mirror of
https://github.com/Shawn-Shan/fawkes.git
synced 2024-11-09 13:41:31 +05:30
e44cb646ff
Former-commit-id: dcd4fc63fe48676f4312fa3a9702ab934d0e268c [formerly 72337c5fdc6132a9b8522969720ab9ecb2ebba28] Former-commit-id: 25d5795192b8fb499d3bc510c89a3660899b59e6
63 lines
2.5 KiB
Markdown
63 lines
2.5 KiB
Markdown
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}
|
|
}
|
|
```
|