Listen to Your Fingers: User Authentication Based on Geometry Biometrics of Touch Gesture

Inputting a pattern or PIN code on the touch screen is a popular method to prevent unauthorized access to mobile devices. However, these sensitive tokens are highly susceptible to being inferred by various types of side-channel attacks, which can compromise the security of the private data stored in the device. This paper presents a second-factor authentication method, TouchPrint, which relies on the user's hand posture shape traits (dependent on the individual different posture type and unique hand geometry biometrics) when the user inputs PIN or pattern. It is robust against the behavioral variability of inputting a passcode and places no restrictions on input manner (e.g., number of the finger touching the screen, moving speed, or pressure). To capture the spatial characteristic of the user's hand posture shape when input the PIN or pattern, TouchPrint performs active acoustic sensing to scan the user's hand posture when his/her finger remains static at some reference positions on the screen (e.g., turning points for the pattern and the number buttons for the PIN code), and extracts the multipath effect feature from the echo signals reflected by the hand. Then, TouchPrint fuses with the spatial multipath feature-based identification results generated from the multiple reference positions to facilitate a reliable and secure MFA system. We build a prototype on smartphone and then evaluate the performance of TouchPrint comprehensively in a variety of scenarios. The experiment results demonstrate that TouchPrint can effectively defend against the replay attacks and imitate attacks. Moreover, TouchPrint can achieve an authentication accuracy of about 92% with only ten training samples.

CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing.