Securing Liveness Detection for Voice Authentication via Pop Noises

Voice authentication is increasingly used for sensitive operations in mobile devices. However, voice biometrics focuses on distinguishing individuals by their spectral features, which cannot deal with spoofing attacks. In this paper, we design and implement a novel software-only anti-spoofing system on smartphones. Our system leverages the pop noise, which is generated by the users oral airflow when speaking the passphrase opposite the microphone. The pop noise is delicate and subject to user diversity, making it hard to be recorded by replay attacks beyond a certain distance and to be imitated precisely by impersonators. Especially, we design a new pop noise detection scheme to pinpoint pop noises at the phonemic level, based on which we establish a theoretical model to calculate the sound pressure level from the speech signal in order to get the estimated pressure signal, and then analyze the consistency with the actual pressure signal extracted from the pop noise. Our evaluation on a dataset of 30 participants and three smartphones shows that our system achieves over 94.79% accuracy. Our system requires no additional hardware and is robust to various factors including authentication angle, authentication distance, the length of passphrase, ambient noise, etc.