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
Find a adversarial paper list here: Adversarial learning papers
If our work could help your research, please cite: DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses
python >= 3.6 (python 3.5 should also work)pytorch >= 1.2.0see setup.py or requirements.txt for more information.
git clone https://github.com/DSE-MSU/DeepRobust.git
cd DeepRobust
python setup.py installpython 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 0.05Train 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
import torch
import deeprobust.image.netmodels.resnet as resnet
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.pyLoad 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_adjFor more details please refer to mettack.py or run
python examples/graph/test_mettack.py --dataset cora --ptb_rate 0.05Load 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.adjTest
# 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 coraadversary 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!