VOW: Verifiable and Oblivious Watermark Detection for Large Language Models
VOW protocol lets organisations verify LLM watermarks on sensitive documents in-house without exposing content to the watermark provider, addressing a key gap for EU AI Act provenance compliance.
Summary written by editorial AI · Source link below
arXiv:2604.27666v2 Announce Type: replace Abstract: Regulation requires generated text to be marked and detectable, but symmetric-key watermarks leave detection to the key-holding provider. We study first-party provenance audit (FPA), where an organization examines sensitive content in its custody. Such an audit should not disclose the audited text and should provide a verifiable result and checkable record. We present VOW, which uses a verifiable oblivious pseudorandom function to evaluate the
Editorial Analysis
Enterprises subject to the EU AI Act need provenance verification for AI-generated content, but sending sensitive documents to external providers for watermark checks creates new data-protection risks that VOW resolves.
Assess verifiable oblivious watermark detection for integration into your AI content governance workflow, enabling in-house provenance audits.
In-house AI content provenance verification without external data exposure closes a practical gap in EU AI Act compliance.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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