpolation artifacts in smooth image parts. To identify the source cameramodel of a digital image, new features that can detect traces of low-order interpolation are introduced and used in conjunction with a support vector machine based multi-class classifier. Performance results due to newly added features are obtained considering source identification among two and three digital cameras. Also, these results are combined with those of [2] to further improve our methodology.
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paper_title: Color processing in digital cameras

paper_content:

In seconds, a digital camera performs full-color rendering that includes color filter array interpolation, color calibration, anti-aliasing, infrared rejection, and white-point correction. This article describes the design decisions that make this processing possible.
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paper_title: Robustness of color interpolation identification against anti-forensic operations

paper_content:

Color interpolation identification using digital images has been shown to be a powerful tool for addressing a range of digital forensic questions. However, due to the existence of adversaries who have the incentive to counter the identification, it is necessary to understand how color interpolation identification performs against anti-forensic operations that intentionally manipulate identification results. This paper proposes two anti-forensic techniques against which the robustness of color interpolation identification is investigated. The first technique employs parameter perturbation to circumvent identification. Various options that achieve different trade-offs between image quality and identification manipulation are examined. The second technique involves algorithm mixing and demonstrates that one can not only circumvent but also mislead the identification system while preserving the image quality. Additional discussions are also provided to enhance the understanding of anti-forensics and its implications to the design of identification systems.
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paper_title: Nonintrusive component forensics of visual sensors using output images

paper_content:

Rapid technology development and the widespread use of visual sensors have led to a number of new problems related to protecting intellectual property rights, handling patent infringements, authenticating acquisition sources, and identifying content manipulations. This paper introduces nonintrusive component forensics as a new methodology for the forensic analysis of visual sensing information, aiming to identify the algorithms and parameters employed inside various processing modules of a digital device by only using the device output data without breaking the device apart. We propose techniques to estimate the algorithms and parameters employed by important camera components, such as color filter array and color interpolation modules. The estimated interpolation coefficients provide useful features to construct an efficient camera identifier to determine the brand and model from which an image was captured. The results obtained from such component analysis are also useful to examine the similarities between the technologies employed by different camera models to identify potential infringement/licensing and to facilitate studies on technology evolution
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paper_title: Source digital camcorder identification using sensor photo response non-uniformity

paper_content:

Photo-response non-uniformity (PRNU) of digital sensors was recently proposed [1] as a unique identification fingerprint ::: for digital cameras. The PRNU extracted from a specific image can be used to link it to the digital camera that took the ::: image. Because digital camcorders use the same imaging sensors, in this paper, we extend this technique for ::: identification of digital camcorders from video clips. We also investigate the problem of determining whether two video ::: clips came from the same camcorder and the problem of whether two differently transcoded versions of one movie came ::: from the same camcorder. The identification technique is a joint estimation and detection procedure consisting of two ::: steps: (1) estimation of PRNUs from video clips using the Maximum Likelihood Estimator and (2) detecting the presence ::: of PRNU using normalized cross-correlation. We anticipate this technology to be an essential tool for fighting piracy of ::: motion pictures. Experimental results demonstrate the reliability and generality of our approach.
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paper_title: Digital camera identification from sensor pattern noise

paper_content:

In this paper, we propose a new method for the problem of digital camera identification from its images based on the sensor's pattern noise. For each camera under investigation, we first determine its reference pattern noise, which serves as a unique identification fingerprint. This is achieved by averaging the noise obtained from multiple images using a denoising filter. To identify the camera from a given image, we consider the reference pattern noise as a spread-spectrum watermark, whose presence in the image is established by using a correlation detector. Experiments on approximately 320 images taken with nine consumer digital cameras are used to estimate false alarm rates and false rejection rates. Additionally, we study how the error rates change with common image processing, such as JPEG compression or gamma correction.
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paper_title: Source Camera Identification Using Enhanced Sensor Pattern Noise

paper_content:

Sensor pattern noises (SPNs), extracted from digital images to serve as the fingerprints of imaging devices, have been proved as an effective way for digital device identification. However, as we demonstrate in this work, the limitation of the current method of extracting SPNs is that the SPNs extracted from images can be severely contaminated by details from scenes, and as a result, the identification rate is unsatisfactory unless images of a large size are used. In this work, we propose a novel approach for attenuating the influence of details from scenes on SPNs so as to improve the device identification rate of the identifier. The hypothesis underlying our SPN enhancement method is that the stronger a signal component in an SPN is, the less trustworthy the component should be, and thus should be attenuated. This hypothesis suggests that an enhanced SPN can be obtained by assigning weighting factors inversely proportional to the magnitude of the SPN components.
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paper_title: Exploring compression effects for improved source camera identification using strongly compressed video

paper_content:

This paper presents a study of the video compression effect on source camera identification based on the Photo-Response Non-Uniformity (PRNU). Specifically, the reliability of different types of frames in a compressed video is first investigated, which shows quantitatively that I-frames are more reliable than P-frames for PRNU estimation. Motivated by this observation, a new mechanism for estimating the reference PRNU and two mechanisms for estimating the test-video PRNU are proposed to achieve higher accuracy with fewer frames used. Experiments are performed to validate the effectiveness of the proposed mechanisms.
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paper_title: Scanner identification using sensor pattern noise

paper_content:

Digital images can be captured or generated by a variety of sources including digital cameras and scanners. In ::: many cases it is important to be able to determine the source of a digital image. This paper presents methods for ::: authenticating images that have been acquired using flatbed desktop scanners. The method is based on using the ::: pattern noise of the imaging sensor as a fingerprint for the scanner, similar to methods that have been reported ::: for identifying digital cameras. To identify the source scanner of an image a reference pattern is estimated for ::: each scanner and is treated as a unique fingerprint of the scanner. An anisotropic local polynomial estimator is ::: used for obtaining the reference patterns. To further improve the classification accuracy a feature vector based ::: approach using an SVM classifier is used to classify the pattern noise. This feature vector based approach is ::: shown to achieve a high classification accuracy.
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paper_title: Scanner Identification Using Feature-Based Processing and Analysis

paper_content:

Digital images can be obtained through a variety of sources including digital cameras and scanners. In many cases, the ability to determine the source of a digital image is important. This paper presents methods for authenticating images that have been acquired using flatbed desktop scanners. These methods use scanner fingerprints based on statistics of imaging sensor pattern noise. To capture different types of sensor noise, a denoising filterbank consisting four different denoising filters is used for obtaining the noise patterns. To identify the source scanner, a support vector machine classifier based on these fingerprints is used. These features are shown to achieve high classification accuracy. Furthermore, the selected fingerprints based on statistical properties of the sensor noise are shown to be robust under postprocessing operations, such as JPEG compression, contrast stretching, and sharpening.
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paper_title: Physics-motivated features for distinguishing photographic images and computer graphics

paper_content:

The increasing photorealism for computer graphics has made computer graphics a convincing form of image forgery. Therefore, classifying photographic images and photorealistic computer graphics has become an important problem for image forgery detection. In this paper, we propose a new geometry-based image model, motivated by the physical image generation process, to tackle the above-mentioned problem. The proposed model reveals certain physical differences between the two image categories, such as the gamma correction in photographic images and the sharp structures in computer graphics. For the problem of image forgery detection, we propose two levels of image authenticity definition, i.e., imaging-process authenticity and scene authenticity, and analyze our technique against these definitions. Such definition is important for making the concept of image authenticity computable. Apart from offering physical insights, our technique with a classification accuracy of 83.5% outperforms those in the prior work, i.e., wavelet features at 80.3% and cartoon features at 71.0%. We also consider a recapturing attack scenario and propose a counter-attack measure. In addition, we constructed a publicly available benchmark dataset with images of diverse content and computer graphics of high photorealism.
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paper_title: Forensic techniques for classifying scanner, computer generated and digital camera images

paper_content:

Digital images can be captured or generated by a variety of sources including digital cameras, scanners and computer graphics softwares. In many cases it is important to be able to determine the source of a digital image such as for criminal and forensic investigation. This paper presents methods for distinguishing between an image captured using a digital camera, a computer generated image and an image captured using a scanner. The method proposed here is based on the differences in the image generation processes used in these devices and is independent of the image content. The method is based on using features of the residual pattern noise that exist in images obtained from digital cameras and scanners. The residual noise present in computer generated images does not have structures similar to the pattern noise of cameras and scanners. The experiments show that a feature based approach using an SVM classifier gives high accuracy.
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paper_title: Robust scanner identification based on noise features

paper_content:

A large portion of digital image data available today is acquired using digital cameras or scanners. While cameras ::: allow digital reproduction of natural scenes, scanners are often used to capture hardcopy art in more controlled ::: scenarios. This paper proposes a new technique for non-intrusive scanner model identification, which can be ::: further extended to perform tampering detection on scanned images. Using only scanned image samples that ::: contain arbitrary content, we construct a robust scanner identifier to determine the brand/model of the scanner ::: used to capture each scanned image. The proposed scanner identifier is based on statistical features of scanning ::: noise. We first analyze scanning noise from several angles, including through image de-noising, wavelet analysis, ::: and neighborhood prediction, and then obtain statistical features from each characterization. Experimental ::: results demonstrate that the proposed method can effectively identify the correct scanner brands/models with ::: high accuracy.
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paper_title: Intrinsic Sensor Noise Features for Forensic Analysis on Scanners and Scanned Images

paper_content:

A large portion of digital images available today are acquired using digital cameras or scanners. While cameras provide digital reproduction of natural scenes, scanners are often used to capture hard-copy art in a more controlled environment. In this paper, new techniques for nonintrusive scanner forensics that utilize intrinsic sensor noise features are proposed to verify the source and integrity of digital scanned images. Scanning noise is analyzed from several aspects using only scanned image samples, including through image denoising, wavelet analysis, and neighborhood prediction, and then obtain statistical features from each characterization. Based on the proposed statistical features of scanning noise, a robust scanner identifier is constructed to determine the model/brand of the scanner used to capture a scanned image. Utilizing these noise features, we extend the scope of acquisition forensics to differentiating scanned images from camera-taken photographs and computer-generated graphics. The proposed noise features also enable tampering forensics to detect postprocessing operations on scanned images. Experimental results are presented to demonstrate the effectiveness of employing the proposed noise features for performing various forensic analysis on scanners and scanned images.
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paper_title: Noise Features for Image Tampering Detection and Steganalysis

paper_content:

