def get_model(idx, device):
model_file = DATASETS[idx] + '_400.pt'
if idx == 0:
model = NumericModel(n_features=4,
n_hidden=4 * 4,
n_classes=2,
use_prob=True)
elif idx == 1:
model = NumericModel(n_features=30,
n_hidden=30 * 4,
n_classes=2,
use_prob=True)
elif idx == 2:
model = NumericModel(n_features=8,
n_hidden=8 * 4,
n_classes=2,
use_prob=True)
elif idx == 3:
model = Resnet(use_prob=True)
model_file = '{}_{}_200.pt'.format(DATASETS[idx], MODEL_NAMES[idx])
elif idx == 4:
model = Vgg(use_prob=True)
model_file = '{}_{}_200.pt'.format(DATASETS[idx], MODEL_NAMES[idx])
elif idx == 5:
model = BaseModel(use_prob=True)
model_file = '{}_200.pt'.format(DATASETS[idx])
else:
raise NotImplementedError
path_model = os.path.join(RESULT_PATH, model_file)
model.load_state_dict(torch.load(path_model, map_location=device))
model = model.to(device)
return model
def train_model(data, model_name, dataset_train, dataset_test, epochs, device,
file_model):
dataloader_train = DataLoader(dataset_train, batch_size=128, shuffle=True)
dataloader_test = DataLoader(dataset_test, batch_size=128, shuffle=False)
print('Train set: {}, Test set: {}'.format(len(dataset_train),
len(dataset_test)))
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('Device: {}'.format(device))
if data == 'mnist':
model = BaseModel(use_prob=True).to(device)
elif data == 'cifar10' and model_name == 'resnet':
model = Resnet(use_prob=True).to(device)
elif data == 'cifar10' and model_name == 'vgg':
model = Vgg(use_prob=True).to(device)
else:
raise NotImplementedError
optimizer = optim.SGD(model.parameters(),
lr=0.01,
momentum=0.9,
weight_decay=5e-4)
loss = nn.CrossEntropyLoss()
scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs)
if not os.path.exists(file_model):
since = time.time()
for e in range(epochs):
start = time.time()
tr_loss, tr_acc = train(model, dataloader_train, loss, optimizer,
device)
va_loss, va_acc = validate(model, dataloader_test, loss, device)
scheduler.step()
time_elapsed = time.time() - start
print((
'{:2d}/{:d}[{:s}] Train Loss: {:.4f} Acc: {:.4f}%, Test Loss: {:.4f} Acc: {:.4f}%'
).format(e + 1, epochs,
str(datetime.timedelta(seconds=time_elapsed)), tr_loss,
tr_acc * 100., va_loss, va_acc * 100.))
time_elapsed = time.time() - since
print('Total run time:', str(datetime.timedelta(seconds=time_elapsed)))
torch.save(model.state_dict(), file_model)
print('Save base model to:', file_model)
else:
print('Found existing file:', file_model)
model.load_state_dict(torch.load(file_model, map_location=device))
return model
def main():
with open('data.json') as data_json:
data_params = json.load(data_json)
parser = argparse.ArgumentParser()
parser.add_argument('--data', type=str)
parser.add_argument('--data_path', type=str, default='data')
parser.add_argument('--output_path', type=str, default='results')
parser.add_argument('--pretrained', type=str, required=True)
parser.add_argument('--batch_size', type=int, default=128)
parser.add_argument('--attack', type=str, required=True, choices=data_params['attacks'])
parser.add_argument('--eps', type=float, default=0.3)
