Verifiable Provenance and Watermarking for Generative AI: An Evidentiary Framework for International Operational Law and Domestic Courts

Computer Science > Cryptography and Security [Submitted on 20 May 2026] Verifiable Provenance and Watermarking for Generative AI: An Evidentiary Framework for International Operational Law and Domestic Courts Gustav Olaf Yunus Laitinen-Fredriksson Lundström-Imanov, Nurana Abdullayeva 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 benchmark of 12000 generated items across image, audio, and video modalities under six laundering pipelines for 72000 evaluation samples. We evaluate four representative schemes and report true positive rate at fixed false positive rate, robustness area under the curve, computational overhead, and a regime conditioned legal sufficiency score. We translate empirical detection bounds into legal sufficiency thresholds for command decisions under the law of armed conflict, for criminal and civil admissibility under domestic procedure, and for persistence audits under the European Union Artificial Intelligence Act and analogous regimes. The result is a reproducible reference pipeline, a public benchmark, and model annexes that lawyers, engineers, and operators can deploy together. Comments: 13 pages, 4 figures, 10 tables. Submitted to IEEE Transactions on Information Forensics and Security Subjects: Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV); Computers and Society (cs.CY); Multimedia (cs.MM) ACM classes: K.5.m; I.2.0; D.4.6; K.4.1 Cite as: arXiv:2605.21002 [cs.CR]   (or arXiv:2605.21002v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2605.21002 Focus to learn more Submission history From: Gustav Olaf Yunus Laitinen-Fredriksson Lundström-Imanov [view email] [v1] Wed, 20 May 2026 10:39:56 UTC (93 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-05 Change to browse by: cs cs.CV cs.CY cs.MM References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)