# deepdefense.pytorch

**Repository Path**: xieck13/deepdefense.pytorch

## Basic Information

- **Project Name**: deepdefense.pytorch
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-02-17
- **Last Updated**: 2021-02-17

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# deepdefense.pytorch
Code for NeurIPS 2018 paper [Deep Defense: Training DNNs with Improved Adversarial Robustness](https://papers.nips.cc/paper/7324-deep-defense-training-dnns-with-improved-adversarial-robustness).

Deep Defense is recipe to improve the robustness of DNNs to adversarial perturbations. We integrate an adversarial perturbation-based regularizer into the training objective, such that the obtained models learn to resist potential attacks in a principled way.

## Environments
* Python 3.5
* PyTorch 0.4.1
* glog 0.3.1

## Datasets and Reference Models
For fair comparison with DeepFool, we follow it to use [matconvnet](https://github.com/vlfeat/matconvnet/releases/tag/v1.0-beta24) to pre-process data and train reference models for MNIST and CIFAR-10.

Please download processed datasets and reference models (including MNIST and CIFAR-10) at [Google Drive](https://drive.google.com/open?id=15xoZ-LUbc9GZpTlxmCJmvL_DR2qYEu2J) or [Baidu Pan](https://pan.baidu.com/s/1-TSXR8kVcat7IXtuE74nJg).

For the MLP with batch normalization example [(issue 2)](https://github.com/ZiangYan/deepdefense.pytorch/issues/2), please download the reference model at [Google Drive](https://drive.google.com/open?id=1Vy4xWeXhOX_QluYH33SHVE3q_KDOOBeF) or [Baidu Pan](https://pan.baidu.com/s/1cIgGX6b-1AQ4ybSyX2xDew).

## Usage
To train a Deep Defense LeNet model using default parameters on MNIST:

```
python3 deepdefense.py --pretest --dataset mnist --arch LeNet
```

Argument ```--pretest``` indicates evaluating performance before fine-tuning, thus we can check the performance of reference model.

Currently we've implemented ```MLP``` and ```LeNet``` for mnist, and ```ConvNet``` for CIFAR-10.

## Citation
Please cite our work in your publications if it helps your research:

```
@inproceedings{yan2018deep,
  title={Deep Defense: Training DNNs with Improved Adversarial Robustness},
  author={Yan, Ziang and Guo, Yiwen and Zhang, Changshui},
  booktitle={Advances in Neural Information Processing Systems},
  pages={417--426},
  year={2018}
}
```