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

2025-08-15

AI Summary

Current LLM copyright protection suffers from conceptual ambiguity—particularly the conflation of text watermarking, model watermarking, and model fingerprinting—and lacks systematic comparative analysis. Method: We propose the first unified taxonomy of model fingerprinting, explicitly incorporating model watermarking into the fingerprinting framework and formally defining fingerprint transfer and removal techniques. Through a comprehensive literature review and technical categorization, we delineate mechanistic distinctions among the three paradigms—including generative watermarking, embedding-layer binding, and output-distribution modulation—and construct a five-dimensional evaluation benchmark. Contribution/Results: Our work establishes conceptual clarity, unifies fragmented methodologies under a coherent framework, and standardizes evaluation criteria. It provides both theoretical foundations and practical guidelines for robust, verifiable LLM intellectual property protection.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• Exploring copyright protection methods for large language models
• Clarifying relationships among text watermarking, model watermarking, and fingerprinting
• Surveying model fingerprinting techniques and evaluation metrics

Innovation

• Surveying model fingerprinting for LLM copyright protection
• Clarifying text and model watermarking relationships
• Introducing fingerprint transfer and removal techniques