CT-Auth: Capacitive Touchscreen-Based Continuous Authentication on Smartphones

Abstract

Continuous authentication, which provides identity verification using behavioral biometrics in an implicit and transparent manner, has shown potentials for protecting privacy. As the most common way of human-computer interaction, touch behavior pattern of each user has been proven distinctive and widely adopted for continuous authentication. However, most touch based solutions rely on the touchscreen signals obtained from high-level application programming interfaces, which are hard to characterize fine-grained appearance and contour profile of contact fingertips as well as dynamic sliding information in a touch gesture. In this paper, we propose a continuous authentication framework called CT-Auth, which leverages raw capacitive value collected from capacitive touchscreen on smartphone as a descriptor of touch behavior for authentication. Specifically, we first develop a three-dimensional convolution neural network model for capturing intra-gesture spatial-temporal feature and a structure extraction model for capturing structural information between moving fingertips of a touch gesture and touchscreen. A recurrent neural network based model is also applied for capturing temporal patterns among a sequence of touch gestures. To evaluate the effectiveness of our framework, we recruit 100 volunteers over 2 months and collect a large-scale dataset in the unconstrained conditions. Extensive experiments reveal that CT-Auth provides the state-of-the-art authentication accuracy.