# GANonymization: A GAN-based Face Anonymization Framework for Preserving Emotional Expressions

arXiv: https://arxiv.org/abs/2305.02143 · HTML: https://mlsanity.com/paper/2305.02143/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 56 references that are in the map.
- First posted on arXiv 3 May 2023.

## Builds on (most cited first, 29 of 29)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [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)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild](https://mlsanity.com/paper/1708.03985/) (2017)
- [WIDER FACE: A Face Detection Benchmark](https://mlsanity.com/paper/1511.06523/) (2015)
- [Machine Learning with Membership Privacy using Adversarial Regularization](https://mlsanity.com/paper/1807.05852/) (2018)
- [DSFD: Dual Shot Face Detector](https://mlsanity.com/paper/1810.10220/) (2018)
- [CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks](https://mlsanity.com/paper/2005.09544/) (2020)
- [A Study of Face Obfuscation in ImageNet](https://mlsanity.com/paper/2103.06191/) (2021)
- [Continuous Surface Embeddings](https://mlsanity.com/paper/2011.12438/) (2020)
- [Protecting Facial Privacy: Generating Adversarial Identity Masks via Style-robust Makeup Transfer](https://mlsanity.com/paper/2203.03121/) (2022)
- [Real-time Facial Surface Geometry from Monocular Video on Mobile GPUs](https://mlsanity.com/paper/1907.06724/) (2019)
- [Natural and Effective Obfuscation by Head Inpainting](https://mlsanity.com/paper/1711.09001/) (2017)
- [DeepPrivacy2: Towards Realistic Full-Body Anonymization](https://mlsanity.com/paper/2211.09454/) (2022)
- [Towards Privacy-Preserving Visual Recognition via Adversarial Training: A Pilot Study](https://mlsanity.com/paper/1807.08379/) (2018)
- [MOON: A Mixed Objective Optimization Network for the Recognition of Facial Attributes](https://mlsanity.com/paper/1603.07027/) (2016)
- [Privacy Preserving Face Recognition Utilizing Differential Privacy](https://mlsanity.com/paper/2005.10486/) (2020)
- [IdentityDP: Differential Private Identification Protection for Face Images](https://mlsanity.com/paper/2103.01745/) (2021)
- [Realistic Full-Body Anonymization with Surface-Guided GANs](https://mlsanity.com/paper/2201.02193/) (2022)
- [Obfuscation of Images via Differential Privacy: From Facial Images to General Images](https://mlsanity.com/paper/2102.11072/) (2021)
- [StyleID: Identity Disentanglement for Anonymizing Faces](https://mlsanity.com/paper/2212.13791/) (2022)
- [Privacy Issues and Data Protection in Big Data: A Case Study Analysis under GDPR](https://mlsanity.com/paper/1811.08531/) (2018)
- [Alternative Data Augmentation for Industrial Monitoring using Adversarial Learning](https://mlsanity.com/paper/2205.04222/) (2022)

## Built on by (most cited first, 9 of 9)

- [Face De-identification: State-of-the-art Methods and Comparative Studies](https://mlsanity.com/paper/2411.09863/) (2024)
- [RID-TWIN: An end-to-end pipeline for automatic face de-identification in videos](https://mlsanity.com/paper/2403.10058/) (2024)
- [FAKER: Full-body Anonymization with Human Keypoint Extraction for Real-time Video Deidentification](https://mlsanity.com/paper/2408.11829/) (2024)
- [Reverse Personalization](https://mlsanity.com/paper/2512.22984/) (2025)
- [Position: Life-Logging Video Streams Make the Privacy-Utility Trade-off Inevitable](https://mlsanity.com/paper/2605.10404/) (2026)
- [PrivacyGAN: robust generative image privacy](https://mlsanity.com/paper/2310.12590/) (2023)
- [Assessing the Use of Face Swapping Methods as Face Anonymizers in Videos](https://mlsanity.com/paper/2505.20985/) (2025)
- [Facial Attribute Based Text Guided Face Anonymization](https://mlsanity.com/paper/2505.21002/) (2025)
- [Privacy-Preserving Action Recognition: Taxonomy, Methods, and Privacy-Utility Trade-offs](https://mlsanity.com/paper/2608.04501/) (2026)

## Abstract (arXiv)

In recent years, the increasing availability of personal data has raised concerns regarding privacy and security. One of the critical processes to address these concerns is data anonymization, which aims to protect individual privacy and prevent the release of sensitive information. This research focuses on the importance of face anonymization. Therefore, we introduce GANonymization, a novel face anonymization framework with facial expression-preserving abilities. Our approach is based on a high-level representation of a face, which is synthesized into an anonymized version based on a generative adversarial network (GAN). The effectiveness of the approach was assessed by evaluating its performance in removing identifiable facial attributes to increase the anonymity of the given individual face. Additionally, the performance of preserving facial expressions was evaluated on several affect recognition datasets and outperformed the state-of-the-art methods in most categories. Finally, our approach was analyzed for its ability to remove various facial traits, such as jewelry, hair color, and multiple others. Here, it demonstrated reliable performance in removing these attributes. Our results suggest that GANonymization is a promising approach for anonymizing faces while preserving facial expressions.