With increasing availability of low-cost image editing softwares, the authenticity of digital images can no longer be taken for granted. Digital images have also been used as cover data for transmitting secret information in the field of steganography. In this paper, we introduce a new set of features for multimedia forensics to determine if a digital image is an authentic camera output or if it has been tampered or embedded with hidden data. We perform such image forensic analysis employing three sets of statistical noise features, including those from denoising operations, wavelet analysis, and neighborhood prediction. Our experimental results demonstrate that the proposed method can effectively distinguish digital images from their tampered or stego versions.
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paper_title: Determining Image Origin and Integrity Using Sensor Noise

paper_content:

In this paper, we provide a unified framework for identifying the source digital camera from its images and for revealing digitally altered images using photo-response nonuniformity noise (PRNU), which is a unique stochastic fingerprint of imaging sensors. The PRNU is obtained using a maximum-likelihood estimator derived from a simplified model of the sensor output. Both digital forensics tasks are then achieved by detecting the presence of sensor PRNU in specific regions of the image under investigation. The detection is formulated as a hypothesis testing problem. The statistical distribution of the optimal test statistics is obtained using a predictor of the test statistics on small image blocks. The predictor enables more accurate and meaningful estimation of probabilities of false rejection of a correct camera and missed detection of a tampered region. We also include a benchmark implementation of this framework and detailed experimental validation. The robustness of the proposed forensic methods is tested on common image processing, such as JPEG compression, gamma correction, resizing, and denoising.
---
paper_title: Exposing digital forgeries in color filter array interpolated images

paper_content:

With the advent of low-cost and high-resolution digital cameras, and sophisticated photo editing software, digital images can be easily manipulated and altered. Although good forgeries may leave no visual clues of having been tampered with, they may, nevertheless, alter the underlying statistics of an image. Most digital cameras, for example, employ a single sensor in conjunction with a color filter array (CFA), and then interpolate the missing color samples to obtain a three channel color image. This interpolation introduces specific correlations which are likely to be destroyed when tampering with an image. We quantify the specific correlations introduced by CFA interpolation, and describe how these correlations, or lack thereof, can be automatically detected in any portion of an image. We show the efficacy of this approach in revealing traces of digital tampering in lossless and lossy compressed color images interpolated with several different CFA algorithms.
---
paper_title: Detecting digital image forgeries using sensor pattern noise

paper_content:

We present a new approach to detection of forgeries in digital images under the assumption that either the camera that took the image is available or other images taken by that camera are available. Our method is based on detecting the presence of the camera pattern noise, which is a unique stochastic characteristic of imaging sensors, in individual regions in the image. The forged region is determined as the one that lacks the pattern noise. The presence of the noise is established using correlation as in detection of spread spectrum watermarks. We proposed two approaches. In the first one, the user selects an area for integrity verification. The second method attempts to automatically determine the forged area without assuming any a priori knowledge. The methods are tested both on examples of real forgeries and on non-forged images. We also investigate how further image processing applied to the forged image, such as lossy compression or filtering, influences our ability to verify image integrity.
---
paper_title: Image tamper detection based on demosaicing artifacts

paper_content:

In this paper, we introduce tamper detection techniques based on artifacts created by Color Filter Array (CFA) processing in most digital cameras. The techniques are based on computing a single feature and a simple threshold based classifier. The efficacy of the approach was tested over thousands of authentic, tampered, and computer generated images. Experimental results demonstrate reasonably low error rates.
---
paper_title: Image Tampering Identification using Blind Deconvolution

paper_content:

Digital images have been used in growing number of applications from law enforcement and surveillance, to medical diagnosis and consumer photography. With such widespread popularity and the presence of low-cost image editing softwares, the integrity of image content can no longer be taken for granted. In this paper, we propose a novel technique based on blind deconvolution to verify image authenticity. We consider the direct output images of a camera as authentic, and introduce algorithms to detect further processing such as tampering applied to the image. Our proposed method is based on the observation that many tampering operations can be approximated as a combination of linear and non-linear components. We model the linear part of the tampering process as a filter, and obtain its coefficients using blind deconvolution. These estimated coefficients are then used to identify possible manipulations. We demonstrate the effectiveness of the proposed image authentication technique and compare our results with existing works.
---
paper_title: Tampering identification using Empirical Frequency Response

paper_content:

With the widespread popularity of digital images and the presence of easy-to-use image editing software, content integrity can no longer be taken for granted, and there is a strong need for techniques that not only detect the presence of tampering but also identify its type. This paper focusses on tampering-type identification and introduces a new approach based on the Empirical Frequency Response (EFR) to address this problem. We show that several types of tampering operations, both linear shift invariant (LSI) and non-LSI, can be characterized consistently and distinctly by their EFRs. We then extend the approach to estimate the EFR for scenarios where only the final image is available. Theoretical reasoning supported by experimental results verify the effectiveness of this method for identifying the type of a tampering operation.
---
paper_title: Digital image forensics via intrinsic fingerprints

paper_content:

Digital imaging has experienced tremendous growth in recent decades, and digital camera images have been used in a growing number of applications. With such increasing popularity and the availability of low-cost image editing software, the integrity of digital image content can no longer be taken for granted. This paper introduces a new methodology for the forensic analysis of digital camera images. The proposed method is based on the observation that many processing operations, both inside and outside acquisition devices, leave distinct intrinsic traces on digital images, and these intrinsic fingerprints can be identified and employed to verify the integrity of digital data. The intrinsic fingerprints of the various in-camera processing operations can be estimated through a detailed imaging model and its component analysis. Further processing applied to the camera captured image is modelled as a manipulation filter, for which a blind deconvolution technique is applied to obtain a linear time-invariant approximation and to estimate the intrinsic fingerprints associated with these postcamera operations. The absence of camera-imposed fingerprints from a test image indicates that the test image is not a camera output and is possibly generated by other image production processes. Any change or inconsistencies among the estimated camera-imposed fingerprints, or the presence of new types of fingerprints suggest that the image has undergone some kind of processing after the initial capture, such as tampering or steganographic embedding. Through analysis and extensive experimental studies, this paper demonstrates the effectiveness of the proposed framework for nonintrusive digital image forensics.
---
paper_title: Intrinsic Sensor Noise Features for Forensic Analysis on Scanners and Scanned Images

paper_content:

A large portion of digital images available today are acquired using digital cameras or scanners. While cameras provide digital reproduction of natural scenes, scanners are often used to capture hard-copy art in a more controlled environment. In this paper, new techniques for nonintrusive scanner forensics that utilize intrinsic sensor noise features are proposed to verify the source and integrity of digital scanned images. Scanning noise is analyzed from several aspects using only scanned image samples, including through image denoising, wavelet analysis, and neighborhood prediction, and then obtain statistical features from each characterization. Based on the proposed statistical features of scanning noise, a robust scanner identifier is constructed to determine the model/brand of the scanner used to capture a scanned image. Utilizing these noise features, we extend the scope of acquisition forensics to differentiating scanned images from camera-taken photographs and computer-generated graphics. The proposed noise features also enable tampering forensics to detect postprocessing operations on scanned images. Experimental results are presented to demonstrate the effectiveness of employing the proposed noise features for performing various forensic analysis on scanners and scanned images.
---
paper_title: Applications of ENF criterion in forensic audio, video, computer and telecommunication analysis.

paper_content:

This article reports on the electric network frequency criterion as a means of assessing the integrity of digital audio/video evidence and forensic IT and telecommunication analysis. A brief description is given to different ENF types and phenomena that determine ENF variations. In most situations, to reach a non-authenticity opinion, the visual inspection of spectrograms and comparison with an ENF database are enough. A more detailed investigation, in the time domain, requires short time windows measurements and analyses. The stability of the ENF over geographical distances has been established by comparison of synchronized recordings made at different locations on the same network. Real cases are presented, in which the ENF criterion was used to investigate audio and video files created with secret surveillance systems, a digitized audio/video recording and a TV broadcasted reportage. By applying the ENF Criterion in forensic audio/video analysis, one can determine whether and where a digital recording has been edited, establish whether it was made at the time claimed, and identify the time and date of the registering operation.
---
paper_title: Instantaneous frequency estimation and localization for ENF signals

paper_content:

Forensic analysis based on Electric Network Frequency (ENF) fluctuations is an emerging technology for authenticating multimedia recordings. This class of techniques requires extracting frequency fluctuations from multimedia recordings and comparing them with the ground truth frequencies, obtained from the power mains, at the corresponding time. Most current guidelines for frequency estimation from the ENF signal use non-parametric approaches. Such approaches have limited temporal-frequency resolution due to the tradeoffs of the time-frequency resolutions as well as computational power. To facilitate robust high-resolution matching, it is important to estimate instantaneous frequency using as few samples as possible. The use of subspace-based methods for high resolution frequency estimation is fairly new for ENF analysis. In this paper, a systematic study of several high resolution low-complexity frequency estimation algorithms is conducted, focusing on estimating the frequencies in short time-frames. After establishing the performance of several frequency estimation algorithms, a study towards using the ENF signal for estimating the location-of-recording is carried out. Experiments conducted on ENF data collected in several cities indicate the presence of location-specific signatures that can be exploited for future forensic applications.
---
paper_title: Audio Authenticity: Detecting ENF Discontinuity With High Precision Phase Analysis

paper_content:

This paper addresses a forensic tool used to assess audio authenticity. The proposed method is based on detecting phase discontinuity of the power grid signal; this signal, referred to as electric network frequency (ENF), is sometimes embedded in audio signals when the recording is carried out with the equipment connected to an electrical outlet or when certain microphones are in an ENF magnetic field. After down-sampling and band-filtering the audio around the nominal value of the ENF, the result can be considered a single tone such that a high-precision Fourier analysis can be used to estimate its phase. The estimated phase provides a visual aid to locating editing points (signalled by abrupt phase changes) and inferring the type of audio editing (insertion or removal of audio segments). From the estimated values, a feature is used to quantify the discontinuity of the ENF phase, allowing an automatic decision concerning the authenticity of the audio evidence. The theoretical background is presented along with practical implementation issues related to the proposed technique, whose performance is evaluated on digitally edited audio signals.
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paper_title: "Seeing" ENF: natural time stamp for digital video via optical sensing and signal processing

paper_content:

Electric Network Frequency (ENF) fluctuates slightly over time from its nominal value of 50 Hz/60 Hz. The fluctuations in the ENF remain consistent across the entire power grid even when measured at physically distant locations. The near-invisible flickering of fluorescent lights connected to the power mains reflect these fluctuations present in the ENF. In this paper, mechanisms using optical sensors and video cameras to record and validate the presence of the ENF fluctuations in fluorescent lighting are presented. Signal processing techniques are applied to demonstrate a high correlation between the fluctuations in the ENF signal captured from fluorescent lighting and the ENF signal captured directly from power mains supply. The proposed technique is then used to demonstrate the presence of the ENF signal in video recordings taken in various geographical areas. Experimental results show that the ENF signal can be used as a natural timestamp for optical sensor recordings and video surveillance recordings from indoor environments under fluorescent lighting. Application of the ENF signal analysis to tampering detection of surveillance video recordings is also demonstrated.
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paper_title: Signal Processing of Power Quality Disturbances

paper_content:

