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arXiv · 2012.11138

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

Shoma Ishida·Satoshi Ono
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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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Shoma Ishida, Satoshi Ono. 2020-12-21. Adjust-free adversarial example generation in speech recognition using evolutionary multi-objective optimization under black-box condition. https://doi.org/10.1007/s10015-020-00671-x

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