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.
@article{arxiv.2508.08559,
title = {Multi-Target Backdoor Attacks Against Speaker Recognition},
author = {Alexandrine Fortier and Sonal Joshi and Thomas Thebaud and Jesús Villalba and Najim Dehak and Patrick Cardinal},
journal= {arXiv preprint arXiv:2508.08559},
year = {2025}
}
Comments
Accepted to IEEE Automatic Speech Recognition and Understanding Workshop 2025