PREFACE. ACKNOWLEDGMENTS. 1 INTRODUCTION. 1.1 Modern View of Power Systems. 1.2 Power Quality. 1.3 Signal Processing and Power Quality. 1.4 Electromagnetic Compatibility Standards. 1.5 Overview of Power Quality Standards. 1.6 Compatibility Between Equipment and Supply. 1.7 Distributed Generation. 1.8 Conclusions. 1.9 About This Book. 2 ORIGIN OF POWER QUALITY VARIATIONS. 2.1 Voltage Frequency Variations. 2.2 Voltage Magnitude Variations. 2.3 Voltage Unbalance. 2.4 Voltage Fluctuations and Light Flicker. 2.5 Waveform Distortion. 2.6 Summary and Conclusions. 3 PROCESSING OF STATIONARY SIGNALS. 3.1 Overview of Methods. 3.2 Parameters That Characterize Variations. 3.3 Power Quality Indices. 3.4 Frequency-Domain Analysis and Signal Transformation. 3.5 Estimation of Harmonics and Interharmonics. 3.6 Estimation of Broadband Spectrum. 3.7 Summary and Conclusions. 3.8 Further Reading. 4 PROCESSING OF NONSTATIONARY SIGNALS. 4.1 Overview of Some Nonstationary Power Quality Data Analysis Methods. 4.2 Discrete STFT for Analyzing Time-Evolving Signal Components. 4.3 Discrete Wavelet Transforms for Time-Scale Analysis of Disturbances. 4.4 Block-Based Modeling. 4.5 Models Directly Applicable to Nonstationary Data. 4.6 Summary and Conclusion. 4.7 Further Reading. 5 STATISTICS OF VARIATIONS. 5.1 From Features to System Indices. 5.2 Time Aggregation. 5.3 Characteristics Versus Time. 5.4 Site Indices. 5.5 System Indices. 5.6 Power Quality Objectives. 5.7 Summary and Conclusions. 6 ORIGIN OF POWER QUALITY EVENTS. 6.1 Interruptions. 6.2 Voltage Dips. 6.3 Transients. 6.4 Summary and Conclusions. 7 TRIGGERING AND SEGMENTATION. 7.1 Overview of Existing Methods. 7.2 Basic Concepts of Triggering and Segmentation. 7.3 Triggering Methods. 7.4 Segmentation. 7.5 Summary and Conclusions. 8 CHARACTERIZATION OF POWER QUALITY EVENTS. 8.1 Voltage Magnitude Versus Time. 8.2 Phase Angle Versus Time. 8.3 Three-Phase Characteristics Versus Time. 8.4 Distortion During Event. 8.5 Single-Event Indices: Interruptions. 8.6 Single-Event Indices: Voltage Dips. 8.7 Single-Event Indices: Voltage Swells. 8.8 Single-Event Indices Based on Three-Phase Characteristics. 8.9 Additional Information from Dips and Interruptions. 8.10 Transients. 8.11 Summary and Conclusions. 9 EVENT CLASSIFICATION. 9.1 Overview of Machine Data Learning Methods for Event Classification. 9.2 Typical Steps Used in Classification System. 9.3 Learning Machines Using Linear Discriminants. 9.4 Learning and Classification Using Probability Distributions. 9.5 Learning and Classification Using Artificial Neural Networks. 9.6 Learning and Classification Using Support Vector Machines. 9.7 Rule-Based Expert Systems for Classification of Power System Events. 9.8 Summary and Conclusions. 10 EVENT STATISTICS. 10.1 Interruptions. 10.2 Voltage Dips: Site Indices. 10.3 Voltage Dips: Time Aggregation. 10.4 Voltage Dips: System Indices. 10.5 Summary and Conclusions. 11 CONCLUSIONS. 11.1 Events and Variations. 11.2 Power Quality Variations. 11.3 Power Quality Events. 11.4 Itemization of Power Quality. 11.5 Signal-Processing Needs. APPENDIX A IEC STANDARDS ON POWER QUALITY. APPENDIX B IEEE STANDARDS ON POWER QUALITY. BIBLIOGRAPHY. INDEX.
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paper_title: Determining Image Origin and Integrity Using Sensor Noise

paper_content:

In this paper, we provide a unified framework for identifying the source digital camera from its images and for revealing digitally altered images using photo-response nonuniformity noise (PRNU), which is a unique stochastic fingerprint of imaging sensors. The PRNU is obtained using a maximum-likelihood estimator derived from a simplified model of the sensor output. Both digital forensics tasks are then achieved by detecting the presence of sensor PRNU in specific regions of the image under investigation. The detection is formulated as a hypothesis testing problem. The statistical distribution of the optimal test statistics is obtained using a predictor of the test statistics on small image blocks. The predictor enables more accurate and meaningful estimation of probabilities of false rejection of a correct camera and missed detection of a tampered region. We also include a benchmark implementation of this framework and detailed experimental validation. The robustness of the proposed forensic methods is tested on common image processing, such as JPEG compression, gamma correction, resizing, and denoising.
---
paper_title: Can we trust digital image forensics?

paper_content:

Compared to the prominent role digital images play in nowadays multimedia society, research in the field of image authenticity is still in its infancy. Only recently, research on digital image forensics has gained attention by addressing tamper detection and image source identification. However, most publications in this emerging field still lack rigorous discussions of robustness against strategic counterfeiters, who anticipate the existence of forensic techniques. As a result, the question of trustworthiness of digital image forensics arises. This work will take a closer look at two state-of-the-art forensic methods and proposes two counter-techniques; one to perform resampling operations undetectably and another one to forge traces of image origin. Implications for future image forensic systems will be discussed.
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paper_title: Digital camera identification from sensor pattern noise

paper_content:

In this paper, we propose a new method for the problem of digital camera identification from its images based on the sensor's pattern noise. For each camera under investigation, we first determine its reference pattern noise, which serves as a unique identification fingerprint. This is achieved by averaging the noise obtained from multiple images using a denoising filter. To identify the camera from a given image, we consider the reference pattern noise as a spread-spectrum watermark, whose presence in the image is established by using a correlation detector. Experiments on approximately 320 images taken with nine consumer digital cameras are used to estimate false alarm rates and false rejection rates. Additionally, we study how the error rates change with common image processing, such as JPEG compression or gamma correction.
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paper_title: Hiding Traces of Resampling in Digital Images

paper_content:

Resampling detection has become a standard tool for forensic analyses of digital images. This paper presents new variants of image transformation operations which are undetectable by resampling detectors based on periodic variations in the residual signal of local linear predictors in the spatial domain. The effectiveness of the proposed method is supported with evidence from experiments on a large image database for various parameter settings. We benchmark detectability as well as the resulting image quality against conventional linear and bicubic interpolation and interpolation with a sinc kernel. These early findings on ldquocounter-forensicrdquo techniques put into question the reliability of known forensic tools against smart counterfeiters in general, and might serve as benchmarks and motivation for the development of much improved forensic techniques.
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paper_title: Anti-forensics of digital image compression

paper_content:

As society has become increasingly reliant upon digital images to communicate visual information, a number of forensic techniques have been developed to verify the authenticity of digital images. Amongst the most successful of these are techniques that make use of an image's compression history and its associated compression fingerprints. Little consideration has been given, however, to anti-forensic techniques capable of fooling forensic algorithms. In this paper, we present a set of anti-forensic techniques designed to remove forensically significant indicators of compression from an image. We do this by first developing a generalized framework for the design of anti-forensic techniques to remove compression fingerprints from an image's transform coefficients. This framework operates by estimating the distribution of an image's transform coefficients before compression, then adding anti-forensic dither to the transform coefficients of a compressed image so that their distribution matches the estimated one. We then use this framework to develop anti-forensic techniques specifically targeted at erasing compression fingerprints left by both JPEG and wavelet-based coders. Additionally, we propose a technique to remove statistical traces of the blocking artifacts left by image compression algorithms that divide an image into segments during processing. Through a series of experiments, we demonstrate that our anti-forensic techniques are capable of removing forensically detectable traces of image compression without significantly impacting an image's visual quality. Furthermore, we show how these techniques can be used to render several forms of image tampering such as double JPEG compression, cut-and-paste image forgery, and image origin falsification undetectable through compression-history-based forensic means.
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paper_title: Temporal Forensics and Anti-Forensics for Motion Compensated Video

paper_content:

Due to the ease with which digital information can be altered, many digital forensic techniques have been developed to authenticate multimedia content. Similarly, a number of anti-forensic operations have recently been designed to make digital forgeries undetectable by forensic techniques. However, like the digital manipulations they are designed to hide, many anti-forensic operations leave behind their own forensically detectable traces. As a result, a digital forger must balance the trade-off between completely erasing evidence of their forgery and introducing new evidence of anti-forensic manipulation. Because a forensic investigator is typically bound by a constraint on their probability of false alarm (P_fa), they must also balance a trade-off between the accuracy with which they detect forgeries and the accuracy with which they detect the use of anti-forensics. In this paper, we analyze the interaction between a forger and a forensic investigator by examining the problem of authenticating digital videos. Specifically, we study the problem of adding or deleting a sequence of frames from a digital video. We begin by developing a theoretical model of the forensically detectable fingerprints that frame deletion or addition leaves behind, then use this model to improve upon the video frame deletion or addition detection technique proposed by Wang and Farid. Next, we propose an anti-forensic technique designed to fool video forensic techniques and develop a method for detecting the use of anti-forensics. We introduce a new set of techniques for evaluating the performance of anti-forensic operations and develop a game theoretic framework for analyzing the interplay between a forensic investigator and a forger. We use these new techniques to evaluate the performance of each of our proposed forensic and anti-forensic techniques, and identify the optimal actions of both the forger and forensic investigator.
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paper_title: Undetectable image tampering through JPEG compression anti-forensics

paper_content:

Recently, a number of digital image forensic techniques have been developed which are capable of identifying an image's origin, tracing its processing history, and detecting image forgeries. Though these techniques are capable of identifying standard image manipulations, they do not address the possibility that anti-forensic operations may be designed and used to hide evidence of image tampering. In this paper, we propose an anti-forensic operation capable of removing blocking artifacts from a previously JPEG compressed image. Furthermore, we show that by using this operation along with another anti-forensic operation which we recently proposed, we are able to fool forensic methods designed to detect evidence of JPEG compression in decoded images, determine an image's origin, detect double JPEG compression, and identify cut-and-paste image forgeries.
---
paper_title: Temporal Forensics and Anti-Forensics for Motion Compensated Video

paper_content:

Due to the ease with which digital information can be altered, many digital forensic techniques have been developed to authenticate multimedia content. Similarly, a number of anti-forensic operations have recently been designed to make digital forgeries undetectable by forensic techniques. However, like the digital manipulations they are designed to hide, many anti-forensic operations leave behind their own forensically detectable traces. As a result, a digital forger must balance the trade-off between completely erasing evidence of their forgery and introducing new evidence of anti-forensic manipulation. Because a forensic investigator is typically bound by a constraint on their probability of false alarm (P_fa), they must also balance a trade-off between the accuracy with which they detect forgeries and the accuracy with which they detect the use of anti-forensics. In this paper, we analyze the interaction between a forger and a forensic investigator by examining the problem of authenticating digital videos. Specifically, we study the problem of adding or deleting a sequence of frames from a digital video. We begin by developing a theoretical model of the forensically detectable fingerprints that frame deletion or addition leaves behind, then use this model to improve upon the video frame deletion or addition detection technique proposed by Wang and Farid. Next, we propose an anti-forensic technique designed to fool video forensic techniques and develop a method for detecting the use of anti-forensics. We introduce a new set of techniques for evaluating the performance of anti-forensic operations and develop a game theoretic framework for analyzing the interplay between a forensic investigator and a forger. We use these new techniques to evaluate the performance of each of our proposed forensic and anti-forensic techniques, and identify the optimal actions of both the forger and forensic investigator.
---
paper_title: Anti-forensics for frame deletion/addition in MPEG video

paper_content:

Due to the ease with which digital information can be altered, many digital forensic techniques have recently been developed to authenticate multimedia content. One important digital forensic result is that adding or deleting frames from an MPEG video sequence introduces a temporally distributed fingerprint into the video can be used to identify frame deletion or addition. By contrast, very little research exists into anti-forensic operations designed to make digital forgeries undetectable by forensic techniques. In this paper, we propose an anti-forensic technique capable of removing the temporal fingerprint from MPEG videos that have undergone frame addition or deletion. We demonstrate that our proposed anti-forensic technique can effectively remove this fingerprint through a series of experiments.
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paper_title: Defending Against Fingerprint-Copy Attack in Sensor-Based Camera Identification

paper_content:

Sensor photoresponse nonuniformity has been proposed as a unique identifier (fingerprint) for various forensic tasks, including digital-camera ballistics in which an image is matched to the specific camera that took it. The problem investigated here concerns the situation when an adversary estimates the sensor fingerprint from a set of images and superimposes it onto an image from a different camera to frame an innocent victim. This paper proposes a reliable method for detecting such fake fingerprints under rather mild and general assumptions about the adversary's activity and the means available to the victim. The proposed method is subjected to experiments to evaluate its reliability as well as its limitations. The conclusion that can be made from this study is that planting a sensor fingerprint in an image without leaving a trace is significantly more difficult than previously thought.
---
paper_title: Anti-forensics for frame deletion/addition in MPEG video

paper_content:

Due to the ease with which digital information can be altered, many digital forensic techniques have recently been developed to authenticate multimedia content. One important digital forensic result is that adding or deleting frames from an MPEG video sequence introduces a temporally distributed fingerprint into the video can be used to identify frame deletion or addition. By contrast, very little research exists into anti-forensic operations designed to make digital forgeries undetectable by forensic techniques. In this paper, we propose an anti-forensic technique capable of removing the temporal fingerprint from MPEG videos that have undergone frame addition or deletion. We demonstrate that our proposed anti-forensic technique can effectively remove this fingerprint through a series of experiments.
---
paper_title: Temporal Forensics and Anti-Forensics for Motion Compensated Video

paper_content:

Due to the ease with which digital information can be altered, many digital forensic techniques have been developed to authenticate multimedia content. Similarly, a number of anti-forensic operations have recently been designed to make digital forgeries undetectable by forensic techniques. However, like the digital manipulations they are designed to hide, many anti-forensic operations leave behind their own forensically detectable traces. As a result, a digital forger must balance the trade-off between completely erasing evidence of their forgery and introducing new evidence of anti-forensic manipulation. Because a forensic investigator is typically bound by a constraint on their probability of false alarm (P_fa), they must also balance a trade-off between the accuracy with which they detect forgeries and the accuracy with which they detect the use of anti-forensics. In this paper, we analyze the interaction between a forger and a forensic investigator by examining the problem of authenticating digital videos. Specifically, we study the problem of adding or deleting a sequence of frames from a digital video. We begin by developing a theoretical model of the forensically detectable fingerprints that frame deletion or addition leaves behind, then use this model to improve upon the video frame deletion or addition detection technique proposed by Wang and Farid. Next, we propose an anti-forensic technique designed to fool video forensic techniques and develop a method for detecting the use of anti-forensics. We introduce a new set of techniques for evaluating the performance of anti-forensic operations and develop a game theoretic framework for analyzing the interplay between a forensic investigator and a forger. We use these new techniques to evaluate the performance of each of our proposed forensic and anti-forensic techniques, and identify the optimal actions of both the forger and forensic investigator.
---
paper_title: How secure are power network signature based time stamps?

paper_content:

A time stamp based on the power network signature called the Electrical Network Frequency (ENF) has been used by an emerging class of approaches for authenticating digital audio and video recordings in computer-to-computer communications. However, the presence of adversaries may render the time stamp insecure, and it is crucial to understand the robustness of ENF analysis against anti-forensic operations. This paper investigates possible anti-forensic operations that can remove and alter the ENF signal while trying to preserve the host signal, and develops detection methods targeting these operations. Improvements over anti-forensic operations that can circumvent the detection are also examined, for which various trade-offs are discussed. To develop an understanding of the dynamics between a forensic analyst and an adversary, an evolutionary perspective and a game-theoretical perspective are proposed, which allow for a comprehensive characterization of plausible anti-forensic strategies and countermeasures. Such an understanding has the potential to lead to more secure and reliable time stamp schemes based on ENF analysis.
---
paper_title: Temporal Forensics and Anti-Forensics for Motion Compensated Video

paper_content:

Due to the ease with which digital information can be altered, many digital forensic techniques have been developed to authenticate multimedia content. Similarly, a number of anti-forensic operations have recently been designed to make digital forgeries undetectable by forensic techniques. However, like the digital manipulations they are designed to hide, many anti-forensic operations leave behind their own forensically detectable traces. As a result, a digital forger must balance the trade-off between completely erasing evidence of their forgery and introducing new evidence of anti-forensic manipulation. Because a forensic investigator is typically bound by a constraint on their probability of false alarm (P_fa), they must also balance a trade-off between the accuracy with which they detect forgeries and the accuracy with which they detect the use of anti-forensics. In this paper, we analyze the interaction between a forger and a forensic investigator by examining the problem of authenticating digital videos. Specifically, we study the problem of adding or deleting a sequence of frames from a digital video. We begin by developing a theoretical model of the forensically detectable fingerprints that frame deletion or addition leaves behind, then use this model to improve upon the video frame deletion or addition detection technique proposed by Wang and Farid. Next, we propose an anti-forensic technique designed to fool video forensic techniques and develop a method for detecting the use of anti-forensics. We introduce a new set of techniques for evaluating the performance of anti-forensic operations and develop a game theoretic framework for analyzing the interplay between a forensic investigator and a forger. We use these new techniques to evaluate the performance of each of our proposed forensic and anti-forensic techniques, and identify the optimal actions of both the forger and forensic investigator.
---
paper_title: The Source Identification Game: An Information-Theoretic Perspective

paper_content:

We introduce a theoretical framework in which to cast the source identification problem. Thanks to the adoption of a game-theoretic approach, the proposed framework permits us to derive the ultimate achievable performance of the forensic analysis in the presence of an adversary aiming at deceiving it. The asymptotic Nash equilibrium of the source identification game is derived under an assumption on the resources on which the forensic analyst may rely. The payoff at the equilibrium is analyzed, deriving the conditions under which a successful forensic analysis is possible and the error exponent of the false-negative error probability in such a case. The difficulty of deriving a closed-form solution for general instances of the game is alleviated by the introduction of an efficient numerical procedure for the derivation of the optimum attacking strategy. The numerical analysis is applied to a case study to show the kind of information it can provide.
---
paper_title: Multimedia Data Hiding

paper_content:

Introduction.- Preliminaries.- Classification and capacity of embedding.- Handling uneven embedding capacity.- Data hiding in binary images.- Multilevel data hiding for image & video.- Data hiding for image authentication.- Data hiding for video communication.- Attacks on known data-hiding algorithms.- Attacks on unknown data-hiding algorithms.- Conclusions and perspectives.
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paper_title: Image-adaptive watermarking using visual models

paper_content:

The huge success of the Internet allows for the transmission, wide distribution, and access of electronic data in an effortless manner. Content providers are faced with the challenge of how to protect their electronic data. This problem has generated a flurry of research activity in the area of digital watermarking of electronic content for copyright protection. The challenge here is to introduce a digital watermark that does not alter the perceived quality of the electronic content, while being extremely robust to attack. For instance, in the case of image data, editing the picture or illegal tampering should not destroy or transform the watermark into another valid signature. Equally important, the watermark should not alter the perceived visual quality of the image. From a signal processing perspective, the two basic requirements for an effective watermarking scheme, robustness and transparency, conflict with each other. We propose two watermarking techniques for digital images that are based on utilizing visual models which have been developed in the context of image compression. Specifically, we propose watermarking schemes where visual models are used to determine image dependent upper bounds on watermark insertion. This allows us to provide the maximum strength transparent watermark which, in turn, is extremely robust to common image processing and editing such as JPEG compression, rescaling, and cropping. We propose perceptually based watermarking schemes in two frameworks: the block-based discrete cosine transform and multiresolution wavelet framework and discuss the merits of each one. Our schemes are shown to provide very good results both in terms of image transparency and robustness.
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paper_title: Information-Theoretic Analysis of Information Hiding

paper_content:

