While convenient in daily life, face recognition technologies also raise privacy concerns for regular users on the social media since they could be used to analyze face images and videos, efficiently and surreptitiously without any security restrictions. In this paper, we investigate the face privacy protection from a technology standpoint based on a new type of customized cloak, which can be applied to all the images of a regular user, to prevent malicious face recognition systems from uncovering their identity. Specifically, we propose a new method, named one person one mask (OPOM), to generate person-specific (class-wise) universal masks by optimizing each training sample in the direction away from the feature subspace of the source identity. To make full use of the limited training images, we investigate several modeling methods, including affine hulls, class centers, and convex hulls, to obtain a better description of the feature subspace of source identities. The effectiveness of the proposed method is evaluated on both common and celebrity datasets against black-box face recognition models with different loss functions and network architectures. In addition, we discuss the advantages and potential problems of the proposed method. In particular, we conduct an application study on the privacy protection of a video dataset, Sherlock, to demonstrate the potential practical usage of the proposed method. Datasets and code are available at https://github.com/zhongyy/OPOM.

翻译：虽然在日常生活中很方便,但面对面的识别技术也给社交媒体上的经常用户带来隐私问题,因为这些技术可以用来在没有任何安全限制的情况下高效和秘密地分析脸部图像和视频;在本文件中,我们从基于新型定制斗篷的技术角度对面部隐私保护进行调查,这种斗篷可以适用于普通用户的所有图像,以防止恶意面部识别系统暴露身份;具体地说,我们提议采用新方法,将一个人命名为一个面部面具(OPOM),通过优化来自源身份特征子空间的每个培训样本,在远离源码特征子空间的方向上制作个人专用(类)通用面罩;为了充分利用有限的培训图像,我们从技术角度对面部保护隐私的方法进行调查,包括飞轮、班级中心和软体,以便更好地了解源码特征的子空间特征;对普通和名人数据集的有效性进行评估,以不同损失功能和网络结构为对象。此外,我们讨论了拟议方法的优点和潜在问题。我们特别要对有限培训图像进行一项示范方法的研究,包括松式船体、班机中心和软体数据库的潜在用途。