Deep Image Restoration For Image Anti-Forensics
This work addresses the challenge of making tampered images more realistic and less detectable for forensic analysis, representing an incremental advancement in anti-forensics.
The study tackles the problem of improving image quality after applying anti-forensic techniques like JPEG compression, blurring, and noising, which degrade images and leave detectable traces, by using deep image restoration models to make forged images harder to detect; results show that existing image forgery detection models fail against these enhanced methods.
While image forensics is concerned with whether an image has been tampered with, image anti-forensics attempts to prevent image forensics methods from detecting tampered images. The competition between these two fields started long before the advancement of deep learning. JPEG compression, blurring and noising, which are simple methods by today's standards, have long been used for anti-forensics and have been the subject of much research in both forensics and anti-forensics. Although these traditional methods are old, they make it difficult to detect fake images and are used for data augmentation in training deep image forgery detection models. In addition to making the image difficult to detect, these methods leave traces on the image and consequently degrade the image quality. Separate image forensics methods have also been developed to detect these traces. In this study, we go one step further and improve the image quality after these methods with deep image restoration models and make it harder to detect the forged image. We evaluate the impact of these methods on image quality. We then test both our proposed methods with deep learning and methods without deep learning on the two best existing image manipulation detection models. In the obtained results, we show how existing image forgery detection models fail against the proposed methods. Code implementation will be publicly available at https://github.com/99eren99/DIRFIAF .