TouchEnc: a Novel Behavioural Encoding Technique to Enable Computer Vision for Continuous Smartphone User Authentication

Abstract

We are increasingly required to prove our identity when using smartphones through explicit authentication processes such as passwords or physiological biometrics, e.g., authorising online banking transactions or unlocking smartphones. However, these methods are often annoying to input and do not guarantee that the genuine user remains the same. Thus, a modern verification process should differ from traditional authentication. In touch-based biometrics, a new approach must not verify what we draw but how we draw it. Our research proposes TouchEnc, a Deep Learning approach that outperforms conventional methods. Unlike Machine Learning methods, TouchEnc automates the feature extraction from touch gestures. TouchEnc achieves this by transforming and encoding touch behaviour into images, enabling continuous authentication through modern computer vision. Our approach has been tested on a popular and publicly available dataset to demonstrate its effectiveness. Results show that users can authenticate using TouchEnc with a single gesture containing users' on-screen navigational behaviour, independent of drawing up, down, left, or right. TouchEnc achieves an 8.4% Equal Error Rate and a 96.7% Area Under the Curve using a single gesture. Furthermore, TouchEnc achieves up to 65% better Equal Error Rates when combining gestures compared to the related work.