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

• URL: http://arxiv.org/abs/2602.06530v1
• Date: Fri, 06 Feb 2026 09:32:10 GMT
• Title: Universal Anti-forensics Attack against Image Forgery Detection via Multi-modal Guidance
• Authors: Haipeng Li, Rongxuan Peng, Anwei Luo, Shunquan Tan, Changsheng Chen, Anastasia Antsiferova,
• Abstract summary: ForgeryEraser is a framework designed to execute universal anti-forensics attack without access to the target AIGC detectors.<n>We show that ForgeryEraser causes substantial performance degradation to advanced AIGC detectors on global synthesis and local editing benchmarks.
• Score: 22.94094331220455
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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