def attack_loader(args, net):
# Gradient Clamping based Attack
if args.attack == "pgd":
return torchattacks.PGD(model=net,
eps=args.eps,
alpha=args.eps / args.steps * 2.3,
steps=args.steps,
random_start=True)
elif args.attack == "auto":
return torchattacks.APGD(model=net, eps=args.eps)
elif args.attack == "fab":
return torchattacks.FAB(model=net,
eps=args.eps,
n_classes=args.n_classes)
elif args.attack == "cw":
return torchattacks.CW(model=net, c=0.1, lr=0.1, steps=200)
elif args.attack == "fgsm":
return torchattacks.FGSM(model=net, eps=args.eps)
elif args.attack == "bim":
return torchattacks.BIM(model=net, eps=args.eps, alpha=1 / 255)
elif args.attack == "deepfool":
return torchattacks.DeepFool(model=net, steps=10)
elif args.attack == "sparse":
return torchattacks.SparseFool(model=net)
elif args.attack == "gn":
return torchattacks.GN(model=net, sigma=args.eps)
def get_atk(model, atk_name, eps, steps):
if atk_name == 'fgsm':
return torchattacks.FGSM(model, eps=eps)
elif atk_name == 'bim':
return torchattacks.BIM(model,
eps=eps,
steps=steps,
alpha=eps / (steps * .5))
elif atk_name == 'deepfool':
return torchattacks.DeepFool(model, steps=steps)
elif atk_name == 'cw':
return torchattacks.CW(model)
elif atk_name == 'pgd':
return torchattacks.PGD(model,
eps=eps,
steps=steps,
alpha=eps / (steps * .5))
elif atk_name == 'rfgsm':
return torchattacks.RFGSM(model, eps=eps, alpha=eps)
elif atk_name == 'auto-attack':
return torchattacks.AutoAttack(model, eps=eps)
elif atk_name == 'mifgsm':
return torchattacks.MIFGSM(model, eps=eps, steps=steps)
elif atk_name == 'square':
return torchattacks.Square(model, eps=eps)
elif atk_name == 'fab':
return torchattacks.FAB(model, eps=eps)
elif atk_name == 'one-pixel':
return torchattacks.OnePixel(model)
elif atk_name == 'gn':
return torchattacks.GN(model, sigma=eps)
elif atk_name == 'apgd':
return torchattacks.APGD(model, eps=eps)
elif atk_name == 'eotpgd':
return torchattacks.EOTPGD(model,
eps=eps,
steps=steps,
alpha=eps / (steps * .5))
elif atk_name == 'pgddlr':
return torchattacks.PGDDLR(model,
eps=eps,
steps=steps,
alpha=eps / (steps * .5))
elif atk_name == 'ffgsm':
return torchattacks.FFGSM(model, eps=eps, alpha=eps)
elif atk_name == 'sparsefool':
return torchattacks.SparseFool(model)
else:
print("Attack not valid")
sys.exit(-1)
def basic_iterative_method(model, X, Y, eps, eps_iter, test_loader=None):
print(X.shape, Y.shape)
atk = torchattacks.BIM(model, eps=eps, alpha=eps_iter, steps=7)
if test_loader is not None:
x_adv_list = []
for batch in test_loader:
x = batch[0]
y = batch[1]
x_adv_list.append(atk(x, y))
X_adv = torch.cat(x_adv_list)
print('adv', X_adv.shape)
X_adv = X_adv.cpu()
return X_adv
def load_attack(model, attack: str):
import torchattacks
if attack == 'PGD':
return torchattacks.PGD(model, eps=2 / 255, alpha=2 / 255, steps=7)
elif attack == 'CW':
return torchattacks.CW(model,
targeted=False,
c=1,
kappa=0,
steps=1000,
lr=0.01)
elif attack == 'BIM':
