Splitting Wolves Category in Doddington Zoo: Impacts on Keystroke Dynamics

Biometrics has for objective to identify or verify the identity of an individual based on morphological or behavioral characteristics. A biometric system can be attacked by presenting a biometric data to the capture subsystem with the goal of interfering it, that is called a presentation attack. Covid, panther, shadow monster and dragon are the investigated presentation attacks associated to the Doddington Zoo Menagerie (which classify users in different categories considering their performance behavior when using biometric systems). In this work, we examined the robustness of each genuine class of the biometric menagerie against the proposed presentation attacks. The achieved experiments are applied to the keystroke dynamics modality. Owing to the adaptive strategy, we depicted each genuine category that is most vulnerable to a specific presentation attack class. We find that the impact of covid, panther, shadow monster and dragon attempts are more pronounced when compared to chameleons, worms, doves and phantoms classes respectively. The obtained results, point out that adding imposter labels to Doddington zoo may lead to a better assessment of biometric authentication systems and promotes the interpretation of their performances.