Model Extraction Attack against Self-supervised Speech Models

Autor:	Hsu, Tsu-Yuan, Li, Chen-An, Wu, Tung-Yu, Lee, Hung-yi
Rok vydání:	2022
Předmět:	Computer Science - Sound Computer Science - Computation and Language Computer Science - Cryptography and Security Computer Science - Machine Learning Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2211.16044 Zobrazit plný text záznamu View this record from Arxiv