Precision-Varying Prediction (PVP): Robustifying ASR systems against adversarial attacks
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866917359230386176 |
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| author | Pizarro, Matías Narasimhan, Raghavan Fischer, Asja |
| author_facet | Pizarro, Matías Narasimhan, Raghavan Fischer, Asja |
| contents | With the increasing deployment of automated and agentic systems, ensuring the adversarial robustness of automatic speech recognition (ASR) models has become critical. We observe that changing the precision of an ASR model during inference reduces the likelihood of adversarial attacks succeeding. We take advantage of this fact to make the models more robust by simple random sampling of the precision during prediction. Moreover, the insight can be turned into an adversarial example detection strategy by comparing outputs resulting from different precisions and leveraging a simple Gaussian classifier. An experimental analysis demonstrates a significant increase in robustness and competitive detection performance for various ASR models and attack types. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_22590 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Precision-Varying Prediction (PVP): Robustifying ASR systems against adversarial attacks Pizarro, Matías Narasimhan, Raghavan Fischer, Asja Machine Learning Cryptography and Security Audio and Speech Processing With the increasing deployment of automated and agentic systems, ensuring the adversarial robustness of automatic speech recognition (ASR) models has become critical. We observe that changing the precision of an ASR model during inference reduces the likelihood of adversarial attacks succeeding. We take advantage of this fact to make the models more robust by simple random sampling of the precision during prediction. Moreover, the insight can be turned into an adversarial example detection strategy by comparing outputs resulting from different precisions and leveraging a simple Gaussian classifier. An experimental analysis demonstrates a significant increase in robustness and competitive detection performance for various ASR models and attack types. |
| title | Precision-Varying Prediction (PVP): Robustifying ASR systems against adversarial attacks |
| topic | Machine Learning Cryptography and Security Audio and Speech Processing |
| url | https://arxiv.org/abs/2603.22590 |