return torchattacks.BIM(model, eps=4 / 255, alpha=1 / 255, steps=0)
elif attack == 'FGSM':
return torchattacks.FGSM(model, eps=1 / 255)
else:
raise NotImplementedError()
def Greedy_Decode_Eval(Net, datasets, args):
# TestNet = Net.eval()
epoch_size = len(datasets) // args.test_batch_size # 整除，多余的末尾就不会包括进来了
# collate_fn：如何取样本的，我们可以定义自己的函数来准确地实现想要的功能
# shuffle：设置为True的时候，每个世代都会打乱数据集
batch_iterator = iter(DataLoader(datasets, args.test_batch_size, shuffle=True, num_workers=args.num_workers, collate_fn=collate_fn))
attack = torchattacks.BIM(Net, eps=args.epsilon, alpha=1/255, iters=0)
total = epoch_size * args.test_batch_size # 总识别样本数
correct = 0
for i in range(epoch_size):
# load train data
images, labels, lengths = next(batch_iterator) # 提取iter的元素，注意images里面是整个batch的图像，但这时候类型是tensor了
# print(lengths) # 如果batch_size=100，那么lengths就是[7,7,...,7]，100个7(list类型)
# print(images.shape)
start = 0
targets = []
for length in lengths: # 事到如今又要将tabel一个个提取出来，那为什么之前要用extend方法而不用append？
label = labels[start:start+length]
targets.append(label)
start += length
targets = np.array([el.numpy() for el in targets], dtype=np.int32)
perturbed_images = attack(images, labels, lengths)
# 重新进行识别
preb_atk = Net(perturbed_images)
# 获得标签
preb_labels_atk = np.array(get_preb_labels(preb_atk))
for i in range(preb_labels_atk.shape[0]):
# print(preb_labels_atk[i])
# print(targets[i])
if len(preb_labels_atk[i]) == len(targets[i]) and (preb_labels_atk[i] == targets[i]).all():
correct += 1
return correct * 1.0 / total
CUDA_MODE = torch.cuda.is_available()
if not CUDA_MODE:
logger.warn(
"CUDA not available. Run on a CUDA enabled platform (NVIDIA GPU with compute capability >= 3) to get memory usage and timing stats (this code makes use of CUDA events to accurately measure memory and timing). Press [ENTER] to continue anyways."
)
input()
# How many images we want to test
IMG_NUM = 500
model.eval()
attacks = [
torchattacks.PGD(model),
torchattacks.DeepFool(model),
torchattacks.StepLL(model),
torchattacks.BIM(model)
]
for attack in attacks:
time = []
attack_l2 = []
peak_cuda = []
avg_cuda = []
total_success = 0
logger.info(f"Benchmarking {str(attack)} on {IMG_NUM} images")
for img_id in range(IMG_NUM):
# Load the image and reshape it to [NxCxWxH] (which is what the models expect)
target_im = data[str(model)][img_id][None, :, :, :].to(device)
_, TRUECLASS = torch.max(model(target_im), 1)
def train_model(device, dataloaders, batch_size, len_dataset, model, criterion, optimizer, scheduler, num_epochs=25):
since = time.time()
'''
* state_dict: 각 layer 를 매개변수 텐서로 매핑하는 Python 사전(dict) 객체
- layer; learnable parameters (convolutional layers, linear layers, etc.), registered buffers (batchnorm’s running_mean)
- Optimizer objects (torch.optim)
'''
best_model_wts = copy.deepcopy(model.state_dict())
best_acc = 0.0
train_loss, train_acc, valid_loss, valid_acc = [], [], [], []
for epoch in range(num_epochs):
print('Epoch {}/{}'.format(epoch, num_epochs - 1))
print('-' * 10)
