Virtual camera detection: Catching video injection attacks in remote biometric systems
This addresses security vulnerabilities in biometric systems for users of web-based applications, but it is incremental as it builds on existing virtual camera detection concepts.
The study tackled the problem of video injection attacks in remote biometric authentication systems by introducing a machine learning-based virtual camera detection approach, which effectively identified injection attempts and reduced the risk of bypassing face anti-spoofing systems.
Face anti-spoofing (FAS) is a vital component of remote biometric authentication systems based on facial recognition, increasingly used across web-based applications. Among emerging threats, video injection attacks -- facilitated by technologies such as deepfakes and virtual camera software -- pose significant challenges to system integrity. While virtual camera detection (VCD) has shown potential as a countermeasure, existing literature offers limited insight into its practical implementation and evaluation. This study introduces a machine learning-based approach to VCD, with a focus on its design and validation. The model is trained on metadata collected during sessions with authentic users. Empirical results demonstrate its effectiveness in identifying video injection attempts and reducing the risk of malicious users bypassing FAS systems.