标题： 基于不变量的鲁棒权重水印用于大型语言模型 显示英文标题

标题： Invariant-based Robust Weights Watermark for Large Language Models

摘要： 水印技术由于知识产权（IP）权利的重要性日益增加而受到广泛关注，尤其是在大型语言模型（LLMs）在数十亿资源受限的边缘设备上部署的情况下。 为了应对恶意用户可能造成的知识产权盗窃威胁，本文引入了一种无需重新训练或微调的变压器模型鲁棒水印方案。 该方案为每个用户生成一个唯一密钥，并通过求解从模型不变量构建的线性约束来推导出稳定的水印值。 此外，该技术利用噪声机制在多用户场景中隐藏水印位置，以抵御共谋攻击。 本文在三个流行的模型（Llama3、Phi3、Gemma）上评估了该方法，实验结果证实了在各种攻击方法（微调、剪枝、量化、排列、缩放、可逆矩阵和共谋攻击）下的强鲁棒性。 显示英文摘要

摘要： Watermarking technology has gained significant attention due to the increasing importance of intellectual property (IP) rights, particularly with the growing deployment of large language models (LLMs) on billions resource-constrained edge devices. To counter the potential threats of IP theft by malicious users, this paper introduces a robust watermarking scheme without retraining or fine-tuning for transformer models. The scheme generates a unique key for each user and derives a stable watermark value by solving linear constraints constructed from model invariants. Moreover, this technology utilizes noise mechanism to hide watermark locations in multi-user scenarios against collusion attack. This paper evaluates the approach on three popular models (Llama3, Phi3, Gemma), and the experimental results confirm the strong robustness across a range of attack methods (fine-tuning, pruning, quantization, permutation, scaling, reversible matrix and collusion attacks).