Rethinking LLM Watermark Detection in Black-Box Settings: A Non-Intrusive Third-Party Framework
Year: 2026
Type: conference-paper
Abstract: While watermarking serves as a critical mechanism for LLM provenance, existing secret-key schemes tightly couple detection with injection, requiring access to keys or provider-side scheme-specific detectors for verification.This dependency creates a fundamental barrier for real-world governance, as independent auditing becomes impossible without compromising model security or relying on the opaque claims of service providers.To resolve this dilemma, we introduce TTP-Detect, a pioneering black-box framework designed for nonintrusive, third-party watermark verification.By decoupling detection from injection, TTP-Detect reframes verification as a relative hypothesis testing problem.It employs a proxy model to amplify watermark-relevant signals and a suite of complementary relative measurements to assess the alignment of the query text with watermarked distributions.Extensive experiments across representative watermarking schemes, datasets and models demonstrate that TTP-Detect achieves superior detection performance and robustness against diverse attacks.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Award: G5077752208
OpenAlex ID: W7166881387