{
  "id": 14583,
  "url": "https://arxiv.org/abs/2607.26641v1",
  "title": "FakeIDet3-DB: Refining Digital Attacks and Patch Extraction for Secure ID Benchmarking",
  "summary": "Identity document (ID) authentication relies on the structural integrity of complex, high-frequency security patterns. However, advanced Generative AI models can now inject localized, high-fidelity manipulations, creating deceptive attacks that bypass standard verification. Training robust image forensic models to detect these anomalies is hindered by privacy regulations, forcing reliance on synthetic templates lacking the intricate visual patterns of real IDs. To bridge this domain gap, we intr",
  "authors": "Muñoz-Haro Javier, Teruel Andres, Tolosana Ruben, DeAlcala Daniel, Vera-Rodriguez Ruben, Morales Aythami et al.",
  "category": "research",
  "topics": "regulation,privacy-surveillance",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-29T09:03:29.000Z",
  "fetched_at": "2026-07-30T05:10:24.387Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/14583",
  "original_url": "https://arxiv.org/abs/2607.26641v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}