Mitigating undesired interactions between liveness detection components in biometric authentication

Biometric authentication has made great strides throughout the years thanks to better hardware and software support. However, attackers are unrelenting in finding new ways to spoof a subject, hereby breaking existing presentation attack detection schemes. Similar to combining multiple authentication factors, a combination of liveness detection defenses is expected to strengthen security against spoofing attacks. The problem that we address is that many defenses have only been evaluated in isolation or in ideal circumstances. In this work, we demonstrate how different liveness components for face authentication can interfere with one another, thereby jeopardizing security. Furthermore, contextual and environmental influences can endanger their robustness. In this work, we propose a security framework for biometric authentication that supports adaptive liveness detection by reasoning upon undesired interactions between defenses, the impact of new attacks, and the context in which they emerge. We validate the flexibility of our framework to account for both historic and novel interplays between attacks and defenses. Our experiments show that our framework effectively accounts for undesired interactions while only incurring a limited and acceptable performance overhead.