# NOTE: In CW_L2 attack, eps is the upper bound of c.
parser.add_argument('--n_samples', type=int, default=2000)
parser.add_argument('--random_state', type=int, default=1234)
args = parser.parse_args()
print(args)
set_seeds(args.random_state)
if not os.path.exists(args.output_path):
print('Output folder does not exist. Create:', args.output_path)
os.mkdir(args.output_path)
print('Dataset:', args.data)
print('Pretrained model:', args.pretrained)
print('Running attack: {}'.format(args.attack))
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('Device: {}'.format(device))
# Prepare data
transforms = tv.transforms.Compose([tv.transforms.ToTensor()])
if args.data == 'mnist':
dataset_train = datasets.MNIST(args.data_path, train=True, download=True, transform=transforms)
dataset_test = datasets.MNIST(args.data_path, train=False, download=True, transform=transforms)
elif args.data == 'cifar10':
dataset_train = datasets.CIFAR10(args.data_path, train=True, download=True, transform=transforms)
dataset_test = datasets.CIFAR10(args.data_path, train=False, download=True, transform=transforms)
else:
data_path = os.path.join(args.data_path, data_params['data'][args.data]['file_name'])
print('Read file:', data_path)
X, y = load_csv(data_path)
X_train, X_test, y_train, y_test = train_test_split(
X, y,
test_size=data_params['data'][args.data]['n_test'],
random_state=args.random_state)
scaler = MinMaxScaler().fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
dataset_train = TensorDataset(torch.from_numpy(X_train).type(torch.float32), torch.from_numpy(y_train).type(torch.long))
dataset_test = TensorDataset(torch.from_numpy(X_test).type(torch.float32), torch.from_numpy(y_test).type(torch.long))
dataloader_train = DataLoader(dataset_train, 256, shuffle=False)
dataloader_test = DataLoader(dataset_test, 256, shuffle=False)
shape_train = get_shape(dataloader_train.dataset)
shape_test = get_shape(dataloader_test.dataset)
print('Train set:', shape_train)
print('Test set:', shape_test)
# Load model
use_prob = args.attack not in ['apgd', 'apgd1', 'apgd2', 'cw2', 'cwinf']
print('Attack:', args.attack)
print('Using softmax layer:', use_prob)
if args.data == 'mnist':
model = BaseModel(use_prob=use_prob).to(device)
model_name = 'basic'
elif args.data == 'cifar10':
model_name = args.pretrained.split('_')[1]
if model_name == 'resnet':
model = Resnet(use_prob=use_prob).to(device)
elif model_name == 'vgg':
model = Vgg(use_prob=use_prob).to(device)
else:
raise ValueError('Unknown model: {}'.format(model_name))
else:
n_features = data_params['data'][args.data]['n_features']
n_classes = data_params['data'][args.data]['n_classes']
model = NumericModel(
n_features,
n_hidden=n_features * 4,
n_classes=n_classes,
use_prob=use_prob).to(device)
model_name = 'basic' + str(n_features * 4)
optimizer = optim.SGD(model.parameters(), lr=0.01,
momentum=0.9, weight_decay=5e-4)
loss = nn.CrossEntropyLoss()
pretrained_path = os.path.join(args.output_path, args.pretrained)
model.load_state_dict(torch.load(pretrained_path, map_location=device))
_, acc_train = validate(model, dataloader_train, loss, device)
_, acc_test = validate(model, dataloader_test, loss, device)
print('Accuracy on train set: {:.4f}%'.format(acc_train * 100))
print('Accuracy on test set: {:.4f}%'.format(acc_test * 100))
