Paper Info
Title
Practical Adversarial Attacks Against Speaker Recognition Systems
Abstract
Unlike other biometric-based user identification methods (e.g., fingerprint and iris), speaker recognition systems can identify individuals relying on their unique voice biometrics without requiring users to be physically present. Therefore, speaker recognition systems have been becoming increasingly popular recently in various domains, such as remote access control, banking services and criminal investigation. In this paper, we study the vulnerability of this kind of systems by launching a practical and systematic adversarial attack againstX-vector, the state-of-the-art deep neural network (DNN) based speaker recognition system. In particular, by adding a well-crafted inconspicuous noise to the original audio, our attack can fool the speaker recognition system to make false predictions and even force the audio to be recognized as any adversary-desired speaker. Moreover, our attack integrates the estimated room impulse response (RIR) into the adversarial example training process toward practical audio adversarial examples which could remain effective while being played over the air in the physical world. Extensive experiment using a public dataset of $109$ speakers shows the effectiveness of our attack with a high attack success rate for both digital attack ($98%$) and practical over-the-air attack ($50%$).
Year
DOI
Venue
2020
10.1145/3376897.3377856
HotMobile '20: The 21st International Workshop on Mobile Computing Systems and Applications Austin TX USA March, 2020
Keywords
Field
DocType
Speaker Recognition, Deep Learning, Adversarial Example, Room Impulse Response
Computer science,Speech recognition,Speaker recognition,Adversarial system
Conference
ISBN
Citations 
PageRank 
978-1-4503-7116-2
4
0.48
References 
Authors
0
6
Name
Order
Citations
PageRank
Zhuohang Li152.89
Cong Shi248940.60
Yi Xie342.17
Jian Liu428959.26
Bo Yuan5103362.40
Yingying Chen62495193.14