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Voice biometrics is drawing increasing attention to user authentication on smart devices. However, voice biometrics is vulnerable to replay attacks, where adversaries try to spoof voice authentication systems using pre-recorded voice samples collected from genuine users. To this end, we propose VoiceGesture, a liveness detection solution for voice authentication on smart devices such as smartphones and smart speakers. With audio hardware advances on smart devices, VoiceGesture leverages built-in speaker and microphone pairs on smart devices as Doppler Radar to sense articulatory gestures for liveness detection during voice authentication. The experiments with 21 participants and different smart devices show that VoiceGesture achieves over 99% and around 98% detection accuracy for text-dependent and text-independent liveness detection, respectively. Moreover, VoiceGesture is robust to different device placements, low audio sampling frequency, and supports medium range liveness detection on smart speakers in various use scenarios, including smart homes and smart vehicles. 

This paper presents GoPose, a 3D skeleton-based human pose estimation system that uses WiFi devices at home. Our system leverages the WiFi signals reflected off the human body for 3D pose estimation. In contrast to prior systems that need specialized hardware or dedicated sensors, our system does not require a user to wear or carry any sensors and can reuse the WiFi devices that already exist in a home environment for mass adoption. To realize such a system, we leverage the 2D AoA spectrum of the signals reflected from the human body and the deep learning techniques. In particular, the 2D AoA spectrum is proposed to locate different parts of the human body as well as to enable environment-independent pose estimation. Deep learning is incorporated to model the complex relationship between the 2D AoA spectrums and the 3D skeletons of the human body for pose tracking. Our evaluation results show GoPose achieves around 4.7cm of accuracy under various scenarios including tracking unseen activities and under NLoS scenarios.