Voice presentation attack detection through text-converted voice command analysis
- Authors
- Kwak, I.-Y.; Huh, J.H.; Han, S.T.; Kim, I.; Yoon, J.
- Issue Date
- 2019
- Publisher
- Association for Computing Machinery
- Keywords
- Attack detection; Voice assistant security; Voice command analysis
- Citation
- Conference on Human Factors in Computing Systems - Proceedings
- Journal Title
- Conference on Human Factors in Computing Systems - Proceedings
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/63793
- DOI
- 10.1145/3290605.3300828
- ISSN
- 0000-0000
- Abstract
- Voice assistants are quickly being upgraded to support advanced, security-critical commands such as unlocking devices, checking emails, and making payments. In this paper, we explore the feasibility of using users’ text-converted voice command utterances as classification features to help identify users’ genuine commands, and detect suspicious commands. To maintain high detection accuracy, our approach starts with a globally trained attack detection model (immediately available for new users), and gradually switches to a user-specific model tailored to the utterance patterns of a target user. To evaluate accuracy, we used a real-world voice assistant dataset consisting of about 34.6 million voice commands collected from 2.6 million users. Our evaluation results show that this approach is capable of achieving about 3.4% equal error rate (EER), detecting 95.7% of attacks when an optimal threshold value is used. As for those who frequently use security-critical (attack-like) commands, we still achieve EER below 5%. © 2019 Association for Computing Machinery.
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Collections - College of Business & Economics > Department of Applied Statistics > 1. Journal Articles
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