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.
- Publication:
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arXiv e-prints
- Pub Date:
- November 2022
- DOI:
- 10.48550/arXiv.2211.09454
- arXiv:
- arXiv:2211.09454
- Bibcode:
- 2022arXiv221109454H
- Keywords:
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- Computer Science - Computer Vision and Pattern Recognition
- E-Print:
- Accepted at WACV2023