  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.
