Deep Image Restoration For Image Anti-Forensics

TL;DR

This paper proposes deep image restoration techniques to improve image quality after anti-forensics methods, making tampered images harder to detect and revealing vulnerabilities in current forgery detection models.

Contribution

It introduces a novel deep image restoration approach to counteract anti-forensics methods, enhancing image quality and challenging existing forgery detection techniques.

Findings

Deep restoration reduces detectability of tampered images.

Existing forgery detection models fail against the proposed methods.

Restoration improves image quality post anti-forensics techniques.

Abstract

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 .