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import requests
import torch
from torchvision import datasets,models,transforms
import torch.nn.functional as F
import os
import numpy as np
import argparse
import matplotlib.pyplot as plt
import random
from deeprobust.image import utils
def run_attack(attackmethod, batch_size, batch_num, device, test_loader, random_targeted = False, target_label = -1, **kwargs):
test_loss = 0
correct = 0
samplenum = 1000
count = 0
classnum = 10
for count, (data, target) in enumerate(test_loader):
if count == batch_num:
break
print('batch:{}'.format(count))
data, target = data.to(device), target.to(device)
if(random_targeted == True):
r = list(range(0, target)) + list(range(target+1, classnum))
target_label = random.choice(r)
adv_example = attackmethod.generate(data, target, target_label = target_label, **kwargs)
elif(target_label >= 0):
adv_example = attackmethod.generate(data, target, target_label = target_label, **kwargs)
else:
adv_example = attackmethod.generate(data, target, **kwargs)
output = model(adv_example)
test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss
pred = output.argmax(dim = 1, keepdim = True) # get the index of the max log-probability.
correct += pred.eq(target.view_as(pred)).sum().item()
batch_num = count+1
test_loss /= len(test_loader.dataset)
print("===== ACCURACY =====")
print('Attack Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, batch_num * batch_size,
100. * correct / (batch_num * batch_size)))
def load_net(attack_model, filename, path):
if(attack_model == "CNN"):
from deeprobust.image.netmodels.CNN import Net
model = Net()
if(attack_model == "ResNet18"):
import deeprobust.image.netmodels.resnet as Net
model = Net.ResNet18()
model.load_state_dict(torch.load(path + filename))
model.eval()
return model
def generate_dataloader(dataset, batch_size):
if(dataset == "MNIST"):
test_loader = torch.utils.data.DataLoader(
datasets.MNIST('deeprobust/image/data', train = False,
download = True,
transform = transforms.Compose([transforms.ToTensor()])),
batch_size = args.batch_size,
shuffle = True)
print("Loading MNIST dataset.")
elif(dataset == "CIFAR" or args.dataset == 'CIFAR10'):
test_loader = torch.utils.data.DataLoader(
datasets.CIFAR10('deeprobust/image/data', train = False,
download = True,
transform = transforms.Compose([transforms.ToTensor()])),
batch_size = args.batch_size,
shuffle = True)
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
print("Loading CIFAR10 dataset.")
elif(dataset == "ImageNet"):
test_loader = torch.utils.data.DataLoader(
datasets.CIFAR10('deeprobust/image/data', train=False,
download = True,
transform = transforms.Compose([transforms.ToTensor()])),
batch_size = args.batch_size,
shuffle = True)
print("Loading ImageNet dataset.")
return test_loader
def parameter_parser():
parser = argparse.ArgumentParser(description = "Run attack algorithms.", usage ='Use -h for more information.')
parser.add_argument("--attack_method",
default = 'PGD',
help = "Choose a attack algorithm from: PGD(default), FGSM, LBFGS, CW, deepfool, onepixel, Nattack")
parser.add_argument("--attack_model",
default = "CNN",
help = "Choose network structure from: CNN, ResNet")
parser.add_argument("--path",
default = "./trained_models/",
help = "Type the path where the model is saved.")
parser.add_argument("--file_name",
default = 'MNIST_CNN_epoch_20.pt',
help = "Type the file_name of the model that is to be attack. The model structure should be matched with the ATTACK_MODEL parameter.")
parser.add_argument("--dataset",
default = 'MNIST',
help = "Choose a dataset from: MNIST(default), CIFAR(or CIFAR10), ImageNet")
parser.add_argument("--epsilon", type = float, default = 0.3)
parser.add_argument("--batch_num", type = int, default = 1000)
parser.add_argument("--batch_size", type = int, default = 1000)
parser.add_argument("--num_steps", type = int, default = 40)
parser.add_argument("--step_size", type = float, default = 0.01)
parser.add_argument("--random_targeted", type = bool, default = False,
help = "default: False. By setting this parameter be True, the program would random generate target labels for the input samples.")
