  This paper describes the results of the IEEE BigData 2023 Keystroke
Verification Challenge (KVC), that considers the biometric verification
performance of Keystroke Dynamics (KD), captured as tweet-long sequences of
variable transcript text from over 185,000 subjects. The data are obtained from
two of the largest public databases of KD up to date, the Aalto Desktop and
Mobile Keystroke Databases, guaranteeing a minimum amount of data per subject,
age and gender annotations, absence of corrupted data, and avoiding excessively
unbalanced subject distributions with respect to the considered demographic
attributes. Several neural architectures were proposed by the participants,
leading to global Equal Error Rates (EERs) as low as 3.33% and 3.61% achieved
by the best team respectively in the desktop and mobile scenario, outperforming
the current state of the art biometric verification performance for KD. Hosted
on CodaLab, the KVC will be made ongoing to represent a useful tool for the
research community to compare different approaches under the same experimental
conditions and to deepen the knowledge of the field.
