论文标题
模型提取攻击针对自我监督的语音模型
Model Extraction Attack against Self-supervised Speech Models
论文作者
论文摘要
自我监督的学习（SSL）语音模型产生了给定剪辑的有意义表示，并在各种下游任务中实现了令人难以置信的性能。模型提取攻击（MEA）通常是指只有查询访问的对手窃取受害者模型的功能。在这项工作中，我们研究了针对SSL语音模型的MEA问题。我们提出了一个两阶段的框架来提取模型。在第一阶段，SSL是在大规模的未标记语料库上进行的，以预先培训小型语音模型。其次，我们从未标记的语料库中积极采样一小部分剪辑，并使用这些夹子查询目标模型，以获取其作为小型模型的第二阶段训练的标签。实验结果表明，我们的采样方法可以有效地提取目标模型，而无需了解有关其模型体系结构的任何信息。
Self-supervised learning (SSL) speech models generate meaningful representations of given clips and achieve incredible performance across various downstream tasks. Model extraction attack (MEA) often refers to an adversary stealing the functionality of the victim model with only query access. In this work, we study the MEA problem against SSL speech model with a small number of queries. We propose a two-stage framework to extract the model. In the first stage, SSL is conducted on the large-scale unlabeled corpus to pre-train a small speech model. Secondly, we actively sample a small portion of clips from the unlabeled corpus and query the target model with these clips to acquire their representations as labels for the small model's second-stage training. Experiment results show that our sampling methods can effectively extract the target model without knowing any information about its model architecture.
