The importance of protecting personal information, like, a person's address or health history, is well known and commonly discussed. However, images also contain sensitive information that can compromise a person's privacy or be used for nefarious purposes. To date, most methods for preserving privacy with images have relied on obfuscation techniques, such as pixelation, blurring, or masking parts of the image. However, new face‐recognition technologies driven by deep learning are showing cracks in the old techniques. Moreover, faceless recognition is presenting a whole new set of challenges for image privacy. The core of these issues it is how to ensure privacy while still being able to see and use the image. Our solution is a model based on a generative adversarial network that protects identity information while preserving face features of the original image as much as possible. The premise is to generate a fake image of a face that shares all the same attributes as the original image, for example, a brown‐eyed child smiling. With this strategy, the image remains useful, but no person or algorithm could determine the identity of the pictured individual. The framework consists of three parts: a detection module, an image creation module, and an image transformation module. The detection module extracts the attribute labels. The image creation module generates images of faces, and the image transformation module transforms the fake features to match the attributes in the original image. A comprehensive set of experiments shows the effectiveness of the proposed framework.

中文翻译：

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图像数据的隐私保护：基于GAN的方法

保护个人信息（例如一个人的住址或健康史）的重要性是众所周知的，并且经常被讨论。但是，图像还包含敏感信息，这些信息可能会危及人的隐私或用于邪恶目的。迄今为止，大多数用于保护图像隐私的方法都依赖于混淆技术，例如像素化，模糊或掩盖图像的某些部分。但是，由深度学习驱动的新的面部识别技术正在显示旧技术的缺陷。此外，不露面的识别给图像隐私带来了全新的挑战。这些问题的核心是如何确保隐私，同时仍然能够查看和使用图像。我们的解决方案是基于生成对抗网络的模型，该模型在保护身份信息的同时尽可能保留原始图像的面部特征。前提是生成与原始图像具有所有相同属性的假脸图像，例如棕色眼睛的孩子微笑。使用这种策略，图像仍然有用，但是没有人或算法可以确定所描绘个人的身份。该框架由三部分组成：检测模块，图像创建模块和图像转换模块。检测模块提取属性标签。图像创建模块生成人脸图像，图像转换模块转换伪造特征以匹配原始图像中的属性。