# StyleGAN as a Utility-Preserving Face De-identification Method

arXiv: https://arxiv.org/abs/2212.02611 · HTML: https://mlsanity.com/paper/2212.02611/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 66 references that are in the map.
- First posted on arXiv 5 Dec 2022.

## Builds on (most cited first, 27 of 27)

- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Neural Style Transfer: A Review](https://mlsanity.com/paper/1705.04058/) (2017)
- [DeepFaceLab: Integrated, flexible and extensible face-swapping framework](https://mlsanity.com/paper/2005.05535/) (2020)
- [DeepPrivacy: A Generative Adversarial Network for Face Anonymization](https://mlsanity.com/paper/1909.04538/) (2019)
- [LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition](https://mlsanity.com/paper/2101.07922/) (2021)
- [CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks](https://mlsanity.com/paper/2005.09544/) (2020)
- [Learning to Anonymize Faces for Privacy Preserving Action Detection](https://mlsanity.com/paper/1803.11556/) (2018)
- [Natural and Effective Obfuscation by Head Inpainting](https://mlsanity.com/paper/1711.09001/) (2017)
- [AnonymousNet: Natural Face De-Identification with Measurable Privacy](https://mlsanity.com/paper/1904.12620/) (2019)
- [Live Face De-Identification in Video](https://mlsanity.com/paper/1911.08348/) (2019)
- [Defeating Image Obfuscation with Deep Learning](https://mlsanity.com/paper/1609.00408/) (2016)
- [A Hybrid Model for Identity Obfuscation by Face Replacement](https://mlsanity.com/paper/1804.04779/) (2018)
- [Adversarial Image Perturbation for Privacy Protection -- A Game Theory Perspective](https://mlsanity.com/paper/1703.09471/) (2017)
- [Learning Privacy Preserving Encodings through Adversarial Training](https://mlsanity.com/paper/1802.05214/) (2018)
- [Face-Off: Adversarial Face Obfuscation](https://mlsanity.com/paper/2003.08861/) (2020)
- [FoggySight: A Scheme for Facial Lookup Privacy](https://mlsanity.com/paper/2012.08588/) (2020)
- [Naive-Deep Face Recognition: Touching the Limit of LFW Benchmark or Not?](https://mlsanity.com/paper/1501.04690/) (2015)
- [InfoScrub: Towards Attribute Privacy by Targeted Obfuscation](https://mlsanity.com/paper/2005.10329/) (2020)
- [Fairness Properties of Face Recognition and Obfuscation Systems](https://mlsanity.com/paper/2108.02707/) (2021)
- [Face Deidentification with Generative Deep Neural Networks](https://mlsanity.com/paper/1707.09376/) (2017)
- [A Systematical Solution for Face De-identification](https://mlsanity.com/paper/2107.08581/) (2021)
- [Robustness Analysis of Face Obscuration](https://mlsanity.com/paper/1905.05243/) (2019)
- [A Utility-Preserving GAN for Face Obscuration](https://mlsanity.com/paper/1906.11979/) (2019)
- [De-identification without losing faces](https://mlsanity.com/paper/1902.04202/) (2019)

## Built on by (most cited first, 2 of 2)

- [FairDeFace: Evaluating the Fairness and Adversarial Robustness of Face Obfuscation Methods](https://mlsanity.com/paper/2503.08731/) (2025)
- [Face De-Identification: A Domain-Centric Survey from Capture to Processing](https://mlsanity.com/paper/2607.25926/) (2026)

## Abstract (arXiv)

Face de-identification methods have been proposed to preserve users' privacy by obscuring their faces. These methods, however, can degrade the quality of photos, and they usually do not preserve the utility of faces, i.e., their age, gender, pose, and facial expression. Recently, GANs, such as StyleGAN, have been proposed, which generate realistic, high-quality imaginary faces. In this paper, we investigate the use of StyleGAN in generating de-identified faces through style mixing. We examined this de-identification method for preserving utility and privacy by implementing several face detection, verification, and identification attacks and conducting a user study. The results from our extensive experiments, human evaluation, and comparison with two state-of-the-art methods, i.e., CIAGAN and DeepPrivacy, show that StyleGAN performs on par or better than these methods, preserving users' privacy and images' utility. In particular, the results of the machine learning-based experiments show that StyleGAN0-4 preserves utility better than CIAGAN and DeepPrivacy while preserving privacy at the same level. StyleGAN0-3 preserves utility at the same level while providing more privacy. In this paper, for the first time, we also performed a carefully designed user study to examine both privacy and utility-preserving properties of StyleGAN0-3, 0-4, and 0-5, as well as CIAGAN and DeepPrivacy from the human observers' perspectives. Our statistical tests showed that participants tend to verify and identify StyleGAN0-5 images more easily than DeepPrivacy images. All the methods but StyleGAN0-5 had significantly lower identification rates than CIAGAN. Regarding utility, as expected, StyleGAN0-5 performed significantly better in preserving some attributes. Among all methods, on average, participants believe gender has been preserved the most while naturalness has been preserved the least.