Model Extraction Attack against Self-supervised Speech Models. (arXiv:2211.16044v1 [cs.SD])

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 …

attack speech