A Survey of Text Watermarking in the Era of Large Language Models-Reference-Cited by-同舟云学术

A Survey of Text Watermarking in the Era of Large Language Models

Published:2024-09-03 Issue: Volume: Page:
ISSN:0360-0300
Container-title:ACM Computing Surveys
language:en
Short-container-title:ACM Comput. Surv.

Author:

Liu Aiwei¹^ORCID,Pan Leyi¹^ORCID,Lu Yijian²^ORCID,Li Jingjing²^ORCID,Hu Xuming³^ORCID,Zhang Xi⁴^ORCID,Wen Lijie¹^ORCID,King Irwin²^ORCID,Xiong Hui³^ORCID,Yu Philip⁵^ORCID

Affiliation:

1. Tsinghua University, Beijing, China

2. The Chinese University of Hong Kong, Hong Kong Hong Kong

3. The Hong Kong University of Science and Technology - Guangzhou Campus, Guangzhou, China

4. Beijing University of Posts and Telecommunications, Beijing, China

5. Department of Computer Science, University of Illinois at Chicago, Chicago, United States

Abstract

Text watermarking algorithms are crucial for protecting the copyright of textual content. Historically, their capabilities and application scenarios were limited. However, recent advancements in large language models (LLMs) have revolutionized these techniques. LLMs not only enhance text watermarking algorithms with their advanced abilities but also create a need for employing these algorithms to protect their own copyrights or prevent potential misuse. This paper conducts a comprehensive survey of the current state of text watermarking technology, covering four main aspects: (1) an overview and comparison of different text watermarking techniques; (2) evaluation methods for text watermarking algorithms, including their detectability, impact on text or LLM quality, robustness under target or untargeted attacks; (3) potential application scenarios for text watermarking technology; (4) current challenges and future directions for text watermarking. This survey aims to provide researchers with a thorough understanding of text watermarking technology in the era of LLM, thereby promoting its further advancement.

Publisher

Association for Computing Machinery (ACM)

Link

https://dl.acm.org/doi/pdf/10.1145/3691626

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