This repository provide famous adversarial attacks.

• python 3.6.1
• pytorch 1.4.0

• Explaining and harnessing adversarial example: FGSM
• Towards Evaluating the Robustness of Neural Networks: CW
• Towards Deep Learning Models Resistant to Adversarial Attacks: PGD
• DeepFool: a simple and accurate method to fool deep neural networks: DeepFool

Multi GPUs are allowed

foo@bar:.../Attack-repo$ ./attack.sh

if you want to change hyper-parameter such as attack method or epsilons for attack quality, open the attack.sh file and just change arguments.

Open the file on terminal or your favorite editor,

foo@bar:.../Attack-repo$ vim attack.sh

and change values in "Set parameters" block.

You can check description in config.py

foo@bar:.../Attack-repo$ ./visualize.sh

You can set option to save all images or just the one you have selected.

foo@bar:.../Attack-repo$ vim visualize.sh

• parameters
  • normal: Set 'true' when you need save normal images. If 'false', adversarial examples will be saved
  • n_rows: Number of rows in saved figure
  • batch_size: Mini batch-size for torch.utils.data.DataLoader. If you don't need to compare images one by one, you can use a size as large as your GPU resource allows.
  • set_idx: If you set 'true', only the image of index belonging to the indices variable.specified below will be saved. On the other hand, if set to 'false', all images will be saved.
  • indices: Image indices to save.
  • attack_method: 'DeepFool', 'FGSM', 'PGD', and 'DeepFool' are allowed. You have to be careful about the case.
  • dataset: 'cifar10' and 'cifar100' are allowed.

Customized ResNet-based models are pre-treained on 'CIFAR-10' and 'CIFAR-100' dataset.

There are three pre-trained models on each dataset and you can download pre-trained weights from the links as follows:

• CIFAR10
  • resnet18
  • resnet50
  • resnet101
• CIFAR100
  • resnet18 and resnet50 will be available
  • resnet18
  • resnet50
  • resnet101

The location of those files are

....|
    - Attack-repo
           |
           - resnet
                |
                - pretrained_models
                            |
                            -cifar10
                                |
                                -resnet18.pth
                                -resnet50.pth
                                -resnet101.pth
                            -cifar100
                                |
                                -resnet101.pth