Over-the-air White-box Attack on the Wav2Vec Speech Recognition Neural Network
Fuente:
arXiv
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| Formato: | Preprint |
| Publicado: |
2026
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| Acceso en línea: | |
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| _version_ | 1866914404408229888 |
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| author | Alexey, Protopopov |
| author_facet | Alexey, Protopopov |
| contents | Automatic speech recognition systems based on neural networks are vulnerable to adversarial attacks that alter transcriptions in a malicious way. Recent works in this field have focused on making attacks work in over-the-air scenarios, however such attacks are typically detectable by human hearing, limiting their potential applications. In the present work we explore different approaches of making over-the-air attacks less detectable, as well as the impact these approaches have on the attacks' effectiveness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_16972 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Over-the-air White-box Attack on the Wav2Vec Speech Recognition Neural Network Alexey, Protopopov Audio and Speech Processing Machine Learning Sound Automatic speech recognition systems based on neural networks are vulnerable to adversarial attacks that alter transcriptions in a malicious way. Recent works in this field have focused on making attacks work in over-the-air scenarios, however such attacks are typically detectable by human hearing, limiting their potential applications. In the present work we explore different approaches of making over-the-air attacks less detectable, as well as the impact these approaches have on the attacks' effectiveness. |
| title | Over-the-air White-box Attack on the Wav2Vec Speech Recognition Neural Network |
| topic | Audio and Speech Processing Machine Learning Sound |
| url | https://arxiv.org/abs/2603.16972 |