Black Box Face Recognition

Black box face recognition research focuses on understanding and mitigating vulnerabilities in face recognition systems where the internal workings of the model are unknown. Current efforts concentrate on developing adversarial attacks, such as generating imperceptible perturbations to images or creating personalized cloaks, and on inverting the model's latent space to reconstruct faces from their feature representations, often employing diffusion models or generative adversarial networks (GANs). These investigations are crucial for assessing the robustness and security of face recognition technology, with implications for privacy protection, authentication systems, and the broader field of AI security.