4.7 Article

Adversarial Examples for Automatic Speech Recognition: Attacks and Countermeasures

Journal

IEEE COMMUNICATIONS MAGAZINE
Volume 57, Issue 10, Pages 120-126

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/MCOM.2019.1900006

Keywords

Speech recognition; Mel frequency cepstral coefficient; Neural networks; Mobile handsets; Optimization; Genetic algorithms

Funding

  1. NSFC [61822207, U1636219]
  2. Equipment Pre-Research Joint Fund of the Ministry of Education of China (Youth Talent) [6141A02033327]
  3. Outstanding Youth Foundation of Hubei Province [2017CFA047]
  4. Research Grants Council of Hong Kong [CityU 11276816, CityU 11212717, CityU C1008-16G]
  5. National Natural Science Foundation of China [61572412]

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Speech is a common and effective approach for communication between humans and modern mobile devices such as smartphones or home hubs. The remarkable advances in computing and networking have popularized automatic speech recognition (ASR) systems, which can interpret received speech signals on mobile devices and enable us to remotely control and interact with those devices. Despite promising development, audio adversarial examples, a new kind of attack on advanced ASR systems, are found to be extremely effective in imitating human speech while fooling mobile devices to produce incorrect commands. In this article, we provide a systematic survey of audio adversarial examples in the literature. We first present an overview of the architecture of ASR systems and outline the basic attack philosophy. Followed by a brief introduction of the state-of-the-art solutions to audio adversarial examples, a comprehensive comparison is presented. Finally, after discussing existing countermeasures to defend ASR, we highlight several promising future research directions and challenges on constructing more robust and practical audio adversarial examples.

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