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Artificial Life and Robotics
Erschienen in: Artificial Life and Robotics 2/2021

06.01.2021 | Original Article

Adjust-free adversarial example generation in speech recognition using evolutionary multi-objective optimization under black-box condition

verfasst von: Shoma Ishida, Satoshi Ono

Erschienen in: Artificial Life and Robotics | Ausgabe 2/2021

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Abstract

This paper proposes a black-box adversarial attack method to automatic speech recognition systems. Some studies have attempted to attack neural networks for speech recognition; however, these methods did not consider the robustness of generated adversarial examples against timing lag with a target speech. The proposed method in this paper adopts Evolutionary Multi-objective Optimization (EMO) that allows it generating robust adversarial examples under black-box scenario. Experimental results showed that the proposed method successfully generated adjust-free adversarial examples, which are sufficiently robust against timing lag so that an attacker does not need to take the timing of playing it against the target speech.
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Fußnoten
1
Tested 60 adversarial examples can be available at https://​mediaeng.​ics.​kagoshima-u.​ac.​jp/​adjustFreeAE.​html.
 
2
Pairred t test with 95% confidence level was performed for each comparison.
 
Literatur
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Alzantot M, Balaji B, Srivastava M (2018) Did you hear that? adversarial examples against automatic speech recognition. arXiv preprint arXiv:​1801.​00554
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Qin Y, Carlini N, Goodfellow IA (2019) Imperceptible, robust, and targeted adversarial examples for automatic speech recognition. arXiv preprint arXiv:​1903.​10346
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Metadaten
Titel
Adjust-free adversarial example generation in speech recognition using evolutionary multi-objective optimization under black-box condition
verfasst von
Shoma Ishida
Satoshi Ono
Publikationsdatum
06.01.2021
Verlag
Springer Japan
Erschienen in
Artificial Life and Robotics / Ausgabe 2/2021
Print ISSN: 1433-5298
Elektronische ISSN: 1614-7456
DOI
https://doi.org/10.1007/s10015-020-00671-x

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