BackLip: Passphrase-Independent Lip-reading User Authentication with Backscatter Signals

User authentication is essential for threat defense and data protection. Existed authentication systems have some known limitations, such as spoofing attacks, privacy leakage, and user-unfriendliness. In this paper, we propose BackLip, a novel anti-spoofing authentication system based on lip reading. We employ Wi-Fi backscatter-based technology to recognize users lip reading due to its various advantages, e.g. privacy protection, low power consumption, and low cost. Our system is touch-free and passphrase-independent, making it user-friendly, especially for the elderly and disabled. We first filter out irrelevant interference and enhance the signal-to-noise ratio of lip-reading signals by modulating the backscatter tags and constructing a series of suitable filters. Then, we study the energy distribution and steady-state characteristics of the backscattered signal caused by lip-reading movement at different frequencies to extract passphrase-independen lip-reading fingerprints. We build a theoretical model to analyze and prove the feasibility of our method and design an adaptive correction method to resist the interference caused by distance and angle changes. Additionally, we propose an adaptive segmentation algorithm to label lip-reading motions automatically. Extensive experiments demonstrate that BackLip has an average accuracy of 92.1% and is improved to 96.5% when users use the same passphrases.