论文标题
Deepprivacy2：迈向现实的全身匿名化
DeepPrivacy2: Towards Realistic Full-Body Anonymization
论文作者
论文摘要
生成的对抗网络（GAN）广泛适合人物的匿名化。但是，当前最新的匿名化将面对匿名的任务限制。在本文中，我们提出了一个新颖的匿名框架（Deepprivacy2），以实现人物和面部的匿名化。我们引入了一个新的大型且多样化的数据集，以供人类人物合成，该数据集显着提高了产生图像的图像质量和多样性。此外，我们提出了一种基于样式的GAN，可产生高质量，多样化和可编辑的匿名化。我们证明，与以前提出的方法相比，我们的全身匿名框架提供了更强的隐私保证。
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.
