An Adversarial Perturbation Generation Method for Image Anti-Forensics Based on Dual-Path Spatial Attention GAN

Yihong Lu, Jianyi Liu, Ru Zhang

Abstract: Adversarial attacks are essential for evaluating the robustness of deep learning-based forensics, revealing potential vulnerabilities. However, most existing adversarial sample generation methods face significant trade-offs between anti-forensic ability, transferability, and visual quality, as they typically apply perturbations either uniformly across entire images or modify only a limited number of arbitrary pixels. This paper proposes a novel method for generating anti-forensic images through a salient region-focused adversarial GAN based on meta-learning. By developing a dual-path perturbation generation model, we enable the generation of inconspicuous perturbations based on the spatial attention module. During the model’s training process, the perturbation generator uses a multi-task training strategy based on meta-learning to enhance anti-forensics transferability. Experimental results demonstrate that the proposed method outperforms state-of-the-art anti-forensic methods in maintaining rich image details while achieving higher anti-forensic ability.

External IDs:dblp:conf/icassp/LuLZ25