DBFIA: Diffusion-Based Face Image Anonymization

Hanyu Xue, Xin Yuan, Bo Liu, Ming Ding

Abstract: This paper presents a novel approach to address the issue of identity protection in facial image datasets. Our goal is to prevent any violation of privacy for the individuals depicted in the dataset while ensuring that the dataset is still useful for downstream training tasks and maintains the quality of the images. Previous methods have predominantly used Generative Adversarial Network (GAN)-based models to anonymize facial datasets. However, such models suffer from distortion and loss of detail when processing real-life images.