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pgd_attack_cifar10.py
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pgd_attack_cifar10.py
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from __future__ import print_function
import os
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from torch.autograd import Variable
import torch.optim as optim
from torchvision import datasets, transforms
from models.wideresnet import *
from models.resnet import *
parser = argparse.ArgumentParser(description='PyTorch CIFAR PGD Attack Evaluation')
parser.add_argument('--test-batch-size', type=int, default=200, metavar='N',
help='input batch size for testing (default: 200)')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='disables CUDA training')
parser.add_argument('--epsilon', default=0.031,
help='perturbation')
parser.add_argument('--num-steps', default=20,
help='perturb number of steps')
parser.add_argument('--step-size', default=0.003,
help='perturb step size')
parser.add_argument('--random',
default=True,
help='random initialization for PGD')
parser.add_argument('--model-path',
default='./checkpoints/model_cifar_wrn.pt',
help='model for white-box attack evaluation')
parser.add_argument('--source-model-path',
default='./checkpoints/model_cifar_wrn.pt',
help='source model for black-box attack evaluation')
parser.add_argument('--target-model-path',
default='./checkpoints/model_cifar_wrn.pt',
help='target model for black-box attack evaluation')
parser.add_argument('--white-box-attack', default=True,
help='whether perform white-box attack')
args = parser.parse_args()
# settings
use_cuda = not args.no_cuda and torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
# set up data loader
transform_test = transforms.Compose([transforms.ToTensor(),])
testset = torchvision.datasets.CIFAR10(root='../data', train=False, download=True, transform=transform_test)
test_loader = torch.utils.data.DataLoader(testset, batch_size=args.test_batch_size, shuffle=False, **kwargs)
def _pgd_whitebox(model,
X,
y,
epsilon=args.epsilon,
num_steps=args.num_steps,
step_size=args.step_size):
out = model(X)
err = (out.data.max(1)[1] != y.data).float().sum()
X_pgd = Variable(X.data, requires_grad=True)
if args.random:
random_noise = torch.FloatTensor(*X_pgd.shape).uniform_(-epsilon, epsilon).to(device)
X_pgd = Variable(X_pgd.data + random_noise, requires_grad=True)
for _ in range(num_steps):
opt = optim.SGD([X_pgd], lr=1e-3)
opt.zero_grad()
with torch.enable_grad():
loss = nn.CrossEntropyLoss()(model(X_pgd), y)
loss.backward()
eta = step_size * X_pgd.grad.data.sign()
X_pgd = Variable(X_pgd.data + eta, requires_grad=True)
eta = torch.clamp(X_pgd.data - X.data, -epsilon, epsilon)
X_pgd = Variable(X.data + eta, requires_grad=True)
X_pgd = Variable(torch.clamp(X_pgd, 0, 1.0), requires_grad=True)
err_pgd = (model(X_pgd).data.max(1)[1] != y.data).float().sum()
print('err pgd (white-box): ', err_pgd)
return err, err_pgd
def _pgd_blackbox(model_target,
model_source,
X,
y,
epsilon=args.epsilon,
num_steps=args.num_steps,
step_size=args.step_size):
out = model_target(X)
err = (out.data.max(1)[1] != y.data).float().sum()
X_pgd = Variable(X.data, requires_grad=True)
if args.random:
random_noise = torch.FloatTensor(*X_pgd.shape).uniform_(-epsilon, epsilon).to(device)
X_pgd = Variable(X_pgd.data + random_noise, requires_grad=True)
for _ in range(num_steps):
opt = optim.SGD([X_pgd], lr=1e-3)
opt.zero_grad()
with torch.enable_grad():
loss = nn.CrossEntropyLoss()(model_source(X_pgd), y)
loss.backward()
eta = step_size * X_pgd.grad.data.sign()
X_pgd = Variable(X_pgd.data + eta, requires_grad=True)
eta = torch.clamp(X_pgd.data - X.data, -epsilon, epsilon)
X_pgd = Variable(X.data + eta, requires_grad=True)
X_pgd = Variable(torch.clamp(X_pgd, 0, 1.0), requires_grad=True)
err_pgd = (model_target(X_pgd).data.max(1)[1] != y.data).float().sum()
print('err pgd black-box: ', err_pgd)
return err, err_pgd
def eval_adv_test_whitebox(model, device, test_loader):
"""
evaluate model by white-box attack
"""
model.eval()
robust_err_total = 0
natural_err_total = 0
for data, target in test_loader:
data, target = data.to(device), target.to(device)
# pgd attack
X, y = Variable(data, requires_grad=True), Variable(target)
err_natural, err_robust = _pgd_whitebox(model, X, y)
robust_err_total += err_robust
natural_err_total += err_natural
print('natural_err_total: ', natural_err_total)
print('robust_err_total: ', robust_err_total)
def eval_adv_test_blackbox(model_target, model_source, device, test_loader):
"""
evaluate model by black-box attack
"""
model_target.eval()
model_source.eval()
robust_err_total = 0
natural_err_total = 0
for data, target in test_loader:
data, target = data.to(device), target.to(device)
# pgd attack
X, y = Variable(data, requires_grad=True), Variable(target)
err_natural, err_robust = _pgd_blackbox(model_target, model_source, X, y)
robust_err_total += err_robust
natural_err_total += err_natural
print('natural_err_total: ', natural_err_total)
print('robust_err_total: ', robust_err_total)
def main():
if args.white_box_attack:
# white-box attack
print('pgd white-box attack')
model = WideResNet().to(device)
model.load_state_dict(torch.load(args.model_path))
eval_adv_test_whitebox(model, device, test_loader)
else:
# black-box attack
print('pgd black-box attack')
model_target = WideResNet().to(device)
model_target.load_state_dict(torch.load(args.target_model_path))
model_source = WideResNet().to(device)
model_source.load_state_dict(torch.load(args.source_model_path))
eval_adv_test_blackbox(model_target, model_source, device, test_loader)
if __name__ == '__main__':
main()