Towards Query Efficient and Generalizable Black-Box Face Reconstruction Attack

Hojin Park, Jaewoo Park, Xingbo Dong, Andrew Beng Jin Teoh

Abstract: In this paper, we address the black-box face reconstruction attack with two crucial requirements: query efficiency and generalizability. A practical attack must be query efficient due to limited access to the target black-box model, and the reconstructed face must be generalizable so it can be used to attack other face recognition systems. To this end, we propose a novel face reconstruction attack that optimizes the latent vector of a pre-trained StyleGAN generator. Unlike existing methods, our method is query efficient as neither training nor simultaneous updating of multiple latent vectors is required. Furthermore, we propose a simple initialization scheme that greatly enhances the generalizability of the proposed method. We demonstrate the effectiveness of our method by a thorough evaluation on LFW and CFP-FP datasets across multiple state-of-the-art face recognition models. Project Code: github.com/1ho0jin1/Black-box-Face-Reconstruction.