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, WaterSeeker's localization ability supports the development of interpretable AI detection systems. This work pioneers a new direction in watermarked segment detection, facilitating more reliable AI-generated content identification.Our code is available at https://github.com/THU-BPM/WaterSeeker.

本研究解决了现有水印检测方法未能有效识别大型文档中仅部分水印文本的问题。提出的WaterSeeker方法通过高效的异常提取和局部遍历，实现在大规模自然文本中精确检测水印段落。研究结果表明，该方法在检测准确性和计算效率之间达到了优越的平衡，推进了可解释AI检测系统的发展。

WaterSeeker：有效检测大型文档中水印段落的工具