Generative Adversarial Networks (GANs) are widely adapted for anonymization of human figures. However, current state-of-the-art limit anonymization to the task of face anonymization. In this paper, we propose a novel anonymization framework (DeepPrivacy2) for realistic anonymization of human figures and faces. We introduce a new large and diverse dataset for human figure synthesis, which significantly improves image quality and diversity of generated images. Furthermore, we propose a style-based GAN that produces high quality, diverse and editable anonymizations. We demonstrate that our full-body anonymization framework provides stronger privacy guarantees than previously proposed methods.
@article{arxiv.2211.09454,
title = {DeepPrivacy2: Towards Realistic Full-Body Anonymization},
author = {Håkon Hukkelås and Frank Lindseth},
journal= {arXiv preprint arXiv:2211.09454},
year = {2022}
}