fawkes 0.0.6

An utility to protect user privacy

Fawkes

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.

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

If you would like to use Fawkes to protect your images, please check out our binary implementation on the website.

Usage

$ fawkes

Options:

• -m, --mode : the tradeoff between privacy and perturbation size
• -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
• --format : format of the output image.

when --mode is custom:

• --th : perturbation threshold
• --max-step : number of optimization steps to run
• --lr : learning rate for the optimization
• --feature-extractor : name of the feature extractor to use
• --separate_target : whether select separate targets for each faces in the diectory.

Example

fawkes -d ./imgs --mode mid

Tips

• Select the best mode for your need. Low protection is effective against most model trained by individual trackers with commodity face recongition model. mid is robust against most commercial models, such as Facebook tagging system. high is robust against powerful modeled trained using different face recongition API.
• 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.
• Turn on separate target if the images in the directory belong to different person, otherwise, turn it off.

Quick Installation

Install from [PyPI][pypi_fawkes]:

pip install fawkes

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

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}
}