{
  "id": 7025,
  "url": "https://arxiv.org/abs/2603.18577v2",
  "title": "MedForge: Interpretable Medical Deepfake Detection via Forgery-aware Reasoning",
  "summary": "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",
  "authors": "Zhihui Chen, Kai He, Qingyuan Lei, Bin Pu, Jian Zhang, Yuling Xu et al.",
  "category": "research",
  "topics": "misinformation,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-19T07:38:11.000Z",
  "fetched_at": "2026-07-14T16:32:54.535Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/7025",
  "original_url": "https://arxiv.org/abs/2603.18577v2",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}