Copyright Protection for Large Language Models: A Survey of Methods,
  Challenges, and Trends
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.
68a28870a4caabb4320e63cc
Zhenhua Xu
68a28870a4caabb4320e63cd
Xubin Yue
68a28870a4caabb4320e63ce
Zhebo Wang
claimed_verified
2025-08-18T06:53:58.643Z
6746d09dfb7a1de1dca3cc21
https://cdn-avatars.huggingface.co/v1/production/uploads/6746d09dfb7a1de1dca3cc21/uGNJVBm3g_O6N_R77yoMs.png
Zhebo Wang
user
BreynaldDva
68a28870a4caabb4320e63cf
Qichen Liu
68a28870a4caabb4320e63d0
Xixiang Zhao
68a28870a4caabb4320e63d1
Jingxuan Zhang
68a28870a4caabb4320e63d2
Wenjun Zeng
68a28870a4caabb4320e63d3
Wengpeng Xing
68a28870a4caabb4320e63d4
Dezhang Kong
68a28870a4caabb4320e63d5
Changting Lin
68a28870a4caabb4320e63d6
Meng Han
Copyright Protection for Large Language Models: A Survey of Methods,
  Challenges, and Trends
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.
A survey of LLM copyright protection technologies, focusing on model fingerprinting, clarifies its relationship with text watermarking and provides a comprehensive overview of fingerprinting techniques, evaluation metrics, and open challenges.
text watermarking model watermarking model fingerprinting fingerprint transfer fingerprint removal evaluation metrics effectiveness harmlessness robustness stealthiness reliability