# Create a subset which only contains recognisable samples.
tensor_test_X, tensor_test_y = get_correct_examples(
model, dataset_test, device=device, return_tensor=True)
dataset_perfect = TensorDataset(tensor_test_X, tensor_test_y)
loader_perfect = DataLoader(dataset_perfect, batch_size=512, shuffle=False)
_, acc_perfect = validate(model, loader_perfect, loss, device)
print('Accuracy on {} filtered test examples: {:.4f}%'.format(
len(dataset_perfect), acc_perfect * 100))
# Generate adversarial examples
n_features = data_params['data'][args.data]['n_features']
n_classes = data_params['data'][args.data]['n_classes']
if isinstance(n_features, int):
n_features = (n_features,)
classifier = PyTorchClassifier(
model=model,
loss=loss,
input_shape=n_features,
optimizer=optimizer,
nb_classes=n_classes,
clip_values=(0.0, 1.0),
device_type='gpu')
if args.attack == 'apgd':
eps_step = args.eps / 10.0 if args.eps <= 0.1 else 0.1
attack = AutoProjectedGradientDescent(
estimator=classifier,
eps=args.eps,
eps_step=eps_step,
max_iter=1000,
batch_size=args.batch_size,
targeted=False)
elif args.attack == 'apgd1':
attack = AutoProjectedGradientDescent(
estimator=classifier,
norm=1,
eps=args.eps,
eps_step=0.1,
max_iter=1000,
batch_size=args.batch_size,
targeted=False)
elif args.attack == 'apgd2':
attack = AutoProjectedGradientDescent(
estimator=classifier,
norm=2,
eps=args.eps,
eps_step=0.1,
max_iter=1000,
batch_size=args.batch_size,
targeted=False)
elif args.attack == 'bim':
eps_step = args.eps / 10.0
attack = BasicIterativeMethod(
estimator=classifier,
eps=args.eps,
eps_step=eps_step,
max_iter=1000,
batch_size=args.batch_size,
targeted=False)
elif args.attack == 'boundary':
attack = BoundaryAttack(
estimator=classifier,
max_iter=1000,
sample_size=args.batch_size,
targeted=False)
elif args.attack == 'cw2':
# NOTE: Do NOT increase the batch size!
attack = CarliniWagnerAttackL2(
model=model,
n_classes=n_classes,
confidence=args.eps,
verbose=True,
check_prob=False,
batch_size=args.batch_size,
targeted=False)
elif args.attack == 'cwinf':
attack = CarliniLInfMethod(
classifier=classifier,
confidence=args.eps,
max_iter=1000,
batch_size=args.batch_size,
targeted=False)
elif args.attack == 'deepfool':
attack = DeepFool(
classifier=classifier,
epsilon=args.eps,
batch_size=args.batch_size)
elif args.attack == 'fgsm':
attack = FastGradientMethod(
estimator=classifier,
eps=args.eps,
batch_size=args.batch_size)
elif args.attack == 'jsma':
attack = SaliencyMapMethod(
classifier=classifier,
gamma=args.eps,
batch_size=args.batch_size)
elif args.attack == 'line':
if args.data == 'mnist':
color = args.eps
elif args.data == 'cifar10':
color = (args.eps, args.eps, args.eps)
else:
raise NotImplementedError
attack = LineAttack(color=color, thickness=1)
elif args.attack == 'shadow':
attack = ShadowAttack(
estimator=classifier,
batch_size=args.batch_size,
targeted=False,
verbose=False)
elif args.attack == 'watermark':
attack = WaterMarkAttack(
eps=args.eps,
n_classes=data_params['data'][args.data]['n_classes'],
x_min=0.0,
x_max=1.0,
targeted=False)
X_train, y_train = get_correct_examples(model, dataset_train, device=device, return_tensor=True)
X_train = X_train.cpu().detach().numpy()
y_train = y_train.cpu().detach().numpy()
attack.fit(X_train, y_train)
else:
raise NotImplementedError
if len(dataset_perfect) > args.n_samples:
n = args.n_samples
else:
n = len(dataset_perfect)
X_benign = tensor_test_X[:n].cpu().detach().numpy()
y = tensor_test_y[:n].cpu().detach().numpy()
print('Creating {} adversarial examples with eps={} (Not all attacks use eps)'.format(n, args.eps))
time_start = time.time()
# Shadow attack only takes single sample!
if args.attack == 'shadow':
adv = np.zeros_like(X_benign)
for i in trange(len(X_benign)):
adv[i] = attack.generate(x=np.expand_dims(X_benign[i], axis=0))
elif args.attack == 'watermark':