An information-theoretic analysis of information hiding is presented, forming the theoretical basis for design of information-hiding systems. Information hiding is an emerging research area which encompasses applications such as copyright protection for digital media, watermarking, fingerprinting, steganography, and data embedding. In these applications, information is hidden within a host data set and is to be reliably communicated to a receiver. The host data set is intentionally corrupted, but in a covert way, designed to be imperceptible to a casual analysis. Next, an attacker may seek to destroy this hidden information, and for this purpose, introduce additional distortion to the data set. Side information (in the form of cryptographic keys and/or information about the host signal) may be available to the information hider and to the decoder. We formalize these notions and evaluate the hiding capacity, which upper-bounds the rates of reliable transmission and quantifies the fundamental tradeoff between three quantities: the achievable information-hiding rates and the allowed distortion levels for the information hider and the attacker. The hiding capacity is the value of a game between the information hider and the attacker. The optimal attack strategy is the solution of a particular rate-distortion problem, and the optimal hiding strategy is the solution to a channel-coding problem. The hiding capacity is derived by extending the Gel'fand-Pinsker (1980) theory of communication with side information at the encoder. The extensions include the presence of distortion constraints, side information at the decoder, and unknown communication channel. Explicit formulas for capacity are given in several cases, including Bernoulli and Gaussian problems, as well as the important special case of small distortions. In some cases, including the last two above, the hiding capacity is the same whether or not the decoder knows the host data set. It is shown that many existing information-hiding systems in the literature operate far below capacity.
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paper_title: Robust content-dependent high-fidelity watermark for tracking in digital cinema

paper_content:

Forensic digital watermarking is a promising tool in the fight against piracy of copyrighted motion imagery content, but to be effective it must be (1) imperceptibly embedded in high-definition motion picture source, (2) reliably retrieved, even from degraded copies as might result from camcorder capture and subsequent very-low-bitrate compression and distribution on the Internet, and (3) secure against unauthorized removal. No existing watermarking technology has yet to meet these three simultaneous requirements of fidelity, robustness, and security. We describe here a forensic watermarking approach that meets all three requirements. It is based on the inherent robustness and imperceptibility of very low spatiotemporal frequency watermark carriers, and on a watermark placement technique that renders jamming attacks too costly in picture quality, even if the attacker has complete knowledge of the embedding algorithm. The algorithm has been tested on HD Cinemascope source material exhibited in a digital cinema viewing room. The watermark is imperceptible, yet recoverable after exhibition capture with camcorders, and after the introduction of other distortions such as low-pass filtering, noise addition, geometric shifts, and the manipulation of brightness and contrast.© (2003) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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paper_title: Secure spread spectrum watermarking for multimedia

paper_content:

This paper presents a secure (tamper-resistant) algorithm for watermarking images, and a methodology for digital watermarking that may be generalized to audio, video, and multimedia data. We advocate that a watermark should be constructed as an independent and identically distributed (i.i.d.) Gaussian random vector that is imperceptibly inserted in a spread-spectrum-like fashion into the perceptually most significant spectral components of the data. We argue that insertion of a watermark under this regime makes the watermark robust to signal processing operations (such as lossy compression, filtering, digital-analog and analog-digital conversion, requantization, etc.), and common geometric transformations (such as cropping, scaling, translation, and rotation) provided that the original image is available and that it can be successfully registered against the transformed watermarked image. In these cases, the watermark detector unambiguously identifies the owner. Further, the use of Gaussian noise, ensures strong resilience to multiple-document, or collusional, attacks. Experimental results are provided to support these claims, along with an exposition of pending open problems.
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paper_title: Quantization Index Modulation: A Class of Provably Good Methods for Digital Watermarking and Information Embedding

paper_content:

We consider the problem of embedding one signal (e.g., a digital watermark), within another "host" signal to form a third, "composite" signal. The embedding is designed to achieve efficient tradeoffs among the three conflicting goals of maximizing the information-embedding rate, minimizing the distortion between the host signal and composite signal, and maximizing the robustness of the embedding. We introduce new classes of embedding methods, termed quantization index modulation (QIM) and distortion-compensated QIM (DC-QIM), and develop convenient realizations in the form of what we refer to as dither modulation. Using deterministic models to evaluate digital watermarking methods, we show that QIM is "provably good" against arbitrary bounded and fully informed attacks, which arise in several copyright applications, and in particular it achieves provably better rate distortion-robustness tradeoffs than currently popular spread-spectrum and low-bit(s) modulation methods. Furthermore, we show that for some important classes of probabilistic models, DC-QIM is optimal (capacity-achieving) and regular QIM is near-optimal. These include both additive white Gaussian noise (AWGN) channels, which may be good models for hybrid transmission applications such as digital audio broadcasting, and mean-square-error-constrained attack channels that model private-key watermarking applications.
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paper_title: Rotation-, scale-, and translation-resilient public watermarking for images

paper_content:

Many electronic watermarks for still images and video content are sensitive to geometric distortions. For example, simple rotation, scaling, and/or translation (RST) of an image can prevent detection of a public watermark. In this paper, we propose a watermarking algorithm that is robust to RST distortions. The watermark is embedded into a 1-dimensional signal obtained by first taking the Fourier transform of the image, resampling the Fourier magnitudes into log-polar coordinates, and then summing a function of those magnitudes along the log-radius axis. If the image is rotated, the resulting signal is cyclically shifted. If it is scaled, the signal is multiplied by some value. And if the image is translated, the signal is unaffected. We can therefore compensate for rotation with a simple search, and for scaling by using the correlation coefficient for the detection metric. False positive results on a database of 10,000 images are reported. Robustness results on a database of 2,000 images are described. It is shown that the watermark is robust to rotation, scale and translation. In addition, the algorithm shows resistance to cropping.
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paper_title: Resistance of digital watermarks to collusive attacks

paper_content:

In digital watermarking (also called digital fingerprinting), extra information is embedded imperceptibly into digital content (such as an audio track, a still image, or a movie). This extra information can be read by authorized parties, and other users attempting to remove the watermark cannot do so without destroying the value of the content by making perceptible changes to the content. This provides a disincentive to copying by allowing copies to be traced to their original owner. Unlike cryptography, digital watermarking provides protection to content that is in the clear. It is not easy to design watermarks that are hard to erase, especially if an attacker has access to several differently marked copies of the same base content. Cox et al. (see IEEE Trans. on Image Processing, vol.6, no.12, p.1673-87, 1997) have proposed the use of additive normally distributed values as watermarks, and have sketched an argument showing that, in a certain theoretical model, such watermarks are resistant to collusive attacks. Here, we fill in the mathematical justification for this claim.
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paper_title: Performance Study of ECC-based Collusion-resistant Multimedia Fingerprinting

paper_content:

Digital fingerprinting is a tool to protect multimedia content from illegal redistribution by uniquely marking copies of the content distributed to each user. Fingerprinting based on error correction coding (ECC) handle the important issue of how to embed the fingerprint into host data in an abstract way known as the marking assumptions, which often do not fully account for multimedia specific issues. In this paper, we examine the performance of ECC based fingerprinting by considering both coding and embedding issues. We provide performance comparison of ECC-based scheme and a major alternative of orthogonal fingerprinting. As averaging is a feasible and cost-effective collusion attack against multimedia fingerprints yet is generally not considered in the ECC-based system, we also investigate the resistance against averaging collusion and identify avenues for improving collusion resistance.
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paper_title: Resistance of orthogonal Gaussian fingerprints to collusion attacks

paper_content:

Digital fingerprinting is a means to offer protection to digital data by which fingerprints embedded in the multimedia are capable of identifying unauthorized use of digital content. A powerful attack that can be employed to reduce this tracing capability is collusion. In this paper, we study the collusion resistance of a fingerprinting system employing Gaussian distributed fingerprints and orthogonal modulation. We propose a likelihood-based approach to estimate the number of colluders, and introduce the thresholding detector for colluder identification. We first analyze the collusion resistance of a system to the average attack by considering the probability of a false negative and the probability of a false positive when identifying colluders. Lower and upper bounds for the maximum number of colluders K/sub max/ are derived. We then show that the detectors are robust to different attacks. We further study different sets of performance criteria.
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paper_title: Joint coding and embedding techniques for MultimediaFingerprinting

paper_content:

Digital fingerprinting protects multimedia content from illegal redistribution by uniquely marking every copy of the content distributed to each user. The collusion attack is a powerful attack where several different fingerprinted copies of the same content are combined together to attenuate or even remove the fingerprints. One major category of collusion-resistant fingerprinting employs an explicit step of coding. Most existing works on coded fingerprinting mainly focus on the code-level issues and treat the embedding issues through abstract assumptions without examining the overall performance. In this paper, we jointly consider the coding and embedding issues for coded fingerprinting systems and examine their performance in terms of collusion resistance, detection computational complexity, and distribution efficiency. Our studies show that coded fingerprinting has efficient detection but rather low collusion resistance. Taking advantage of joint coding and embedding, we propose a permuted subsegment embedding technique and a group-based joint coding and embedding technique to improve the collusion resistance of coded fingerprinting while maintaining its efficient detection. Experimental results show that the number of colluders that the proposed methods can resist is more than three times as many as that of the conventional coded fingerprinting approaches.
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paper_title: Performance of Orthogonal Fingerprinting Codes Under Worst-Case Noise

paper_content:

We study the effect of the noise distribution on the error probability of the detection test when a class of randomly rotated spherical fingerprints is used. The detection test is performed by a focused correlation detector, and the spherical codes studied here form a randomized orthogonal constellation. The colluders create a noise-free forgery by uniform averaging of their individual copies, and then add a noise sequence to form the actual forgery. We derive the noise distribution that maximizes the error probability of the detector under average and almost-sure distortion constraints. Moreover, we characterize the noise distribution that minimizes the decoder's error exponent under a large-deviations distortion constraint.
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paper_title: Collusion-resistant fingerprinting for multimedia

paper_content:

Digital fingerprinting is a technology for enforcing digital rights policies whereby unique labels, known as digital fingerprints, are inserted into content prior to distribution. For multimedia content, fingerprints can be embedded using conventional watermarking techniques that are typically concerned with robustness against a variety of attacks mounted by an individual. These attacks, known as multiuser collusion attacks, provide a cost-effective method for attenuating each of the colluder's fingerprints and poses a real threat to protecting media data and enforcing usage policies. In this article, we review some major design methodologies for collusion-resistant fingerprinting of multimedia and highlight common and unique issues of different fingerprinting techniques. It also provides detailed discussions on the two major classes of fingerprinting strategies, namely, orthogonal fingerprinting and correlated fingerprinting.
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paper_title: Nonlinear collusion attacks on independent fingerprints for multimedia

paper_content:

