Verifiable Provenance and Watermarking for Generative AI: An Evidentiary Framework for International Operational Law and Domestic Courts
Generative artificial intelligence now synthesizes photorealistic imagery, audio, and video at a cost that defeats traditional forensic intuition. The legal consequences span three regimes studied so far in isolation: international operational law, domestic procedure, and product regulation. This article presents a unified evidentiary framework that maps cryptographic content provenance, robust statistical watermarking, and zero knowledge attestation to the proof requirements of each regime. We define a five tier threat model spanning naive regeneration, adversarial laundering, cross model regeneration, active watermark removal, and insider provenance forgery. We release a public benchmar...
| Authors | Gustav Olaf Yunus Laitinen-Fredriksson Lundström-Imanov, Nurana Abdullayeva |
|---|---|
| Primary category | cs.CR |
| Published | 20 May 2026 |
| Last updated at source | 20 May 2026 |
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