Privacy-Preserving Liveness Detection for Securing Smart Voice Interfaces

Yan Meng, Jiachun Li, Haojin Zhu, Yuan Tian, Jiming Chen

Abstract: Smart speakers are widely used as the primary user interface in intelligent systems, including smart homes and industrial IoT. However, they are vulnerable to voice spoofing attacks which result in malicious command execution or privacy information leakage. Passive liveness detection, which thwarts voice spoofing via analyzing the collected audio rather than deploying sensors to distinguish between live-human and spoofing voices, has drawn increasing attention. But existing schemes either face performance degradation under environmental factor changes or require the user to keep fixed gestures, which limit their deployment in real-world scenarios. Besides, the space distributed property of smart speakers causes building a universal classifier for all involved users to be cumbersome and increases privacy leakage issues. To address the challenges mentioned above, we propose LiveArray, an efficient, lightweight, and privacy-preserving passive liveness detection system. LiveArray exploits a novel liveness feature, array fingerprint, which utilizes the microphone array inherently adopted by the smart speaker to improve the accuracy of liveness detection. LiveArray's further employs the federated learning-based architecture to reduce the dataset collection overhead during classifier building and eliminate the potential privacy leakage during data transmission. Experimental results show that LiveArray achieves an accuracy of 99.16%, which is superior to existing passive schemes.