@inproceedings{pan-etal-2025-waterseeker,
title = "{W}ater{S}eeker: Pioneering Efficient Detection of Watermarked Segments in Large Documents",
author = "Pan, Leyi and
Liu, Aiwei and
Lu, Yijian and
Gao, Zitian and
Di, Yichen and
Wen, Lijie and
King, Irwin and
Yu, Philip S.",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.156/",
doi = "10.18653/v1/2025.findings-naacl.156",
pages = "2866--2882",
ISBN = "979-8-89176-195-7",
abstract = "Watermarking algorithms for large language models (LLMs) have attained high accuracy in detecting LLM-generated text. However, existing methods primarily focus on distinguishing fully watermarked text from non-watermarked text, overlooking real-world scenarios where LLMs generate only small sections within large documents. In this scenario, balancing time complexity and detection performance poses significant challenges. This paper presents WaterSeeker, a novel approach to efficiently detect and locate watermarked segments amid extensive natural text. It first applies an efficient anomaly extraction method to preliminarily locate suspicious watermarked regions. Following this, it conducts a local traversal and performs full-text detection for more precise verification. Theoretical analysis and experimental results demonstrate that WaterSeeker achieves a superior balance between detection accuracy and computational efficiency. Moreover, its localization capability lays the foundation for building interpretable AI detection systems. Our code is available at https://github.com/THU-BPM/WaterSeeker."
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%0 Conference Proceedings
%T WaterSeeker: Pioneering Efficient Detection of Watermarked Segments in Large Documents
%A Pan, Leyi
%A Liu, Aiwei
%A Lu, Yijian
%A Gao, Zitian
%A Di, Yichen
%A Wen, Lijie
%A King, Irwin
%A Yu, Philip S.
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F pan-etal-2025-waterseeker
%X Watermarking algorithms for large language models (LLMs) have attained high accuracy in detecting LLM-generated text. However, existing methods primarily focus on distinguishing fully watermarked text from non-watermarked text, overlooking real-world scenarios where LLMs generate only small sections within large documents. In this scenario, balancing time complexity and detection performance poses significant challenges. This paper presents WaterSeeker, a novel approach to efficiently detect and locate watermarked segments amid extensive natural text. It first applies an efficient anomaly extraction method to preliminarily locate suspicious watermarked regions. Following this, it conducts a local traversal and performs full-text detection for more precise verification. Theoretical analysis and experimental results demonstrate that WaterSeeker achieves a superior balance between detection accuracy and computational efficiency. Moreover, its localization capability lays the foundation for building interpretable AI detection systems. Our code is available at https://github.com/THU-BPM/WaterSeeker.
%R 10.18653/v1/2025.findings-naacl.156
%U https://aclanthology.org/2025.findings-naacl.156/
%U https://doi.org/10.18653/v1/2025.findings-naacl.156
%P 2866-2882
Markdown (Informal)
[WaterSeeker: Pioneering Efficient Detection of Watermarked Segments in Large Documents](https://aclanthology.org/2025.findings-naacl.156/) (Pan et al., Findings 2025)
ACL
- Leyi Pan, Aiwei Liu, Yijian Lu, Zitian Gao, Yichen Di, Lijie Wen, Irwin King, and Philip S. Yu. 2025. WaterSeeker: Pioneering Efficient Detection of Watermarked Segments in Large Documents. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 2866–2882, Albuquerque, New Mexico. Association for Computational Linguistics.