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Rethinking LLM Watermark Detection in Black-Box Settings: A Non-Intrusive Third-Party Framework

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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 non-intrusive, 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.

Zhuoshang Wang, Yubing Ren, Yanan Cao, Fang Fang, Xiaoxue Li, Li Guo• 2026

Related benchmarks

TaskDatasetResultRank
Watermark DetectionC4 OPT-6.7B
ROC-AUC100
26
Watermark DetectionC4 Llama-3.1-8B
TPR100
8
Watermark DetectionOpenGen Llama-3.1-8B
TPR100
8
Watermark DetectionOpenGen OPT-6.7B
TPR100
8
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