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celeb-create_patched_input_with_saliency.py
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celeb-create_patched_input_with_saliency.py
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#"This is to generate adv samples by applying the patch to the clean images, and also calculate its saliency map using smoothgrad"
#"The patched image is saved as npy format and saliency map as png"
# code from https://github.com/A-LinCui/Adversarial_Patch_Attack
# Adversarial Patch Attack
# Created by Junbo Zhao 2020/3/17
"""
Reference:
[1] Tom B. Brown, Dandelion Mané, Aurko Roy, Martín Abadi, Justin Gilmer
Adversarial Patch. arXiv:1712.09665
"""
from __future__ import print_function
import cv2
import numpy as np
import torch
import torch.nn as nn
from PIL import Image
from torch.autograd import Variable
from torch.nn import functional as F
from tqdm import tqdm
import torch
import torch.nn as nn
from torch.autograd import Variable
import torchvision
from torchvision import models
import torchvision.utils as vutils
import argparse
import csv
import os
import numpy as np
from torchvision.utils import save_image
import random
from patch_utils import*
from utils import*
from smooth_grad import SmoothGrad
from torchvision import datasets, transforms
import torchvision.utils as vutils
def gen_patched_img(patch_type, target, patch, test_loader, model, org_img_folder, adv_img_folder, org_saliency_folder, adv_saliency_folder, smooth_grad):
model.eval()
inv_normalize = transforms.Normalize(
mean=[-0.485/0.229, -0.456/0.224, -0.406/0.255],
std=[1/0.229, 1/0.224, 1/0.255]
)
test_total, test_actual_total, test_success = 0, 0, 0
for (image, label) in test_loader:
test_total += label.shape[0]
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() != target:
test_actual_total += 1
applied_patch, mask, x_location, y_location = mask_generation(patch_type, patch, image_size=(3, 224, 224))
applied_patch = torch.from_numpy(applied_patch)
mask = torch.from_numpy(mask)
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 = perturbated_image.cuda()
output = model(perturbated_image)
_, predicted = torch.max(output.data, 1)
if predicted[0].data.cpu().numpy() == target:
test_success += 1
#print(" ====== {} input ".format(test_success))
# get saliency from org img
smooth_grad.feed_image(image)
prob, idx = smooth_grad.forward()
smooth_grad.generate(filename= org_saliency_folder + "/" + str(test_success) + "_" + str(label.item()), idx=idx[0]) # "0" means only calculate the saliency for the top label
smooth_grad.feed_image(perturbated_image)
prob, idx = smooth_grad.forward()
smooth_grad.generate(filename= adv_saliency_folder + "/" + str(test_success) + "_" + str(label.item()) , idx=idx[0])
np.save( adv_img_folder + "/" + str(test_success) + "_" + str(label.item()) + ".npy" , perturbated_image.cpu().numpy() )
np.save( org_img_folder + "/" + str(test_success) + "_" + str(label.item()) + ".npy" , image.cpu().numpy() )
print("%d successful adv samples out of %d samples"%(test_success, test_actual_total))
return test_success / test_actual_total
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('--test_size', type=int, default=2000, help="number of test 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=0.5, 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=20, help="total epoch")
parser.add_argument('--data_dir', type=str, default='imagenet-val/ILSVRC2012_img_val', 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_file', type=str, required=True, help='file to the adv patch')
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')
parser.add_argument('--save_folder', type=str, default='./')
"for smoothgrad"
parser.add_argument('--image', type=str, required=False)
parser.add_argument('--sigma', type=float, default=0.10)
parser.add_argument('--n_samples', type=int, default=50)
parser.add_argument('--cuda', action='store_true', default=True)
parser.add_argument('--guided', action='store_true', default=False)
args = parser.parse_args()
'''
# Load the synset words
idx2cls = list()
with open('samples/synset_words.txt') as lines:
for line in lines:
line = line.strip().split(' ', 1)[1]
line = line.split(', ', 1)[0].replace(' ', '_')
idx2cls.append(line)
'''
os.environ["CUDA_VISIBLE_DEVICES"] = args.GPU
seed = 1
np.random.seed(seed)
random.seed(seed)
torch.manual_seed(seed)
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 = np.load(args.patch_file)
print('The shape of the patch is', patch.shape)
# Setup the SmoothGrad
smooth_grad = SmoothGrad(model=model, cuda=args.cuda, sigma=args.sigma,
n_samples=args.n_samples, guided=args.guided)
test_success_rate = 0
train_success_rate = 0
save_folder2 = "{}/celeb_{}_{}_npy_test_org_result_{}".format(args.save_folder.rstrip("/"), args.patch_type, args.target, str(args.noise_percentage).replace('.', ''))
save_folder22 = "{}/celeb_{}_{}_npy_test_adv_result_{}".format(args.save_folder.rstrip("/"), args.patch_type, args.target, str(args.noise_percentage).replace('.', ''))
save_folder222 = "{}/celeb_{}_{}_npy_test_org_saliency_{}".format(args.save_folder.rstrip("/"),args.patch_type, args.target, str(args.noise_percentage).replace('.', ''))
save_folder2222 = "{}/celeb_{}_{}_npy_test_adv_saliency_{}".format(args.save_folder.rstrip("/"), args.patch_type, args.target, str(args.noise_percentage).replace('.', ''))
if(not os.path.exists(save_folder2)):
os.mkdir(save_folder2)
if(not os.path.exists(save_folder22)):
os.mkdir(save_folder22)
if(not os.path.exists(save_folder222)):
os.mkdir(save_folder222)
if(not os.path.exists(save_folder2222)):
os.mkdir(save_folder2222)
#train_success_rate = gen_patched_img(patch_type= args.patch_type, target= args.target, patch= patch, test_loader= train_loader, model= model, org_img_folder = save_folder1, adv_img_folder=save_folder11, org_saliency_folder=save_folder111, adv_saliency_folder=save_folder1111, smooth_grad=smooth_grad)
#print(" Patch attack success rate on trainset using the patch: {:.3f}%".format( 100 * train_success_rate))
test_success_rate = gen_patched_img(patch_type= args.patch_type, target= args.target, patch= patch, test_loader= test_loader, model= model, org_img_folder = save_folder2, adv_img_folder=save_folder22, org_saliency_folder=save_folder222, adv_saliency_folder=save_folder2222, smooth_grad=smooth_grad)
print(" Patch attack success rate on testset: {:.3f}%".format( 100 * test_success_rate))
print( args.patch_file )