Digital fingerprinting is a technology for tracing the distribution of multimedia content and protecting them from unauthorized redistribution. Collusion attack is a cost effective attack against digital fingerprinting where several copies with the same content but different fingerprints are combined to remove the original fingerprints. In this paper, we investigate average and nonlinear collusion attacks of independent Gaussian fingerprints and study both their effectiveness and the perceptual quality. We also propose the bounded Gaussian fingerprints to improve the perceptual quality of the fingerprinted copies. We further discuss the tradeoff between the robustness against collusion attacks and the perceptual quality of a fingerprinting system.
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paper_title: Anti-Collusion Fingerprinting for Multimedia

paper_content:

Digital fingerprinting is a technique for identifying users who use multimedia content for unintended purposes, such as redistribution. These fingerprints are typically embedded into the content using watermarking techniques that are designed to be robust to a variety of attacks. A cost-effective attack against such digital fingerprints is collusion, where several differently marked copies of the same content are combined to disrupt the underlying fingerprints. We investigate the problem of designing fingerprints that can withstand collusion and allow for the identification of colluders. We begin by introducing the collusion problem for additive embedding. We then study the effect that averaging collusion has on orthogonal modulation. We introduce a tree-structured detection algorithm for identifying the fingerprints associated with K colluders that requires O(Klog(n/K)) correlations for a group of n users. We next develop a fingerprinting scheme based on code modulation that does not require as many basis signals as orthogonal modulation. We propose a new class of codes, called anti-collusion codes (ACCs), which have the property that the composition of any subset of K or fewer codevectors is unique. Using this property, we can therefore identify groups of K or fewer colluders. We present a construction of binary-valued ACC under the logical AND operation that uses the theory of combinatorial designs and is suitable for both the on-off keying and antipodal form of binary code modulation. In order to accommodate n users, our code construction requires only O(/spl radic/n) orthogonal signals for a given number of colluders. We introduce three different detection strategies that can be used with our ACC for identifying a suspect set of colluders. We demonstrate the performance of our ACC for fingerprinting multimedia and identifying colluders through experiments using Gaussian signals and real images.
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paper_title: Joint coding and embedding techniques for MultimediaFingerprinting

paper_content:

Digital fingerprinting protects multimedia content from illegal redistribution by uniquely marking every copy of the content distributed to each user. The collusion attack is a powerful attack where several different fingerprinted copies of the same content are combined together to attenuate or even remove the fingerprints. One major category of collusion-resistant fingerprinting employs an explicit step of coding. Most existing works on coded fingerprinting mainly focus on the code-level issues and treat the embedding issues through abstract assumptions without examining the overall performance. In this paper, we jointly consider the coding and embedding issues for coded fingerprinting systems and examine their performance in terms of collusion resistance, detection computational complexity, and distribution efficiency. Our studies show that coded fingerprinting has efficient detection but rather low collusion resistance. Taking advantage of joint coding and embedding, we propose a permuted subsegment embedding technique and a group-based joint coding and embedding technique to improve the collusion resistance of coded fingerprinting while maintaining its efficient detection. Experimental results show that the number of colluders that the proposed methods can resist is more than three times as many as that of the conventional coded fingerprinting approaches.
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paper_title: Collusion-Resistant Video Fingerprinting for Large User Group

paper_content:

Digital fingerprinting protects multimedia content from illegal redistribution by uniquely marking copies of the content distributed to each user. Most existing multimedia fingerprinting schemes consider userset on the scale of thousands for evaluation. However, in real applications such as cable TV and DVD distribution, the potential users can be as many as 10 ~ 100 million. This large scale user set demands not only high collusion resistance but also high efficiency in the fingerprint construction and detection, which makes most existing schemes incapable of practical application. A recently proposed joint coding and embedding fingerprinting framework provides a promising balance between collusion resistance, efficient construction and detection. In this paper, we explore how to employ such a framework and develop practical algorithms to fingerprint video in such challenging practical settings as to accommodate more than ten million users and resisting hundreds of users' collusion. Both analysis and experimental results show the great potential of joint coding and embedding fingerprinting for real video fingerprinting applications.
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paper_title: Multiresolution scene-based video watermarking using perceptual models

paper_content:

We present a watermarking procedure to embed copyright protection into digital video. Our watermarking procedure is scene-based and video dependent. It directly exploits spatial masking, frequency masking, and temporal properties to embed an invisible and robust watermark. The watermark consists of static and dynamic temporal components that are generated from a temporal wavelet transform of the video scenes. The resulting wavelet coefficient frames are modified by a perceptually shaped pseudorandom sequence representing the author. The noise-like watermark is statistically undetectable to thwart unauthorized removal. Furthermore, the author representation resolves the deadlock problem. The multiresolution watermark may be detected on single frames without knowledge of the location of the frames in the video scene. We demonstrate the robustness of the watermarking procedure to several video degradations and distortions.
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paper_title: Statistical invisibility for collusion-resistant digital video watermarking

paper_content:

We present a theoretical framework for the linear collusion analysis of watermarked digital video sequences, and derive a new theorem equating a definition of statistical invisibility, collusion-resistance, and two practical watermark design rules. The proposed framework is simple and intuitive; the basic processing unit is the video frame and we consider second-order statistical descriptions of their temporal inter-relationships. Within this analytical setup, we define the linear frame collusion attack, the analytic notion of a statistically invisible video watermark, and show that the latter is an effective counterattack against the former. Finally, to show how the theoretical results detailed in this paper can easily be applied to the construction of collusion-resistant video watermarks, we encapsulate the analysis into two practical video watermark design rules that play a key role in the subsequent development of a novel collusion-resistant video watermarking algorithm discussed in a companion paper.
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paper_title: Collusion-Secure Fingerprinting for Digital Data

paper_content:

This paper discusses methods for assigning code-words for the purpose of fingerprinting digital data, e.g., software, documents, music, and video. Fingerprinting consists of uniquely marking and registering each copy of the data. This marking allows a distributor to detect any unauthorized copy and trace it back to the user. This threat of detection will deter users from releasing unauthorized copies. A problem arises when users collude: for digital data, two different fingerprinted objects can be compared and the differences between them detected. Hence, a set of users can collude to detect the location of the fingerprint. They can then alter the fingerprint to mask their identities. We present a general fingerprinting solution which is secure in the context of collusion. In addition, we discuss methods for distributing fingerprinted data.
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paper_title: Collusion-resistant fingerprinting for multimedia

paper_content:

Digital fingerprinting is a technology for enforcing digital rights policies whereby unique labels, known as digital fingerprints, are inserted into content prior to distribution. For multimedia content, fingerprints can be embedded using conventional watermarking techniques that are typically concerned with robustness against a variety of attacks mounted by an individual. These attacks, known as multiuser collusion attacks, provide a cost-effective method for attenuating each of the colluder's fingerprints and poses a real threat to protecting media data and enforcing usage policies. In this article, we review some major design methodologies for collusion-resistant fingerprinting of multimedia and highlight common and unique issues of different fingerprinting techniques. It also provides detailed discussions on the two major classes of fingerprinting strategies, namely, orthogonal fingerprinting and correlated fingerprinting.
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paper_title: Optimal probabilistic fingerprint codes

paper_content:

We construct binary codes for fingerprinting digital documents. Our codes for n users that are e-secure against c pirates have length O(c2log(n/e)). This improves the codes proposed by Boneh and Shaw l1998r whose length is approximately the square of this length. The improvement carries over to works using the Boneh--Shaw code as a primitive, for example, to the dynamic traitor tracing scheme of Tassa l2005r. By proving matching lower bounds we establish that the length of our codes is best within a constant factor for reasonable error probabilities. This lower bound generalizes the bound found independently by Peikert et al. l2003r that applies to a limited class of codes. Our results also imply that randomized fingerprint codes over a binary alphabet are as powerful as over an arbitrary alphabet and the equal strength of two distinct models for fingerprinting.
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paper_title: Combining digital watermarks and collusion secure fingerprints for digital images

paper_content:

Digital watermarking is the enabling technology to prove ownership on copyrighted material, detect originators of illegally made copies, monitor the usage of the copyrighted multimedia data, and analyze the spread spectrum of the data over networks and servers. Embedding of unique customer identification as a watermark into data is called fingerprinting to identify illegal copies of documents. Basically, watermarks embedded into multimedia data for enforcing copyrights must uniquely identify the data and must be difficult to remove, even after various media transformation processes. Digital fingerprinting raises the additional problem that we produce different copies for each customer. Attackers can compare several fingerprinted copies to find and destroy the embedded identification string by altering the data in those places where a difference was detected. In our paper we present a technology for combining a collusion-secure fingerprinting scheme based on finite geometries and a watermarking mechanism with special marking points for digital images. The only marking positions the pirates cannot detect are those positions which contain the same letter in all the compared documents, called intersection of different fingerprints. The proposed technology for a maximal number d of pirates, puts enough information in the intersection of up to d fingerprints to uniquely identify all the pirates.
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paper_title: Combinatorial Properties of Frameproof and Traceability Codes

paper_content:

In order to protect copyrighted material, codes may be embedded in the content or codes may be associated with the keys used to recover the content. Codes can offer protection by providing some form of traceability (TA) for pirated data. Several researchers have studied different notions of TA and related concepts in previous years. "Strong" versions of TA allow at least one member of a coalition that constructs a "pirate decoder" to be traced. Weaker versions of this concept ensure that no coalition can "frame" a disjoint user or group of users. All these concepts can be formulated as codes having certain combinatorial properties. We study the relationships between the various notions, and we discuss equivalent formulations using structures such as perfect hash families. We use methods from combinatorics and coding theory to provide bounds (necessary conditions) and constructions (sufficient conditions) for the objects of interest.
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paper_title: Performance Study of ECC-based Collusion-resistant Multimedia Fingerprinting

paper_content:

Digital fingerprinting is a tool to protect multimedia content from illegal redistribution by uniquely marking copies of the content distributed to each user. Fingerprinting based on error correction coding (ECC) handle the important issue of how to embed the fingerprint into host data in an abstract way known as the marking assumptions, which often do not fully account for multimedia specific issues. In this paper, we examine the performance of ECC based fingerprinting by considering both coding and embedding issues. We provide performance comparison of ECC-based scheme and a major alternative of orthogonal fingerprinting. As averaging is a feasible and cost-effective collusion attack against multimedia fingerprints yet is generally not considered in the ECC-based system, we also investigate the resistance against averaging collusion and identify avenues for improving collusion resistance.
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paper_title: Efficient Watermark Detection and Collusion Security

paper_content:

