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

TL;DR

This paper presents a machine learning-based virtual camera detection method to identify video injection attacks, enhancing the security of remote facial recognition systems against deepfake and virtual camera threats.

Contribution

It introduces a practical VCD approach trained on session metadata, with thorough validation demonstrating its effectiveness against video injection attacks.

Findings

Effective identification of video injection attempts

Reduces risk of bypassing facial recognition security

Validated through empirical experiments

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.