Towards Effective Image Forensics via A Novel Computationally Efficient
  Framework and A New Image Splice Dataset
Ankit Yadav Dinesh Kumar Vishwakarma
Splice detection models are the need of the hour since splice manipulations
can be used to mislead, spread rumors and create disharmony in society.
However, there is a severe lack of image splicing datasets, which restricts the
capabilities of deep learning models to extract discriminative features without
overfitting. This manuscript presents two-fold contributions toward splice
detection. Firstly, a novel splice detection dataset is proposed having two
variants. The two variants include spliced samples generated from code and
through manual editing. Spliced images in both variants have corresponding
binary masks to aid localization approaches. Secondly, a novel
Spatio-Compression Lightweight Splice Detection Framework is proposed for
accurate splice detection with minimum computational cost. The proposed
dual-branch framework extracts discriminative spatial features from a
lightweight spatial branch. It uses original resolution compression data to
extract double compression artifacts from the second branch, thereby making it
'information preserving.' Several CNNs are tested in combination with the
proposed framework on a composite dataset of images from the proposed dataset
and the CASIA v2.0 dataset. The best model accuracy of 0.9382 is achieved and
compared with similar state-of-the-art methods, demonstrating the superiority
of the proposed framework.