Localization of JPEG double compression through multi-domain convolutional neural networks
This work addresses the challenge of image forensics for detecting tampering in JPEG images, representing an incremental improvement in machine learning-based forgery detection.
The paper tackles the problem of localizing forged areas in JPEG images by detecting single or double compression using convolutional neural networks, achieving reliable localization through various input types and experimental evaluations.
When an attacker wants to falsify an image, in most of cases she/he will perform a JPEG recompression. Different techniques have been developed based on diverse theoretical assumptions but very effective solutions have not been developed yet. Recently, machine learning based approaches have been started to appear in the field of image forensics to solve diverse tasks such as acquisition source identification and forgery detection. In this last case, the aim ahead would be to get a trained neural network able, given a to-be-checked image, to reliably localize the forged areas. With this in mind, our paper proposes a step forward in this direction by analyzing how a single or double JPEG compression can be revealed and localized using convolutional neural networks (CNNs). Different kinds of input to the CNN have been taken into consideration, and various experiments have been carried out trying also to evidence potential issues to be further investigated.