用适配器将嵌入向量转成基础模型空间，实现高精度人脸重建。
Face Reconstruction from Face Embeddings using Adapter to a Face Foundation Model
- 通过适配器将黑盒模型的嵌入向量映射到基础模型空间
- 在多个数据集和模型上重建图像，准确率超越以往方法
- 适合研究人脸识别安全与隐私攻击的人员参考
人脸识别系统从人脸图像中提取嵌入向量，并用其进行身份验证或识别。人脸重建攻击（又称模板逆向）指从嵌入向量中重构人脸图像，并用于绕过人脸识别系统。本文提出利用一个训练了4200万张人脸图像的面部基础模型，从黑盒人脸识别模型的嵌入向量中重建人脸图像。我们设计适配器将目标嵌入向量转换至基础模型的嵌入空间。重建图像在多个人脸识别模型和数据集上进行评估，验证了该方法对不同模型嵌入向量的兼容性。同时评估了重建图像在攻击不同人脸识别模型时的迁移能力。实验结果表明，本方法生成的人脸图像在对抗攻击中表现优于现有重建技术。
原文摘要 · Abstract (English)
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.
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