ERASE: Bypassing Collaborative Detection of AI Counterfeit via Comprehensive Artifacts Elimination

The rapid advancement of AI-Generated Images (AIGI) has amplified concerns about increasingly undetectable deepfakes. Recent adversarial techniques further worsen this problem by enhancing the imperceptibility of synthetic forgeries to both human viewers and automated detection systems. To simulate realistic adversaries and expose detection vulnerabilities, AI-Generated Image Stealth (AIGI-S) methods specifically aim to make synthetic images harder to detect. However, existing AIGI-S approaches often lack universality and transferability across diverse detection models—especially in collaborative detection settings—and tend to prioritize machine deception over human perceptual fidelity, resulting in visible artifacts. Inspired by real-world antique painting forgery, we propose ERASE (comprehensivE counteRfeit ArtifactS Elimination), a stealth-oriented optimization framework designed for multi-detector environments. ERASE comprehensively suppresses generative artifacts and incorporates a perceptual optimization objective to improve deception against both detection algorithms and human examiners. Extensive evaluations across eight distinct generative subsets from the GenImage benchmark and fifteen detection models demonstrate that ERASE delivers substantially improved attack performance—improving single-detector evasion by +10.5% and collaborative detection evasion by +17.9%—while preserving high image quality.