Metadata-Version: 2.1
Name: fawkes
Version: 0.0.6
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: MIT
Description: Fawkes
        ------
        
        Fawkes is a privacy protection system developed by researchers at [SANDLab](http://sandlab.cs.uchicago.edu/), University of Chicago. For more information about the project, please refer to our project [webpage](http://sandlab.cs.uchicago.edu/fawkes/).  
        
        We published an academic paper to summary our work "[Fawkes: Protecting Personal Privacy against Unauthorized Deep Learning Models](https://www.shawnshan.com/files/publication/fawkes.pdf)" at *USENIX Security 2020*. 
        
        If you would like to use Fawkes to protect your images, please check out our binary implementation on the [website](http://sandlab.cs.uchicago.edu/fawkes/#code). 
        
        
        
        Usage
        -----
        
        `$ fawkes`
        
        Options:
        
        * `-m`, `--mode`       : the tradeoff between privacy and perturbation size
        * `-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 
        * `--format`      : format of the output image. 
        
        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 mid`
        
        ### Tips
        
        - Select the best mode for your need. `Low` protection is effective against most model trained by individual trackers with commodity face recongition model. `mid` is robust against most commercial models, such as Facebook tagging system. `high` is robust against powerful modeled trained using different face recongition API. 
        - 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 person, otherwise, turn it off. 
        
        
        Quick Installation
        ------------------
        
        Install from [PyPI][pypi_fawkes]:
        
        ```
        pip install fawkes
        ```
        
        If you don't have root privilege, please try to install on user namespace: `pip install --user fawkes`.
        
        ### 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
Description-Content-Type: text/markdown
