DeepPrivacy2: Towards Realistic Full-Body Anonymization

• URL: http://arxiv.org/abs/2211.09454v1
• Date: Thu, 17 Nov 2022 10:52:27 GMT
• Title: DeepPrivacy2: Towards Realistic Full-Body Anonymization
• Authors: H{\aa}kon Hukkel{\aa}s, Frank Lindseth
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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