LLM Copyright Protection Research
π Exploring cutting-edge techniques to safeguard the intellectual property of large language models in the AI era.
About This Project
In this project, we begin with a comprehensive preliminary section that introduces essential background knowledge for understanding various LLM IP protection methods. We provide updated definitions of key concepts such as model watermarking and model fingerprinting in the context of 2025, enabling newcomers to clearly distinguish between these often-confused concepts.
Our ultimate goal is to present a comprehensive pipeline of copyright protection methods for large language models. From contemporary model fingerprint definitions to fingerprint embedding (and extraction) techniques, and from fingerprint transfer to removal strategies, we provide an end-to-end overview of the entire process.
π We hope this project serves as a valuable resource for researchers and practitioners in the field of AI copyright protection.
Start with Preliminary β𧬠Model Fingerprinting
Systematic categorization of fingerprinting methods, tracing their evolution from traditional deep learning to current LLM applications.
Explore Methods βπ Fingerprint Transfer
Investigation of transferable characteristics in invasive fingerprinting methods and their applications in model protection.
Learn More βπ‘οΈ Detection & Removal
Latest developments in fingerprint detection and removal techniques for comprehensive model protection.
Discover More βOur team is currently preparing a comprehensive survey in this exciting field! This project serves as a part of our upcoming review. In the official release, we will provide in-depth explanations for each module, cover a broader range of references, and offer detailed descriptions of evaluation metrics and properties for
π’ Latest Updates
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