论文标题
OPOM：定制的无形斗篷朝面部隐私保护
OPOM: Customized Invisible Cloak towards Face Privacy Protection
论文作者
论文摘要
虽然在日常生活中方便，但面部识别技术还为社交媒体上的普通用户引起了隐私问题，因为它们可以用来分析面部图像和视频，而无需任何安全限制，从而有效地秘密地分析了面部图像和视频。在本文中，我们根据技术的角度研究了面部隐私保护，基于一种新型的定制斗篷，可以应用于常规用户的所有图像，以防止恶意的面部识别系统发现其身份。具体而言，我们提出了一种新方法，称为一个人，一个蒙版（OPOM），以通过在远离源身份的特征子空间方向上优化每个训练样本来生成特定于人的通用掩码。为了充分利用有限的训练图像，我们研究了几种建模方法，包括仿射船体，班级中心和凸面船体，以更好地描述源身份的特征子空间。针对具有不同损失功能和网络体系结构的黑框面部识别模型，对所提出方法的有效性进行了评估。此外，我们讨论了所提出方法的优势和潜在问题。特别是，我们对视频数据集的隐私保护Sherlock进行了申请研究，以证明该方法的潜在实际用法。数据集和代码可在https://github.com/zhongyy/opom上找到。
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.
