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.

翻译：随着移动设备的广泛使用、设备中存储信息的敏感性以及当前移动用户身份验证方法的局限性，亟需创新、安全且无侵入性的解决方案来验证用户身份。本文提出TypeFormer——一种新型Transformer架构，用于对移动设备上自由文本击键动态进行建模，以实现用户身份验证。该模型由时间模块和通道模块组成，包含两个长短期记忆（LSTM）循环层、高斯范围编码（GRE）、多头自注意力机制及块循环结构。在迄今最大规模的公开数据库之一——阿尔托移动击键数据库上的实验表明，TypeFormer仅使用5次注册会话（每次含50次击键）即可实现3.25%的等错误率（EER），性能超越现有最先进系统。这一成果有效缩小了移动自由文本场景与传统桌面/固定文本场景之间的性能差距。此外，我们通过分析不同实验配置（如击键序列长度与注册会话数量）下模型的行为，发现增加注册数据可带来进一步性能提升空间。最后，跨数据库评估结果证明，相较现有方法，TypeFormer提取的特征具有更强的鲁棒性。