Diffusion Models for Adversarial Attack Generation

Pip install the requirements

pandas
torch==1.13.1
torchattacks==3.3.0
torchvision==0.14.1
tqdm==4.62.3

pip install -r requirements.txt

Download the ResNet trained checkpoints for every dataset from https://drive.google.com/file/d/1SowQ-KSCwM9lhUhIyaIWyJGH6GWssGo0/view . Copy the ResNet_18, ResNet_18 and ResNet_18 inside the zip into a new directory under repo root models/.

Our code is easily reproducible. The experiments can simply be triggered from experiments.py by modifying the main code to run the desired experiment as run_experiment{N}() where N = {1, 2, 3, 4}.

Results of the experiments are dumped under figures/experiment_{N}.csv directory. It will include the fields [dataset,attack,target_model,clean_accuracy,robust_accuracy,model_robust_accuracy,l2_norm,linf_norm]. The rows pertain to some combination of Target Architecture, Attack set and Datasets and we record the metrics corresponding to these experiments.

Also trained models get created under models/.

Single target model, single attack on single dataset. We run it for 4 combos.

attacks = [ATTACKS["fgsm"], ATTACKS['pgd']]
dataset_classes = [KMNIST, MNIST]

Single target model, fused attack set and run on different datasets. We run it for the below combos.

attacks = [ATTACKS['pgd'], ATTACKS["fgsm"]]
dataset_classes = [MNIST, KMNIST]

Measure how well can a diffusion attack trained on some datasets generalize to some unseen data, run for PGD.

train_dataset_classes = [MNIST, KMNIST]
test_dataset_classes = [FashionMNIST]
attacks = [ATTACKS['pgd']]

Here the goal is to understand how accurately a diffusion attack learnt for some target model architectures work against another target architecture, checked with PGD.

test_model_archs = [(resnet50, "50")]
train_dataset_classes = [MNIST]
attacks = [ATTACKS['pgd']]