针对医学影像伪造问题，提出可解释的实时检测方法
MedForge: Interpretable Medical Deepfake Detection via Forgery-aware Reasoning
- 采用先定位后分析的推理框架，提前识别可疑区域
- 在19种病灶类型上实现领先检测准确率，支持专家级解释
- 专为医疗场景设计，减少误判与虚构证据风险
文本引导的图像编辑器如今能以高保真度操控真实医学影像，实现病灶植入或删除，严重威胁临床信任与安全。现有防御手段在医疗领域表现不足：医学检测模型多为黑箱，基于多模态大模型的解释工具通常为事后生成，缺乏医学专业知识，且在模糊病例中易产生虚构证据。本文提出MedForge，一种数据与方法协同的预判式、证据驱动的医学伪造检测方案。构建了MedForge-90K，一个涵盖19种病理类型的大型真实病灶编辑基准数据集，通过医生检查指南与真实编辑位置提供专家指导的推理标注。在此基础上，MedForge-Reasoner采用“定位-分析”推理流程，先预测可疑区域再给出判断，并通过伪造感知的广义策略优化（Forgery-aware GSPO）增强证据关联性，降低幻觉。实验表明，该方法在检测精度和可信、专家对齐的解释方面均达到当前最优水平。
原文摘要 · Abstract (English)
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.
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