基于文本水印的AIGC用户溯源技术

doi:10.3969/j.issn.0255-8297.2025.03.001

摘要/Abstract

摘要： 本文主要针对文本水印技术在中文语境下研究的不足，使用修改式水印与生成式水印两种方案对于中英文文本水印技术进行了实现。利用针对英文的Bert模型和针对中文的WoBert模型，设计了可移植的词替换水印模块，通过替换源文本中指定词元的方式在源文本中嵌入水印信息。对于生成式水印，采用对抗生成式文本水印模型，在中文语料上进行了针对性地修改与迁移，以适应中文文本的语义结构和语言习惯。使用中英文下的人类-ChatGPT对比语料库进行实验，结合准确与语义两方面的文本水印评估指标对2个数据集下不同模型的水印质量进行了评估，以说明水印在多种语料下的有效性。

关键词: 文本水印, 预训练语言模型, 生成式模型, 对比语料库

Abstract: This study addresses the limitations of text watermarking technology in the Chinese language context, and proposes both modified watermarking and generative watermarking schemes for implementation in English and Chinese. Using the Bert model for English and the WoBert model for Chinese, this study designs a portable word substitution watermarking module, which embeds watermarking information by replacing the specified lexical elements in the source text. For generative watermarking, this study adopts the adversarial generative text watermarking model with targeted modifications and migrations on the Chinese corpus, ensuring compatibility with Chinese semantic structures and linguistic conventions of Chinese text. Experiments are conducted using a human-ChatGPT comparison corpus in both Chinese and English. The effectiveness of the proposed watermarking schemes is evaluated based on text watermarking evaluation metrics in terms of both accuracy and semantics. Results demonstrate the proposed methods’ effectiveness in enhancing watermark robustness and traceability in multilingual text.

Key words: text watermarking, pre-trained language model, generative model, comparison corpus

中图分类号:

P751.1

宋轶, 刘功申. 基于文本水印的AIGC用户溯源技术[J]. 应用科学学报, 2025, 43(3): 361-369.

SONG Yimin, LIU Gongshen. AIGC Users Traceability Technology Based on Text Watermarking[J]. Journal of Applied Sciences, 2025, 43(3): 361-369.

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