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attack.py
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attack.py
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import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
import torch.optim as optim
import numpy as np
import argparse
from train_vgg19 import vgg19
from utils import Noisy,transform,random_label
import os
class L3Attack(torch.autograd.Function):
@staticmethod
def forward(self, model, img, target_lable, dataset, allstep, sink_lr, s_radius):
return L3_function(model, img, target_lable, dataset=dataset, allstep=allstep, lr=sink_lr, s_radius=s_radius)
@staticmethod
def backward(self, grad_output):
return None, grad_output, None, None, None, None, None
class L4Attack(torch.autograd.Function):
@staticmethod
def forward(self, model, img, dataset, allstep, sink_lr, u_radius):
return L4_function(model, img, dataset=dataset, allstep=allstep, lr=sink_lr, u_radius=u_radius)
@staticmethod
def backward(self, grad_output):
return None, grad_output, None, None, None, None
"""
Return the variable used for L3 function specified in paper
[lr] specifies the learning rate of the attack algorithm A
[s_radius] specifies the maximum l infinity distance between the origianl [img]
"""
def L3_function(model,
img,
target_lable,
dataset,
allstep,
lr,
s_radius,
margin=20,
use_margin=False):
x_var = torch.autograd.Variable(img.clone().cuda(), requires_grad=True)
optimizer_s = optim.SGD([x_var], lr=lr)
with torch.enable_grad():
for step in range(allstep):
optimizer_s.zero_grad()
output = model(transform(x_var, dataset=dataset))
if use_margin:
target_lable = target_lable[0].item()
_, top2_1 = output.data.cpu().topk(2)
argmax11 = top2_1[0][0]
if argmax11 == target_l:
argmax11 = top2_1[0][1]
loss = (output[0][argmax11] - output[0][target_l] + margin).clamp(min=0)
else:
loss = F.cross_entropy(output, target_lable)
loss.backward()
x_var.data = torch.clamp(x_var - lr * x_var.grad.data, min=0, max=1)
x_var.data = torch.clamp(x_var - img, min=-s_radius, max=s_radius) + img
return x_var
"""
Return the variable used for L4 function specified in paper
[lr] specifies the learning rate of the attack algorithm A
[s_radius] specifies the maximum l infinity distance between the origianl [img]
"""
def L4_function(model,
img,
dataset,
allstep,
lr,
u_radius,
margin=20,
use_margin=False):
x_var = torch.autograd.Variable(img.clone().cuda(), requires_grad=True)
true_label = model(transform(x_var.clone(), dataset=dataset)).data.max(1, keepdim=True)[1][0].item()
optimizer_s = optim.SGD([x_var], lr=lr)
with torch.enable_grad():
for step in range(allstep):
optimizer_s.zero_grad()
output = model(transform(x_var, dataset=dataset))
if use_margin:
_, top2_1 = output.data.cpu().topk(2)
argmax11 = top2_1[0][0]
if argmax11 == true_label:
argmax11 = top2_1[0][1]
loss = (output[0][true_label] - output[0][argmax11] + margin).clamp(min=0)
else:
loss = -F.cross_entropy(output, torch.LongTensor([true_label]).cuda())
loss.backward()
x_var.data = torch.clamp(x_var - lr * x_var.grad.data, min=0, max=1)
x_var.data = torch.clamp(x_var - img, min=-u_radius, max=u_radius) + img
return x_var
def noisy_img(img, n_radius):
return img + n_radius * torch.randn_like(img)
""" Return the probability-match cross entropy """
def cross_entropy(pred, target):
logsoftmax = nn.LogSoftmax()
return torch.mean(torch.sum(- target * logsoftmax(pred), dim=1))
def target_distribution(original_softmax, target_label):
true_label = original_softmax.max(1, keepdim=True)[1][0].item()
target_l = original_softmax.clone()
temp = target_l.clone()[0, int(true_label)]
target_l[0, int(true_label)] = target_l[0, int(target_label)]
target_l[0, int(target_label)] = temp
return target_l
""" Return the best effort whitebox or gray box PGD attack
[allstep] specifies the number of steps
[lr] specifies the learning rate
[radius] specifies the maximum l infinity distance between the origianl [img]
[lbd] specifies the weight we add on L1 loss
[setting] can be 'white' or 'gray'
"""
def PGD(model,
img,
dataset='imagenet',
allstep=30,
lr=0.03,
radius=0.1,
lbd=2,
setting='white',
noise_radius=0.1,
targeted_lr = 0.005,
targeted_radius = 0.03,
untargeted_lr = 0.1,
untargeted_radius = 0.03):
model.eval()
x_var = torch.autograd.Variable(img.clone().cuda(), requires_grad=True)
true_label = model(transform(x_var.clone(), dataset=dataset)).data.max(1, keepdim=True)[1][0].item()
original_softmax = F.softmax(model(transform(x_var.clone(), dataset=dataset))).data