# 각 epoch마다 training, validation phase 나눠줌.
for phase in ['train', 'valid']:
if phase == 'train':
model.train() # training mode
else:
model.eval() # evaluate mode
running_loss, running_corrects, num_cnt = 0.0, 0, 0
ratio_adv_ori = int((len_dataset // batch_size + 1) * 0.4) # adversarial, original data 비율 정하기
# batch 별로 나눠진 데이터 불러오기
for i, (inputs, labels) in enumerate(dataloaders[phase]):
# 설정한 비율에 따라 adversarial, original input으로 나누기
if (phase == 'train' and (i < ratio_adv_ori)) or (phase == 'valid' and i % 2 == 0):
inputs = inputs.to(device)
else:
# adversarial attack 정의
atks = [torchattacks.FGSM(model, eps=8 / 255),
torchattacks.BIM(model, eps=8 / 255, alpha=2 / 255, steps=7),
torchattacks.PGD(model, eps=8 / 255, alpha=2 / 255, steps=7),
]
inputs = atks[i % 3](inputs, labels).to(device)
# Image Processing Based Defense Methods --> tensor를 image로 변환하여 적용
for batch in range(inputs.shape[0]):
tensor2pil = transforms.ToPILImage()(inputs[batch]).convert('RGB')
# 1. Resizing
# Image.resize(size, resample=3, box=None, reducing_gap=None)
# resample(filter): PIL.Image.NEAREST, PIL.Image.BOX, PIL.Image.BILINEAR, PIL.Image.HAMMING, PIL.Image.BICUBIC
tensor2pil.resize((74, 74))
tensor2pil.resize((224, 224))
# 다시 이미지를 tensor로 바꾸기
tensor_img = transforms.ToTensor()(tensor2pil)
inputs[batch] = tensor_img
# 2. jpeg compression
tensor2numpy = inputs[batch].cpu().numpy()
cv_img = np.transpose(tensor2numpy, (1, 2, 0)) # [w, h, c]
cv_img = cv_img * 255
encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), 15]
result, encimg = cv2.imencode('.jpg', cv_img, encode_param)
if False == result:
print('could not encode image!')
quit()
# decode from jpeg format
jpeg_img = cv2.imdecode(encimg, 1)
jpeg2input = np.transpose(jpeg_img, (2, 0, 1)) / 255
inputs[batch] = torch.Tensor(jpeg2input).to(device)
# # save adversarial examples
# save_inputs = inputs.cpu().numpy()
# labels = labels.cpu().numpy()
# from matplotlib.pyplot import imsave
#
# for j in range(batch_size):
# image = save_inputs[j, :, :, :]
# label = labels[j]
# if label == 0:
# imsave(
# f"C:/Users/mmclab1/Desktop/fakecheck/dataset/adv_img_examples/"
# f"fake_adversarial_image_{j}.png",
# np.transpose(image, (1, 2, 0)))
# else:
# imsave(
# f"C:/Users/mmclab1/Desktop/fakecheck/dataset/adv_img_examples/"
# f"real_adversarial_image_{j}.png",
# np.transpose(image, (1, 2, 0)))
labels = labels.to(device)
# 학습 가능한 가중치인 "optimizer 객체" 사용하여, 갱신할 변수들에 대한 모든 변화도 0으로 설정
# backward() 호출시, 변화도가 buffer 에 덮어쓰지 않고 누적되기 때문.
optimizer.zero_grad()
# forward pass
# gradient 계산하는 모드로, 학습 시에만 연산 기록을 추적
with torch.set_grad_enabled(phase == 'train'):
outputs = model(inputs) # h(x) 값, 모델의 예측 값
_, preds = torch.max(outputs, 1) # dim = 1, output의 각 sample 결과값(row)에서 max값 1개만 뽑음.
