From TV news to Google StreetView, face obscuration has been used for privacy protection. Due to recent advances in the field of deep learning, obscuration methods such as Gaussian blurring and pixelation are not guaranteed to conceal identity. In this paper, we propose a utility-preserving generative model, UP-GAN, that is able to provide an effective face obscuration, while preserving facial utility. By utility-preserving we mean preserving facial features that do not reveal identity, such as age, gender, skin tone, pose, and expression. We show that the proposed method achieves the best performance in terms of obscuration and utility preservation.
@article{arxiv.1906.11979,
title = {A Utility-Preserving GAN for Face Obscuration},
author = {Hanxiang Hao and David Güera and Amy R. Reibman and Edward J. Delp},
journal= {arXiv preprint arXiv:1906.11979},
year = {2019}
}
Comments
6 pages, 5 figures, presented at the ICML 2019 Worksop on Synthetic Realities: Deep Learning for Detecting AudioVisual Fakes