Image Attack and Defense¶
We introduce the usage of attacks and defense API in image package.
Attack Example¶
from deeprobust.image.attack.pgd import PGD from deeprobust.image.config import attack_params from deeprobust.image.utils import download_model import torch import deeprobust.image.netmodels.resnet as resnet URL = "https://github.com/I-am-Bot/deeprobust_model/raw/master/CIFAR10_ResNet18_epoch_50.pt" download_model(URL, "$MODEL_PATH$") model = resnet.ResNet18().to('cuda') model.load_state_dict(torch.load("$MODEL_PATH$")) model.eval() transform_val = transforms.Compose([transforms.ToTensor()]) test_loader = torch.utils.data.DataLoader( datasets.CIFAR10('deeprobust/image/data', train = False, download=True, transform = transform_val), batch_size = 10, shuffle=True) x, y = next(iter(test_loader)) x = x.to('cuda').float() adversary = PGD(model, device) Adv_img = adversary.generate(x, y, **attack_params['PGD_CIFAR10'])
Defense Example¶
model = Net() train_loader = torch.utils.data.DataLoader( datasets.MNIST('deeprobust/image/defense/data', train=True, download=True, transform=transforms.Compose([transforms.ToTensor()])), batch_size=100, shuffle=True) test_loader = torch.utils.data.DataLoader( datasets.MNIST('deeprobust/image/defense/data', train=False, transform=transforms.Compose([transforms.ToTensor()])), batch_size=1000,shuffle=True) defense = PGDtraining(model, 'cuda') defense.generate(train_loader, test_loader, **defense_params["PGDtraining_MNIST"])