Language generation models have been an increasingly powerful enabler for many applications. Many such models offer free or affordable API access, which makes them potentially vulnerable to model extraction attacks through distillation. To protect intellectual property (IP) and ensure fair use of these models, various techniques such as lexical watermarking and synonym replacement have been proposed. However, these methods can be nullified by obvious countermeasures such as "synonym randomization". To address this issue, we propose GINSEW, a novel method to protect text generation models from being stolen through distillation. The key idea of our method is to inject secret signals into the probability vector of the decoding steps for each target token. We can then detect the secret message by probing a suspect model to tell if it is distilled from the protected one. Experimental results show that GINSEW can effectively identify instances of IP infringement with minimal impact on the generation quality of protected APIs. Our method demonstrates an absolute improvement of 19 to 29 points on mean average precision (mAP) in detecting suspects compared to previous methods against watermark removal attacks.

翻译：语言生成模型已成为众多应用日益强大的赋能工具。许多此类模型提供免费或可负担的API访问，这使其可能易受通过蒸馏进行的模型提取攻击。为保护知识产权并确保这些模型的公平使用，研究者已提出多种技术，例如词汇水印和同义词替换。然而，这些方法可能被诸如“同义词随机化”等明显对抗措施所抵消。为解决此问题，我们提出GINSEW，一种新颖的保护文本生成模型免于通过蒸馏被盗用的方法。该方法的核心思想是将秘密信号注入解码步骤中每个目标词元的概率向量。随后，我们可通过探测可疑模型检测秘密消息，以判断其是否从受保护模型蒸馏而来。实验结果表明，GINSEW能有效识别知识产权侵权实例，同时对受保护API的生成质量影响极小。与先前方法相比，我们的方法在平均精确率均值上实现了19至29个百分点的绝对提升，以抵御水印移除攻击。