用频率信号在生成文本中嵌入水印，可精准识别大模型写作。
FreqMark: Frequency-Based Watermark for Sentence-Level Detection of LLM-Generated Text
- 在采样时加入周期性频率信号引导选词，形成可检测水印。
- 面对改写、替换攻击仍保持高精度，AUC最高达0.98。
- 适合需要验证内容来源的学术、媒体等场景使用。
大型语言模型生成高度连贯且语境相关的文本，带来虚假信息或学术不端等风险。为此，我们提出FreqMark，一种在令牌采样过程中嵌入可检测频率水印的新方法。该方法利用周期性信号引导令牌选择，通过短时傅里叶变换（STFT）分析实现水印检测。即使在混合文本中（含人工与模型生成段落），也能准确识别模型生成内容。实验表明，FreqMark对改写、令牌替换等攻击具有强鲁棒性，检测性能显著优于现有方法，最大AUC达0.98。
原文摘要 · Abstract (English)
The increasing use of Large Language Models (LLMs) for generating highly coherent and contextually relevant text introduces new risks, including misuse for unethical purposes such as disinformation or academic dishonesty. To address these challenges, we propose FreqMark, a novel watermarking technique that embeds detectable frequency-based watermarks in LLM-generated text during the token sampling process. The method leverages periodic signals to guide token selection, creating a watermark that can be detected with Short-Time Fourier Transform (STFT) analysis. This approach enables accurate identification of LLM-generated content, even in mixed-text scenarios with both human-authored and LLM-generated segments. Our experiments demonstrate the robustness and precision of FreqMark, showing strong detection capabilities against various attack scenarios such as paraphrasing and token substitution. Results show that FreqMark achieves an AUC improvement of up to 0.98, significantly outperforming existing detection methods.
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