optimizer = optim.Adam([x_var], lr=lr)
target_label = random_label(true_label, dataset=dataset)
target_l = torch.LongTensor([target_label]).cuda()
target_dist = target_distribution(original_softmax, target_label)
for i in range(allstep):
optimizer.zero_grad()
total_loss = 0
output_ori = model(transform(x_var, dataset=dataset))
loss1 = cross_entropy(output_ori, target_dist) # loss of original image, should descend
if setting == 'white':
total_loss += lbd * loss1
noise_var = noisy(x_var, noise_radius)
output_noise = model(transform(noise_var, dataset=dataset))
loss2 = torch.norm(F.softmax(output_noise) - F.softmax(output_ori),
1) # l1(noisy_img-origin_img), should descend
total_loss += loss2
new_target = torch.LongTensor([random_label(target_label, dataset=dataset)]).cuda()
t_attack_var = t_attack(model, x_var, new_target, dataset, 1, targeted_lr, targeted_radius) # 1 step t_attack
output_t_attack = model(transform(t_attack_var, dataset=dataset))
loss3 = F.cross_entropy(output_t_attack,
new_target) # 1 step of targeted attack image, should be new_target, descend
total_loss += loss3
u_attack_var = u_attack(model, x_var, dataset, 1, untargeted_lr, untargeted_radius) # 1 step u_attack, if you want to do white box attack for inception, then you will need to change 0.1 to 3 here
output_u_attack = model(transform(u_attack_var, dataset=dataset))
loss4 = F.cross_entropy(output_u_attack,
target_l) # 1 step of u_targeted attack, should be away from target_l, ascend
total_loss -= loss4
elif setting == 'gray':
total_loss += loss1
else:
raise "attack setting is not supported"
total_loss.backward()
optimizer.step()
x_var.data = torch.clamp(torch.clamp(x_var, min=0, max=1) - img, min=-radius, max=radius) + img
return x_var
""" Return the best effort whitebox or gray box CW attack
[allstep] specifies the number of steps
[lr] specifies the learning rate
[radius] specifies the maximum l infinity distance between the origianl [img]
[lbd] specifies the weight we add on L1 loss
[setting] can be 'white' or 'gray'
"""
def CW(model,
img,
dataset='imagenet',
allstep=30,
lr=0.03,
radius=0.1,
margin=20.0,
lbd=2,
setting='white',
noise_radius=0.1,
targeted_lr = 0.005,
targeted_radius = 0.03,
untargeted_lr = 0.1,
untargeted_radius = 0.03):
model.eval()
x_var = torch.autograd.Variable(img.clone().cuda(), requires_grad=True)
true_label = model(transform(x_var.clone(), dataset=dataset)).data.max(1, keepdim=True)[1][0].item()
optimizer = optim.Adam([x_var], lr=lr)
target_label = random_label(true_label, dataset=dataset)
for step in range(allstep):
optimizer.zero_grad()
total_loss = 0
output_ori = model(transform(x_var, dataset=dataset))
_, top2_1 = output_ori.data.cpu().topk(2)
argmax11 = top2_1[0][0]
if argmax11 == target_label:
argmax11 = top2_1[0][1]
loss1 = (output_ori[0][argmax11] - output_ori[0][target_label] + margin).clamp(min=0)
if setting == 'white':
total_loss += lbd * loss1 # loss of original image, should descend
noise_var = noisy(x_var, noise_radius)
output_noise = model(transform(noise_var, dataset=dataset))
loss2 = torch.norm(F.softmax(output_noise) - F.softmax(output_ori),
1) # l1(noisy_img-origin_img), should descend
total_loss += loss2
new_tl = random_label(target_label, dataset=dataset)
new_target = torch.LongTensor([new_tl]).cuda()
t_attack_var = t_attack(model, x_var, new_target, dataset, 1, targeted_lr, targeted_radius) # 1 step t_attack
output_t_attack = model(transform(t_attack_var, dataset=dataset))
_, top2_3 = output_t_attack.data.cpu().topk(2)
argmax13 = top2_3[0][0]
if argmax13 == new_tl:
argmax13 = top2_3[0][1]
loss3 = (output_t_attack[0][argmax13] - output_t_attack[0][new_tl] + margin).clamp(
min=0) # 1 step of targeted attack image, should be new_target, descend
total_loss += loss3 # loss of sink image, should descend
u_attack_var = u_attack(model, x_var, dataset, 1, untargeted_lr, untargeted_radius) # 1 step u_attack, if you want to do white box attack for inception, then you will need to change 0.1 to 3 here
output_u_attack = model(transform(u_attack_var, dataset=dataset))
_, top2_4 = output_u_attack.data.cpu().topk(2)
argmax14 = top2_4[0][1]
if argmax14 == target_label:
argmax14 = top2_4[0][0]
loss4 = (output_u_attack[0][argmax14] - output_u_attack[0][target_label] + margin).clamp(
min=0) # 1 step of u_targeted attack, should be away from target_l, ascend
total_loss -= loss4
elif setting == 'gray':
total_loss += loss1
else:
raise "attack setting is not supported"
total_loss.backward()
optimizer.step()
