fawkes：Fawkes，针对面部识别系统的隐私保护工具。 有关更多信息，请访问https：//sandlab.cs.uchi...

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]`.  ### 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} } ```