Voice Presentation Attack Detection through Text Converted Voice Command Analysis
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%
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