Secure mmWave-Radar-Based Speaker Verification for IoT Smart Home

Abstract

Voice assistant devices function as interaction gateways in the Internet-of-Things (IoT) smart home. By using voice assistants, users are able to control smart homes via speech commands. However, voice assistants introduce potential security risks and privacy disclosures. For example, malicious actors could impersonate genuine users to send smart home speech commands. Speaker/user verification thus becomes a critical issue for smart home security. This article proposes a secure method for speaker verification in IoT smart homes using millimeter-wave (mmWave) radar. Specifically, we utilize the radar to capture both vocal cord vibration (VCV) and lip motion (LM) as multimodal biometrics for identifying speakers. Traditional voice-based speaker verification methods are vulnerable to impostor attacks, such as replay attacks and voice synthesis attacks, that use recorded or imitated voice audio to spoof the system. Our approach is able to protect IoT smart homes from these attacks by continuously detecting the liveness of the user using mmWave sensing and deep learning techniques. Extensive experiments show that the proposed approach can achieve high verification accuracy and be more robust against imposter attacks.