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celeb-attack.py
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celeb-attack.py
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import torch
import torch.nn as nn
from torch.autograd import Variable
import torchvision
from torchvision import datasets, models, transforms
import argparse
import csv
import os
import numpy as np
import random
from patch_utils import*
from utils import*
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', type=int, default=1, help="batch size")
parser.add_argument('--num_workers', type=int, default=2, help="num_workers")
parser.add_argument('--train_size', type=int, default=2000, help="number of training images")
parser.add_argument('--noise_percentage', type=float, required=True, help="percentage of the patch size compared with the image size")
parser.add_argument('--probability_threshold', type=float, default=0.9, help="minimum target probability")
parser.add_argument('--lr', type=float, default=1.0, help="learning rate")
parser.add_argument('--max_iteration', type=int, default=1000, help="max iteration")
parser.add_argument('--target', type=int, default=859, help="target label")
parser.add_argument('--epochs', type=int, default=30, help="total epoch")
parser.add_argument('--data_dir', type=str, default='./CelebA_HQ_facial_identity_dataset', help="dir of the dataset")
parser.add_argument('--GPU', type=str, default='0', help="index pf used GPU")
parser.add_argument('--log_dir', type=str, default='patch_attack_log.csv', help='dir of the log')
parser.add_argument('--patch_type', type=str, default='square', help="patch type: rectangle or square")
parser.add_argument('--model_path', type=str, default='facial_identity_ResNet18.pth')
seed = 1
np.random.seed(seed)
random.seed(seed)
torch.manual_seed(seed)
args = parser.parse_args()
# Patch attack via optimization
# According to reference [1], one image is attacked each time
# Assert: applied patch should be a numpy
# Return the final perturbated picture and the applied patch. Their types are both numpy
def patch_attack(image, applied_patch, mask, target, probability_threshold, model, lr=1, max_iteration=100):
model.eval()
applied_patch = torch.from_numpy(applied_patch)
mask = torch.from_numpy(mask)
target_probability, count = 0, 0
perturbated_image = torch.mul(mask.type(torch.FloatTensor), applied_patch.type(torch.FloatTensor)) + torch.mul((1 - mask.type(torch.FloatTensor)), image.type(torch.FloatTensor))
while target_probability < probability_threshold and count < max_iteration:
count += 1
# Optimize the patch
perturbated_image = Variable(perturbated_image.data, requires_grad=True)
per_image = perturbated_image
per_image = per_image.cuda()
output = model(per_image)
target_log_softmax = torch.nn.functional.log_softmax(output, dim=1)[0][target]
target_log_softmax.backward()
patch_grad = perturbated_image.grad.clone().cpu()
perturbated_image.grad.data.zero_()
applied_patch = lr * patch_grad + applied_patch.type(torch.FloatTensor)
applied_patch = torch.clamp(applied_patch, min=-3, max=3)
# Test the patch
perturbated_image = torch.mul(mask.type(torch.FloatTensor), applied_patch.type(torch.FloatTensor)) + torch.mul((1-mask.type(torch.FloatTensor)), image.type(torch.FloatTensor))
perturbated_image = torch.clamp(perturbated_image, min=-3, max=3)
perturbated_image = perturbated_image.cuda()
output = model(perturbated_image)
target_probability = torch.nn.functional.softmax(output, dim=1).data[0][target]
#print("{}\t, target prob: {}".format(count, target_probability))
perturbated_image = perturbated_image.cpu().numpy()
applied_patch = applied_patch.cpu().numpy()
return perturbated_image, applied_patch
os.environ["CUDA_VISIBLE_DEVICES"] = args.GPU
model = models.resnet18(pretrained=True)
num_features = model.fc.in_features
model.fc = nn.Linear(num_features, 307) # multi-class classification (num_of_class == 307)
model.load_state_dict(torch.load(args.model_path))
model.eval()
if(args.GPU!='-1'):
model.cuda()