# This is untargeted.
adv = attack.generate(X_benign, y)
else:
adv = attack.generate(x=X_benign)
time_elapsed = time.time() - time_start
print('Total time spend: {}'.format(str(datetime.timedelta(seconds=time_elapsed))))
pred_benign = np.argmax(classifier.predict(X_benign), axis=1)
acc_benign = np.sum(pred_benign == y) / n
pred_adv = np.argmax(classifier.predict(adv), axis=1)
acc_adv = np.sum(pred_adv == y) / n
print("Accuracy on benign samples: {:.4f}%".format(acc_benign * 100))
print("Accuracy on adversarial examples: {:.4f}%".format(acc_adv * 100))
# Save results
if args.n_samples < 2000:
output_file = '{}_{}_{}_{}_size{}'.format(args.data, model_name, args.attack, str(args.eps), args.n_samples)
else:
output_file = '{}_{}_{}_{}'.format(args.data, model_name, args.attack, str(args.eps))
path_x = os.path.join(args.output_path, '{}_x.npy'.format(output_file))
path_y = os.path.join(args.output_path, '{}_y.npy'.format(output_file))
path_adv = os.path.join(args.output_path, '{}_adv.npy'.format(output_file))
np.save(path_x, X_benign)
np.save(path_y, y)
np.save(path_adv, adv)
print('Saved to:', '{}_adv.npy'.format(output_file))
print()
def main():
with open('data.json') as data_json:
data_params = json.load(data_json)
parser = argparse.ArgumentParser()
parser.add_argument('--data', type=str, required=True)
parser.add_argument('--model', type=str, required=True)
parser.add_argument('--pretrained', type=str, required=True)
parser.add_argument('--data_path', type=str, default='data')
parser.add_argument('--output_path', type=str, default='results')
parser.add_argument('--random_state', type=int, default=1234)
args = parser.parse_args()
print(args)
set_seeds(args.random_state)
if not os.path.exists(args.output_path):
print('Output folder does not exist. Create:', args.output_path)
os.mkdir(args.output_path)
print('data:', args.data)
print('model:', args.model)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('Device: {}'.format(device))
# Prepare data
transforms = tv.transforms.Compose([tv.transforms.ToTensor()])
if args.data == 'mnist':
dataset_train = datasets.MNIST(args.data_path,
train=True,
download=True,
transform=transforms)
dataset_test = datasets.MNIST(args.data_path,
train=False,
download=True,
transform=transforms)
elif args.data == 'cifar10':
dataset_train = datasets.CIFAR10(args.data_path,
train=True,
download=True,
transform=transforms)
dataset_test = datasets.CIFAR10(args.data_path,
train=False,
download=True,
transform=transforms)
else:
data_path = os.path.join(args.data_path,
data_params['data'][args.data]['file_name'])
print('Read file:', data_path)
X, y = load_csv(data_path)
X_train, X_test, y_train, y_test = train_test_split(
X,
y,
test_size=data_params['data'][args.data]['n_test'],
random_state=args.random_state)
scaler = MinMaxScaler().fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
dataset_train = TensorDataset(
torch.from_numpy(X_train).type(torch.float32),
torch.from_numpy(y_train).type(torch.long))
dataset_test = TensorDataset(
torch.from_numpy(X_test).type(torch.float32),
torch.from_numpy(y_test).type(torch.long))
loader_train = DataLoader(dataset_train, batch_size=512, shuffle=False)
loader_test = DataLoader(dataset_test, batch_size=512, shuffle=False)
shape_train = get_shape(loader_train.dataset)
shape_test = get_shape(loader_test.dataset)
print('Train set:', shape_train)
print('Test set:', shape_test)
use_prob = True
print('Using softmax layer:', use_prob)
# Load model
if args.data == 'mnist':
model = BaseModel(use_prob=use_prob).to(device)
model_name = 'basic'
elif args.data == 'cifar10':
model_name = args.pretrained.split('_')[1]
if model_name == 'resnet':
model = Resnet(use_prob=use_prob).to(device)
elif model_name == 'vgg':
model = Vgg(use_prob=use_prob).to(device)
else:
raise NotImplementedError
else:
n_features = data_params['data'][args.data]['n_features']
n_classes = data_params['data'][args.data]['n_classes']
model = NumericModel(n_features,
n_hidden=n_features * 4,
n_classes=n_classes,
use_prob=use_prob).to(device)
model_name = 'basic' + str(n_features * 4)
loss = nn.CrossEntropyLoss()
pretrained_path = os.path.join(args.output_path, args.pretrained)
model.load_state_dict(torch.load(pretrained_path, map_location=device))
_, acc_train = validate(model, loader_train, loss, device)
_, acc_test = validate(model, loader_test, loss, device)
print('Accuracy on train set: {:.4f}%'.format(acc_train * 100))
print('Accuracy on test set: {:.4f}%'.format(acc_test * 100))