loss = criterion(outputs, labels) # h(x) 모델이 잘 예측했는지 판별하는 loss function
# training phase에서만 backward + optimize 수행
if phase == 'train':
loss.backward() # gradient 계산
optimizer.step() # parameter update
# statistics
running_loss += loss.item() * inputs.size(0) # inputs.size(0) == batch size
running_corrects += torch.sum(preds == labels.data) # True == 1, False == 0, 총 정답 수
num_cnt += len(labels) # len(labels) == batch size
if phase == 'train':
scheduler.step() # Learning Rate Scheduler
epoch_loss = float(running_loss / num_cnt)
epoch_acc = float((running_corrects.double() / num_cnt).cpu() * 100)
if phase == 'train':
train_loss.append(epoch_loss)
train_acc.append(epoch_acc)
else:
valid_loss.append(epoch_loss)
valid_acc.append(epoch_acc)
print('{} Loss: {:.2f} Acc: {:.1f}'.format(phase, epoch_loss, epoch_acc))
# deep copy the model
if phase == 'valid' and epoch_acc > best_acc:
best_idx = epoch
best_acc = epoch_acc
best_model_wts = copy.deepcopy(model.state_dict())
# best_model_wts = copy.deepcopy(model.module.state_dict())
print('==> best model saved - %d / %.1f' % (best_idx, best_acc))
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))
print('Best valid Acc: %d - %.1f' % (best_idx, best_acc))
# load best model weights
PATH = 'pytorch_model_adv_epoch30_4_sgd_resize3_comp15.pt'
model.load_state_dict(best_model_wts)
# torch.save(model.state_dict(), PATH) # 모델 객체의 state_dict 저장
torch.save(model, PATH) # 전체모델 저장
torch.save(model.state_dict(), f'C:/Users/mmclab1/.cache/torch/hub/checkpoints/{PATH}')
print('model saved')
# train, validation의 loss, acc 그래프로 나타내기
plt.subplot(311)
plt.plot(train_loss)
plt.plot(valid_loss)
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper left')
plt.subplot(313)
plt.plot(train_acc)
plt.plot(valid_acc)
plt.ylabel('acc')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper left')
plt.savefig('graph_adv_epoch30_4_sgd_resize3_comp15.png')
plt.show()
return model, best_idx, best_acc, train_loss, train_acc, valid_loss, valid_acc, inputs
def train_epoch(model, loader, optimizer):
model.train()
train_loss = []
bar = tqdm(loader)
for i, (data, target, face_name, df_method) in enumerate(bar):
optimizer.zero_grad()
if args.use_meta:
data, meta = data
data, meta, target = data.to(device), meta.to(device), target.to(
device)
logits = model(data, meta)
else:
# attack 추가
method = {
'0_PGD': [20, 70, 2],
'1_APGD': [20, 70, 2],
'2_FGSM': [2, 8],
'3_FFGSM': [4, 7, 10],
'4_MIFGSM': [3, 6],
'5_RFGSM': [4, 7, 8],
'6_BIM': [4, 10, 1],
'7_CW': [1e-4, 2e-4]
}
# 1. original data save
# img_o
# 2. small sized data
# img_s = scaling(image_o, scaling_factor=0.5)
# out_attack = attack(small_data, target~~~)
# img_gen = normalize ( scaling ((out_attack - small_data), 1/scaling_factor) + img_o)
for eps in range(2):
globals()['atk{}'.format(0)] = torchattacks.PGD(
model,
eps=method['0_PGD'][eps] / 255,
alpha=method['0_PGD'][-1] / 255,
steps=4)
globals()['atk{}'.format(1)] = torchattacks.APGD(
model,
eps=method['1_APGD'][eps] / 255,
alpha=method['1_APGD'][-1] / 255,
steps=4)
globals()['atk{}'.format(2)] = torchattacks.FGSM(
model, eps=method['2_FGSM'][eps] / 255)
globals()['atk{}'.format(3)] = torchattacks.FFGSM(
model,
eps=method['3_FFGSM'][eps] / 255,
