MedForge: Interpretable Medical Deepfake Detection via Forgery-aware Reasoning
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 introduc
Record details
Published: 19 March 2026
Source: arXiv
Category: Research
Topics: Misinformation · Healthcare
Retrieved: 14 July 2026
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How to cite this record
ethics.ai (19 March 2026), “MedForge: Interpretable Medical Deepfake Detection via Forgery-aware Reasoning,” evidence record 7025, https://ethics.ai/record/7025 (originally published by arXiv).
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