JECA^2: Judgment-Explanation Consistent Adversarial Attack against Forensic Vision-Language Models 事件

JECA^2: Judgment-Explanation Consistent Adversarial Attack against Forensic Vision-Language Models arXiv:2605.28609v1 Announce Type: new Abstract: Forensic vision-language models (VLMs) have recently been developed to detect image tampering and provide natural-language explanations. However, their robustness against adversarial manipulation remains underexplored. Existing adversarial attacks typically aim to flip the model's binary judgment, while the accompanying explanation may still reveal f