An Efficient White-box LLM Watermarking for IP Protection on Online Market Platforms

Online market platforms serve as a central hub for sharing and deploying AI models among researchers, developers, and companies. In this context, watermarking techniques are essential to protect intellectual property (IP), preventing unauthorized use and duplication of large language models (LLMs). Two key challenges arise: (i) These platforms host diverse LLMs, yet current watermarking techniques are only tailored to specific models, such as fine-tuned or quantized LLMs. (ii) Efficient watermarking is critical. However, traditional methods require substantial data and costly hardware, which limits their feasibility. In this paper, we propose an efficient white-box LLM watermarking technique called ELLMark. This method treats LLMs as multi-layered matrices while embedding watermarks only relies on modifying the model's weights. To preserve LLMs' performance, it filters weights by correlations with the activation magnitudes and downstream tasks, then modifies weights as minimal as possible via histogram modulation. Notably, all phases are training-free with low hardware resources, making it efficient for online platforms. We conduct extensive experiments to evaluate the effectiveness of ELLMark on LLaMA-3, OPT, and Phi-3 LLMs. The results demonstrate that it achieves 100% success in watermark detection while preserving model performance. Moreover, the preprocessing, encoding, and decoding processes remain efficient, taking less than 7 minutes, 12 minutes, and 18 seconds, respectively, for models with 80B parameters. Lastly, it exhibits robustness against parameter overwriting, re-watermarking, forging, fine-tuning, and pruning attacks.