Metadata-Version: 2.1
Name: fawkes
Version: 0.3.2
Summary: An utility to protect user privacy
Home-page: https://github.com/Shawn-Shan/fawkes
Author: Shawn Shan
Author-email: shansixiong@cs.uchicago.edu
License: BSD
Description: Fawkes
        ------
        
        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). 
        
        
        
        Copyright
        ---------
        This code is intended only for personal privacy protection or academic research. 
        
        We are currently exploring the filing of a provisional patent on the Fawkes algorithm. 
        
        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`.
        
        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}
        }
        ```
        
Keywords: fawkes privacy clearview
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: System :: Monitoring
Requires-Python: >=3.5,<3.8
Description-Content-Type: text/markdown
