论文标题
stylegan作为一种保养的脸部去识别方法
StyleGAN as a Utility-Preserving Face De-identification Method
论文作者
论文摘要
已经提出了面部去识别方法，以通过遮盖用户的脸来保护用户的隐私。但是，这些方法会降低照片的质量，并且通常不会保留面部效用，即它们的年龄，性别，姿势和面部表情。最近，已经提出了诸如stylegan之类的甘斯，它产生了逼真的，高质量的假想面孔。在本文中，我们调查了StyleGAN通过样式混合产生去识别面孔的使用。我们通过实施多个面部检测，验证和识别攻击并进行用户研究来研究这种保存效用和隐私的去识别方法。我们广泛的实验，人类评估以及与两种最先进的方法（即Ciagan和Ciagan and Depprivacy）进行了比较的结果表明，Stylegan在PAR或PAR上的表现比这些方法更好，从而保留了用户的隐私和图像的实用性。特别是，基于机器学习的实验的结果表明，stylegan0-4比Ciagan和Ciagan和省力更好地保留了效用，同时将隐私保留在同一级别上。 StyleGAN0-3在提供更多隐私的同时，将实用程序保留在同一级别上。在本文中，我们还首次进行了一项精心设计的用户研究，以研究Stylegan0-3、0-4和0-5的隐私和公用事业的属性，以及从人类观察者的角度来看Ciagan和Ciagan和Ciagan和Deepprivacy。我们的统计测试表明，参与者倾向于比弱化图像更容易验证和识别stylegan0-5图像。除了stylegan0-5，所有方法的识别率明显低于ciagan。关于实用程序，如预期的那样，StyleGAN0-5在保留某些属性方面的表现明显更好。在所有方法中，平均而言，参与者认为性别保留最多，而自然性的保留最少。
Face de-identification methods have been proposed to preserve users' privacy by obscuring their faces. These methods, however, can degrade the quality of photos, and they usually do not preserve the utility of faces, i.e., their age, gender, pose, and facial expression. Recently, GANs, such as StyleGAN, have been proposed, which generate realistic, high-quality imaginary faces. In this paper, we investigate the use of StyleGAN in generating de-identified faces through style mixing. We examined this de-identification method for preserving utility and privacy by implementing several face detection, verification, and identification attacks and conducting a user study. The results from our extensive experiments, human evaluation, and comparison with two state-of-the-art methods, i.e., CIAGAN and DeepPrivacy, show that StyleGAN performs on par or better than these methods, preserving users' privacy and images' utility. In particular, the results of the machine learning-based experiments show that StyleGAN0-4 preserves utility better than CIAGAN and DeepPrivacy while preserving privacy at the same level. StyleGAN0-3 preserves utility at the same level while providing more privacy. In this paper, for the first time, we also performed a carefully designed user study to examine both privacy and utility-preserving properties of StyleGAN0-3, 0-4, and 0-5, as well as CIAGAN and DeepPrivacy from the human observers' perspectives. Our statistical tests showed that participants tend to verify and identify StyleGAN0-5 images more easily than DeepPrivacy images. All the methods but StyleGAN0-5 had significantly lower identification rates than CIAGAN. Regarding utility, as expected, StyleGAN0-5 performed significantly better in preserving some attributes. Among all methods, on average, participants believe gender has been preserved the most while naturalness has been preserved the least.
