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arXiv 2603.18577cs.AI
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MedForge:基于伪造感知推理的可解释医学深度伪造检测

MedForge: Interpretable Medical Deepfake Detection via Forgery-aware Reasoning

National University of Singapore(新加坡国立大学);The Chinese University of Hong Kong(香港中文大学);Hunan University(湖南大学);Xi’an Jiaotong University(西安交通大学);Guangdong Provincial People’s Hospital(广东省人民医院)

Zhihui Chen, Kai He, Qingyuan Lei, Bin Pu, Jian Zhang, Yuling Xu, Mengling Feng

AI总结:

提出MedForge框架,通过构建大规模基准数据集MedForge-90K和局部-分析推理方法,实现可解释的医学图像伪造检测,在准确性和专家对齐解释上达到最优。

摘要:

文本引导的图像编辑器现在能够以高保真度操纵真实的医学扫描,实现病灶植入/移除,威胁临床信任和安全。现有防御措施不足以应对医疗领域。医学检测器大多是黑箱,而基于MLLM的解释器通常是事后解释,缺乏医学专业知识,并可能在模糊案例上产生幻觉证据。我们提出MedForge,一种用于事前、基于证据的医学伪造检测的数据和方法解决方案。我们引入了MedForge-90K,这是一个大规模基准数据集,包含19种病理的真实病灶编辑,并通过医生检查指南和黄金编辑位置提供专家指导的推理监督。在此基础上,MedForge-Reasoner执行局部-分析推理,在产生判决前预测可疑区域,并通过伪造感知GSPO进一步对齐,以加强基础并减少幻觉。实验表明,该方法在检测准确性和可信、专家对齐的解释方面达到了最先进水平。

英文摘要:

Text-guided image editors can now manipulate authentic medical scans with high fidelity, enabling lesion implantation/removal that threatens clinical trust and safety. Existing defenses are inadequate for healthcare. Medical detectors are largely black-box, while MLLM-based explainers are typically post-hoc, lack medical expertise, and may hallucinate evidence on ambiguous cases. We present MedForge, a data-and-method solution for pre-hoc, evidence-grounded medical forgery detection. We introduce MedForge-90K, a large-scale benchmark of realistic lesion edits across 19 pathologies with expert-guided reasoning supervision via doctor inspection guidelines and gold edit locations. Building on it, MedForge-Reasoner performs localize-then-analyze reasoning, predicting suspicious regions before producing a verdict, and is further aligned with Forgery-aware GSPO to strengthen grounding and reduce hallucinations. Experiments demonstrate state-of-the-art detection accuracy and trustworthy, expert-aligned explanations.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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