Biometric Encoding for Replay-Resistant Smartphone User Authentication Using Handgrips

Abstract

Biometrics have been widely applied for user authentication. However, existing biometric authentications are vulnerable to biometric spoofing, because they can be observed and forged. In addition, they rely on verifying biometric features that rarely change. To address this issue, we propose to verify the handgrip biometric that can be unobtrusively extracted by acoustic signals when the user holds the phone. This biometric is uniquely associated with the user’s hand geometry, body-fat ratio, and gripping strength, which are hard to reproduce. Furthermore, we propose two biometric encoding techniques (i.e., temporal-frequential and spatial) to convert static biometrics into dynamic biometric features to prevent data reuse. In particular, we develop a biometric authentication system to work with the challenge-response protocol. We encode the ultrasonic signal according to a random challenge sequence and extract a distinct biometric code as the response. We further develop two decoding algorithms to decode the biometric code for user authentication. Additionally, we investigate multiple new attacks and explore using a latent diffusion model to solve the acoustic noise discrepancies between the training and testing data to improve system performance. Extensive experiments show our system achieves 97% accuracy in distinguishing users and rejects 100% replay attacks with $ 0.6 \, s$ challenge sequence.