Query-efficient Attack for Black-box Image Inpainting Forensics via Reinforcement Learning

Recently, image inpainting has become a common tool for manipulating nature images in a malicious manner, which has led to the rapid advancement of inpainting forensics. Although current forensics methods have shown precise location of inpainting regions and reliable robustness against image post-processing operations, it remains unclear whether they can effectively resist the possible attacks in real-world scenarios. To identify potential flaws, we propose a novel black-box anti-forensics framework to attack inpainting forensics methods, which employs reinforcement learning to generate a query-efficient countermeasure, named RLGC. To this end, we define reinforcement learning paradigm to model the Markov Decision Process of query-based black-box anti-forensics scenario. Specifically, pixel-wise agents are used to modulate anti-forensics images based on action selection and query forensics methods to obtain corresponding outputs. Later, reward function evaluates attack effect and image distortion with these outputs. To maximize the cumulative reward, policy and value networks are integrated and trained by Asynchronous Advantage Actor-Critic algorithm. Experimental results demonstrate that, without visually detectable distortion on anti-forensics images, RLGC achieves remarkable attack effects in a highly query-effcient way against various black-box inpainting forensics methods, even outperforming the most representative white-box attack method.