Fast segmentation of watermarked texts from large language models through an epidemic change-point framework
arXiv:2509.21160v2 Announce Type: replace-cross Abstract: With the growing use of large language models, concerns over content authenticity have spurred a variety of watermarking schemes. These schemes use secret keys to detect machine-generated text while remaining imperceptible to readers. Detection typically reduces to statistical hypothesis testing for the presence of watermarks, a topic that is now well studied. In contrast, the finer-grained task of localizing which segments of a text are watermarked is much less explored; existing approaches often lack scalability or guarantees robust t
The rapid proliferation of large language models necessitates robust methods for content authentication, making watermark detection and localization increasingly critical.
This development enhances the ability to differentiate truly human-generated content from machine-generated content, impacting intellectual property, trust in information, and the legal landscape surrounding AI outputs.
The ability to localize watermarked text segments allows for more granular control and identification of AI-generated portions within a larger document, moving beyond simple presence detection.
- · Content creators
- · Intellectual property rights holders
- · AI ethicists
- · Digital forensics professionals
- · Malicious actors using LLMs
- · Purveyors of AI-generated misinformation
- · Platforms without robust detection capabilities
Improved detection of AI-generated text segments will lead to enhanced content verification processes.
This could foster greater trust in online content by making the origin of text more transparent.
The development may inform future regulatory frameworks for AI-generated content, especially concerning authorship and intellectual property.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG