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*.
NEW! If you would like to use Fawkes to protect your identity, please check out our software and binary implementation on the [website](https://sandlab.cs.uchicago.edu/fawkes/#code).
*`-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.
- 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]`.
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.
