Realistic Face Reconstruction from Deep Embeddings

Modern face recognition systems use deep convolution neural networks to extract latent embeddings from face images. Since basic arithmetic operations on embeddings are needed to make comparisons, generic encryption schemes cannot be used. This leaves facial embedding unprotected and susceptible to privacy attacks that reconstruction facial identity. We propose a search algorithm on the latent vector space of StyleGAN to find a matching face. Our process yields latent vectors that generate face images that are high-resolution, realistic, and reconstruct relevant attributes of the original face. Further, we demonstrate that our process is capable of fooling FaceNet, a state-of-the-art face recognition system.