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MedForge addresses a critical gap in healthcare security by shifting from post-hoc explanations to pre-hoc, evidence-grounded detection of high-fidelity medical deepfakes. The approach combines a large-scale benchmark of realistic lesion edits with a "localize-then-analyze" reasoning strategy to reduce hallucinations and align AI outputs with expert medical guidelines. This work is significant for building trustworthy diagnostic systems where explainability and clinical accuracy are non-negotiable requirements.
MedForge: Interpretable Medical Deepfake Detection via Forgery-aware Reasoning
arXiv:2603.18577v2 Announce Type: replace Abstract: 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.
Abstract: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.
Artificial Intelligence (cs.AI)
arXiv:2603.18577 [cs.AI]
(or arXiv:2603.18577v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2603.18577 Focus to learn more arXiv-issued DOI via DataCite
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