Adversarial Attacks on ASR Systems: An Overview
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 21 of its 44 references that are in the map. First posted on arXiv 3 Aug 2022.
Builds on
21 of 44 references.
References · 21
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9 more
- 2018Targeted Adversarial Examples for Black Box Audio Systemscited by 41
- 2019Universal Adversarial Perturbations for Speech Recognition Systemscited by 41
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