{"title":"通过定制词汇替换实现可逆源感知自然语言水印技术","authors":"Ziyu Jiang, Hongxia Wang, Zhenhao Shi, Run Jiao","doi":"10.1016/j.ipm.2024.103977","DOIUrl":null,"url":null,"abstract":"<div><div>Current natural language watermarking (NLW) methods generate suitable watermark words based on local context using pre-trained models (PLMs), minimizing semantic loss in watermarked text. However, these methods still exhibit some limitations. Specifically, there is room for improvement on substitutes quality and watermark imperceptibility since they integrate off-the-shelf lexical substitution (LS) models, which are not specifically tailored for watermarking algorithms. They make strict synchronization constraints to generate identical substitutes list from the original and the watermarked text, and therefore precludes consideration of some high-quality substitutes, which curtails the watermark capacity. Additionally, the local context changes via watermarking embedding, and these methods cannot losslessly recover the original text, limiting the application of NLW to high-precision scenarios such as government documents, military, and medical applications. To address these issues, we propose a reversible source-aware NLW approach, which performs proactive mining to identify potential reversible watermark positions by virtue of a PLM and subsequently embeds the watermark into the text via source-aware LS. Also, we have designed a novel LS algorithm tailored for NLW to enhance the imperceptibility and textual fidelity of watermarked content. Experiments validate the efficiency of our LS method in generating the most suitable substitutes and verifies that our NLW approach achieves complete reversibility while enhancing watermark capacity and textual fidelity compared to prior arts.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 2","pages":"Article 103977"},"PeriodicalIF":7.4000,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Reversible source-aware natural language watermarking via customized lexical substitution\",\"authors\":\"Ziyu Jiang, Hongxia Wang, Zhenhao Shi, Run Jiao\",\"doi\":\"10.1016/j.ipm.2024.103977\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Current natural language watermarking (NLW) methods generate suitable watermark words based on local context using pre-trained models (PLMs), minimizing semantic loss in watermarked text. However, these methods still exhibit some limitations. Specifically, there is room for improvement on substitutes quality and watermark imperceptibility since they integrate off-the-shelf lexical substitution (LS) models, which are not specifically tailored for watermarking algorithms. They make strict synchronization constraints to generate identical substitutes list from the original and the watermarked text, and therefore precludes consideration of some high-quality substitutes, which curtails the watermark capacity. Additionally, the local context changes via watermarking embedding, and these methods cannot losslessly recover the original text, limiting the application of NLW to high-precision scenarios such as government documents, military, and medical applications. To address these issues, we propose a reversible source-aware NLW approach, which performs proactive mining to identify potential reversible watermark positions by virtue of a PLM and subsequently embeds the watermark into the text via source-aware LS. Also, we have designed a novel LS algorithm tailored for NLW to enhance the imperceptibility and textual fidelity of watermarked content. Experiments validate the efficiency of our LS method in generating the most suitable substitutes and verifies that our NLW approach achieves complete reversibility while enhancing watermark capacity and textual fidelity compared to prior arts.</div></div>\",\"PeriodicalId\":50365,\"journal\":{\"name\":\"Information Processing & Management\",\"volume\":\"62 2\",\"pages\":\"Article 103977\"},\"PeriodicalIF\":7.4000,\"publicationDate\":\"2024-11-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information Processing & Management\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0306457324003364\",\"RegionNum\":1,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Processing & Management","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0306457324003364","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
摘要
当前的自然语言水印(NLW)方法使用预先训练的模型(PLM),根据本地上下文生成合适的水印单词,从而最大限度地减少水印文本中的语义损失。然而,这些方法仍然存在一些局限性。具体来说,由于这些方法整合了现成的词汇替换(LS)模型,而这些模型并不是专门为水印算法定制的,因此在替换质量和水印不可察觉性方面还有改进的余地。这些模型有严格的同步限制,必须从原文和水印文本中生成完全相同的替换列表,因此排除了对一些高质量替换列表的考虑,从而削弱了水印容量。此外,由于水印嵌入会改变局部上下文,这些方法无法无损地恢复原文,因此无损水印的应用仅限于政府文件、军事和医疗等高精度场景。为了解决这些问题,我们提出了一种可逆源感知无损检测方法,该方法通过 PLM 进行主动挖掘,识别潜在的可逆水印位置,然后通过源感知 LS 将水印嵌入文本。此外,我们还设计了一种专为无LW量身定制的新型LS算法,以提高水印内容的不可感知性和文本保真度。实验验证了我们的 LS 方法在生成最合适替代品方面的效率,并验证了我们的无LW 方法实现了完全的可逆性,同时与之前的技术相比提高了水印容量和文本保真度。
Reversible source-aware natural language watermarking via customized lexical substitution
Current natural language watermarking (NLW) methods generate suitable watermark words based on local context using pre-trained models (PLMs), minimizing semantic loss in watermarked text. However, these methods still exhibit some limitations. Specifically, there is room for improvement on substitutes quality and watermark imperceptibility since they integrate off-the-shelf lexical substitution (LS) models, which are not specifically tailored for watermarking algorithms. They make strict synchronization constraints to generate identical substitutes list from the original and the watermarked text, and therefore precludes consideration of some high-quality substitutes, which curtails the watermark capacity. Additionally, the local context changes via watermarking embedding, and these methods cannot losslessly recover the original text, limiting the application of NLW to high-precision scenarios such as government documents, military, and medical applications. To address these issues, we propose a reversible source-aware NLW approach, which performs proactive mining to identify potential reversible watermark positions by virtue of a PLM and subsequently embeds the watermark into the text via source-aware LS. Also, we have designed a novel LS algorithm tailored for NLW to enhance the imperceptibility and textual fidelity of watermarked content. Experiments validate the efficiency of our LS method in generating the most suitable substitutes and verifies that our NLW approach achieves complete reversibility while enhancing watermark capacity and textual fidelity compared to prior arts.
期刊介绍:
Information Processing and Management is dedicated to publishing cutting-edge original research at the convergence of computing and information science. Our scope encompasses theory, methods, and applications across various domains, including advertising, business, health, information science, information technology marketing, and social computing.
We aim to cater to the interests of both primary researchers and practitioners by offering an effective platform for the timely dissemination of advanced and topical issues in this interdisciplinary field. The journal places particular emphasis on original research articles, research survey articles, research method articles, and articles addressing critical applications of research. Join us in advancing knowledge and innovation at the intersection of computing and information science.