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Implementation of adversarial training under fast-gradient sign method (FGSM), projected gradient descent (PGD) and CW using Wide-ResNet-28-10 on cifar-10. Sample code is re-usable despite changing the model or dataset.

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AlbertMillan/adversarial-training-pytorch

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Adversarial Training in PyTorch

This is an implementation of adversarial training using the Fast Gradient Sign Method (FGSM) [1], Projected Gradient Descent (PGD) [2], and Momentum Iterative FGSM (MI-FGSM) [3] attacks to generate adversarial examples. The model employed to compute adversarial examples is WideResNet-28-10 [4]. An implementation of this model is retrieved from [5]. The dataset used to conduct the experiment is CIFAR-10.

Usage

Installation

The training environment (PyTorch and dependencies) can be installed as follows:

git clone https://github.com/AlbertMillan/adversarial-training-pytorch.git
python setup.py install

Tested under Python 3.8.0 and PyTorch 1.4.0

Arguments

This model offers a significant degree of customization. The following are the list of arguments:

Storage Variables
Command Default Value Description
--ds_path 'datasets/' Path to dataset.
--load_dir 'chkpt/chkpt_plain/' Path to pre-trained model. Used to generate adversarial examples from the test set.
--load_name 'chkpt__model_best.pth.tar'
--load_adv_dir 'chkpt/chkpt_plain/'
--load_adv_name 'chkpt__model_best.pth.tar' File name
--save_dir 'chkpt/new/' Path to store model checkpoints on each iteration.
Model Hyper-parameters
Command Default Value Description
--lr 0.1 Learning rate.
--itr 76 Number of training iterations.
--batch_size 64 Batch size.
--momentum 0.9 Momentum constant.
--nesterov True Whether to apply Nesterov momentum.
--weight_decay 2e-4 Weight decay.
--topk 1 Compute accuracy over top k-predictions
Adversarial Generator Properties
Command Default Value Description
--eps (8./255.) Epsilon (float)
--attack 0 Attack type (0: no-attack; 1: PGD)
--adv_momentum None Momentum constant used to generate adversarial examples if given (float).
--train_max_iter 1 Iterations performed to generate adversarial examples from train set.
--test_max_iter 0 Iterations performed to generate adversarial examples from test set.
--train_mode 0 Training on raw images (0), adversarial images (1) or both (2).
--test_mode 0 Testing on raw images (0), adversarial images (1) or both (2).
Other Properties
Command Default Value Description
--gpu "0,1" Epsilon
--zero_norm False Whether to perform zero-mean normalization on the dataset.
--skip_train False Wether to perform testing without training, loading pre-trained model.

Setup

Examples of Use

Acknowledgements

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Implementation of adversarial training under fast-gradient sign method (FGSM), projected gradient descent (PGD) and CW using Wide-ResNet-28-10 on cifar-10. Sample code is re-usable despite changing the model or dataset.

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