DeepPrivacy2: Towards Realistic Full-Body Anonymization
Håkon Hukkelås, Frank Lindseth
Abstract
Generative Adversarial Networks (GANs) are widely adopted for anonymization of human figures. However, current state-of-the-art limits 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 full-body 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. Source code and appendix is available at: github.com/hukkelas/deep_privacy2.