# Create a subset which only contains recognisable samples.
# The original train and test sets are no longer needed.
tensor_train_X, tensor_train_y = get_correct_examples(model,
dataset_train,
device=device,
return_tensor=True)
dataset_train = TensorDataset(tensor_train_X, tensor_train_y)
loader_train = DataLoader(dataset_train, batch_size=512, shuffle=False)
_, acc_perfect = validate(model, loader_train, loss, device)
print('Accuracy on {} filtered train set: {:.4f}%'.format(
len(dataset_train), acc_perfect * 100))
tensor_test_X, tensor_test_y = get_correct_examples(model,
dataset_test,
device=device,
return_tensor=True)
dataset_test = TensorDataset(tensor_test_X, tensor_test_y)
loader_test = DataLoader(dataset_test, batch_size=512, shuffle=False)
_, acc_perfect = validate(model, loader_test, loss, device)
print('Accuracy on {} filtered test set: {:.4f}%'.format(
len(dataset_test), acc_perfect * 100))
X_train = tensor_train_X.cpu().detach().numpy()
y_train = tensor_train_y.cpu().detach().numpy()
X_baard = baard_preprocess(args.data,
tensor_train_X).cpu().detach().numpy()
obj = {'X_train': X_baard, 'y_train': y_train}
path_ouput = os.path.join(
args.output_path,
'{}_{}_baard_train.pt'.format(args.data, args.model, args.model))
torch.save(obj, path_ouput)
print('Save to:', path_ouput)
print()
def get_baard_output(data, model_name, data_path, output_path, file_name,
param, batch_size, device):
"""This function reads a dataset object. It runs BAARD, applies clipping and
adds label_as_adv to the object.
"""
file_path = os.path.join(output_path, file_name)
print('file_path:', file_path)
obj = torch.load(file_path)
X = obj['X']
adv = obj['adv']
y = obj['y']
# Load model
transforms = tv.transforms.Compose([tv.transforms.ToTensor()])
if data == 'mnist':
dataset_train = datasets.MNIST(data_path,
train=True,
download=True,
transform=transforms)
model = BaseModel(use_prob=False).to(device)
pretrained = 'mnist_200.pt'
elif data == 'cifar10':
dataset_train = datasets.CIFAR10(data_path,
train=True,
download=True,
transform=transforms)
if model_name == 'resnet':
model = Resnet(use_prob=False).to(device)
pretrained = 'cifar10_resnet_200.pt'
elif model_name == 'vgg':
model = Vgg(use_prob=False).to(device)
pretrained = 'cifar10_vgg_200.pt'
else:
raise NotImplementedError
else:
raise NotImplementedError
pretrained_path = os.path.join(output_path, pretrained)
model.load_state_dict(torch.load(pretrained_path, map_location=device))
pred = predict_numpy(model, X, device)
acc = np.mean(pred == y)
print('Accuracy on {} clean samples: {}'.format(X.shape[0], acc))
tensor_train_X, tensor_train_y = get_correct_examples(model,
dataset_train,
device=device,
return_tensor=True)
X_train = tensor_train_X.cpu().detach().numpy()
y_train = tensor_train_y.cpu().detach().numpy()
# Load the preprocessed training set
baard_train_path = os.path.join(
output_path, '{}_{}_baard_train.pt'.format(data, model_name))
obj = torch.load(baard_train_path)
X_baard = obj['X_train']
# Load the original validation set for BAARD
# eg: ./results/mnist_basic_apgd2_2.0_adv.npy
file_root = '{}_{}_apgd2_2.0'.format(data, model_name)
path_benign = os.path.join(output_path, file_root + '_x.npy')
path_y = os.path.join(output_path, file_root + '_y.npy')
X_val = np.load(path_benign)
y_val = np.load(path_y)
n = X_val.shape[0] // 2
X_val = X_val[n:]
y_val = y_val[n:]
stages = []
stages.append(ApplicabilityStage(n_classes=N_CLASSES,
quantile=param['q1']))
stages.append(
ReliabilityStage(n_classes=N_CLASSES,
k=param['k_re'],
quantile=param['q2']))
stages.append(
DecidabilityStage(n_classes=N_CLASSES,
k=param['k_de'],
quantile=param['q3']))
print('BAARD: # of stages:', len(stages))
detector = BAARDOperator(stages=stages)
detector.stages[0].fit(X_baard, y_train)
detector.stages[1].fit(X_train, y_train)
detector.stages[2].fit(X_train, y_train)
detector.search_thresholds(X_val, y_val, np.zeros_like(y_val))
pred_adv = predict_numpy(model, adv, device)
print('Acc on adv without clip:', np.mean(pred_adv == y))
