.png){kind=icon}
返回
Dictionary Attacks on Speaker Verification
.png){kind=icon}
OA
AI
DOI：10.1109/TIFS.2022.3229583.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we propose dictionary attacks against speaker verification-a novel attack vector that aims to match a large fraction of speaker population by chance. We introduce a generic formulation of the attack that can be used with various speech representations and threat models. The attacker uses adversarial optimization to maximize raw similarity of speaker embeddings between a seed speech sample and a proxy population. The resulting master voice successfully matches a non-trivial fraction of people in an unknown population. Adversarial waveforms obtained with our approach can match on average 69% of females and 38% of males enrolled in the target system at a strict decision threshold calibrated to yield false alarm rate of 1%. By using the attack with a black-box voice cloning system, we obtain master voices that are effective in the most challenging conditions and transferable between speaker encoders. We also show that, combined with multiple attempts, this attack opens even more to serious issues on the security of these systems.
Keyword:
Authentication
biometrics (access control)
speaker recognition
adversarial machine learning
impersonation attacks
期刊
IF:
8
论文数:
5.2K
被引数:
2.3W
机构
引用论文
.png){kind=icon}
分享收藏The shadow economy in three Mediterranean countries: France, Spain and Greece. A MIMIC approach
PREAI
分享收藏
查看更多内容