transforms_train = transforms.Compose([
transforms.Resize((224, 224)),
transforms.RandomHorizontalFlip(), # data augmentation
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) # normalization
])
transforms_test = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
data_dir = args.data_dir
train_dataset = datasets.ImageFolder(os.path.join(data_dir, 'train'), transforms_train)
test_dataset = datasets.ImageFolder(os.path.join(data_dir, 'test'), transforms_test)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=1, shuffle=True, num_workers=2)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=1, shuffle=True, num_workers=2)
print('Train dataset size:', len(train_dataset))
print('Test dataset size:', len(test_dataset))
class_names = train_dataset.classes
print('Class names:', class_names)
# Test the accuracy of model on trainset and testset
trainset_acc, test_acc = test(model, train_loader), test(model, test_loader)
print('Accuracy of the model on clean trainset and testset is {:.3f}% and {:.3f}%'.format(100*trainset_acc, 100*test_acc))
# Initialize the patch
patch = patch_initialization(args.patch_type, image_size=(3, 224, 224), noise_percentage=args.noise_percentage)
print('The shape of the patch is', patch.shape)
#with open(args.log_dir, 'w') as f:
# writer = csv.writer(f)
# writer.writerow(["epoch", "train_success", "test_success"])
best_patch_epoch, best_patch_success_rate = 0, 0
# Generate the patch
for epoch in range(args.epochs):
#print("=========== attack under {} epoch =========".format(epoch+1))
cnt = 0
train_total, train_actual_total, train_success = 0, 0, 0
for (image, label) in train_loader:
if( cnt >= args.train_size ):
break
train_total += label.shape[0]
print("\tepoch {} | target {} | {} image so far, total: {} | {} succeeded".format(epoch+1 , args.target, cnt, args.train_size, train_success), flush=True)
assert image.shape[0] == 1, 'Only one picture should be loaded each time.'
image = image.cuda()
label = label.cuda()
output = model(image)
_, predicted = torch.max(output.data, 1)
if predicted[0] == label and predicted[0].data.cpu().numpy() != args.target:
train_actual_total += 1
applied_patch, mask, x_location, y_location = mask_generation(args.patch_type, patch, image_size=(3, 224, 224))
perturbated_image, applied_patch = patch_attack(image, applied_patch, mask, args.target, args.probability_threshold, model, args.lr, args.max_iteration)
perturbated_image = torch.from_numpy(perturbated_image).cuda()
output = model(perturbated_image)
_, predicted = torch.max(output.data, 1)
if predicted[0].data.cpu().numpy() == args.target:
train_success += 1
patch = applied_patch[0][:, x_location:x_location + patch.shape[1], y_location:y_location + patch.shape[2]]
cnt += 1
print("Epoch:{} Patch attack success rate on trainset: {:.3f}%".format(epoch+1, 100 * train_success / train_actual_total), flush=True)
#train_success_rate = test_patch(args.patch_type, args.target, patch, test_loader, model)
#print("Epoch:{} Patch attack success rate on trainset with universal patch: {:.3f}%".format(epoch+1, 100 * train_success_rate))
test_success_rate = test_patch(args.patch_type, args.target, patch, test_loader, model)
print("Epoch:{} Patch attack success rate on testset with universal patch: {:.3f}%".format(epoch+1, 100 * test_success_rate), flush=True)
# Record the statistics
#with open(args.log_dir, 'a') as f:
# writer = csv.writer(f)
# writer.writerow([epoch, train_success_rate, test_success_rate])
if test_success_rate > best_patch_success_rate:
best_patch_success_rate = test_success_rate
best_patch_epoch = epoch
#np.save("best_patch.npy", torch.from_numpy(cur_patch))
patch_name = "celeb_{}_best_org_patch_{}_{}.npy".format(args.patch_type, str(args.noise_percentage).replace('.', ''), str(args.target))
np.save(patch_name, torch.from_numpy(patch))
# Load the statistics and generate the line
#log_generation(args.log_dir)
print("The best patch is found at epoch {} with success rate {}% on testset".format(best_patch_epoch, 100 * best_patch_success_rate))