# count_class(pred_adv)
# TODO: After clipping, the 1st stage still blocks samples. I don't know why?!
# To bypass the 1st stage, we want to clip all adversarial examples with the bounding boxes
applicability = detector.stages[0]
thresholds = applicability.thresholds_
adv_clipped = adv.copy()
for c in range(N_CLASSES):
idx = np.where(pred_adv == c)[0]
# Adversarial examples do NOT have the same distribution as the true classes
if len(idx) == 0:
continue
bounding_boxes = thresholds[c]
low = bounding_boxes[0]
high = bounding_boxes[1]
shape = adv_clipped[idx].shape
subset = flatten(adv[idx])
# clipped_subset = np.clip(subset, low, high)
subset = np.minimum(subset, high)
subset = np.maximum(subset, low)
adv_clipped[idx] = subset.reshape(shape)
pred_adv_clip = predict_numpy(model, adv_clipped, device)
print('Acc on adv with clip:', np.mean(pred_adv_clip == y))
print('Class changed after clipping:', np.sum(pred_adv != pred_adv_clip))
pred_X = predict_numpy(model, X, device)
assert not np.all([pred_X, y])
baard_label_adv = detector.detect(adv_clipped, pred_adv_clip)
s1_blocked = detector.stages[0].predict(adv_clipped, pred_adv_clip)
print('Blocked by Stage1:', np.sum(s1_blocked))
acc = acc_on_adv(pred_adv_clip, y, baard_label_adv)
print('Acc_on_adv:', acc)
baard_label_x = detector.detect(X, y)
print('FPR:', np.mean(baard_label_x))
output = {
'X': X,
'adv': adv_clipped,
'y': y,
'baard_label_x': baard_label_x,
'baard_label_adv': baard_label_adv
}
torch.save(output, file_path)
print('Save to:', file_path)
print()
def train_adv(data='mnist',
model_name='basic',
n_samples=2000,
eps=2.,
path_output='results',
path_data='data',
is_test=False,
batch_size=128,
device='cpu'):
# Prepare data
transforms = tv.transforms.Compose([tv.transforms.ToTensor()])
if data == 'mnist':
dataset_test = datasets.MNIST(path_data,
train=False,
download=True,
transform=transforms)
elif data == 'cifar10':
dataset_test = datasets.CIFAR10(path_data,
train=False,
download=True,
transform=transforms)
else:
raise NotImplementedError
loader_test = DataLoader(dataset_test,
batch_size=batch_size,
shuffle=False)
# Load model
if data == 'mnist':
model = BaseModel(use_prob=False).to(device)
n_features = (1, 28, 28)
pretrained = 'mnist_200.pt'
elif data == 'cifar10':
n_features = (3, 32, 32)
if model_name == 'resnet':
model = Resnet(use_prob=False).to(device)
pretrained = 'cifar10_resnet_200.pt'
elif model_name == 'vgg':
model = Vgg(use_prob=False).to(device)
pretrained = 'cifar10_vgg_200.pt'
else:
raise NotImplementedError
else:
raise NotImplementedError
pretrained_path = os.path.join(path_output, pretrained)
model.load_state_dict(torch.load(pretrained_path, map_location=device))
optimizer = optim.SGD(model.parameters(),
lr=0.01,
momentum=0.9,
weight_decay=5e-4)
loss = nn.CrossEntropyLoss()
_, acc_test = validate(model, loader_test, loss, device)
print('Accuracy on test set: {:.4f}%'.format(acc_test * 100))
tensor_test_X, tensor_test_y = get_correct_examples(model,
dataset_test,
device=device,
return_tensor=True)
# Get samples from the tail
if not is_test:
# This is for training the surrogate model
tensor_test_X = tensor_test_X[-n_samples:]
tensor_test_y = tensor_test_y[-n_samples:]
else:
# This is for testing the surrogate model
tensor_test_X = tensor_test_X[-n_samples - 2000:-2000]
tensor_test_y = tensor_test_y[-n_samples - 2000:-2000]
dataset_test = TensorDataset(tensor_test_X, tensor_test_y)
loader_test = DataLoader(dataset_test,
batch_size=batch_size,
shuffle=False)
_, acc_perfect = validate(model, loader_test, loss, device)
