Signature filtering: a lightweight enhancement for statistical watermark detection in large language models
arXiv:2606.18430v1 Announce Type: new Abstract: Statistical watermarks help organizations attribute large language model (LLM) outputs, yet existing detectors often struggle when watermark signals are weak, texts are repetitive, or watermarks are edited. We propose signature filtering, a detection-time module that enhances watermark detection without modifying watermark embedding and text generation. It learns a small set of ``signature'' tokens whose presence makes watermark tests unreliable, and removes these tokens before detection. The signatures are obtained by solving a mixed-integer lin
The rapid proliferation and increasing sophistication of large language models necessitate robust methods for verifying their provenance and maintaining ethical use, making watermark detection a critical, immediate challenge.
This development offers a practical and efficient solution for organizations to better attribute AI-generated content, enhancing trust and accountability in AI outputs without requiring fundamental changes to generation processes.
The ability to more reliably detect watermarks, even when signals are weak or texts are modified, improves the integrity and traceability of LLM outputs across various applications.
- · Organizations deploying LLMs
- · Content creators and intellectual property owners
- · AI ethics and governance bodies
- · LLM auditing and security firms
- · Malicious actors attempting to obfuscate AI-generated content
- · Bad-faith actors misrepresenting content origin
Improved watermark detection directly enhances the ability to distinguish human-generated content from AI-generated content.
This improved detection can lead to higher public trust in AI systems and better enforcement of responsible AI usage policies.
More reliable content attribution might reduce disinformation campaigns and copyright infringement associated with LLMs, fostering a more secure digital information ecosystem.
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Read at arXiv cs.LG