Image Anonymization

Image anonymization aims to protect individual identities in images and videos while preserving data utility for research and applications. Current research focuses on developing advanced generative models, such as diffusion models and GANs, often incorporating techniques like ControlNets and surface-guided generation, to create realistic anonymized images that maintain scene context and avoid artifacts. This field is crucial for addressing privacy concerns in various domains, particularly healthcare and computer vision, by enabling the use of sensitive data while mitigating re-identification risks and minimizing the impact on downstream tasks like object detection and pose estimation. The development of robust and efficient anonymization methods is essential for responsible data sharing and AI development.