print('Accuracy on {} filtered test set: {:.4f}%'.format(
len(dataset_test), acc_perfect * 100))
classifier = PyTorchClassifier(model=model,
loss=loss,
input_shape=n_features,
optimizer=optimizer,
nb_classes=10,
clip_values=(0.0, 1.0),
device_type='gpu')
attack = AutoProjectedGradientDescent(estimator=classifier,
eps=eps,
eps_step=0.1,
max_iter=1000,
batch_size=batch_size,
targeted=False)
X_benign = tensor_test_X.cpu().detach().numpy()
y_true = tensor_test_y.cpu().detach().numpy()
adv = attack.generate(x=X_benign)
pred_adv = np.argmax(classifier.predict(adv), axis=1)
acc_adv = np.mean(pred_adv == y_true)
print("Accuracy on adversarial examples: {:.4f}%".format(acc_adv * 100))
if not is_test:
output_file = '{}_{}_baard_surro_train_eps{}_size{}.pt'.format(
data, model_name, eps, n_samples)
else:
output_file = '{}_{}_baard_surro_test_eps{}_size{}.pt'.format(
data, model_name, eps, n_samples)
file_path = os.path.join(path_output, output_file)
output = {'X': X_benign, 'adv': adv, 'y': y_true}
torch.save(output, file_path)
print('Save to:', file_path)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--data_path', type=str, default='data')
parser.add_argument('--output_path', type=str, default='results')
parser.add_argument('--model',
type=str,
default='resnet18',
choices=['resnet', 'vgg'])
parser.add_argument('--batch_size', type=int, default=128)
parser.add_argument('--epochs', type=int, default=5)
parser.add_argument('--pretrained', type=str, nargs='?')
parser.add_argument('--random_state', type=int, default=1234)
args = parser.parse_args()
set_seeds(args.random_state)
if not os.path.exists(args.data_path):
os.makedirs(args.data_path)
if not os.path.exists(args.output_path):
os.makedirs(args.output_path)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('Device: {}'.format(device))
# Fetch dataset
transform_train = tv.transforms.Compose([
tv.transforms.RandomHorizontalFlip(),
tv.transforms.RandomCrop(32, padding=4),
tv.transforms.ToTensor()
])
transform_test = tv.transforms.Compose([tv.transforms.ToTensor()])
dataset_train = datasets.CIFAR10(args.data_path,
train=True,
download=True,
transform=transform_train)
dataset_test = datasets.CIFAR10(args.data_path,
train=False,
download=True,
transform=transform_test)
dataloader_train = DataLoader(dataset_train,
batch_size=args.batch_size,
shuffle=True)
dataloader_test = DataLoader(dataset_test,
batch_size=args.batch_size,
shuffle=False)
print('Train set: {}, Test set: {}'.format(len(dataset_train),
len(dataset_test)))
# Prepare model
if args.model == 'resnet':
model = Resnet().to(device)
elif args.model == 'vgg':
model = Vgg().to(device)
else:
raise ValueError('Does not support {}'.format(args.model))
optimizer = optim.SGD(model.parameters(),
lr=0.01,
momentum=0.9,
weight_decay=5e-4)
loss = nn.CrossEntropyLoss()
scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer,
T_max=args.epochs)
# Load pre-trained model
if args.pretrained is not None:
pretrained_path = os.path.join(args.output_path, args.pretrained)
model.load_state_dict(torch.load(pretrained_path))
# Train model
since = time.time()
for epoch in range(args.epochs):
start = time.time()
tr_loss, tr_acc = train(model, dataloader_train, loss, optimizer,
device)
va_loss, va_acc = validate(model, dataloader_test, loss, device)
scheduler.step()
time_elapsed = time.time() - start
print(('{:2d}/{:d}[{:s}] Train Loss: {:.4f} Acc: {:.4f}%, ' +
'Test Loss: {:.4f} Acc: {:.4f}%').format(
epoch + 1, args.epochs,
str(datetime.timedelta(seconds=time_elapsed)), tr_loss,
tr_acc * 100., va_loss, va_acc * 100.))
