DRE: Density-Based Data Selection With Entropy for Adversarial-Robust Deep Learning Models

This is the implementation for the project of DRE.

Problem definition

Integrate adversarial training into active learning to produce accurate and adversarial robust deep neural networks.

Benchmarks

• Random sampling
• Max Entropy
• DeepGini
• Bayesian active learning by disagreement (BALD)
• Dropout Entropy
• Least confidence (LC)
• Margin sampling
• Multiple-boundary clustring and prioritization (MCP)
• DeepFool active learning (DFAL)
• Expected gradient length (EGL)
• Core-set

Dependency

• python 3.6.10
• torch 1.6.0
• torchattacks 2.14.2
• torchvision 0.7.0
• foolbox 3.3.0
• scikit-learn 0.23.2
• apex please refer to NVIDA/apex for the installation

Download the dataset

Description of Dataset:

• MNIST, Fashion-MNIST, CIFAR-10: loaded from the corresponding dataset of TorchVision.

• SVHN: download the "train_32x32.mat, test_32x32.mat" from the site, then take the first 50,000 and 10,000 from each file for training and testing, respectively.

How to use

Training models without active learning

To obtain models using adversarial training:

python main_full.py --dataName mnist --train adv --ite 0 

Training models with active learning

To obtain initial models before starting active learning:

python main_warmUp.py --dataName mnist --ite 0

To perform robust active learning using the random selection as the acquisition function:

python main_al.py --dataName mnist --train adv --metric random --ite 0

Evaluate the accuracy and adversarial robustness of trained models

python main_evaluate.py --type al --train adv --dataName mnist --attack pgd --metric random --ite 0

[Notice] Be careful with the saving path in config.py.

Reference

More experimental results can be found at our companion site.

If you use this project, please consider citing us:

@article{guo2022dre,
  title={DRE: density-based data selection with entropy for adversarial-robust deep learning models},
  author={Guo, Yuejun and Hu, Qiang and Cordy, Maxime and Papadakis, Michail and Le Traon, Yves},
  journal={Neural Computing and Applications},
  pages={1--18},
  year={2022},
  publisher={Springer},
  doi={10.1007/s00521-022-07812-2}
}

Contact

Please contact Yuejun Guo (yuejun.guo@list.lu; yuejun.guo@yahoo.com) if you have further questions or want to contribute.