The widespread application of face recognition poses unprecedented threats to individual privacy, as face images can be easily and stealthily analyzed. Efforts have been made to employ adversarial perturbations to disrupt the automatic inference of unauthorized face recognition systems. However, existing schemes fail to satisfy the personalized protection requirements of individuals, which may diminish the user experience. In this paper, we propose a novel scheme that provides tailor-made face privacy protection for individuals via class-wise targeted universal adversarial perturbations (CT-UAPs). In our scheme, each individual can utilize a user-specific CT-UAP to exclusively generate protected faces whose identification outputs are a virtual identity predefined by themselves. For the generation of CT-UAPs, we develop an optimization-based method that guides the feature vectors of the protected faces to approach the class-wise feature space of the predefined virtual identity while simultaneously approaching that of the original identity. Extensive experiment results demonstrate the effectiveness of our scheme against five face recognition models. In addition, the interpretability of CT-UAPs is highlighted by the experimental results obtained through two-dimensional principal component analysis.

As billions of face images stored on cloud platforms contain sensitive information to human vision, the public confronts substantial threats to visual face privacy. In response, the community has proposed some perturbation-based schemes to mitigate visual privacy leakage. However, these schemes need to generate a new protective perturbation for each image, failing to satisfy the real-time requirement of cloud platforms. To address this issue, we present an efficient visual face privacy protection scheme by utilizing person-specific veils, which can be conveniently applied to all images of the same user without regeneration. The protected images exhibit significant visual differences from the originals but remain identifiable to face recognition models. Furthermore, the protected images can be recovered to originals under certain circumstances. In the process of generating the veils, we propose a feature alignment loss to promote consistency between the recognition outputs of protected and original images with approximate construction of feature subspace. Meanwhile, the block variance loss is designed to enhance the concealment of visual identity information. Extensive experimental results demonstrate that our scheme can significantly eliminate the visual appearance of original images and almost has no impact on face recognition models.