Defying Depth: Exploring the Vulnerability of RGB-D Face Recognition Systems to RGB Attacks

The success of machine learning has significantly impacted biometric authentication systems, such as facial recognition, but it has also exposed them to substantial risks through adversarial examples. This work investigates physically realizable adversarial examples against RGB-D facial recognition systems, which combine traditional RGB images with depth data. While showing increased performance in a benign setting, we demonstrate that RGB-D systems remain vulnerable to adversarial examples in white and black-box settings. Furthermore, we show that attacks targeting a single modality, such as only making perturbations on the RGB color space without changing the depth data, remain highly successful, even against RGB-D models. Our research compares the robustness of RGB and RGB-D systems to identify the benefits and limitations of incorporating depth data into face recognition systems. We quantify how different fusion architectures of RGB-D systems influence their resistance to attack and evaluate how the size and positioning of adversarial patches impact attack success rates. Our findings highlight that while multi-modal systems offer potential advantages, they still face challenges in mitigating adversarial vulnerabilities.