Code for NeurIPS 2018 paper 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.
- Python 3.5
- PyTorch 0.4.1
- glog 0.3.1
For fair comparison with DeepFool, we follow it to use matconvnet 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 or Baidu Pan.
For the MLP with batch normalization example (issue 2), please download the reference model at Google Drive or Baidu Pan.
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.
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}
}