Attacks on Image Forgery and Deepfake Detectors

2026

1. arXiv

   Universal Anti-forensics Attack against Image Forgery Detection via Multi-modal Guidance

   Haipeng Li, Rongxuan Peng, Anwei Luo, Shunquan Tan, Changsheng Chen, and Anastasia Antsiferova

   2026

   Abs Bib HTML

   The rapid advancement of AI-Generated Content (AIGC) technologies poses significant challenges for authenticity assessment. However, existing evaluation protocols largely overlook anti-forensics attack, failing to ensure the comprehensive robustness of state-of-the-art AIGC detectors in real-world applications. To bridge this gap, we propose ForgeryEraser, a framework designed to execute universal anti-forensics attack without access to the target AIGC detectors. We reveal an adversarial vulnerability stemming from the systemic reliance on Vision-Language Models (VLMs) as shared backbones (e.g., CLIP), where downstream AIGC detectors inherit the feature space of these publicly accessible models. Instead of traditional logit-based optimization, we design a multi-modal guidance loss to drive forged image embeddings within the VLM feature space toward text-derived authentic anchors to erase forgery traces, while repelling them from forgery anchors. Extensive experiments demonstrate that ForgeryEraser causes substantial performance degradation to advanced AIGC detectors on both global synthesis and local editing benchmarks. Moreover, ForgeryEraser induces explainable forensic models to generate explanations consistent with authentic images for forged images. Our code will be made publicly available.

   @misc{antsiferovaUniversal2026,
     title = {{Universal Anti-forensics Attack against Image Forgery Detection via Multi-modal Guidance}},
     author = {Li, Haipeng and Peng, Rongxuan and Luo, Anwei and Tan, Shunquan and Chen, Changsheng and Antsiferova, Anastasia},
     year = {2026},
     journal = {arXiv preprint arXiv:2602.06530},
   }