mirror of
https://github.com/Shawn-Shan/fawkes.git
synced 2024-11-09 13:41:31 +05:30
78 lines
3.5 KiB
Markdown
78 lines
3.5 KiB
Markdown
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]`.
|
|
|
|
|
|
|
|
### 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}
|
|
}
|
|
```
|