On the exploitation of DCT statistics for cropping detectors
Claudio Vittorio Ragaglia Francesco Guarnera Sebastiano Battiato
{The study of frequency components derived from Discrete Cosine Transform
(DCT) has been widely used in image analysis. In recent years it has been
observed that significant information can be extrapolated from them about the
lifecycle of the image, but no study has focused on the analysis between them
and the source resolution of the image. In this work, we investigated a novel
image resolution classifier that employs DCT statistics with the goal to detect
the original resolution of images; in particular the insight was exploited to
address the challenge of identifying cropped images. Training a Machine
Learning (ML) classifier on entire images (not cropped), the generated model
can leverage this information to detect cropping. The results demonstrate the
classifier's reliability in distinguishing between cropped and not cropped
images, providing a dependable estimation of their original resolution. This
advancement has significant implications for image processing applications,
including digital security, authenticity verification, and visual quality
analysis, by offering a new tool for detecting image manipulations and
enhancing qualitative image assessment. This work opens new perspectives in the
field, with potential to transform image analysis and usage across multiple
domains.}