[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.

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.

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.

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

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.

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.

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

Face anonymization. Test it on Hugginface.

python3 -m gradio_demos.face

Full-body anonymization. Test it on Hugginface.

python3 -m gradio_demos.body_cse

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.

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}}