alpha=method['3_FFGSM'][-1] / 255)
globals()['atk{}'.format(4)] = torchattacks.MIFGSM(
model, eps=method['4_MIFGSM'][eps] / 255, steps=4)
globals()['atk{}'.format(5)] = torchattacks.RFGSM(
model,
eps=method['5_RFGSM'][eps] / 255,
alpha=method['5_RFGSM'][-1] / 255,
steps=4)
globals()['atk{}'.format(6)] = torchattacks.BIM(
model,
eps=method['6_BIM'][eps] / 255,
alpha=method['6_BIM'][-1] / 255)
globals()['atk{}'.format(7)] = torchattacks.CW(
model, c=method['7_CW'][eps], steps=10)
for count in range(8):
# globals()['data_atk{}'.format(i)]
globals()['data_atk{}'.format(count)] = globals()[
'atk{}'.format(count)](data, (target + 1) % 2)
globals()['data_atk{}'.format(count)], target = (
globals()['data_atk{}'.format(count)]
).to(device), target.to(device)
logits = model(globals()['data_atk{}'.format(count)])
globals()['data_atk{}'.format(count)] = (
globals()['data_atk{}'.format(count)]).cpu().numpy()
method_keys = list(method.keys())
bat_size = args.batch_size
for j in range(bat_size):
for save_cnt in range(8):
globals()['im{}'.format(save_cnt)] = (globals()[
'data_atk{}'.format(save_cnt)])[j, :, :, :]
# imsave(
# f"./confirm_attack2img/AE-classification/{method_keys[save_cnt]}/"
# f"{target[j]}_{face_name[j]}_{i * bat_size + j}_{method_keys[save_cnt]}_eps{method[method_keys[save_cnt]][eps]}_wsbs.png",
# np.transpose(globals()['im{}'.format(save_cnt)], (1, 2, 0)))
imsave(
f"./confirm_attack2img/AE-classification/train/"
f"{target[j]}_{face_name[j]}_{df_method[j]}_{i * bat_size + j}_{method_keys[save_cnt]}_eps{method[method_keys[save_cnt]][eps]}_wsbs.png",
np.transpose(globals()['im{}'.format(save_cnt)],
(1, 2, 0)))
loss = criterion(logits, target)
if not args.use_amp:
loss.backward()
# else:
# with amp.scale_loss(loss, optimizer) as scaled_loss:
# scaled_loss.backward()
if args.image_size in [896, 576]:
# 그라디언트가 너무 크면 값을 0.5로 잘라준다 (max_grad_norm=0.5)
torch.nn.utils.clip_grad_norm_(model.parameters(), 0.5)
# gradient accumulation (메모리 부족할때)
if args.accumulation_step:
if (i + 1) % args.accumulation_step == 0:
optimizer.step()
# optimizer.zero_grad()
else:
optimizer.step()
# optimizer.zero_grad()
loss_np = loss.detach().cpu().numpy()
train_loss.append(loss_np)
smooth_loss = sum(train_loss[-100:]) / min(len(train_loss), 100)
bar.set_description('loss: %.5f, smooth_loss: %.5f' %
(loss_np, smooth_loss))
train_loss = np.mean(train_loss)
return train_loss
def train_epoch(model, loader, optimizer):
model.train()
train_loss = []
bar = tqdm(loader)
for i, (data, target, face_name) in enumerate(bar):
optimizer.zero_grad()
if args.use_meta:
data, meta = data
data, meta, target = data.to(device), meta.to(device), target.to(device)
logits = model(data, meta)
else:
# attack 추가
method = {
'1_PGD': [20,70,2],
'2_APGD':[20,70,2],
'3_FGSM': [2,8],
'4_FFGSM': [4,7,10],
'5_MIFGSM': [3,6],
'6_RFGSM': [4,7,8],
'7_BIM':[4,10,1],
'8_CW':[1e-4, 2e-4]}
#TODO: dataset에 original image와 attacked image모두 만들기
# 1. original data save img_o
# 2. small sized data img_s = scaling(image_o, scaling_factor=0.5)
# out_attack = attack(small_data, target~~~)
# img_gen = normalize ( scaling ((out_attack - small_data), 1/scaling_factor) + img_o)
scaling_factor = 0.5
