论文标题
从身份功能中恢复黑框面部
Black-Box Face Recovery from Identity Features
论文作者
论文摘要
在这项工作中，我们介绍了一种新型算法，基于对黑框面部恢复的随机高斯斑点的IT进行采样，仅考虑到深面识别系统的输出特征向量。我们攻击最新的面部识别系统（Arcface）来测试我们的算法。另一个具有不同体系结构（FaceNet）的网络被用作独立批评者，表明即使无法访问受攻击模型，也可以将目标人识别为重建图像。此外，与最先进的解决方案相比，我们的算法需要的查询数量要少得多。
In this work, we present a novel algorithm based on an it-erative sampling of random Gaussian blobs for black-box face recovery, given only an output feature vector of deep face recognition systems. We attack the state-of-the-art face recognition system (ArcFace) to test our algorithm. Another network with different architecture (FaceNet) is used as an independent critic showing that the target person can be identified with the reconstructed image even with no access to the attacked model. Furthermore, our algorithm requires a significantly less number of queries compared to the state-of-the-art solution.
