Multi-Target Backdoor Attacks Against Speaker Recognition

• URL: http://arxiv.org/abs/2508.08559v2
• Date: Wed, 13 Aug 2025 16:03:52 GMT
• Title: Multi-Target Backdoor Attacks Against Speaker Recognition
• Authors: Alexandrine Fortier, Sonal Joshi, Thomas Thebaud, Jesus Villalba Lopez, Najim Dehak, Patrick Cardinal,
• Abstract summary: We propose a multi-target backdoor attack against speaker identification using position-independent clicking sounds.<n>Our method targets up to 50 speakers simultaneously, achieving success rates of up to 95.04%.
• Score: 60.8399833165557
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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