Documentation | Paper | Samples
DeepRobust is a Pytorch adversarial library for attack and defense methods on images and graphs.
List of including algorithms can be found in [Image Package] and [Graph Package].
Usage
For more details about attacks and defenses, you can read the following papers.
- Adversarial Attacks and Defenses on Graphs: A Review and Empirical Study
- Adversarial Attacks and Defenses in Images, Graphs and Text: A Review
If our work could help your research, please cite: DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses
- [07/2020] Add documentation page!
- [06/2020] Add docstring to both image and graph package
python >= 3.6
(python 3.5 should also work)pytorch >= 1.2.0
see setup.py
or requirements.txt
for more information.
git clone https://github.com/DSE-MSU/DeepRobust.git
cd DeepRobust
python setup.py install
python examples/image/test_PGD.py
python examples/image/test_pgdtraining.py
python examples/graph/test_gcn_jaccard.py --dataset cora
python examples/graph/test_mettack.py --dataset cora --ptb_rate 0.05
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Train model
Example: Train a simple CNN model on MNIST dataset for 20 epoch on gpu.
import deeprobust.image.netmodels.train_model as trainmodel trainmodel.train('CNN', 'MNIST', 'cuda', 20)
Model would be saved in deeprobust/trained_models/.
-
Instantiated attack methods and defense methods.
Example: Generate adversary example with PGD attack.
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, "./trained_models/CIFAR10_ResNet18_epoch_50.pt") model = resnet.ResNet18().to('cuda') model.load_state_dict(torch.load("./trained_models/CIFAR10_ResNet18_epoch_50.pt")) 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'])
Example: Train defense model.
from deeprobust.image.defense.pgdtraining import PGDtraining from deeprobust.image.config import defense_params from deeprobust.image.netmodels.CNN import Net import torch from torchvision import datasets, transforms 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"])
More example code can be found in deeprobust/examples.
-
Use our evulation program to test attack algorithm against defense.
Example:
cd DeepRobust python examples/image/test_train.py python deeprobust/image/evaluation_attack.py
-
Load dataset
import torch import numpy as np from deeprobust.graph.data import Dataset from deeprobust.graph.defense import GCN from deeprobust.graph.global_attack import Metattack data = Dataset(root='/tmp/', name='cora', setting='nettack') adj, features, labels = data.adj, data.features, data.labels idx_train, idx_val, idx_test = data.idx_train, data.idx_val, data.idx_test idx_unlabeled = np.union1d(idx_val, idx_test)
-
Set up surrogate model
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") surrogate = GCN(nfeat=features.shape[1], nclass=labels.max().item()+1, nhid=16, with_relu=False, device=device) surrogate = surrogate.to(device) surrogate.fit(features, adj, labels, idx_train)
-
Set up attack model and generate perturbations
model = Metattack(model=surrogate, nnodes=adj.shape[0], feature_shape=features.shape, device=device) model = model.to(device) perturbations = int(0.05 * (adj.sum() // 2)) model.attack(features, adj, labels, idx_train, idx_unlabeled, perturbations, ll_constraint=False) modified_adj = model.modified_adj
For more details please refer to mettack.py or run
python examples/graph/test_mettack.py --dataset cora --ptb_rate 0.05
- Load dataset
import torch from deeprobust.graph.data import Dataset, PtbDataset from deeprobust.graph.defense import GCN, GCNJaccard import numpy as np np.random.seed(15) # load clean graph data = Dataset(root='/tmp/', name='cora', setting='nettack') adj, features, labels = data.adj, data.features, data.labels idx_train, idx_val, idx_test = data.idx_train, data.idx_val, data.idx_test # load pre-attacked graph by mettack perturbed_data = PtbDataset(root='/tmp/', name='cora') perturbed_adj = perturbed_data.adj
- Test
# Set up defense model and test performance device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") model = GCNJaccard(nfeat=features.shape[1], nclass=labels.max()+1, nhid=16, device=device) model = model.to(device) model.fit(features, perturbed_adj, labels, idx_train) model.eval() output = model.test(idx_test) # Test on GCN model = GCN(nfeat=features.shape[1], nclass=labels.max()+1, nhid=16, device=device) model = model.to(device) model.fit(features, perturbed_adj, labels, idx_train) model.eval() output = model.test(idx_test)
For more details please refer to test_gcn_jaccard.py or run
python examples/graph/test_gcn_jaccard.py --dataset cora
adversary examples generated by fgsm:
Left:original, classified as 6; Right:adversary, classified as 4.Serveral trained models can be found here: https://drive.google.com/open?id=1uGLiuCyd8zCAQ8tPz9DDUQH6zm-C4tEL
Some of the algorithms are refer to paper authors' implementations. References can be found at the top of each file.
Implementation of network structure are refer to weiaicunzai's github. Original code can be found here: pytorch-cifar100
Thanks to their outstanding works!