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# Fawkes
Fawkes
------
Fawkes is a privacy protection system developed by researchers at University of Chicago. For more information about the project, please refer to our project [webpage](http://sandlab.cs.uchicago.edu/fawkes/).
Fawkes is a privacy protection system developed by researchers at [SANDLab](http://sandlab.cs.uchicago.edu/), University of Chicago. For more information about the project, please refer to our project [webpage](http://sandlab.cs.uchicago.edu/fawkes/).
We published an academic paper to summary our work "[Fawkes: Protecting Personal Privacy against Unauthorized Deep Learning Models](https://arxiv.org/pdf/2002.08327.pdf)" at *USENIX Security 2020*.
### BEFORE YOU RUN OUR CODE
We published an academic paper to summary our work "[Fawkes: Protecting Personal Privacy against Unauthorized Deep Learning Models](https://www.shawnshan.com/files/publication/fawkes.pdf)" at *USENIX Security 2020*.
If you would like to use Fawkes to protect your images, please check out our binary implementation on the [website](http://sandlab.cs.uchicago.edu/fawkes/#code).
If you are a developer or researcher planning to customize and modify on our existing code. Please refer to [fawkes_dev](https://github.com/Shawn-Shan/fawkes/tree/master/fawkes_dev).
### INSTALL FAWKES
Fawkes can be installed with pip. Simply run:
`pip install fawkes`
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.
### 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
```