Watermarking techniques allow the tracing of pirated copies of data by modifying each copy as it is distributed, embedding hidden information into the data which identifies the owner of that copy The owner of the original data can then identify the source of a pirated copy by reading out the hidden information present in that copy. Naturally, one would like these schemes to be as efficient as possible. Previous analyses measured efficiency in terms of the amount of data needed to allow many different copies to be distributed; in order to hide enough data to distinguish many users, the total original data must be sufficiently large Here, we consider a different notion of efficiency: What resources does the watermark detector need in order to perform this tracing?We address this question in two ways. First, we present a modified version of the CKLS media watermarking algorithm which improves the detector running time from linear to polylogarithmic in the number of users while still maintaining collusion-security. Second, we show that any public, invertible watermarking scheme secure against c colluding adversaries must have at least ?(c) bits of secret information.
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paper_title: Error Control Systems for Digital Communication and Storage

paper_content:

1. Error Control Coding for Digital Communication Systems. 2. Galois Fields. 3. Polynomials over Galois Fields. 4. Linear Block Codes. 5. Cyclic Codes. 6. Hadamard, Quadratic Residue, and Golay Codes. 7. Reed-Muller Codes 8. BCH and Reed-Solomon Codes. 9. Decoding BCH and Reed-Solomon Codes. 10. The Analysis of the Performance of Block Codes. 11. Convolutional Codes. 12. The Viterbi Decoding Algorithm. 13. The Sequential Decoding Algorithms. 14. Trellis Coded Modulation. 15. Error Control for Channels with Feedback. 16. Applications. Appendices: A. Binary Primitive Polynomials. B. Add-on Tables and Vector Space Representations for GF(8) Through GF(1024). C. Cyclotronic Cosets Modulo 2m-1. D. Minimal Polynomials for Elements in GF (2m). E. Generator Polynomials of Binary BCH Codes of Lengths Through 511. Bibliography.
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paper_title: Joint coding and embedding techniques for MultimediaFingerprinting

paper_content:

Digital fingerprinting protects multimedia content from illegal redistribution by uniquely marking every copy of the content distributed to each user. The collusion attack is a powerful attack where several different fingerprinted copies of the same content are combined together to attenuate or even remove the fingerprints. One major category of collusion-resistant fingerprinting employs an explicit step of coding. Most existing works on coded fingerprinting mainly focus on the code-level issues and treat the embedding issues through abstract assumptions without examining the overall performance. In this paper, we jointly consider the coding and embedding issues for coded fingerprinting systems and examine their performance in terms of collusion resistance, detection computational complexity, and distribution efficiency. Our studies show that coded fingerprinting has efficient detection but rather low collusion resistance. Taking advantage of joint coding and embedding, we propose a permuted subsegment embedding technique and a group-based joint coding and embedding technique to improve the collusion resistance of coded fingerprinting while maintaining its efficient detection. Experimental results show that the number of colluders that the proposed methods can resist is more than three times as many as that of the conventional coded fingerprinting approaches.
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paper_title: Collusion Secure q-ary Fingerprinting for Perceptual Content

paper_content:

We propose a q-ary fingerprinting system for stored digital objects such as images, videos and audio clips. A fingerprint is a q-ary sequence. The object is divided into blocks and each symbol of the fingerprint is embedded into one block. Colluders construct a pirate object by assembling parts from their copies. They can also erase some of the marks or cut out part of the object resulting in a shortened fingerprint with some unreadable marks. We give constructions of codes that can identify one of the colluders once a pirate object is found.
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paper_title: Collusion-Resistant Video Fingerprinting for Large User Group

paper_content:

Digital fingerprinting protects multimedia content from illegal redistribution by uniquely marking copies of the content distributed to each user. Most existing multimedia fingerprinting schemes consider userset on the scale of thousands for evaluation. However, in real applications such as cable TV and DVD distribution, the potential users can be as many as 10 ~ 100 million. This large scale user set demands not only high collusion resistance but also high efficiency in the fingerprint construction and detection, which makes most existing schemes incapable of practical application. A recently proposed joint coding and embedding fingerprinting framework provides a promising balance between collusion resistance, efficient construction and detection. In this paper, we explore how to employ such a framework and develop practical algorithms to fingerprint video in such challenging practical settings as to accommodate more than ten million users and resisting hundreds of users' collusion. Both analysis and experimental results show the great potential of joint coding and embedding fingerprinting for real video fingerprinting applications.
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paper_title: Digital fingerprinting codes: Problem statements, constructions, identification of traitors

paper_content:

We consider a general fingerprinting problem of digital data under which coalitions of users can alter or erase some bits in their copies in order to create an illegal copy. Each user is assigned a fingerprint which is a word in a fingerprinting code of size M (the total number of users) and length n. We present binary fingerprinting codes secure against size-t coalitions which enable the distributor (decoder) to recover at least one of the users from the coalition with probability of error exp(-/spl Omega/(n)) for M=exp(/spl Omega/(n)). This is an improvement over the best known schemes that provide the error probability no better than exp(-/spl Omega/(n/sup 1/2/)) and for this probability support at most exp(O(n/sup 1/2/)) users. The construction complexity of codes is polynomial in n. We also present versions of these constructions that afford identification algorithms of complexity poly(n)=polylog(M), improving over the best previously known complexity of /spl Omega/(M). For the case t=2, we construct codes of exponential size with even stronger performance, namely, for which the distributor can either recover both users from the coalition with probability 1-exp(/spl Omega/(n)), or identify one traitor with probability 1.
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paper_title: Behavior forensics for scalable multiuser collusion: fairness versus effectiveness

paper_content:

Multimedia security systems involve many users with different objectives and users influence each other's performance. To have a better understanding of multimedia security systems and offer stronger protection of multimedia, behavior forensics formulate the dynamics among users and investigate how they interact with and respond to each other. This paper analyzes the behavior forensics in multimedia fingerprinting and formulates the dynamics among attackers during multi-user collusion. In particular, this paper focuses on how colluders achieve the fair play of collusion and guarantee that all attackers share the same risk (i.e., the probability of being detected). We first analyze how to distribute the risk evenly among colluders when they receive fingerprinted copies of scalable resolutions due to network and device heterogeneity. We show that generating a colluded copy of higher resolution puts more severe constraints on achieving fairness. We then analyze the effectiveness of fair collusion. Our results indicate that the attackers take a larger risk of being captured when the colluded copy has higher resolution, and they have to take this tradeoff into consideration during collusion. Finally, we analyze the collusion resistance of the scalable fingerprinting systems in various scenarios with different system requirements, and evaluate the maximum number of colluders that the fingerprinting systems can withstand
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paper_title: Game-theoretic strategies and equilibriums in multimedia fingerprinting social networks

paper_content:

Multimedia social network is a network infrastructure in which the social network users share multimedia contents with all different purposes. Analyzing user behavior in multimedia social networks helps design more secured and efficient multimedia and networking systems. Multimedia fingerprinting protects multimedia from illegal alterations and multiuser collusion is a cost-effective attack. The colluder social network is naturally formed during multiuser collusion with which colluders gain reward by redistributing the colluded multimedia contents. Since the colluders have conflicting interest, the maximal-payoff collusion for one colluder may not be the maximal-payoff collusion for others. Hence, before a collusion being successful, the colluders must bargain with each other to reach agreements. We first model the bargaining behavior among colluders as a noncooperative game and study four different bargaining solutions of this game. Moreover, the market value of the redistributed multimedia content is often time-sensitive. The earlier the colluded copy being released, the more the people are willing to pay for it. Thus, the colluders have to reach agreements on how to distribute reward and risk among themselves as soon as possible. This paper further incorporates this time-sensitiveness of the colluders' reward and studies the time-sensitive bargaining equilibrium. The study in this paper reveals the strategies that are optimal for the colluders; thus, all the colluders have no inventive to disagree. Such understanding reduces the possible types of collusion into a small finite set.
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paper_title: Multi-User Collusion Behavior Forensics: Game Theoretic Formulation of Fairness Dynamics

paper_content:

Multi-user collusion is an cost-effective attack against digital fingerprinting, in which a group of attackers collectively undermine the traitor tracing capability of digital fingerprints. However, during multi-user collusion, each colluder wishes to minimize his/her own risk and maximize his/her own profit, and different colluders have different objectives. Thus, an important issue during collusion is to agree on how to distribute the risk/profit among colluders and ensure fairness of the attack. To have a better understanding of the attackers' behavior during collusion to achieve fairness, this paper models the dynamics among colluders as a non-cooperative game. We then study the Pareto-optimal set, where no colluder can further increase his/her own payoff without decreasing others', and analyze the Nash bargaining solution of this game.
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paper_title: Steganography in Digital Media: Principles, Algorithms, and Applications

paper_content:

Steganography, the art of hiding of information in apparently innocuous objects or images, is a field with a rich heritage, and an area of rapid current development. This clear, self-contained guide shows you how to understand the building blocks of covert communication in digital media files and how to apply the techniques in practice, including those of steganalysis, the detection of steganography. Assuming only a basic knowledge in calculus and statistics, the book blends the various strands of steganography, including information theory, coding, signal estimation and detection, and statistical signal processing. Experiments on real media files demonstrate the performance of the techniques in real life, and most techniques are supplied with pseudo-code, making it easy to implement the algorithms. The book is ideal for students taking courses on steganography and information hiding, and is also a useful reference for engineers and practitioners working in media security and information assurance.
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paper_title: Behavior modeling and forensics for multimedia social networks

paper_content:

Factors influencing human behavior have seldom appeared in signal processing disciplines. Therefore, the goals of the article are to illustrate why human factors are important, identify emerging issues strongly related to signal processing, and to demonstrate that signal processing can be effectively used to model, analyze, and perform behavior forensics for multimedia social networks. Since media security and content protection is a major issue, the article illustrates various aspects of issues and problems in multimedia social networks via a case study of human behavior in traitor-tracing multimedia fingerprinting. We focus on the understanding of behavior forensics from signal processing perspective and present a framework to model and analyze user dynamics. The objective is to provide a broad overview of recent advances in behavior modeling and forensics for multimedia social networks.
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paper_title: Behavior forensics with side information for multimedia fingerprinting social networks

paper_content:

In multimedia social networks, there exists complicated dynamics among users who share and exchange multimedia content. Using multimedia fingerprinting as an example, this paper investigates the human behavior dynamics in the multimedia social networks with side information. Side information is the information other than the colluded multimedia content that can help increase the probability of detection. We study the impact of side information in multimedia fingerprinting and show that the statistical means of the detection statistics can help the fingerprint detector significantly improve the collusion resistance. We then investigate how to probe the side information and model the dynamics between the fingerprint detector and the colluders as a two-stage extensive game with perfect information. We model the colluder-detector behavior dynamics as a two-stage game and find the equilibrium of the colluder-detector game using backward induction and show that the min-max solution is a Nash equilibrium, which gives no incentive for everyone in the multimedia fingerprint social network to deviate. This paper demonstrates that the proposed side information can significantly help improve the system performance to almost the same as the optimal correlation-based detector. Such result opens up a new scope in the research of fingerprinting system that given any fingerprint code, leveraging side information can improve the collusion resistance. Also, we provide the solutions to how to reach optimal collusion strategy and the corresponding detection, thus lead to a better protection of the multimedia content.
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paper_title: Impact of Social Network Structure on Multimedia Fingerprinting Misbehavior Detection and Identification

paper_content:

