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Adversarial Attacks on ASR Systems: An Overview

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arxiv 2208.02250 v1 pith:NDXMSVC5 submitted 2022-08-03 cs.SD cs.AIcs.CLcs.CReess.AS

classification cs.SDcs.AIcs.CLcs.CReess.AS
keywords attackssystemsadversarialattackdevelopmentworksassumptionsdifferent
verification ladder T0 review T1 audit T2 compute T3 formal

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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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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Investigation of Whisper ASR Hallucinations Induced by Non-Speech Audio

    cs.SD 2025-01 conditional novelty 6.0 of 10

    Recurring Whisper hallucinations on non-speech audio can be catalogued and removed via post-processing, cutting word error rate in augmented-speech tests.

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