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attack_cls.py
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attack_cls.py
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import os
import argparse
from tools.utils import *
from torch.nn import functional as F
import tools.model as models
from dataset.scene_dataset import *
from torch import nn
from torch.utils import data
from tqdm import tqdm
def main(args):
if args.dataID==1:
DataName = 'UCM'
num_classes = 21
if args.attack_func[:2] == 'mi':
mix_file = './dataset/UCM_'+args.attack_func+'_sample.png'
elif args.dataID==2:
DataName = 'AID'
num_classes = 30
if args.attack_func[:2] == 'mi':
mix_file = './dataset/AID_'+args.attack_func+'_sample.png'
save_path_prefix = args.save_path_prefix+DataName+'_adv/'+args.attack_func+'/'+args.surrogate_model+'/'
if os.path.exists(save_path_prefix)==False:
os.makedirs(save_path_prefix)
composed_transforms = transforms.Compose([
transforms.Resize(size=(args.crop_size,args.crop_size)),
transforms.ToTensor(),
transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))])
imloader = data.DataLoader(
scene_dataset(root_dir=args.root_dir,pathfile='./dataset/'+DataName+'_test.txt', transform=composed_transforms),
batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True)
###################Surrogate Model Definition###################
if args.surrogate_model=='alexnet':
surrogate_model = models.alexnet(pretrained=False)
surrogate_model.classifier._modules['6'] = nn.Linear(4096, num_classes)
elif args.surrogate_model=='resnet18':
surrogate_model = models.resnet18(pretrained=False)
surrogate_model.fc = torch.nn.Linear(surrogate_model.fc.in_features, num_classes)
elif args.surrogate_model=='densenet121':
surrogate_model = models.densenet121(pretrained=False)
surrogate_model.classifier = nn.Linear(1024, num_classes)
elif args.surrogate_model=='regnet_x_400mf':
surrogate_model = models.regnet_x_400mf(pretrained=False)
surrogate_model.fc = torch.nn.Linear(surrogate_model.fc.in_features, num_classes)
surrogate_model_path = args.save_path_prefix+DataName+'/Pretrain/'+args.surrogate_model+'/'
model_path = os.listdir(surrogate_model_path)
for filename in model_path:
filepath = os.path.join(surrogate_model_path, filename)
if os.path.isfile(filepath) and filename.lower().endswith('.pth'):
print(os.path.join(surrogate_model_path, filename))
model_path_resume = os.path.join(surrogate_model_path, filename)
saved_state_dict = torch.load(model_path_resume)
new_params = surrogate_model.state_dict().copy()
for i,j in zip(saved_state_dict,new_params):
new_params[j] = saved_state_dict[i]
surrogate_model.load_state_dict(new_params)
surrogate_model = torch.nn.DataParallel(surrogate_model).cuda()
surrogate_model.eval()
num_batches = len(imloader)
kl_loss = torch.nn.KLDivLoss()
cls_loss = torch.nn.CrossEntropyLoss()
tpgd_loss = torch.nn.KLDivLoss(reduction='sum')
mse_loss = torch.nn.MSELoss(reduction='none')
tbar = tqdm(imloader)
num_iter = 5
alpha = 1
if args.attack_func == 'fgsm':
for batch_index, src_data in enumerate(tbar):
X, Y, img_name = src_data
X = X.cuda()
adv_im = X.clone().cuda()
label = Y.clone().cuda()
Y = Y.numpy().squeeze()
tbar.set_description('Batch: %d/%d' %(batch_index+1,num_batches))
adv_im.requires_grad = True
_,out = surrogate_model(adv_im)
pred_loss = cls_loss(out, label)
grad = torch.autograd.grad(pred_loss, adv_im,
retain_graph=False, create_graph=False)[0]
adv_im = adv_im.detach() + args.epsilon*grad / torch.norm(grad,float('inf'))