Users in video-sharing social networks actively interact with each other, and it is of critical importance to model user behavior and analyze the impact of human factors on video sharing systems. In video-sharing social networks, users have access to extra resources from their peers, and they also contribute their own resources to help others. Each user wants to maximize his/her own payoff, and they negotiate with each other to achieve fairness and address this conflict. However, some selfish users may cheat to their peers and manipulate the system to maximize their own payoffs, and cheat prevention is a critical requirement in many social networks to stimulate user cooperation. It is of ample importance to design monitoring mechanisms to detect and identify misbehaving users, and to design cheat-proof cooperation stimulation strategies. Using video fingerprinting as an example, this paper analyzes the complex dynamics among colluders during multiuser collusion, and explores possible monitoring mechanisms to detect and identify misbehaving colluders in multiuser collusion. We consider two types of colluder networks: one has a centralized structure with a trusted ringleader, and the other is a distributed peer-structured network. We investigate the impact of network structures on misbehavior detection and identification, propose different selfish colluder identification schemes for different colluder networks, and analyze their performance. We show that the proposed schemes can accurately identify selfish colluders without falsely accusing others even under attacks. We also evaluate their robustness against framing attacks and quantify the maximum number of framing colluders that they can resist.
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paper_title: Traitor-Within-Traitor Behavior Forensics: Strategy and Risk Minimization

paper_content:

Multimedia security systems have many users with different objectives and they influence each other's performance and decisions. Behavior forensics analyzes how users with conflicting interests interact with and respond to each other. Such investigation enables a thorough understanding of multimedia security systems and helps the digital rights enforcer offer stronger protection of multimedia. This paper analyzes the dynamics among attackers during multiuser collusion. The colluders share not only the profit from the redistribution of multimedia but also the risk of being detected by the content owner, and an important issue in collusion is fairness of the attack (i.e., whether all attackers share the same risk) (e.g., whether they have the same probability of being detected). While they might agree so, some selfish colluders may break their fair-play agreement in order to further lower their risk. This paper investigates the problem of "traitors within traitors" in multimedia forensics, in an effort to formulate the dynamics among attackers and understand their behavior to minimize their own risk and protect their own interests. As the first work on the analysis of this colluder dynamics, this paper explores some possible strategies that a selfish colluder can use to minimize his or her probability of being caught. We show that processing his or her fingerprinted copy before multiuser collusion helps a selfish colluder further lower his or her risk, especially when the colluded copy has high resolution and good quality. This paper also investigates the optimal precollusion processing strategies for selfish colluders to minimize their risk under the quality constraints
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paper_title: Forensic hash for multimedia information

paper_content:

Digital multimedia such as images and videos are prevalent on today's internet and cause significant ::: social impact, which can be evidenced by the proliferation of social networking sites with user generated ::: contents. Due to the ease of generating and modifying images and videos, it is critical to establish ::: trustworthiness for online multimedia information. In this paper, we propose novel approaches to ::: perform multimedia forensics using compact side information to reconstruct the processing history of ::: a document. We refer to this as FASHION, standing for Forensic hASH for informatION assurance. ::: Based on the Radon transform and scale space theory, the proposed forensic hash is compact and ::: can effectively estimate the parameters of geometric transforms and detect local tampering that an ::: image may have undergone. Forensic hash is designed to answer a broader range of questions regarding ::: the processing history of multimedia data than the simple binary decision from traditional robust ::: image hashing, and also offers more efficient and accurate forensic analysis than multimedia forensic ::: techniques that do not use any side information.
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paper_title: Multimedia forensic hash based on visual words

paper_content:

In recent years, digital images and videos have become increasingly popular over the internet and bring great social impact to a wide audience. In the meanwhile, technology advancement allows people to easily alter the content of digital multimedia and brings serious concern on the trustworthiness of online multimedia information. Forensic hash is a short signature attached to an image before transmission and acts as side information for analyzing the processing history and trustworthiness of the received image. In this paper, we propose a new construction of forensic hash based on visual words representation. We encode SIFT features into a compact visual words representation for robust estimation of geometric transformations and propose a hybrid construction using both SIFT and block-based features to detect and localize image tampering. The proposed hash construction achieves more robust and accurate forensic analysis than prior work.
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paper_title: Seam carving estimation using forensic hash

paper_content:

Seam carving is an adaptive multimedia retargeting technique to resize multimedia data for different display sizes. This technique has found promising applications in media consumption on mobile devices such as tablets and smartphones. However, seam carving can also be used to maliciously alter image content and when combined with other tampering operations, makes tampering detection very difficult by traditional multimedia forensic techniques. In this paper, we study the problem of seam carving estimation and tampering localization using very compact side information called forensic hash. The forensic hash technique bridges two related areas, namely robust image hashing and blind multimedia forensics, to answer a broader scope of forensic questions in a more efficient and accurate manner. We show that our recently proposed forensic hash construction can be extended to accurately estimate seam carving and detect local tampering.
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paper_title: Protection against reverse engineering in digital cameras

paper_content:

Over the past decade, a number of digital forensic techniques have been developed to authenticate digital signals. One important set of forensic techniques operates by estimating signal processing components of a digital camera's signal processing pipeline, then using these estimates to perform forensic tasks such as camera identification or forgery detection. However, because these techniques are capable of estimating a camera's internal signal processing components, these forensic techniques can be used for reverse engineering. In this paper, we propose integrating an anti-forensic module into a digital camera's processing pipeline to protect against forensic reverse engineering. Our proposed technique operates by removing linear dependencies amongst an output images interpolated color values and by disrupting the color sampling grid. Experimental results show that our proposed technique can be effectively used to protect against the forensic reverse engineering of key components of a digital camera's processing pipeline.
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paper_title: A Component Estimation Framework for Information Forensics

paper_content:

With a rapid growth of imaging technologies and an increasingly widespread usage of digital images and videos for a large number of high security and forensic applications, there is a strong need for techniques to verify the source and integrity of digital data. Component forensics is new approach for forensic analysis that aims to estimate the algorithms and parameters in each component of the digital device. In this paper, we develop a novel theoretical foundation to understand the fundamental performance limits of component forensics. We define formal notions of identifiability of components in the information processing chain, and present methods to quantify the accuracies at which the component parameters can be estimated. Building upon the proposed theoretical framework, we devise methods to improve the accuracies of component parameter estimation for a wide range of forensic applications.
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paper_title: A pattern classification framework for theoretical analysis of component forensics

paper_content:

Component forensics is an emerging methodology for forensic analysis that aims at estimating the algorithms and parameters in each component of a digital device. This paper proposes a theoretical foundation to examine the performance limits of component forensics. Using ideas from pattern classification theory, we define formal notions of identifiability of components in the information processing chain. We show that the parameters of certain device components can be accurately identified only in controlled settings through semi non-intrusive forensics, while the parameters of some others can be computed directly from the available sample data via complete non-intrusive analysis. We then extend the proposed theoretical framework to quantify and improve the accuracies and confidence in component parameter identification for several forensic applications.
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paper_title: Robustness of color interpolation identification against anti-forensic operations

paper_content:

Color interpolation identification using digital images has been shown to be a powerful tool for addressing a range of digital forensic questions. However, due to the existence of adversaries who have the incentive to counter the identification, it is necessary to understand how color interpolation identification performs against anti-forensic operations that intentionally manipulate identification results. This paper proposes two anti-forensic techniques against which the robustness of color interpolation identification is investigated. The first technique employs parameter perturbation to circumvent identification. Various options that achieve different trade-offs between image quality and identification manipulation are examined. The second technique involves algorithm mixing and demonstrates that one can not only circumvent but also mislead the identification system while preserving the image quality. Additional discussions are also provided to enhance the understanding of anti-forensics and its implications to the design of identification systems.
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Title: Information Forensics: An Overview of the First Decade

Section 1: INTRODUCTION
Description 1: Provide an overview of the development of information forensics and highlight the need for forensic technologies to ensure the authenticity and trustworthiness of digital content.

Section 2: DETECTION OF TAMPERING AND PROCESSING OPERATIONS
Description 2: Discuss various forensic techniques used to identify manipulations and processing operations within multimedia content.

Section 3: STATISTICAL CLASSIFIERS
Description 3: Explain how statistical classifiers use machine learning to detect changes in multimedia files and identify manipulations.

Section 4: DEVICE FINGERPRINTS
Description 4: Elaborate on how intrinsic fingerprints left by acquisition devices can be used to authenticate multimedia files.

Section 5: MANIPULATION FINGERPRINTS
Description 5: Describe specific fingerprints such as from copy-move forgeries, resampling, contrast enhancement, and median filtering that can be detected in manipulated multimedia content.

Section 6: COMPRESSION AND CODING FINGERPRINTS
Description 6: Detail how compression techniques like JPEG can leave forensic fingerprints that can be used for various forensic tasks.

Section 7: PHYSICAL INCONSISTENCIES
Description 7: Discuss techniques that identify inconsistencies in multimedia content using physics-based models rather than fingerprints.

Section 8: DEVICE FORENSICS
Description 8: Highlight forensic techniques to trace multimedia content back to the acquisition devices and differentiate between individual device units.

Section 9: ANTI-FORENSICS AND COUNTERMEASURES
Description 9: Explore anti-forensic techniques that can disguise manipulations, and discuss countermeasures developed to detect anti-forensic activities.

Section 10: EMERGING FORENSIC USE OF ENVIRONMENTAL SIGNATURES
Description 10: Introduce environmental fingerprints such as Electric Network Frequency (ENF) and their applications in determining the time and location of recordings.

Section 11: EMBEDDED FINGERPRINTING AND FORENSIC WATERMARKING
Description 11: Review techniques involving embedded fingerprints and forensic watermarks used for tracing and anti-collusion to protect multimedia content.

Section 12: BEHAVIOR/HUMAN/SOCIAL FACTORS AND DYNAMICS IN FORENSICS
Description 12: Discuss the social and behavioral aspects of forensic analysis and the adversarial dynamics between users, attackers, and investigators.

Section 13: FINAL THOUGHTS AND FUTURE DIRECTIONS
Description 13: Provide concluding thoughts on the current state of information forensics and discuss potential future directions and theoretical foundations for advancing the field.