@inproceedings{f4f498b16dc04ce6987791e66a73667c,
title = "Generating Master Faces for Dictionary Attacks with a Network-Assisted Latent Space Evolution",
abstract = "A master face is a face image that passes face-based identity-authentication for a large portion of the population. These faces can be used to impersonate, with a high probability of success, any user, without having access to any user-information. We optimize these faces, by using an evolutionary algorithm in the latent embedding space of the StyleGAN face generator. Multiple evolutionary strategies are compared, and we propose a novel approach that employs a neural network in order to direct the search in the direction of promising samples, without adding fitness evaluations. The results we present demonstrate that it is possible to obtain a high coverage of the LFW identities (over 40%) with less than 10 master faces, for three leading deep face recognition systems.",
author = "Ron Shmelkin and Liar Wolf and Tomer Friedlander",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 16th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2021 ; Conference date: 15-12-2021 Through 18-12-2021",
year = "2021",
doi = "https://doi.org/10.1109/FG52635.2021.9666968",
language = "الإنجليزيّة",
series = "Proceedings - 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Vitomir Struc and Marija Ivanovska",
booktitle = "Proceedings - 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2021",
address = "الولايات المتّحدة",
}