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https://github.com/Shawn-Shan/fawkes

Fawkes, privacy preserving tool against facial recognition systems. More info at https://sandlab.cs.uchicago.edu/fawkes
https://github.com/Shawn-Shan/fawkes

adversarial-machine-learning face-recognition privacy-enhancing-technologies privacy-protection

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Fawkes, privacy preserving tool against facial recognition systems. More info at https://sandlab.cs.uchicago.edu/fawkes

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README 

        
Fawkes

------

:warning: Check out our MacOS/Windows Software on our official [webpage](https://sandlab.cs.uchicago.edu/fawkes/#code).



Fawkes is a privacy protection system developed by researchers at [SANDLab](https://sandlab.cs.uchicago.edu/),

University of Chicago. For more information about the project, please refer to our

project [webpage](https://sandlab.cs.uchicago.edu/fawkes/). Contact us at [email protected].



We published an academic paper to summarize our

work "[Fawkes: Protecting Personal Privacy against Unauthorized Deep Learning Models](https://www.shawnshan.com/files/publication/fawkes.pdf)"

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]`.



![](http://sandlab.cs.uchicago.edu/fawkes/files/obama.png)



### 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](https://pypi.org/project/fawkes/):



```

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 separate 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}

}

```