Face recognition systems extract embedding vectors from face images and use these embeddings to verify or identify individuals. Face reconstruction attack (also known as template inversion) refers to reconstructing face images from face embeddings and using the reconstructed face image to enter a face recognition system. In this paper, we propose to use a face foundation model to reconstruct face images from the embeddings of a blackbox face recognition model. The foundation model is trained with 42M images to generate face images from the facial embeddings of a fixed face recognition model. We propose to use an adapter to translate target embeddings into the embedding space of the foundation model. The generated images are evaluated on different face recognition models and different datasets, demonstrating the effectiveness of our method to translate embeddings of different face recognition models. We also evaluate the transferability of reconstructed face images when attacking different face recognition models. Our experimental results show that our reconstructed face images outperform previous reconstruction attacks against face recognition models.

翻译：人脸识别系统从人脸图像中提取嵌入向量，并利用这些嵌入进行身份验证或识别。人脸重建攻击（亦称模板反演）指从人脸嵌入中重建人脸图像，并利用重建图像侵入人脸识别系统。本文提出使用人脸基础模型，从黑盒人脸识别模型的嵌入中重建人脸图像。该基础模型使用4200万张图像训练，能够从固定人脸识别模型的面部嵌入生成人脸图像。我们提出采用适配器将目标嵌入转换至基础模型的嵌入空间。生成图像在不同人脸识别模型和数据集上进行了评估，证明了本方法在转换不同人脸识别模型嵌入方面的有效性。我们还评估了重建人脸图像在攻击不同人脸识别模型时的可迁移性。实验结果表明，本方法重建的人脸图像在对抗人脸识别模型的攻击中优于以往的重建攻击方法。