# CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2005.09544 · HTML: https://mlsanity.com/paper/2005.09544/

## Key facts

- Cited by 88 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2020.

## Built on by (most cited first, 30 of 88)

- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Trustworthy AI: From Principles to Practices](https://mlsanity.com/paper/2110.01167/) (2021)
- [Emu: Generative Pretraining in Multimodality](https://mlsanity.com/paper/2307.05222/) (2023)
- [Coming Down to Earth: Satellite-to-Street View Synthesis for Geo-Localization](https://mlsanity.com/paper/2103.06818/) (2021)
- [Explainability of deep vision-based autonomous driving systems: Review and challenges](https://mlsanity.com/paper/2101.05307/) (2021)
- [Countering Malicious DeepFakes: Survey, Battleground, and Horizon](https://mlsanity.com/paper/2103.00218/) (2021)
- [OpenForensics: Large-Scale Challenging Dataset For Multi-Face Forgery Detection And Segmentation In-The-Wild](https://mlsanity.com/paper/2107.14480/) (2021)
- [Data Poisoning Won't Save You From Facial Recognition](https://mlsanity.com/paper/2106.14851/) (2021)
- [The 2021 Image Similarity Dataset and Challenge](https://mlsanity.com/paper/2106.09672/) (2021)
- [Attribute-preserving Face Dataset Anonymization via Latent Code Optimization](https://mlsanity.com/paper/2303.11296/) (2023)
- [Diff-Privacy: Diffusion-based Face Privacy Protection](https://mlsanity.com/paper/2309.05330/) (2023)
- [IdentityDP: Differential Private Identification Protection for Face Images](https://mlsanity.com/paper/2103.01745/) (2021)
- [LDFA: Latent Diffusion Face Anonymization for Self-driving Applications](https://mlsanity.com/paper/2302.08931/) (2023)
- [RiDDLE: Reversible and Diversified De-identification with Latent Encryptor](https://mlsanity.com/paper/2303.05171/) (2023)
- [Perceptual Indistinguishability-Net (PI-Net): Facial Image Obfuscation with Manipulable Semantics](https://mlsanity.com/paper/2104.01753/) (2021)
- [Controllable Data Generation by Deep Learning: A Review](https://mlsanity.com/paper/2207.09542/) (2022)
- [G2Face: High-Fidelity Reversible Face Anonymization via Generative and Geometric Priors](https://mlsanity.com/paper/2408.09458/) (2024)
- [GAN-based Facial Attribute Manipulation](https://mlsanity.com/paper/2210.12683/) (2022)
- [Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging](https://mlsanity.com/paper/2107.09543/) (2021)
- [Realistic Full-Body Anonymization with Surface-Guided GANs](https://mlsanity.com/paper/2201.02193/) (2022)
- [My Face My Choice: Privacy Enhancing Deepfakes for Social Media Anonymization](https://mlsanity.com/paper/2211.01361/) (2022)
- [CoPE: Conditional image generation using Polynomial Expansions](https://mlsanity.com/paper/2104.05077/) (2021)
- [CFA-Net: Controllable Face Anonymization Network with Identity Representation Manipulation](https://mlsanity.com/paper/2105.11137/) (2021)
- [FIVA: Facial Image and Video Anonymization and Anonymization Defense](https://mlsanity.com/paper/2309.04228/) (2023)
- [Unsupervised Text Deidentification](https://mlsanity.com/paper/2210.11528/) (2022)
- [StyleID: Identity Disentanglement for Anonymizing Faces](https://mlsanity.com/paper/2212.13791/) (2022)
- [Graph-based Generative Face Anonymisation with Pose Preservation](https://mlsanity.com/paper/2112.05496/) (2021)
- [GANonymization: A GAN-based Face Anonymization Framework for Preserving Emotional Expressions](https://mlsanity.com/paper/2305.02143/) (2023)
- [Security Considerations in AI-Robotics: A Survey of Current Methods, Challenges, and Opportunities](https://mlsanity.com/paper/2310.08565/) (2023)
- [Face De-identification: State-of-the-art Methods and Comparative Studies](https://mlsanity.com/paper/2411.09863/) (2024)

## Abstract (arXiv)

The unprecedented increase in the usage of computer vision technology in society goes hand in hand with an increased concern in data privacy. In many real-world scenarios like people tracking or action recognition, it is important to be able to process the data while taking careful consideration in protecting people's identity. We propose and develop CIAGAN, a model for image and video anonymization based on conditional generative adversarial networks. Our model is able to remove the identifying characteristics of faces and bodies while producing high-quality images and videos that can be used for any computer vision task, such as detection or tracking. Unlike previous methods, we have full control over the de-identification (anonymization) procedure, ensuring both anonymization as well as diversity. We compare our method to several baselines and achieve state-of-the-art results.