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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. 

The popularity of Internet-of-Things (IoT) has provided us with unprecedented opportunities to enable a variety of emerging services in a smart home environment. Among those services, sensing the liquid level in a container is critical to building many smart home and mobile healthcare applications that improve the quality of life. This paper presents LiquidSense, a liquid level sensing system that is low-cost, high accuracy, widely applicable to different daily liquids and containers, and can be easily integrated with existing smart home networks. LiquidSense uses existing home WiFi network and a low-cost transducer that attached to the container to sense the resonance of the container for liquid level detection. In particular, our system mounts a low-cost transducer on the surface of the container and emits a well-designed chirp signal to make the container resonant, which introduces subtle changes to the home WiFi signals. By analyzing the subtle phase changes of the WiFi signals, LiquidSense extracts the resonance frequency as a feature for liquid level detection. Our system constructs prediction models for both continuous and discrete predictions using curve fitting and SVM respectively. We evaluate LiquidSense in home environments with containers of three different materials and six types of liquids. Results show that LiquidSense achieves an overall accuracy of 97% for continuous prediction and an overall F-score of 0.968 for discrete predication. Results also show that our system has a large coverage in a home environment and works well under non-line-of-sight (NLOS) scenarios.