Virtual camera detection: Catching video injection attacks in remote biometric systems

• URL: http://arxiv.org/abs/2512.10653v1
• Date: Thu, 11 Dec 2025 14:01:06 GMT
• Title: Virtual camera detection: Catching video injection attacks in remote biometric systems
• Authors: Daniyar Kurmankhojayev, Andrei Shadrikov, Dmitrii Gordin, Mikhail Shkorin, Danijar Gabdullin, Aigerim Kambetbayeva, Kanat Kuatov,
• Abstract summary: Face anti-spoofing (FAS) is a vital component of remote biometric authentication systems.<n>Video injection attacks pose significant challenges to system integrity.<n>This study introduces a machine learning-based approach to virtual camera detection.
• Score: 0.13325819064668773
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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