Pytorch Adversarial Training on CIFAR-10

• This repository provides simple PyTorch implementations for adversarial training methods on CIFAR-10.
• This repository shows accuracies that are similar to the accuracies in the original papers.
• If you have questions about this repository, please send an e-mail to me (dongbinna@postech.ac.kr) or make an issue.

Experiment Settings

• The basic experiment setting used in this repository follows the setting used in Madry Laboratory.
• Dataset: CIFAR-10 (10 classes)
• Attack method: PGD attack
  1. Epsilon size: 0.0314 for L-infinity bound
  2. Epsilon size: 0.25 (for attack) or 0.5 (for training) for L2 bound
• Training batch size: 128
• Weight decay: 0.0002
• Momentum: 0.9
• Learning rate adjustment
  1. 0.1 for epoch [0, 100)
  2. 0.01 for epoch [100, 150)
  3. 0.001 for epoch [150, 200)
• The ResNet-18 architecture used in this repository is smaller than Madry Laboratory, but its performance is similar.

Training Methods

1. Basic Training

• The basic training method adopts ResNet-18 architecture proposed by Kaiming He in CVPR 2016.
  • But, the architecture in this repository uses 32 X 32 inputs for CIFAR-10 (original ResNet-18 is for ImageNet).

python3 basic_training.py

	This repository
Benign accuracy	95.28%
Robust accuracy (L-infinity PGD)	1.02%

• Training time: 2 hours 24 minutes using 1 Titan XP
• Trained model download: Basic Training

2. PGD Adversarial Training

• This defense method was proposed by Aleksander Madry in ICLR 2018.

python3 pgd_adversarial_training.py

	This repository	Original paper (wide)
Benign accuracy	83.53%	87.30%
Robust accuracy (L-infinity PGD)	46.07%	50.00%

• Training time: 11 hours 12 minutes using 1 Titan XP
• Trained model download: PGD Adversarial Training

3. Interpolated Adversarial Training (IAT)

• This defense method was proposed by Alex Lamb in AISec 2019.

python3 interpolated_adversarial_training.py

	This repository	Original paper
Benign accuracy	91.86%	89.88%
Robust accuracy (L-infinity PGD)	44.76%	44.57%

• Training time: 15 hours 18 minutes using 1 Titan XP
• Trained model download: Interpolated Adversarial Training

4. Basic Training with Robust Dataset

• A normal dataset can be split into a robust dataset and a non-robust dataset.
• This robust dataset is conducted from an L2 adversarially trained model (epsilon = 0.5).
• The construction method for a robust dataset is proposed by Andrew Ilyas in NIPS 2019.
• Dataset download: Robust Dataset

python3 basic_training_with_robust_dataset.py

	This repository	Original paper (wide)
Benign accuracy	78.69%	84.10%
Robust accuracy (L2 PGD 0.25)	37.96%	48.27%

• Trained model download: Basic Training with Robust Dataset

5. Basic Training with Non-robust Dataset

• The normal dataset can be split into a robust dataset and a non-robust dataset.
• This non-robust dataset is conducted from an L2 adversarially trained model (epsilon = 0.5).
• The construction method for a non-robust dataset is proposed by Andrew Ilyas in NIPS 2019.
• Dataset download: Non-robust Dataset

python3 basic_training_with_non_robust_dataset.py

	This repository	Original paper (wide)
Benign accuracy	82.00%	87.68%
Robust accuracy (L2 PGD 0.25)	0.10%	0.82%

• Trained model download: Basic Training with Non-robust Dataset

How to Test

• The attack method is the PGD attack.
• All pre-trained models are provided in this repository :)

python3 test.py