Black-Box Face Recovery from Identity Features

Abstract

In this work, we present a novel algorithm based on an iterative 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.

Publication
In ECCV 2020 Workshop on ECCV-2020 Workshop The Bright and Dark Sides of Computer Vision - Challenges and Opportunities for Privacy and Security
Aleksandr Petiushko Александр Петюшко
Aleksandr Petiushko Александр Петюшко
Director, Head of ML Research / Adjunct Professor / PhD

Principal R&D Researcher (15+ years of experience), R&D Technical Leader (10+ years of experience), and R&D Manager (8+ years of experience). Running and managing industrial research and academic collaboration (35+ publications, 30+ patents). Inspired by theoretical computer science and how it changes the world.