# Privacy-Protective-GAN for Face De-identification

arXiv: https://arxiv.org/abs/1806.08906 · HTML: https://mlsanity.com/paper/1806.08906/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2018.

## Built on by (most cited first, 14 of 14)

- [Attribute-preserving Face Dataset Anonymization via Latent Code Optimization](https://mlsanity.com/paper/2303.11296/) (2023)
- [RiDDLE: Reversible and Diversified De-identification with Latent Encryptor](https://mlsanity.com/paper/2303.05171/) (2023)
- [Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging](https://mlsanity.com/paper/2107.09543/) (2021)
- [Differentially Private Imaging via Latent Space Manipulation](https://mlsanity.com/paper/2103.05472/) (2021)
- [DeepBlur: A Simple and Effective Method for Natural Image Obfuscation](https://mlsanity.com/paper/2104.02655/) (2021)
- [Latent Diffusion Models for Attribute-Preserving Image Anonymization](https://mlsanity.com/paper/2403.14790/) (2024)
- [Learnable Privacy-Preserving Anonymization for Pedestrian Images](https://mlsanity.com/paper/2207.11677/) (2022)
- [Face Encryption via Frequency-Restricted Identity-Agnostic Attacks](https://mlsanity.com/paper/2308.05983/) (2023)
- [Privacy of Autonomous Vehicles: Risks, Protection Methods, and Future Directions](https://mlsanity.com/paper/2209.04022/) (2022)
- [GMFIM: A Generative Mask-guided Facial Image Manipulation Model for Privacy Preservation](https://mlsanity.com/paper/2201.03353/) (2022)
- [BLANKET: Anonymizing Faces in Infant Video Recordings](https://mlsanity.com/paper/2512.15542/) (2025)
- [Disentangle Before Anonymize: A Two-stage Framework for Attribute-preserved and Occlusion-robust De-identification](https://mlsanity.com/paper/2311.08786/) (2023)
- [PuFace: Defending against Facial Cloaking Attacks for Facial Recognition Models](https://mlsanity.com/paper/2406.02253/) (2024)
- [Deep Learning Technology for Face Forgery Detection: A Survey](https://mlsanity.com/paper/2409.14289/) (2024)

## Abstract (arXiv)

Face de-identification has become increasingly important as the image sources are explosively growing and easily accessible. The advance of new face recognition techniques also arises people's concern regarding the privacy leakage. The mainstream pipelines of face de-identification are mostly based on the k-same framework, which bears critiques of low effectiveness and poor visual quality. In this paper, we propose a new framework called Privacy-Protective-GAN (PP-GAN) that adapts GAN with novel verificator and regulator modules specially designed for the face de-identification problem to ensure generating de-identified output with retained structure similarity according to a single input. We evaluate the proposed approach in terms of privacy protection, utility preservation, and structure similarity. Our approach not only outperforms existing face de-identification techniques but also provides a practical framework of adapting GAN with priors of domain knowledge.