用大模型改写文本实现隐蔽多比特水印，抗干扰能力强。
Robust Multi-bit Text Watermark with LLM-based Paraphrasers
- 用两组不同行为的LLM改写器交替编码，通过语义差异嵌入水印
- 在1.1B小模型上实现99.99%检测准确率，保持原文语义不变
- 对替换和改写均鲁棒，适合用于生成式AI内容溯源
我们提出一种基于大模型改写器的不可见多比特文本水印方法。通过微调一对行为不同的大语言模型改写器，在句子级别交替使用以编码预设二进制码。利用文本分类器作为解码器识别每个比特的水印信息。大量实验表明，该水印在仅1.1B参数的小型改写器上即可实现超过99.99%的检测AUC，同时保持原始句子的语义完整性。更重要的是，该方案对词替换和句子改写等扰动具有强鲁棒性，并能良好泛化至分布外数据。通过大模型评估验证了水印的隐蔽性。代码已开源：https://github.com/xiaojunxu/multi-bit-text-watermark。
原文摘要 · Abstract (English)
We propose an imperceptible multi-bit text watermark embedded by paraphrasing with LLMs. We fine-tune a pair of LLM paraphrasers that are designed to behave differently so that their paraphrasing difference reflected in the text semantics can be identified by a trained decoder. To embed our multi-bit watermark, we use two paraphrasers alternatively to encode the pre-defined binary code at the sentence level. Then we use a text classifier as the decoder to decode each bit of the watermark. Through extensive experiments, we show that our watermarks can achieve over 99.99\% detection AUC with small (1.1B) text paraphrasers while keeping the semantic information of the original sentence. More importantly, our pipeline is robust under word substitution and sentence paraphrasing perturbations and generalizes well to out-of-distributional data. We also show the stealthiness of our watermark with LLM-based evaluation. We open-source the code: https://github.com/xiaojunxu/multi-bit-text-watermark.
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