Dual-Domain Generative Adversarial Network for Digital Image Operation Anti-Forensics

Abstract

In this letter, we propose a general digital image operation anti-forensic framework based on generative adversarial nets (GANs), called dual-domain generative adversarial network (DDGAN). To tackle the issue of image operation detection, the proposed framework incorporates both operation specific forensic features and machine-learned knowledge to ensure that the generated images exhibit better undetectability performance against various detectors. The DDGAN consists of a generator and two discriminators working on different domains, i.e., the operation-specific feature domain which helps to conceal the artifacts from the perspective of forensic analysis for the target task, and the spatial domain which facilitates to take advantage of machine-learned features from the scratch as a supplementary. Through the experiments on median filtering and JPEG compression anti-forensics, we show the superior performance of the proposed DDGAN compared with state-of-the-art anti-forensic methods in terms of undetectability and visual quality.