Pytorch Implementation of Adversarial Training with Projected Gradient Descent and Fast Gradient Sign Methods
This reopository is made by Byungjoo Kim, Korea University.
- Create your virtual environments ('conda' recommended).
conda create -n ${envname} python=3.6
Activate the virtual environment.
source activate ssp
- Install require packages, this codes require
PyTorch 1.1,overrides,tqdm. For PyTorch, follow this website(note that you must install PyTorch>=1.1).
Make sure that this repository uses Tensorboard, you should install tensorflow.
In my machine, I used CUDA 9.0, so I installed tensorflow 1.9.0.
pip install tensorflow-gpu==1.9.0
pip install overrides
pip install https://github.com/bethgelab/foolbox/archive/master.zip
pip install tqdm
pip install tb-nightly
pip install future
- Now, clone this repository.
git clone https://github.com/matbambbang/pgd_adversarial_training.git
This would make the repository named pgd_adversarial_training.
Move to this repository, and follow below descriptions.
This module is build for MNIST and CIFAR10.
For each task, I implemented different networks.
Please see model/mnist.py and model/cifar10.py.
I would upload the sample trained model soon.
For train the model, use cifar10_train.py.
python mnist_train.py --model ${model} --block ${block} --save ${save_path} --norm ${norm} --tbsize ${batch_size} --adv ${adv}
python cifar10_train.py --model ${model} --block ${block} --save ${save_path} --norm ${norm} --tbsize ${batch_size} --adv ${adv}
When train network while each groups are composed of 6 residual blocks,
python mnist_train.py --model res --block 6 --save res_mnist_6
python cifar10_train.py --model res --block 6 --save res_cifar_6
In the table below, we describe the arguments that you can control:
| args | Valid arguments |
|---|---|
model |
res(default), wres, conv, in MNIST, wres is not available |
block |
6(default), you can use any integer values. In CIFAR10, this argument only effects on res or wres |
epochs |
200(default) , in MNIST, 100 is default |
lr |
0.1(default), 0.001 or 0.0001 recommended when you use adam optimizer |
decay |
0.0005(default), not available when using adam optimizer |
opt |
sgd(default) or adam, when using GroupNorm, adam is recommended |
norm |
b(BatchNorm, default), g |
tbsize |
128(default), you can use any integer values |
adv |
none(default), for adversarial training, use fgsm, pgd, or ball |
save |
identify the folder name in this arguments, I recommend you the name should be the combination of model, block and norm, i.e., res_6_b |
After training your model, the models and logs are saved in experiments/${save_path}.
After the model is trained, you can attack your model with attack algorithms.
This repository supports l_inf bounded attack, with using sign of gradients.
Valid attacks : fgsm, pgd.
python attack_test.py --model ${model} --eval ${eval} --attack ${attack} --eps ${eps} --load ${load} --norm ${norm}
| args | Valid arguments |
|---|---|
attack |
fgsm(default), bim, mim, pgd, ball |
eval |
cifar10(default), mnist |
eps |
8.0(default), any integer pixel intensity values. When the model is for MNIST classification, eps should |
load |
Identify the folder name which includes target model |