面向LLM生成食品安全内容的可信水印框架
A Trustworthy Watermarking Framework for LLM-Generated Food Safety Content
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中文总结 AI 辅助
本文提出ToSS框架，通过自适应双重水印将词汇令牌分为黑白子列表嵌入位级信息，并利用熵自适应选择高不确定性区域插入水印，在保障文本质量的同时实现食品领域LLM生成内容的可靠认证与追溯。
中文摘要 AI 辅助
大型语言模型凭借其文本生成能力正在变革众多行业。然而，其输出容易被篡改，在食品安全报告等关键领域造成严重风险。为保护AI生成内容的完整性和可追溯性，本文提出了ToSS（令牌导向重分区与策略选择），一种采用自适应双重水印的可靠认证方法。ToSS的关键创新在于其双重水印编码方法，将词汇令牌划分为黑名单和白名单子列表，实现可追溯信息的精确位级嵌入。此外，熵自适应机制动态选择预测不确定性高的文本区域进行水印插入，在确保可靠可追溯性的同时，保持文本流畅性和事实准确性。在包括食品领域文本在内的多个数据集上的实验表明，ToSS在水印容量和解码准确率方面均达到了领先性能。
英文摘要
Large language models are transforming many industries with their text generation abilities. However, their outputs can be easily tampered with, creating serious risks in critical areas such as food safety reporting. To protect the integrity and traceability of AI-generated content, this paper introduces ToSS (Token Oriented Repartitioning and Strategic Selection), a reliable authentication method using adaptive dual watermarking. The key innovation of ToSS is its dual watermark encoding approach that divides vocabulary tokens into black and white sublists, enabling precise bit-level embedding of traceability information. Additionally, an entropy adaptive mechanism dynamically selects text regions with high prediction uncertainty for watermark insertion, maintaining text fluency and factual accuracy while ensuring reliable traceability. Experiments on multiple datasets, including food domain texts, demonstrate that ToSS achieves leading performance in both watermark capacity and decoding accuracy.
发表机构
- Fudan University（复旦大学）
- Worcester Polytechnic Institute（伍斯特理工学院）
机构由 AI 辅助整理，请以论文原文为准。
