DeepPrivacy2: Towards Realistic Full-Body Anonymization
Abstract
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.
Cite
Text
Hukkelås and Lindseth. "DeepPrivacy2: Towards Realistic Full-Body Anonymization." Winter Conference on Applications of Computer Vision, 2023.Markdown
[Hukkelås and Lindseth. "DeepPrivacy2: Towards Realistic Full-Body Anonymization." Winter Conference on Applications of Computer Vision, 2023.](https://mlanthology.org/wacv/2023/hukkelas2023wacv-deepprivacy2/)BibTeX
@inproceedings{hukkelas2023wacv-deepprivacy2,
title = {{DeepPrivacy2: Towards Realistic Full-Body Anonymization}},
author = {Hukkelås, Håkon and Lindseth, Frank},
booktitle = {Winter Conference on Applications of Computer Vision},
year = {2023},
pages = {1329-1338},
url = {https://mlanthology.org/wacv/2023/hukkelas2023wacv-deepprivacy2/}
}