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Membership Inference Attacks are Easier on Difficult Problems

Membership Inference Attacks are Easier on Difficult Problems
Avital Shafran, Shmuel Peleg and Yedid Hoshen
International Conference on Computer Vision (ICCV), 2021.

<p align='center'> <img src='imgs/method.png' width='1000'/> </p>

Usage

The current software is tested with Pytorch 1.6.0 and Python 3.6.

Dataset

Download the Cityscapes dataset from the official website (registration required). After downloading, please put it under the ./pix2pixHD/datasets folder in the same way the example images are provided.

Pre-trained model

Please download the pre-trained pix2pixHD Cityscapes model from the official pix2pixHD Pytorch implementation, and put it under ./pix2pixHD/checkpoints/label2city_1024p/

Run attack

Run attack on pre-trained pix2pixHD model:

python run_MIA_pix2pixHD.py --dataroot./pix2pixHD/datasets/Cityscapes --checkpoints_dir ./pix2pixHD/checkpoints

Citation

If you find this project useful for your research, please cite

@article{shafran2021reconstruction,
  title={Reconstruction-Based Membership Inference Attacks are Easier on Difficult Problems},
  author={Shafran, Avital and Peleg, Shmuel and Hoshen, Yedid},
  journal={arXiv preprint arXiv:2102.07762},
  year={2021}
}

Acknowledgments

This code borrows heavily from pix2pixHD.