img_origin = np.transpose(data.cpu().numpy()[i, :, :, :], (1, 2, 0))
img_small = cv2.resize(img_origin, dsize=(0, 0),
fx=scaling_factor,
fy=scaling_factor) #,interpolation=cv2.INTER_AREA
for eps in range(2):
globals()['atk{}'.format(1)] = torchattacks.PGD(model, eps=method['1_PGD'][eps] / 255, alpha=method['1_PGD'][-1] / 255, steps=4)
globals()['atk{}'.format(2)] = torchattacks.APGD(model, eps=method['2_APGD'][eps] / 255, alpha=method['2_APGD'][-1] / 255, steps=4)
globals()['atk{}'.format(3)] = torchattacks.FGSM(model, eps=method['3_FGSM'][eps] / 255)
globals()['atk{}'.format(4)] = torchattacks.FFGSM(model, eps=method['4_FFGSM'][eps] / 255, alpha=method['4_FFGSM'][-1] / 255)
globals()['atk{}'.format(5)] = torchattacks.MIFGSM(model, eps=method['5_MIFGSM'][eps] / 255, steps=4)
globals()['atk{}'.format(6)] = torchattacks.RFGSM(model, eps=method['6_RFGSM'][eps] / 255, alpha=method['6_RFGSM'][-1] / 255, steps=4)
globals()['atk{}'.format(7)] = torchattacks.BIM(model, eps=method['7_BIM'][eps] / 255, alpha=method['7_BIM'][-1] / 255)
globals()['atk{}'.format(8)] = torchattacks.CW(model, c= method['8_CW'][eps], steps=10)
for count in range(1,9):
# regularization
# torch.clamp(images + delta, min=0, max=1).detach()
# torch.from_numpy(img_small)
out_attack = globals()['atk{}'.format(count)](torch.from_numpy(img_small), (target + 1) % 2)
img_gen = torch.clamp(cv2.resize(out_attack-img_small,dsize=(0,0),fx=1/scaling_factor, fy=1/scaling_factor), min=0, max=1).detach() + img_origin
globals()['data_atk{}'.format(count)] = torch.from_numpy(img_gen)
globals()['data_atk{}'.format(count)], target = (globals()['data_atk{}'.format(count)]).to(device), target.to(device)
logits = model(globals()['data_atk{}'.format(count)])
globals()['data_atk{}'.format(count)] = (globals()['data_atk{}'.format(count)]).cpu().numpy()
method_keys = list(method.keys())
bat_size = args.batch_size
for j in range(bat_size):
# save original image
# im0 = data.cpu().numpy()[j, :, :, :]
# imsave(
# f"./confirm_attack2img/AE-real_fake/0_original/"
# f"{target[j]}_{face_name[j]}_{i * bat_size + j}_0_wsbs.png",
# np.transpose(im0, (1, 2, 0)))
# save attacked image
for save_cnt in range(1,9):
globals()['im{}'.format(save_cnt)] = (globals()['data_atk{}'.format(save_cnt)])[j, :, :, :]
# imsave(
# f"./confirm_attack2img/AE-real_fake/{method_keys[save_cnt]}/"
# f"{target[j]}_{face_name[j]}_{i * bat_size + j}_{method_keys[save_cnt]}_eps{method[method_keys[save_cnt]][eps]}_wsbs.png",
# np.transpose(globals()['im{}'.format(save_cnt)], (1, 2, 0)))
imsave(
f"./confirm_attack2img/AE-real_fake/train/"
f"{target[j]}_{face_name[j]}_{i * bat_size + j}_{method_keys[save_cnt]}_eps{method[method_keys[save_cnt]][eps]}_wsbs.png",
np.transpose(globals()['im{}'.format(save_cnt)], (1, 2, 0)))
loss = criterion(logits, target)
if not args.use_amp:
loss.backward()
# else:
# with amp.scale_loss(loss, optimizer) as scaled_loss:
# scaled_loss.backward()
if args.image_size in [896, 576]:
# 그라디언트가 너무 크면 값을 0.5로 잘라준다 (max_grad_norm=0.5)
torch.nn.utils.clip_grad_norm_(model.parameters(), 0.5)
# gradient accumulation (메모리 부족할때)
if args.accumulation_step:
if (i + 1) % args.accumulation_step == 0:
optimizer.step()
# optimizer.zero_grad()
else:
optimizer.step()
