fawkes 1.0.4

An utility to protect user privacy

Fawkes

:warning: Check out our MacOS/Windows Software on our official webpage.

Fawkes is a privacy protection system developed by researchers at SANDLab, University of Chicago. For more information about the project, please refer to our project webpage. Contact us at [e-mail].

We published an academic paper to summarize our work "Fawkes: Protecting Personal Privacy against Unauthorized Deep Learning Models" at USENIX Security 2020.

Copyright

This code is intended only for personal privacy protection or academic research.

Usage

$ fawkes

Options:

• -m, --mode : the tradeoff between privacy and perturbation size. Select from low, mid, high. The higher the mode is, the more perturbation will add to the image and provide stronger protection.
• -d, --directory : the directory with images to run protection.
• -g, --gpu : the GPU id when using GPU for optimization.
• --batch-size : number of images to run optimization together. Change to >1 only if you have extremely powerful compute power.
• --format : format of the output image (png or jpg).

Example

fawkes -d ./imgs --mode low

or python3 protection.py -d ./imgs --mode low

Tips

• The perturbation generation takes ~60 seconds per image on a CPU machine, and it would be much faster on a GPU machine. Use batch-size=1 on CPU and batch-size>1 on GPUs.
• Run on GPU. The current Fawkes package and binary does not support GPU. To use GPU, you need to clone this repo, install the required packages in setup.py, and replace tensorflow with tensorflow-gpu. Then you can run Fawkes by python3 fawkes/protection.py [args].

How do I know my images are secure?

We are actively working on this. Python scripts that can test the protection effectiveness will be ready shortly.

Quick Installation

Install from PyPI:

pip install fawkes

If you don't have root privilege, please try to install on user namespace: pip install --user fawkes.

Academic Research Usage

For academic researchers, whether seeking to improve fawkes or to explore potential vunerability, please refer to the following guide to test Fawkes.

To protect a class in a dataset, first move the label's image to a seperate location and run Fawkes. Please use --debug option and set batch-size to a reasonable number (i.e 16, 32). If the images are already cropped and aligned, then also use the no-align option.

Citation

@inproceedings{shan2020fawkes,
  title={Fawkes: Protecting Personal Privacy against Unauthorized Deep Learning Models},
  author={Shan, Shawn and Wenger, Emily and Zhang, Jiayun and Li, Huiying and Zheng, Haitao and Zhao, Ben Y},
  booktitle={Proc. of {USENIX} Security},
  year={2020}
}