Securing Face Liveness Detection on Mobile Devices Using Unforgeable Lip Motion Patterns

Face authentication usually utilizes deep learning models to verify users with high accuracy. However, it is vulnerable to various attacks that cheat the models by manipulating the digital counterparts of human faces. So far, lots of liveness detection schemes have been developed to prevent such attacks. Unfortunately, the attacker can still bypass them by constructing sophisticated attacks. We study the security of existing face authentication services and typical liveness detection approaches. Particularly, we develop a new type of attack, i.e., the low-cost 3D projection attack that projects manipulated face videos on a 3D face model, which can easily evade these face authentication services and liveness detection approaches. To this end, we propose FaceLip, a novel face liveness detection scheme on mobile devices, which utilizes lip motion patterns built upon well-designed acoustic signals to enable a strong security guarantee. The unique lip motions for each user are unforgeable because FaceLip verifies the patterns by analyzing acoustic signals that are dynamically generated according to random challenges, which ensures that our signals for liveness detection cannot be manipulated. We prototype FaceLip on off-the-shelf smartphones and conduct extensive experiments under different settings. Our evaluation with 44 participants validates the effectiveness and robustness of FaceLip.