delta = torch.clamp(adv_im - X, min=-args.epsilon, max=args.epsilon)
adv_im = (X + delta).detach()
recreated_image = recreate_image(adv_im.cpu())
gen_name = img_name[0]+'_adv.png'
im = Image.fromarray(recreated_image)
im.save(save_path_prefix+gen_name,'png')
elif args.attack_func == 'ifgsm':
for batch_index, src_data in enumerate(tbar):
X, Y, img_name = src_data
X = X.cuda()
adv_im = X.clone().cuda()
label = Y.clone().cuda()
Y = Y.numpy().squeeze()
# Start iteration
for i in range(num_iter):
tbar.set_description('Batch: %d/%d, Iteration:%d' %(batch_index+1,num_batches,i+1))
adv_im.requires_grad = True
_,out = surrogate_model(adv_im)
pred_loss = cls_loss(out, label)
grad = torch.autograd.grad(pred_loss, adv_im,
retain_graph=False, create_graph=False)[0]
adv_im = adv_im.detach() + alpha*grad / torch.norm(grad,float('inf'))
delta = torch.clamp(adv_im - X, min=-args.epsilon, max=args.epsilon)
adv_im = (X + delta).detach()
recreated_image = recreate_image(adv_im.cpu())
# Process confirmation image
adv_im = preprocess_image(Image.fromarray(recreated_image),args)
gen_name = img_name[0]+'_adv.png'
im = Image.fromarray(recreated_image)
im.save(save_path_prefix+gen_name,'png')
elif args.attack_func == 'cw':
for batch_index, src_data in enumerate(tbar):
X, Y, img_name = src_data
X = X.cuda()
label_mask = F.one_hot(Y, num_classes=num_classes).cuda()
adv_im = X.clone().cuda()
label = Y.clone().cuda()
Y = Y.numpy().squeeze()
# Start iteration
for i in range(num_iter):
tbar.set_description('Batch: %d/%d, Iteration:%d' %(batch_index+1,num_batches,i+1))
adv_im.requires_grad = True
_,out = surrogate_model(adv_im)
correct_logit = torch.sum(label_mask * out)
wrong_logit = torch.max((1 - label_mask) * out)
loss = -(correct_logit - wrong_logit + args.C)
grad = torch.autograd.grad(loss, adv_im,
retain_graph=False, create_graph=False)[0]
adv_im = adv_im.detach() + alpha*grad / torch.norm(grad,float('inf'))
delta = torch.clamp(adv_im - X, min=-args.epsilon, max=args.epsilon)
adv_im = (X + delta).detach()
recreated_image = recreate_image(adv_im.cpu())
# Process confirmation image
adv_im = preprocess_image(Image.fromarray(recreated_image),args)
gen_name = img_name[0]+'_adv.png'
im = Image.fromarray(recreated_image)
im.save(save_path_prefix+gen_name,'png')
elif args.attack_func == 'tpgd':
for batch_index, src_data in enumerate(tbar):
X, Y, img_name = src_data
X = X.cuda()
adv_im = X.clone().cuda()
Y = Y.numpy().squeeze()
_,logit_ori = surrogate_model(X)
logit_ori = logit_ori.detach()
# Start iteration
for i in range(num_iter):
tbar.set_description('Batch: %d/%d, Iteration:%d' %(batch_index+1,num_batches,i+1))
adv_im.requires_grad = True
_,logit_adv = surrogate_model(adv_im)
pred_loss = tpgd_loss(F.log_softmax(logit_adv, dim=1),
F.softmax(logit_ori, dim=1))
grad = torch.autograd.grad(pred_loss, adv_im,
retain_graph=False, create_graph=False)[0]
adv_im = adv_im.detach() + alpha*grad / torch.norm(grad,float('inf'))
delta = torch.clamp(adv_im - X, min=-args.epsilon, max=args.epsilon)
adv_im = (X + delta).detach()
recreated_image = recreate_image(adv_im.cpu())
# Process confirmation image
adv_im = preprocess_image(Image.fromarray(recreated_image),args)
gen_name = img_name[0]+'_adv.png'
im = Image.fromarray(recreated_image)
im.save(save_path_prefix+gen_name,'png')
elif args.attack_func == 'jitter':
for batch_index, src_data in enumerate(tbar):
X, Y, img_name = src_data
X = X.cuda()
label_mask = F.one_hot(Y, num_classes=num_classes).cuda().float()
adv_im = X.clone().cuda()
label = Y.clone().cuda()
Y = Y.numpy().squeeze()
