大型語言模型的版權保護：方法、挑戰與趨勢綜述

Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends

August 15, 2025

作者: Zhenhua Xu, Xubin Yue, Zhebo Wang, Qichen Liu, Xixiang Zhao, Jingxuan Zhang, Wenjun Zeng, Wengpeng Xing, Dezhang Kong, Changting Lin, Meng Han

cs.AI

摘要

大型語言模型的版權保護至關重要，這不僅因為其高昂的開發成本和專有價值，更在於其潛在的濫用風險。現有的研究主要集中於追蹤LLM生成內容的技術——即文本水印技術——而對於保護模型本身的方法（如模型水印和模型指紋）的系統性探討仍顯不足。此外，文本水印、模型水印與模型指紋之間的關係與區別尚未得到全面闡明。本文對當前LLM版權保護技術的現狀進行了全面調查，重點關注模型指紋技術，涵蓋以下方面：(1) 闡明從文本水印到模型水印及指紋的概念聯繫，並採用統一術語，將模型水印納入更廣泛的指紋框架；(2) 概述並比較多種文本水印技術，強調這些方法在某些情況下可作為模型指紋使用；(3) 系統分類並比較現有的LLM版權保護模型指紋方法；(4) 首次提出指紋轉移與指紋移除技術；(5) 總結模型指紋的評估指標，包括有效性、無害性、魯棒性、隱蔽性和可靠性；(6) 探討開放性挑戰與未來研究方向。本調查旨在為研究人員提供對LLM時代文本水印與模型指紋技術的深入理解，從而促進在保護其知識產權方面的進一步發展。

English

Copyright protection for large language models is of critical importance, given their substantial development costs, proprietary value, and potential for misuse. Existing surveys have predominantly focused on techniques for tracing LLM-generated content-namely, text watermarking-while a systematic exploration of methods for protecting the models themselves (i.e., model watermarking and model fingerprinting) remains absent. Moreover, the relationships and distinctions among text watermarking, model watermarking, and model fingerprinting have not been comprehensively clarified. This work presents a comprehensive survey of the current state of LLM copyright protection technologies, with a focus on model fingerprinting, covering the following aspects: (1) clarifying the conceptual connection from text watermarking to model watermarking and fingerprinting, and adopting a unified terminology that incorporates model watermarking into the broader fingerprinting framework; (2) providing an overview and comparison of diverse text watermarking techniques, highlighting cases where such methods can function as model fingerprinting; (3) systematically categorizing and comparing existing model fingerprinting approaches for LLM copyright protection; (4) presenting, for the first time, techniques for fingerprint transfer and fingerprint removal; (5) summarizing evaluation metrics for model fingerprints, including effectiveness, harmlessness, robustness, stealthiness, and reliability; and (6) discussing open challenges and future research directions. This survey aims to offer researchers a thorough understanding of both text watermarking and model fingerprinting technologies in the era of LLMs, thereby fostering further advances in protecting their intellectual property.