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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.08559 |
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| _version_ | 1866912637334323200 |
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| author | Fortier, Alexandrine Joshi, Sonal Thebaud, Thomas Villalba, Jesús Dehak, Najim Cardinal, Patrick |
| author_facet | Fortier, Alexandrine Joshi, Sonal Thebaud, Thomas Villalba, Jesús Dehak, Najim Cardinal, Patrick |
| contents | In this work, we propose a multi-target backdoor attack against speaker identification using position-independent clicking sounds as triggers. Unlike previous single-target approaches, our method targets up to 50 speakers simultaneously, achieving success rates of up to 95.04%. To simulate more realistic attack conditions, we vary the signal-to-noise ratio between speech and trigger, demonstrating a trade-off between stealth and effectiveness. We further extend the attack to the speaker verification task by selecting the most similar training speaker - based on cosine similarity - as a proxy target. The attack is most effective when target and enrolled speaker pairs are highly similar, reaching success rates of up to 90% in such cases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_08559 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Target Backdoor Attacks Against Speaker Recognition Fortier, Alexandrine Joshi, Sonal Thebaud, Thomas Villalba, Jesús Dehak, Najim Cardinal, Patrick Sound Machine Learning In this work, we propose a multi-target backdoor attack against speaker identification using position-independent clicking sounds as triggers. Unlike previous single-target approaches, our method targets up to 50 speakers simultaneously, achieving success rates of up to 95.04%. To simulate more realistic attack conditions, we vary the signal-to-noise ratio between speech and trigger, demonstrating a trade-off between stealth and effectiveness. We further extend the attack to the speaker verification task by selecting the most similar training speaker - based on cosine similarity - as a proxy target. The attack is most effective when target and enrolled speaker pairs are highly similar, reaching success rates of up to 90% in such cases. |
| title | Multi-Target Backdoor Attacks Against Speaker Recognition |
| topic | Sound Machine Learning |
| url | https://arxiv.org/abs/2508.08559 |