AI Watermark Evidence Fails Forensic Readiness: An Empirical Evaluation
Governments are increasingly mandating that LLM-generated content carry watermarks. The EU AI Act calls for markings that are "sufficiently reliable and robust." California's SB 942 requires disclosure that is "permanent or extraordinarily difficult to remove." Both mandates rest on an untested assumption: that watermark detection yields evidence reliable enough for courts. This paper tests that assumption directly. We evaluate three representative LLM watermarking methods -- KGW, Unigram, and the MarkLLM implementation of SynthID-Text -- against the Daubert admissibility criteria and the NIST SP 800-86 digital forensic process. To structure this evaluation, we propose a Forensic Readines...
| Authors | Saifur Rahman Tamim, Amir Labib Khan |
|---|---|
| Primary category | cs.CR |
| Published | 17 July 2026 |
| Last updated at source | 17 July 2026 |
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