SRVoice: A Robust Sparse Representation-Based Liveness Detection System

Voiceprint-based authentication is fast becoming the everyday norm since it is much easier to use and provides better security. However, current voiceprint-based authentication systems are vulnerable to various replay attacks. To tackle the spoofing attacks, we propose a new system that leverages the structural differences between human vocal system and loudspeakers and use the unique vibration pattern of both human vocal cord and throat as a key differentiating factor for liveness detection. Specially, we model the relationship between voices collected by two microphones of a smartphone of each live speaker using sparse representation. Compared with existing systems, our solution does not assume any prior knowledge of the attack method and is easy to operate. Moreover, our solution leverages the audio signals within the vocal frequency range and is robust to jamming attacks using high-frequency audio. Experimental results show that our system can achieve accurate live ness detection for a 6-digit passphrase with a mean true acceptance rate of 99.04% and true rejection rate of 100%.