Abstract
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.
Abstract (translated)
面部识别系统从面部图像中提取嵌入向量，并使用这些嵌入向量来验证或识别个人。面部重建攻击（也称为模板反转）指的是从面部嵌入向量重构面部图像，并利用重构的面部图像进入面部识别系统。在本文中，我们提出了一种方法，即使用一个面部基础模型从黑盒面部识别模型的嵌入向量中重构面部图像。该基础模型是通过4200万张图像训练而成，用于生成固定面部识别模型的面部嵌入向量所对应的面部图像。我们建议使用适配器将目标嵌入转换为基础模型的嵌入空间中的表示形式。我们在不同的面部识别模型和数据集上评估了生成的图像，展示了我们的方法在不同面部识别模型的嵌入翻译上的有效性。我们也评估了重构面部图像在攻击不同面部识别模型时的转移性。实验结果表明，我们重构的面部图像优于针对面部识别模型之前的重建攻击。
URL
https://arxiv.org/abs/2411.03960
