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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2012.11138 |
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| _version_ | 1866914860081610752 |
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| author | Ishida, Shoma Ono, Satoshi |
| author_facet | Ishida, Shoma Ono, Satoshi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2012_11138 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Adjust-free adversarial example generation in speech recognition using evolutionary multi-objective optimization under black-box condition Ishida, Shoma Ono, Satoshi Sound Computation and Language Audio and Speech Processing 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. |
| title | Adjust-free adversarial example generation in speech recognition using evolutionary multi-objective optimization under black-box condition |
| topic | Sound Computation and Language Audio and Speech Processing |
| url | https://arxiv.org/abs/2012.11138 |