DeepPrivacy2 - A Toolbox for Realistic Image Anonymization

[Paper] [Appendix] [Video Demo] [Documentation]

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

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

• [PDF] [Appendix] [Conference Presentation] DeepPrivacy2: Towards Realistic Full-Body Anonymization. Published at WACV 2023.
• [PDF] [Appendix] Does Image Anonymization Impact Computer Vision Training? Published at CVPR Workshop on Autonomous Driving 2023.
• [PDF] [Appendix] Synthesizing Anyone, Anywhere, in Any Pose.

DeepPrivacy2 vs DeepPrivacy1

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

• Full-body anonymization: Anonymize the entire human body with a single generator
• Improved Face Anonymization: Improved quality and higher resolution (256x256 vs. 128x128) face anonymization without relying on facial landmark detection.
• Attribute Guided Anonymiation: Anonymize faces guided on text prompts using StyleMC - [Video Demo].
• Code cleanup and general improvements: Extensive refactoring, bugfixes, and improvements yielding improved results and faster training.

Useful Links

• Installation instructions.
• Anonymization Instructions.
• Training Instructions.

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:.

• Code under sg3_torch_utils/. This code is modified from github.com/NVlabs/stylegan2-ada-pytorch. Separate license is attached in the directory.
• Detection network: See Detectron2 License.
• All checkpoints follow the license of the datasets. See the respective datasets for more information.
• Code under dp2/detection/models/vit_pose. This code is modified from https://github.com/gpastal24/ViTPose-Pytorch, where code is adapted from OpenMMLab. Original license is Apache 2-0.

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}}