In this paper, we assess vulnerability of speaker verification systems to dictionary attacks. We seek master voices, i.e., adversarial utterances optimized to match against a large number of users by pure chance. First, we perform menagerie analysis to identify utterances which intrinsically hold this property. Then, we propose an adversarial optimization approach for generating master voices synthetically. Our experiments show that, even in the most secure configuration, on average, a master voice can match approx. 20% of females and 10% of males without any knowledge about the population. We demonstrate that dictionary attacks should be considered as a feasible threat model for sensitive and high-stakes deployments of speaker verification.
Cite as: Marras, M., Korus, P., Memon, N., Fenu, G. (2019) Adversarial Optimization for Dictionary Attacks on Speaker Verification. Proc. Interspeech 2019, 2913-2917, doi: 10.21437/Interspeech.2019-2430
@inproceedings{marras19_interspeech,
author={Mirko Marras and Paweł Korus and Nasir Memon and Gianni Fenu},
title={{Adversarial Optimization for Dictionary Attacks on Speaker Verification}},
year=2019,
booktitle={Proc. Interspeech 2019},
pages={2913--2917},
doi={10.21437/Interspeech.2019-2430}
}