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 fawkes-team@googlegroups.com. 

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 `min`, `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). 

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 min`

### 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. 
- Turn on separate target if the images in the directory belong to different people, otherwise, turn it off. 
- Run on GPU. The current Fawkes package and binary does not support GPU. To use GPU, you need to clone this, 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 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. 



Contribute to Fawkes
--------------------

If you would like to contribute to make Fawkes software better, please checkout our [project list](https://github.com/Shawn-Shan/fawkes/projects/1) which contains our TODOs. If you are confident in helping, please open a pull requests and explain the plans for your changes. We will try our best to approve asap, and once approved, you can work on it. 


### 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}
}
```