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DeepPrivacy2 - A Toolbox for Realistic Image Anonymization

[Paper] [Appendix] [Video Demo] [Documentation] Hugging Face Spaces

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DeepPrivacy2 is a toolbox for realistic anonymization of humans, including a face and a full-body anonymizer.

<p align="center"> <img width="50%" src="https://www.hukkelas.no/images/anonymization_demo.gif" /> </p>

DeepPrivacy first detects, then recursively anonymization all individuals in an image with a Generative Adversarial Network (GAN) that synthesizes one individual at a time.

Published Papers

This repository provide source code for the following papers

DeepPrivacy2 vs DeepPrivacy1

This repository improves over the original DeepPrivacy repository with the following new features:

Useful Links

Quick Start

Installation

We recommend to setup and install pytorch with anaconda following the pytorch installation instructions.

  1. Clone repository: git clone https://github.com/hukkelas/deep_privacy2/.
  2. Install using setup.py:
pip install -e .

or:

pip install git+https://github.com/hukkelas/deep_privacy2/

See the documentation for more installation instructions.

Anonymization

anonymize.py is the main script for anonymization.

Full-Body Anonymization

python3 anonymize.py configs/anonymizers/FB_cse.py -i media/regjeringen.jpg --output_path output.png --visualize

Face Anonymization

python3 anonymize.py configs/anonymizers/face.py -i media/regjeringen.jpg --output_path output.png --visualize

Webcam anonymization

python3 anonymize.py configs/anonymizers/FB_cse.py --webcam

See the documentation for more detailed instructions for anonymization.

Gradio Demos

The repository includes gradio demos to show of the capabilities of DeepPrivacy2.

Face anonymization. Test it on Hugging Face.

python3 -m gradio_demos.face

Full-body anonymization. Test it on Hugging Face.

python3 -m gradio_demos.body_cse

License

This repsitory is released under Apache 2.0 License, except for the following:.

Citation

If you find this repository useful, please cite:

@inproceedings{hukkelas23DP2,
  author={Hukkelås, Håkon and Lindseth, Frank},
  booktitle={2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, 
  title={DeepPrivacy2: Towards Realistic Full-Body Anonymization}, 
  year={2023},
  volume={},
  number={},
  pages={1329-1338},
  doi={10.1109/WACV56688.2023.00138}}