# optimizer.zero_grad()
loss_np = loss.detach().cpu().numpy()
train_loss.append(loss_np)
smooth_loss = sum(train_loss[-100:]) / min(len(train_loss), 100)
bar.set_description('loss: %.5f, smooth_loss: %.5f' % (loss_np, smooth_loss))
train_loss = np.mean(train_loss)
return train_loss
def train_epoch(model, loader, optimizer):
model.train()
train_loss = []
bar = tqdm(loader)
for i, (data, target, image_name) in enumerate(bar):
optimizer.zero_grad()
if args.use_meta:
data, meta = data
data, meta, target = data.to(device), meta.to(device), target.to(
device)
logits = model(data, meta)
else:
# attack 추가: 5,499장 --> 총 7개 attack x epsilon 2개 == 76,986
method = {
'0_FGSM': [2, 5, 8],
'1_PGD': [20, 50, 80, 2],
'2_BIM': [4, 7, 10, 1]
}
for eps in range(3):
globals()['atk{}'.format(0)] = torchattacks.FGSM(
model, eps=method['0_FGSM'][eps] / 255)
globals()['atk{}'.format(1)] = torchattacks.PGD(
model,
eps=method['1_PGD'][eps] / 255,
alpha=method['1_PGD'][-1] / 255,
steps=4)
globals()['atk{}'.format(2)] = torchattacks.BIM(
model,
eps=method['2_BIM'][eps] / 255,
alpha=method['2_BIM'][-1] / 255)
for count in range(3):
# globals()['data_atk{}'.format(i)]
globals()['data_atk{}'.format(count)] = globals()[
'atk{}'.format(count)](data, (target + 1) % 2)
globals()['data_atk{}'.format(count)], target = (
globals()['data_atk{}'.format(count)]
).to(device), target.to(device)
logits = model(globals()['data_atk{}'.format(count)])
globals()['data_atk{}'.format(count)] = (
globals()['data_atk{}'.format(count)]).cpu().numpy()
method_keys = list(method.keys())
bat_size = args.batch_size
for j in range(bat_size):
for save_cnt in range(3):
globals()['im{}'.format(save_cnt)] = (globals()[
'data_atk{}'.format(save_cnt)])[j, :, :, :]
# imsave(
# f"./confirm_attack2img/AE-classification/{method_keys[save_cnt]}/"
# f"{target[j]}_{id[j]}_{i * bat_size + j}_{method_keys[save_cnt]}_eps{method[method_keys[save_cnt]][eps]}.png",
# np.transpose(globals()['im{}'.format(save_cnt)], (1, 2, 0)))
imsave(
f"./data/Adversarial Attack/{method_keys[save_cnt]}/"
f"{image_name[j].split('.')[0]}_{method_keys[save_cnt]}_eps{method[method_keys[save_cnt]][eps]}_{i * bat_size + j}.png",
np.transpose(globals()['im{}'.format(save_cnt)],
(1, 2, 0)))
loss = criterion(logits, target)
if not args.use_amp:
loss.backward()
# else:
# with amp.scale_loss(loss, optimizer) as scaled_loss:
# scaled_loss.backward()
if args.image_size in [896, 576]:
# 그라디언트가 너무 크면 값을 0.5로 잘라준다 (max_grad_norm=0.5)
torch.nn.utils.clip_grad_norm_(model.parameters(), 0.5)
# gradient accumulation (메모리 부족할때)
if args.accumulation_step:
if (i + 1) % args.accumulation_step == 0:
optimizer.step()
# optimizer.zero_grad()
else:
optimizer.step()
# optimizer.zero_grad()
loss_np = loss.detach().cpu().numpy()
train_loss.append(loss_np)
smooth_loss = sum(train_loss[-100:]) / min(len(train_loss), 100)
bar.set_description('loss: %.5f, smooth_loss: %.5f' %
(loss_np, smooth_loss))
train_loss = np.mean(train_loss)
return train_loss
def test_model(model, phase='test'):
# phase = 'train', 'valid', 'test'
model.eval() # evaluate mode; gradient 계산 안함.
running_loss, running_corrects, num_cnt = 0.0, 0, 0
'''
with torch.no_grad(): # memory save를 위해 gradient 저장하지 않음.
