Update README.md

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fawkes/README.md

@@ -1,26 +1,22 @@
-# Fawkes
+# Fawkes Binary
 
 This application is built for individuals to cloak their images before uploading to the Internet. For more information about the project, please refer to our project [webpage](http://sandlab.cs.uchicago.edu/fawkes/).  
 
-If you are a developer or researcher planning to customize and modify on our existing code. Please refer to [fawkes_dev](https://github.com/Shawn-Shan/fawkes/tree/master/fawkes_dev). 
+If you are a developer or researcher planning to customize and modify on our existing code. Please refer to [fawkes](https://github.com/Shawn-Shan/fawkes/tree/master/). 
+
+### How to Setup
+
+#### MAC:
+
+* Download the binary following this [link](http://sandlab.cs.uchicago.edu/fawkes/files/fawkes_binary.zip) and unzip the download file. 
+* 
+* Create a directory and move all the images you wish to protect into that directory. Note the path to that directory (e.g. ~/Desktop/images). 
+* Open [terminal](https://support.apple.com/guide/terminal/open-or-quit-terminal-apd5265185d-f365-44cb-8b09-71a064a42125/mac) and change directory to fawkes (the unzipped folder). 
+* (If your MacOS is Catalina) Run `sudo spctl --master-disable` to enable running apps from unidentified developer. 
+* Run `./fawkes -d IMAGE_DIR_PATH -m low` to generate cloak for images in `IMAGE_DIR_PATH`. 
+* More details on the optional parameters check out the [github repo](https://github.com/Shawn-Shan/fawkes/tree/master/). 
 
 
-# How do I protect my images?
+#### PC:
+More details coming soon. The steps should be similar to the MAC setup. 
 
-TBD
-
-
-
-
-### 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}
-}
-```
-
-
-Code implementation of the paper "[Fawkes: Protecting Personal Privacy against Unauthorized Deep Learning Models](https://arxiv.org/pdf/2002.08327.pdf)", at *USENIX Security 2020*.