Contrastive Learning for Continuous Touch-Based Authentication

2025/04/24 by Qiao, Mengyu, Zhai, Yunpeng, Wang, Yang · 1 citation
#Cryptography and Security (cs.CR) #FOS: Computer and information sciences

paper · doi:10.48550/arxiv.2504.17271

Abstract

Smart mobile devices have become indispensable in modern daily life, where sensitive information is frequently processed, stored, and transmitted-posing critical demands for robust security controls. Given that touchscreens are the primary medium for human-device interaction, continuous user authentication based on touch behavior presents a natural and seamless security solution. While existing methods predominantly adopt binary classification under single-modal learning settings, we propose a unified contrastive learning framework for continuous authentication in a non-disruptive manner. Specifically, the proposed method leverages a Temporal Masked Autoencoder to extract temporal patterns from raw multi-sensor data streams, capturing continuous motion and gesture dynamics. The pre-trained TMAE is subsequently integrated into a Siamese Temporal-Attentive Convolutional Network within a contrastive learning paradigm to model both sequential and cross-modal patterns. To further enhance performance, we incorporate multi-head attention and channel attention mechanisms to capture long-range dependencies and optimize inter-channel feature integration. Extensive experiments on public benchmarks and a self-collected dataset demonstrate that our approach outperforms state-of-the-art methods, offering a reliable and effective solution for user authentication on mobile devices.

Citations

• TSLANet: Rethinking Transformers for Time Series Representation Learning
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• Attention Is All You Need
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• Learning Human Identity from Motion Patterns
• Touchalytics: On the Applicability of Touchscreen Input as a Behavioral Biometric for Continuous Authentication
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Cited by

• Person Identification from Egocentric Human-Object Interactions using 3D Hand Pose

Related

• Touchalytics: On the Applicability of Touchscreen Input as a Behavioral Biometric for Continuous Authentication
• Continuous User Authentication Using Machine Learning and Multi-Finger Mobile Touch Dynamics with a Novel Dataset
• An Omnidirectional Approach to Touch-based Continuous Authentication
• Your Identity is Your Behavior -- Continuous User Authentication based on Machine Learning and Touch Dynamics
• Relative Attention-based One-Class Adversarial Autoencoder for Continuous Authentication of Smartphone Users