Adversarial Attacks on ASR Systems: An Overview
August 03, 2022 ยท Declared Dead ยท ๐ International Conference on Data Science in Cyberspace
"No code URL or promise found in abstract"
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Authors
Xiao Zhang, Hao Tan, Xuan Huang, Denghui Zhang, Keke Tang, Zhaoquan Gu
arXiv ID
2208.02250
Category
cs.SD: Sound
Cross-listed
cs.AI,
cs.CL,
cs.CR,
eess.AS
Citations
3
Venue
International Conference on Data Science in Cyberspace
Last Checked
3 months ago
Abstract
With the development of hardware and algorithms, ASR(Automatic Speech Recognition) systems evolve a lot. As The models get simpler, the difficulty of development and deployment become easier, ASR systems are getting closer to our life. On the one hand, we often use APPs or APIs of ASR to generate subtitles and record meetings. On the other hand, smart speaker and self-driving car rely on ASR systems to control AIoT devices. In past few years, there are a lot of works on adversarial examples attacks against ASR systems. By adding a small perturbation to the waveforms, the recognition results make a big difference. In this paper, we describe the development of ASR system, different assumptions of attacks, and how to evaluate these attacks. Next, we introduce the current works on adversarial examples attacks from two attack assumptions: white-box attack and black-box attack. Different from other surveys, we pay more attention to which layer they perturb waveforms in ASR system, the relationship between these attacks, and their implementation methods. We focus on the effect of their works.
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