Characterizing the Adversarial Vulnerability of Speech self-Supervised Learning
Refereed conference paper presented and published in conference proceedings
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AbstractA leaderboard named Speech processing Universal PERformance Benchmark (SUPERB), which aims at benchmarking the performance of a shared self-supervised learning (SSL) speech model across various downstream speech tasks with minimal modification of architectures and a small amount of data, has fueled the research for speech representation learning. The SUPERB demonstrates speech SSL upstream models improve the performance of various downstream tasks through just minimal adaptation. As the paradigm of the self-supervised learning upstream model followed by downstream tasks arouses more attention in the speech community, characterizing the adversarial robustness of such paradigm is of high priority. In this paper, we make the first attempt to investigate the adversarial vulnerability of such paradigm under the attacks from both zero-knowledge adversaries and limited-knowledge adversaries. The experimental results illustrate that the paradigm proposed by SUPERB is seriously vulnerable to limited-knowledge adversaries, and the attacks generated by zero-knowledge adversaries are with transferability. The XAB test verifies the imperceptibility of crafted adversarial attacks.
All Author(s) ListWu H., Zheng B., Li X., Wu X., Lee H.Y., Meng H.
Name of ConferenceIEEE International Conference on Acoustics, Speech and Signal Processing
Start Date of Conference07/05/2022
End Date of Conference13/05/2022
Place of ConferenceSingapore
Country/Region of ConferenceSingapore
Proceedings TitleIEEE International Conference on Acoustics, Speech and Signal Processing
Year2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3164 - 3168
ISBN9781665405409
LanguagesEnglish-United Kingdom
KeywordsAdversarial attack, self-supervised learning
