Contributed Talk: A Utility-Preserving GAN for Face Obscuration

From TV news to Google StreetView, face obscu- ration has been used for privacy protection. Due to recent advances in the field of deep learning, ob- scuration methods such as Gaussian blurring and pixelation are not guaranteed to conceal identity. In this paper, we propose a utility-preserving gen- erative model, UP-GAN, that is able to provide an effective face obscuration, while preserving facial utility. By utility-preserving we mean pre- serving facial features that do not reveal identity, such as age, gender, skin tone, pose, and expres- sion. We show that the proposed method achieves a better performance than the common obscura- tion methods in terms of obscuration and utility preservation.