Data-Augmentation-Enabled Continuous User Authentication via Passive Vibration Response
Hangcheng Cao, Hongbo Jiang, Kehua Yang, Siyu Chen, Wenqi Wu, Jiangchuan Liu, Schahram Dustdar
Abstract
Continuous identity authentication is critical for privacy protection throughout an entire user login session. In this article, we propose a continuous user authentication mechanism, namely, HandPass, which employs the vibration responses from hand biometrics and is passively activated by natural user-device interaction. Hand vibration responses are embedded in the mechanical vibration of a force-bearing body consisting of one mobile device and one user hand. A built-in accelerometer of the device can capture hand-dependent vibration signals. Considering the concealment of vibration generation and the nonreplicability of hand structure, it is difficult for attackers to counterfeit user identity. Moreover, for ensuring the robustness of authentication performance to tapping behavior interference, we construct a data augmentation module jointly leveraging a signal processing and learning-based pipeline. It can generate enough vibration responses representing hand structure biometrics under various behaviors, thereby making HandPass comprehensively understand vibration response variation. We prototype HandPass on smartphones, and extensive experiments demonstrate that HandPass can achieve satisfactory authentication accuracy.