  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.
