arXiv:2605.00348v2 Announce Type: replace-cross Abstract: Recent multi-bit watermarking methods for large language models (LLMs) prioritize capacity over reliability, often conflating decoding with detection. Our analysis reveals that existing ECC-based extractors suffer from catastrophic false positive rates (FPR), and applying rejection thresholds merely collapses detection sensitivity (TPR) to random guessing. To resolve this structural limitation, we propose BREW (Block-wise Reliable Embedding for Watermarking), a framework shifting the paradigm to designated verification. BREW employs a t
The proliferation of LLMs and the increasing need for content provenance due to synthetic media make robust watermarking solutions critical. This paper directly addresses known issues with current multi-bit watermarking reliability.
Reliable watermarking is essential for trust in AI-generated content, copyright protection, and combating misinformation, impacting industries from media to cybersecurity.
This research introduces a more reliable block-wise method (BREW) for multi-bit text watermarking in LLMs, specifically aiming to resolve issues with high false positive rates and poor detection sensitivity in existing ECC-based extractors.
- · AI developers
- · Content creators
- · Media platforms
- · Cybersecurity firms
- · Malicious actors
- · Creators of unreliable watermarking methods
- · Users relying on flawed content provenance
Improved reliability in LLM watermarking could lead to wider adoption and trust in AI-generated content across various sectors.
Enhanced content provenance stemming from better watermarking may strengthen copyright enforcement and reduce the spread of deepfakes.
The widespread deployment of robust watermarking could foster new regulatory frameworks and industry standards for AI-generated media, influencing platform responsibilities and content distribution.
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Read at arXiv cs.CL