Face Anonymization

Face anonymization aims to protect individual privacy in images and videos by obscuring identifying facial features while preserving other image qualities. Current research focuses on developing reversible anonymization techniques using generative adversarial networks (GANs) and diffusion models, often incorporating geometric priors or attention mechanisms to improve realism and maintain data utility for downstream tasks like computer vision training. These advancements are crucial for balancing privacy regulations with the need for large, high-quality datasets in various applications, including medical imaging, autonomous driving, and metaverse development. The ongoing challenge lies in achieving robust anonymization that resists reconstruction attacks while minimizing the impact on image quality and data utility.