보통 test를 할 때, gradient를 training 시키는 것이 아니기 때문에 위와 같은 코드를 추가한다.
grad = torch.autograd.grad(cost, images, retain_graph=False, create_graph=False)[0]
하지만, adversarial attack은 위와 같이 gradient를 토대로 data에 공격을 가하기 때문에 gradient가 필요하다.
따라서 test_adv 에는 with torch.no_grad()를 제외해야 한다.
'''
for i, (inputs, labels) in enumerate(dataloaders[phase]):
# adversarial attack 정의
atks = [
torchattacks.FGSM(model, eps=8 / 255),
torchattacks.BIM(model, eps=8 / 255, alpha=2 / 255, steps=7),
torchattacks.PGD(model, eps=8 / 255, alpha=2 / 255, steps=7),
]
adv_images = atks[0](inputs, labels).to(device)
# Image Processing Based Defense Methods --> tensor를 image로 변환하여 적용
for batch in range(inputs.shape[0]):
tensor2img = transforms.ToPILImage()(inputs[batch]).convert('RGB')
# 1. Resizing
# Image.resize(size, resample=3, box=None, reducing_gap=None)
# resample(filter): PIL.Image.NEAREST, PIL.Image.BOX, PIL.Image.BILINEAR, PIL.Image.HAMMING, PIL.Image.BICUBIC
tensor2img.resize((74, 74))
tensor2img.resize((224, 224))
# 다시 이미지를 tensor로 바꾸기
tensor_img = transforms.ToTensor()(tensor2img)
inputs[batch] = tensor_img
# 2. jpeg compression
tensor2numpy = inputs[batch].cpu().numpy()
cv_img = np.transpose(tensor2numpy, (1, 2, 0)) # [w, h, c]
cv_img = cv_img * 255
encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), 15]
result, encimg = cv2.imencode('.jpg', cv_img, encode_param)
if False == result:
print('could not encode image!')
quit()
# decode from jpeg format
jpeg_img = cv2.imdecode(encimg, 1)
jpeg2input = np.transpose(jpeg_img, (2, 0, 1)) / 255
inputs[batch] = torch.Tensor(jpeg2input).to(device)
labels = labels.to(device)
outputs = model(adv_images) # forward pass
_, preds = torch.max(outputs, 1) # model이 가장 높은 확률로 예측한 label
loss = criterion(outputs, labels) # loss 계산
running_loss += loss.item() * inputs.size(0)
running_corrects += torch.sum(preds == labels.data)
num_cnt += inputs.size(0) # batch size
test_loss = running_loss / num_cnt
test_acc = running_corrects.double() / num_cnt
print('test done : loss/acc : %.2f / %.1f' %
(test_loss, test_acc * 100))
def ta_bim(x, y, model, eps=4 / 255, alpha=1 / 255, steps=0):
attack = torchattacks.BIM(model, eps=eps, alpha=alpha, steps=steps)
advs = attack(x, y)
return advs
ssim = structural_similarity(ori_image,
adv_image,
data_range=255,
multichannel=False)
psnr = peak_signal_noise_ratio(ori_image, adv_image, data_range=255)
l2 = np.linalg.norm(ori_image - adv_image)
return ssim, psnr, l2
################ 开始攻击 ##################
epsilons = [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3]
for epsilon in epsilons:
attack = torchattacks.BIM(model, eps=epsilon, alpha=1 / 255, iters=0)
correct = 0
ssim = 0
psnr = 0
l2 = 0
for data, target in test_loader:
# Send the data and label to the device
data, target = data.to(device), target.to(device)
# 防止对原始样本进行了更改
data_t = data.clone()
target_t = target.clone()
adv_images = attack(data_t, target_t)
# show_cmp(data, adv_images) # 显示图像，只能batch_size=1的时候才可以
# break
