# A Utility-Preserving GAN for Face Obscuration

arXiv: https://arxiv.org/abs/1906.11979 · HTML: https://mlsanity.com/paper/1906.11979/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2019.

## Built on by (most cited first, 5 of 5)

- [DeepBlur: A Simple and Effective Method for Natural Image Obfuscation](https://mlsanity.com/paper/2104.02655/) (2021)
- [Robust Privacy-Preserving Motion Detection and Object Tracking in Encrypted Streaming Video](https://mlsanity.com/paper/2108.13141/) (2021)
- [Hiding Behind Backdoors: Self-Obfuscation Against Generative Models](https://mlsanity.com/paper/2201.09774/) (2022)
- [StyleGAN as a Utility-Preserving Face De-identification Method](https://mlsanity.com/paper/2212.02611/) (2022)
- [FairDeFace: Evaluating the Fairness and Adversarial Robustness of Face Obfuscation Methods](https://mlsanity.com/paper/2503.08731/) (2025)

## Abstract (arXiv)

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.