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.

中文翻译：

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OPOM：定制隐形斗篷，实现人脸隐私保护


虽然人脸识别技术在日常生活中很方便，但也引起了社交媒体普通用户的隐私问题，因为它们可以用来高效、秘密地分析人脸图像和视频，而没有任何安全限制。在本文中，我们从技术角度研究了基于新型定制斗篷的人脸隐私保护，该斗篷可以应用于普通用户的所有图像，以防止恶意人脸识别系统暴露他们的身份。具体来说，我们提出了一种名为一人一掩码（OPOM）的新方法，通过在远离源身份的特征子空间的方向上优化每个训练样本来生成特定于人的（按类别）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，包括仿射壳、类中心和凸包，以获得对源身份的特征子空间的更好描述。该方法的有效性在常见数据集和名人数据集上针对具有不同损失函数和网络架构的黑盒人脸识别模型进行了评估。此外，我们讨论了所提出方法的优点和潜在问题。特别是，我们对视频数据集 Sherlock 的隐私保护进行了应用研究，以展示所提出方法的潜在实际用途。