Contrastive Learning for Continuous Touch-Based Authentication

• URL: http://arxiv.org/abs/2504.17271v1
• Date: Thu, 24 Apr 2025 05:58:12 GMT
• Title: Contrastive Learning for Continuous Touch-Based Authentication
• Authors: Mengyu Qiao, Yunpeng Zhai, Yang Wang,
• Abstract summary: We propose a unified contrastive learning framework for continuous authentication in a non-disruptive manner.<n>Specifically, the proposed method leverages a Temporal Masked Autoencoder to extract temporal patterns from raw multi-sensor data streams.<n>Our approach outperforms state-of-the-art methods, offering a reliable and effective solution for user authentication on mobile devices.
• Score: 6.5894762902301744
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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