parser.add_argument("--target_label", type = int, default = -1,
help = "default: -1. Generate all attack Fixed target label.")
parser.add_argument("--device", default = 'cuda',
help = "Choose the device.")
return parser.parse_args()
if __name__ == "__main__":
# read arguments
args = parameter_parser() # read argument and creat an argparse object
# download example model
example_model_path = './trained_models/MNIST_CNN_epoch_20.pt'
if not (os.path.exists('./trained_models')):
os.mkdir('./trained_models')
print('create path: ./trained_models')
model_url = "https://github.com/I-am-Bot/deeprobust_trained_model/blob/master/MNIST_CNN_epoch_20.pt?raw=true"
r = requests.get(model_url)
print('Downloading example model...')
with open(example_model_path,'wb') as f:
f.write(r.content)
print('Downloaded.')
# load model
model = load_net(args.attack_model, args.file_name, args.path)
print("===== START ATTACK =====")
if(args.attack_method == "PGD"):
from deeprobust.image.attack.pgd import PGD
test_loader = generate_dataloader(args.dataset, args.batch_size)
attack_method = PGD(model, args.device)
utils.tab_printer(args)
run_attack(attack_method, args.batch_size, args.batch_num, args.device, test_loader, epsilon = args.epsilon)
elif(args.attack_method == "FGSM"):
from deeprobust.image.attack.fgsm import FGSM
test_loader = generate_dataloader(args.dataset, args.batch_size)
attack_method = FGSM(model, args.device)
utils.tab_printer(args)
run_attack(attack_method, args.batch_size, args.batch_num, args.device, test_loader, epsilon = args.epsilon)
elif(args.attack_method == "LBFGS"):
from deeprobust.image.attack.lbfgs import LBFGS
try:
if (args.batch_size >1):
raise ValueError("batch_size shouldn't be larger than 1.")
except ValueError:
args.batch_size = 1
try:
if (args.random_targeted == 0 and args.target_label == -1):
raise ValueError("No target label assigned. Random generate target for each input.")
except ValueError:
args.random_targeted = True
utils.tab_printer(args)
test_loader = generate_dataloader(args.dataset, args.batch_size)
attack_method = LBFGS(model, args.device)
run_attack(attack_method, 1, args.batch_num, args.device, test_loader, random_targeted = args.random_targeted, target_label = args.target_label)
elif(args.attack_method == "CW"):
from deeprobust.image.attack.cw import CarliniWagner
attack_method = CarliniWagner(model, args.device)
try:
if (args.batch_size > 1):
raise ValueError("batch_size shouldn't be larger than 1.")
except ValueError:
args.batch_size = 1
try:
if (args.random_targeted == 0 and args.target_label == -1):
raise ValueError("No target label assigned. Random generate target for each input.")
except ValueError:
args.random_targeted = True
utils.tab_printer(args)
test_loader = generate_dataloader(args.dataset, args.batch_size)
run_attack(attack_method, 1, args.batch_num, args.device, test_loader, random_targeted = args.random_targeted, target_label = args.target_label)
elif(args.attack_method == "deepfool"):
from deeprobust.image.attack.deepfool import DeepFool
attack_method = DeepFool(model, args.device)
try:
if (args.batch_size > 1):
raise ValueError("batch_size shouldn't be larger than 1.")
except ValueError:
args.batch_size = 1
utils.tab_printer(args)
test_loader = generate_dataloader(args.dataset, args.batch_size)
run_attack(attack_method, args.batch_size, args.batch_num, args.device, test_loader)
elif(args.attack_method == "onepixel"):
from deeprobust.image.attack.onepixel import Onepixel
attack_method = Onepixel(model, args.device)
try:
if (args.batch_size > 1):
raise ValueError("batch_size shouldn't be larger than 1.")
except ValueError:
args.batch_size = 1
utils.tab_printer(args)
test_loader = generate_dataloader(args.dataset, args.batch_size)
run_attack(attack_method, args.batch_size, args.batch_num, args.device, test_loader)
elif(args.attack_method == "Nattack"):
pass
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