In 2021, the pioneering work on TypeNet showed that keystroke dynamics verification could scale to hundreds of thousands of users with minimal performance degradation. Recently, the KVC-onGoing competition has provided an open and robust experimental protocol for evaluating keystroke dynamics verification systems of such scale, including considerations of algorithmic fairness. This article describes Type2Branch, the model and techniques that achieved the lowest error rates at the KVC-onGoing, in both desktop and mobile scenarios. The novelty aspects of the proposed Type2Branch include: i) synthesized timing features emphasizing user behavior deviation from the general population, ii) a dual-branch architecture combining recurrent and convolutional paths with various attention mechanisms, iii) a new loss function named Set2set that captures the global structure of the embedding space, and iv) a training curriculum of increasing difficulty. Considering five enrollment samples per subject of approximately 50 characters typed, the proposed Type2Branch achieves state-of-the-art performance with mean per-subject EERs of 0.77% and 1.03% on evaluation sets of respectively 15,000 and 5,000 subjects for desktop and mobile scenarios. With a uniform global threshold for all subjects, the EERs are 3.25% for desktop and 3.61% for mobile, outperforming previous approaches by a significant margin.

翻译：2021年，TypeNet的开创性研究表明，击键动态验证系统可在数十万用户规模下保持极小的性能衰减。近期，KVC-onGoing竞赛为评估此类规模的击键动态验证系统（涵盖算法公平性考量）提供了开放且稳健的实验协议。本文阐述了在KVC-onGoing桌面端和移动端场景中均实现最低错误率的Type2Branch模型及相关技术。提出的Type2Branch创新点包括：i）强调用户行为偏离总体规律的合成时序特征，ii）融合循环路径与卷积路径并集成多种注意力机制的双分支架构，iii）命名为Set2set的新型损失函数，可捕获嵌入空间的全局结构，以及iv）难度递增的训练课程。在每位受试者录入约50个字符的五个注册样本条件下，所提出的Type2Branch在桌面端和移动端场景中分别对15,000名和5,000名受试者的评估集实现了均值0.77%和1.03%的个体等错误率。采用适用于所有受试者的统一全局阈值时，桌面端与移动端等错误率分别为3.25%和3.61%，显著优于既往方法。