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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.18577 |
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| _version_ | 1866908900735844352 |
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| author | Chen, Zhihui He, Kai Lei, Qingyuan Pu, Bin Zhang, Jian Xu, Yuling Feng, Mengling |
| author_facet | Chen, Zhihui He, Kai Lei, Qingyuan Pu, Bin Zhang, Jian Xu, Yuling Feng, Mengling |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_18577 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MedForge: Interpretable Medical Deepfake Detection via Forgery-aware Reasoning Chen, Zhihui He, Kai Lei, Qingyuan Pu, Bin Zhang, Jian Xu, Yuling Feng, Mengling Artificial Intelligence 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. |
| title | MedForge: Interpretable Medical Deepfake Detection via Forgery-aware Reasoning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2603.18577 |