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