[AAAI'21] Modeling Deep Learning Based Privacy Attacks on Physical Mail
[AAAI’21] Modeling Deep Learning Based Privacy Attacks on Physical Mail
PyTorch implementation of Neural-STE (Neural-See-Through-Envelope).
Please refer to supplementary material (~68M) for more results.
Clone this repo:
git clone https://github.com/BingyaoHuang/Neural-STE
cd Neural-STE
Install required packages by typing
pip install -r requirements.txt
data/
Start visdom by typing
visdom -port 8098
Once visdom is successfully started, visit http://localhost:8098
(train locally) or http://serverhost:8098
(train remotely).
Open train_Neural-STE.py
and set which GPUs to use. An example is shown below, we use GPU 0, 2 and 3 to train the model.
os.environ['CUDA_VISIBLE_DEVICES'] = '0, 2, 3'
device_ids = [0, 1, 2]
Run train_Neural-STE.py
to start training and testing
cd src/python
python train_Neural-STE.py
log/%Y-%m-%d_%H_%M_%S.txt
after training.
If you use the dataset or this code, please consider citing our work
@inproceedings{huang2021Neural-STE,
title={Modeling Deep Learning Based Privacy Attacks on Physical Mail},
author={Bingyao Huang and Ruyi Lian and Dimitris Samaras and Haibin Ling},
booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
year={2021}
}
The PyTorch implementation of SSIM loss is modified from Po-Hsun-Su/pytorch-ssim.
We thank the anonymous reviewers for valuable and inspiring comments and suggestions.
We thank the authors of the colorful textured sampling images.
This software is freely available for non-profit non-commercial use, and may be redistributed under the conditions in license.