Policy & Ethicsc2paprovenance metadatawatermarkingopenai
C2PA Fails To Secure Image Authenticity
7.1
Verge reporter Jess Weatherbed discusses C2PA in a 2026 Decoder interview, highlighting the Adobe-led metadata standard backed by Meta, Microsoft, and OpenAI. She says C2PA was built for photography provenance rather than AI-manipulation detection, and that metadata can be stripped, adoption is partial, and platforms may remove it. The result, she warns, undercuts efforts to reliably label real versus synthetic images and videos at scale.
Key Points
- 1Describes C2PA metadata standard backed by Adobe, Meta, Microsoft, OpenAI to record content provenance.
- 2Explains C2PA was designed for photography metadata, not robust AI-manipulation detection, and is easily stripped.
- 3Warns limited adoption and practical tamperability mean platforms and practitioners cannot rely solely on labels.
Scoring Rationale
Industry-standard provenance effort has broad implications, but limited adoption and tampering reduce its practical effectiveness.
Sources
Public references used for this report.
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