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# Fawkes
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*.
### BEFORE YOU RUN OUR CODE
We appreciate your interest in our work and for trying out our code. We've noticed several cases where incorrect configuration leads to poor performances of protection. If you also observe low detection performance far away from what we presented in the paper, please feel free to open an issue in this repo or contact any of the authors directly. We are more than happy to help you debug your experiment and find out the correct configuration.
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/).
### ABOUT
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).
This repository contains 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*.
### DEPENDENCIES
# How do I protect my images?
Our code is implemented and tested on Keras with TensorFlow backend. Following packages are used by our code.
TBD
- `keras==2.3.1`
- `numpy==1.18.4`
- `tensorflow-gpu==1.13.1`
Our code is tested on `Python 3.6.8`
### HOWTO
#### Download and Config Datasets
The first step is to download several datasets for protection and target selection.
1. Download the following dataset to your local machine. After downloading the datasets, restructure it the same way as the FaceScrub dataset downloaded.
- FaceScrub -- used for protection evaluation (link)
- VGGFace1 -- used for target select (link)
- VGGFace2 -- used for target select (link)
- WebFace -- used for target select (link)
2. Config datasets
open `fawkes/config.py` and update the `DATASETS` dictionary with the path to each dataset. Then run `python fawkes/config.py`. Every time the datasets are updated or moved, remember to rerun the command with the updated path.
3. Calculate embeddings using feature extractor.
Run `python3 fawkes/prepare_feature_extractor.py --candidate-datasets scrub vggface1 vggface2 webface`. This will calculate and cache the embeddings using the default feature extractor we provide. To use a customized feature extractor, please look at the Advance section at the end.
#### Generate Cloak for Images
To generate cloak, run
`python3 fawkes/protection.py --gpu 0 --dataset scrub --feature-extractor webface_dense_robust_extract`
For more information about the detailed parameters, please read `fawkes/protection.py`.
The code will output a directory in `results/` with `cloak_data.p` inside. You can check the cloaked images or inspect the changes in `this notebook`.
#### Evaluate Cloak Effectiveness
To evaluate the cloak, run `python3 fawkes/eval_cloak.py --gpu 0 --cloak_data PATH-TO-RESULT-DIRECTORY --transfer_model vggface2_inception_extract`.
The code will print out the tracker model accuracy on uncloaked/original test images of the protected user, which should be close to 0.
### Citation
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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*.