论文标题
Ciagan：有条件的身份匿名生成对抗网络
CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks
论文作者
论文摘要
社会中计算机视觉技术使用的前所未有的增长与对数据隐私的关注点息息相关。在许多现实世界中，例如人们跟踪或行动识别，能够在保护人们的身份时仔细考虑数据，这一点很重要。我们建议和开发Ciagan，这是一种基于条件生成对抗网络的图像和视频匿名化的模型。我们的模型能够删除面部和身体的识别特征，同时产生可用于任何计算机视觉任务（例如检测或跟踪）的高质量图像和视频。与以前的方法不同，我们可以完全控制去识别（匿名）程序，从而确保匿名和多样性。我们将我们的方法与多个基线进行比较，并获得最新的结果。
The unprecedented increase in the usage of computer vision technology in society goes hand in hand with an increased concern in data privacy. In many real-world scenarios like people tracking or action recognition, it is important to be able to process the data while taking careful consideration in protecting people's identity. We propose and develop CIAGAN, a model for image and video anonymization based on conditional generative adversarial networks. Our model is able to remove the identifying characteristics of faces and bodies while producing high-quality images and videos that can be used for any computer vision task, such as detection or tracking. Unlike previous methods, we have full control over the de-identification (anonymization) procedure, ensuring both anonymization as well as diversity. We compare our method to several baselines and achieve state-of-the-art results.
