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96 lines
4.4 KiB
Markdown
96 lines
4.4 KiB
Markdown
Fawkes
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------
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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.
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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*.
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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).
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Copyright
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---------
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This code is intended only for personal privacy protection or academic research.
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Usage
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-----
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`$ fawkes`
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Options:
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* `-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.
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* `-d`, `--directory` : the directory with images to run protection.
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* `-g`, `--gpu` : the GPU id when using GPU for optimization.
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* `--batch-size` : number of images to run optimization together. Change to >1 only if you have extremely powerful compute power.
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* `--format` : format of the output image (png or jpg).
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when --mode is `custom`:
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* `--th` : perturbation threshold
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* `--max-step` : number of optimization steps to run
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* `--lr` : learning rate for the optimization
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* `--feature-extractor` : name of the feature extractor to use
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* `--separate_target` : whether select separate targets for each faces in the diectory.
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### Example
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`fawkes -d ./imgs --mode min`
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### Tips
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- 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.
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- Turn on separate target if the images in the directory belong to different people, otherwise, turn it off.
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- 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]`.
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### How do I know my images are secure?
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We are actively working on this. Python scripts that can test the protection effectiveness will be ready shortly.
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Quick Installation
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------------------
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Install from [PyPI](https://pypi.org/project/fawkes/):
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```
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pip install fawkes
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```
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If you don't have root privilege, please try to install on user namespace: `pip install --user fawkes`.
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## Installation and Usage with Docker
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- clone this repository: `git clone [url]`
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- go into cloned repository: `cd fawkes`
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- create `/imgs` folder in root folder: `mkdir imgs`
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- build docker image in root folder: `docker build -t fawkes .`
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- run container: `docker run -it fawkes sh`
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- run your fawkes command, e.g. `fawkes -d ./imgs --mode min`
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- exit from your container: `exit`
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- find your container id: `docker container ls --all`
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- copy the created data to your local machine: `docker cp [container-id]:/app/imgs ~/output-from-fawkes`
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Academic Research Usage
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-----------------------
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For academic researchers, whether seeking to improve fawkes or to explore potential vunerability, please refer to the following guide to test Fawkes.
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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.
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Contribute to Fawkes
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--------------------
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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.
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### Citation
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```
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@inproceedings{shan2020fawkes,
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title={Fawkes: Protecting Personal Privacy against Unauthorized Deep Learning Models},
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author={Shan, Shawn and Wenger, Emily and Zhang, Jiayun and Li, Huiying and Zheng, Haitao and Zhao, Ben Y},
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booktitle="Proc. of USENIX Security",
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year={2020}
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}
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```
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