With the advancement of face recognition (FR) systems, privacy-preserving face recognition (PPFR) systems have gained popularity for their accurate recognition, enhanced facial privacy protection, and robustness to various attacks. However, there are limited studies to further verify privacy risks by reconstructing realistic high-resolution face images from embeddings of these systems, especially for PPFR. In this work, we propose the face embedding mapping (FEM), a general framework that explores Kolmogorov-Arnold Network (KAN) for conducting the embedding-to-face attack by leveraging pre-trained Identity-Preserving diffusion model against state-of-the-art (SOTA) FR and PPFR systems. Based on extensive experiments, we verify that reconstructed faces can be used for accessing other real-word FR systems. Besides, the proposed method shows the robustness in reconstructing faces from the partial and protected face embeddings. Moreover, FEM can be utilized as a tool for evaluating safety of FR and PPFR systems in terms of privacy leakage. All images used in this work are from public datasets.

翻译：随着人脸识别（FR）系统的发展，隐私保护人脸识别（PPFR）系统因其识别准确、增强的面部隐私保护能力以及对各类攻击的鲁棒性而日益普及。然而，目前很少有研究通过从这些系统（尤其是PPFR系统）的嵌入中重建真实的高分辨率人脸图像来进一步验证其隐私风险。在本工作中，我们提出了人脸嵌入映射（FEM），这是一个通用框架，该框架探索利用Kolmogorov-Arnold网络（KAN），结合预训练的身份保持扩散模型，对最先进的（SOTA）FR和PPFR系统实施嵌入到人脸的攻击。基于大量实验，我们验证了重建的人脸可用于访问其他真实世界的人脸识别系统。此外，所提方法在从部分及受保护的人脸嵌入中重建人脸方面表现出鲁棒性。而且，FEM可作为一种工具，用于从隐私泄露角度评估FR和PPFR系统的安全性。本工作中使用的所有图像均来自公开数据集。