给大模型生成文本嵌入多比特水印，实现无痕溯源。
StealthInk: A Multi-bit and Stealthy Watermark for Large Language Models
- 通过隐蔽编码在文本中嵌入用户ID、时间戳等多比特信息
- 在保持原文分布不变的前提下，可稳定检测水印且误判率可控
- 适合需快速追踪生成内容来源的场景，如版权保护与合规审计
大型语言模型（LLM）的水印技术为识别AI生成文本提供了有前景的解决方案。然而，现有方法要么破坏原始生成文本的分布，要么仅支持零比特信息嵌入，只能检测水印而无法识别来源。本文提出StealthInk，一种隐蔽的多比特水印方案，在保持原始文本分布不变的同时，可嵌入如userID、TimeStamp、modelID等出处信息，实现无需访问API或提示即可快速溯源。我们推导了在固定等错误率下水印检测所需的最小令牌数下界，为提升容量提供指导。在多种任务上的全面实证评估表明，StealthInk具备良好的隐蔽性、可检测性与鲁棒性，是适用于实际应用的高效水印方案。
原文摘要 · Abstract (English)
Watermarking for large language models (LLMs) offers a promising approach to identifying AI-generated text. Existing approaches, however, either compromise the distribution of original generated text by LLMs or are limited to embedding zero-bit information that only allows for watermark detection but ignores identification. We present StealthInk, a stealthy multi-bit watermarking scheme that preserves the original text distribution while enabling the embedding of provenance data, such as userID, TimeStamp, and modelID, within LLM-generated text. This enhances fast traceability without requiring access to the language model's API or prompts. We derive a lower bound on the number of tokens necessary for watermark detection at a fixed equal error rate, which provides insights on how to enhance the capacity. Comprehensive empirical evaluations across diverse tasks highlight the stealthiness, detectability, and resilience of StealthInk, establishing it as an effective solution for LLM watermarking applications.
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