TypeFormer: Transformers for Mobile Keystroke Biometrics

TL;DR

TypeFormer introduces a Transformer-based model for mobile keystroke biometrics, achieving state-of-the-art authentication accuracy and demonstrating robustness across different datasets and configurations.

Contribution

It proposes a novel Transformer architecture incorporating LSTM, GRE, and self-attention for mobile keystroke authentication, outperforming existing methods.

Findings

Achieves 3.25% EER on the Aalto database with minimal enrollment sessions.

Outperforms current state-of-the-art mobile keystroke authentication systems.

Demonstrates robustness across different datasets and experimental setups.

Abstract

The broad usage of mobile devices nowadays, the sensitiveness of the information contained in them, and the shortcomings of current mobile user authentication methods are calling for novel, secure, and unobtrusive solutions to verify the users' identity. In this article, we propose TypeFormer, a novel Transformer architecture to model free-text keystroke dynamics performed on mobile devices for the purpose of user authentication. The proposed model consists in Temporal and Channel Modules enclosing two Long Short-Term Memory (LSTM) recurrent layers, Gaussian Range Encoding (GRE), a multi-head Self-Attention mechanism, and a Block-Recurrent structure. Experimenting on one of the largest public databases to date, the Aalto mobile keystroke database, TypeFormer outperforms current state-of-the-art systems achieving Equal Error Rate (EER) values of 3.25% using only 5 enrolment sessions of 50 keystrokes each. In such way, we contribute to reducing the traditional performance gap of the challenging mobile free-text scenario with respect to its desktop and fixed-text counterparts. Additionally, we analyse the behaviour of the model with different experimental configurations such as the length of the keystroke sequences and the amount of enrolment sessions, showing margin for improvement with more enrolment data. Finally, a cross-database evaluation is carried out, demonstrating the robustness of the features extracted by TypeFormer in comparison with existing approaches.