x_var.data = torch.clamp(torch.clamp(x_var, min=0, max=1) - img, min=-radius, max=radius) + img
return x_var
parser = argparse.ArgumentParser(description='PyTorch White Box Adversary Generation')
parser.add_argument('--real_dir', type=str, required=True, help='directory to store images correctly classified')
parser.add_argument('--adv_dir', type=str, required=True, help='directory to store adversarial images')
parser.add_argument('--name', type=str, default='_demo_',required=True, help='the name of the adversarial example')
parser.add_argument('--dataset', type=str, default='imagenet', help='dataset, imagenet or cifar')
parser.add_argument('--setting', type=str, default='white', help='attack, white or gray')
parser.add_argument('--allstep', type=int, default=50, help='number of steps to run an iterative attack')
parser.add_argument('--base', type=str, default="resnet", help='model, vgg for cifar and resnet/inception for imagenet')
parser.add_argument('--lowbd', type=int, default=0, help='index of the first adversarial example to load')
parser.add_argument('--upbd', type=int, default=1000, help='index of the last adversarial example to load')
parser.add_argument('--radius', type=float, default=0.1, help='adversarial radius')
args = parser.parse_args()
t_attack = L3Attack.apply
u_attack = L4Attack.apply
noisy = Noisy.apply
real_d = os.path.join(args.real_dir,args.base)
adv_d = os.path.join(args.adv_dir,args.base)
if args.dataset == 'imagenet':
data_dir = './imagenetdata/'
if not os.path.exists(adv_d):
os.makedirs(adv_d)
os.makedirs(os.path.join(adv_d,'pgd'))
os.makedirs(os.path.join(adv_d,'cw'))
if not os.path.exists(real_d):
os.makedirs(real_d)
noise_radius = 0.1
targeted_lr = 0.005
targeted_radius = 0.03
untargeted_radius = 0.03
#### use ImageFolder to load images, need to map label correct with target_transform
testset = torchvision.datasets.ImageFolder(root=data_dir,
transform=torchvision.transforms.Compose([
torchvision.transforms.Resize(256),
torchvision.transforms.CenterCrop(224),
torchvision.transforms.ToTensor(),]),
)
if args.base == 'resnet':
model = models.resnet101(pretrained=True)
untargeted_lr = 0.1
elif args.base == 'inception':
model = models.inception_v3(pretrained=True, transform_input=False)
untargeted_lr = 3
else:
raise Exception('No such model predefined.')
model = torch.nn.DataParallel(model).cuda()
elif args.dataset == 'cifar':
data_dir = './cifardata/'
if not os.path.exists(adv_d):
os.makedirs(adv_d)
if not os.path.exists(real_d):
os.makedirs(real_d)
noise_radius = 0.01
targeted_lr = 0.0005
targeted_radius = 0.5
untargeted_radius = 0.5
untargeted_lr = 1
testset = torchvision.datasets.CIFAR10(root=data_dir, train=False, download=True,
transform=torchvision.transforms.Compose(
[torchvision.transforms.ToTensor(), ]))
if args.base == "vgg":
model = vgg19()
model.features = torch.nn.DataParallel(model.features)
model.cuda()
checkpoint = torch.load('./vgg19model/model_best.pth.tar')#save directory for vgg19 model
else:
raise Exception('No such model predefined.')
model.load_state_dict(checkpoint['state_dict'])
else:
raise Exception('Not supported dataset.')
model.eval()
title = args.name + str(args.allstep)
numcout = 0
for i in range(args.lowbd, args.upbd):
view_data, view_data_label = testset[i]
view_data = view_data.unsqueeze(0).cuda()
view_data_label = view_data_label * torch.ones(1).cuda().long()
model.eval()
predicted_label = model(transform(view_data.clone(), dataset=args.dataset)).data.max(1, keepdim=True)[1][0]
if predicted_label != view_data_label:
continue#note that only load images that were classified correctly
torch.save(view_data, os.path.join(real_d, str(numcout) + '_img.pt'))
torch.save(view_data_label, os.path.join(real_d, str(numcout) + '_label.pt'))
torch.save(PGD(model,
view_data,
dataset = args.dataset,
allstep = args.allstep,
radius = args.radius,
setting = args.setting,
noise_radius = noise_radius,
targeted_lr = targeted_lr,
targeted_radius = targeted_radius,
untargeted_lr = untargeted_lr,
untargeted_radius = untargeted_radius),
os.path.join(os.path.join(adv_d, 'pgd'), str(numcout) + title + '.pt'))
torch.save(CW(model,
view_data,
dataset = args.dataset,
allstep = args.allstep,
radius = args.radius,
setting = args.setting,
noise_radius = noise_radius,
targeted_lr = targeted_lr,
targeted_radius = targeted_radius,
untargeted_lr = untargeted_lr,
untargeted_radius = untargeted_radius),
os.path.join(os.path.join(adv_d, 'cw'), str(numcout) + title + '.pt'))
numcout += 1
print('Finish generating white box adversaries')