# Start iteration
for i in range(num_iter):
tbar.set_description('Batch: %d/%d, Iteration:%d' %(batch_index+1,num_batches,i+1))
adv_im.requires_grad = True
_,out = surrogate_model(adv_im)
_, pre = torch.max(out, dim=1)
wrong = (pre != label)
norm_z = torch.norm(out, p=float('inf'), dim=1, keepdim=True)
hat_z = nn.Softmax(dim=1)(args.scale*out/norm_z)
hat_z = hat_z + args.std*torch.randn_like(hat_z)
loss = mse_loss(hat_z, label_mask).mean(dim=1)
norm_r = torch.norm((adv_im - X), p=float('inf'), dim=[1,2,3])
nonzero_r = (norm_r != 0)
loss[wrong*nonzero_r] /= norm_r[wrong*nonzero_r]
loss = loss.mean()
grad = torch.autograd.grad(loss, adv_im,
retain_graph=False, create_graph=False)[0]
adv_im = adv_im.detach() + alpha*grad / torch.norm(grad,float('inf'))
delta = torch.clamp(adv_im - X, min=-args.epsilon, max=args.epsilon)
adv_im = (X + delta).detach()
recreated_image = recreate_image(adv_im.cpu())
# Process confirmation image
adv_im = preprocess_image(Image.fromarray(recreated_image),args)
gen_name = img_name[0]+'_adv.png'
im = Image.fromarray(recreated_image)
im.save(save_path_prefix+gen_name,'png')
elif args.attack_func == 'mixup' or args.attack_func == 'mixcut':
mixup_im = composed_transforms(Image.open(mix_file).convert('RGB')).unsqueeze(0).cuda()
mixup_feature,_ = surrogate_model(mixup_im)
mixup_feature = mixup_feature.data
for batch_index, src_data in enumerate(tbar):
X, Y, img_name = src_data
X = X.cuda()
adv_im = X.clone().cuda()
label = Y.clone().cuda()
Y = Y.numpy().squeeze()
momentum = torch.zeros_like(X).cuda()
# Start iteration
for i in range(num_iter):
tbar.set_description('Batch: %d/%d, Iteration:%d' %(batch_index+1,num_batches,i+1))
adv_im.requires_grad = True
pred_loss = 0
mix_loss = 0
for k in range(5):
# Scale augmentation
feature,pred = surrogate_model(adv_im/(2**(k)))
pred_loss += cls_loss(pred, label)
mix_loss += -kl_loss(feature, mixup_feature)
total = pred_loss*args.beta+mix_loss
grad = torch.autograd.grad(total, adv_im,
retain_graph=False, create_graph=False)[0]
grad = grad / torch.norm(grad,p=1)
grad = grad + momentum*args.decay
momentum = grad
adv_im = adv_im.detach() + alpha*grad / torch.norm(grad,float('inf'))
delta = torch.clamp(adv_im - X, min=-args.epsilon, max=args.epsilon)
adv_im = (X + delta).detach()
recreated_image = recreate_image(adv_im.cpu())
adv_im = preprocess_image(Image.fromarray(recreated_image),args)
gen_name = img_name[0]+'_adv.png'
im = Image.fromarray(recreated_image)
im.save(save_path_prefix+gen_name,'png')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--dataID', type=int, default=1)
parser.add_argument('--surrogate_model', type=str, default='resnet18',help='alexnet,resnet18,densenet121,regnet_x_400mf')
parser.add_argument('--save_path_prefix', type=str,default='./')
parser.add_argument('--root_dir', type=str, default='/iarai/home/yonghao.xu/Data/',help='dataset path.')
parser.add_argument('--crop_size', type=int, default=256)
parser.add_argument('--epsilon', type=float, default=1)
parser.add_argument('--batch_size', type=int, default=1)
parser.add_argument('--num_workers', type=int, default=1)
parser.add_argument('--attack_func', type=str, default='mixup',help='fgsm,ifgsm,cw,tpgd,jitter,mixup,mixcut')
parser.add_argument('--decay', type=float, default=1)
parser.add_argument('--beta', type=float, default=1e-3)
parser.add_argument('--scale', type=float, default=10)
parser.add_argument('--std', type=float, default=0.1)
parser.add_argument('--C', type=float, default=50)
main(parser.parse_args())