time_elapsed = time.time() - since
print('Total run time: {:.0f}m {:.1f}s'.format(time_elapsed // 60,
time_elapsed % 60))
# Save model
file_name = os.path.join(
args.output_path,
'cifar10_{:s}_{:d}.pt'.format(args.model, args.epochs))
print('Output file name: {}'.format(file_name))
torch.save(model.state_dict(), file_name)
# Test accuracy per class:
print('Training set:')
X, y = dataset2tensor(dataset_train)
X = X.cpu().detach().numpy()
y = y.cpu().detach().numpy()
print_acc_per_label(model, X, y, device)
print('Test set:')
X, y = dataset2tensor(dataset_test)
X = X.cpu().detach().numpy()
y = y.cpu().detach().numpy()
print_acc_per_label(model, X, y, device)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--data', type=str, required=True, choices=DATA_NAMES)
parser.add_argument('--data_path', type=str, default='data')
parser.add_argument('--output_path', type=str, default='results')
parser.add_argument('--pretrained', type=str, required=True)
parser.add_argument('--param', type=str, required=True)
parser.add_argument('--random_state', type=int, default=1234)
args = parser.parse_args()
print(args)
set_seeds(args.random_state)
if not os.path.exists(args.output_path):
print('Output folder does not exist. Create:', args.output_path)
os.mkdir(args.output_path)
with open(args.param) as param_json:
param = json.load(param_json)
param['n_classes'] = DATA[args.data]['n_classes']
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('Dataset:', args.data)
print('Pretrained model:', args.pretrained)
print('Param:', param)
print('Device: {}'.format(device))
transforms = tv.transforms.Compose([tv.transforms.ToTensor()])
# the autoencoder2 need a larger temperature value for the softmax function.
use_prob = False
if args.data == 'mnist':
dataset_train = datasets.MNIST(
args.data_path, train=True, download=True, transform=transforms)
dataset_test = datasets.MNIST(
args.data_path, train=False, download=True, transform=transforms)
model = BaseModel(use_prob=use_prob).to(device)
model_name = 'basic'
elif args.data == 'cifar10':
dataset_train = datasets.CIFAR10(
args.data_path, train=True, download=True, transform=transforms)
dataset_test = datasets.CIFAR10(
args.data_path, train=False, download=True, transform=transforms)
model_name = args.pretrained.split('_')[1]
if model_name == 'resnet':
model = Resnet(use_prob=use_prob).to(device)
elif model_name == 'vgg':
model = Vgg(use_prob=use_prob).to(device)
else:
raise ValueError('model_name must be either resnet or vgg.')
else:
raise ValueError('This autoencoder does not support other datasets.')
tensor_X_train, tensor_y_train = dataset2tensor(dataset_train)
X_train = tensor_X_train.cpu().detach().numpy()
y_train = tensor_y_train.cpu().detach().numpy()
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=5000)
loader_train = DataLoader(
dataset_train, batch_size=512, shuffle=False)
loader_test = DataLoader(dataset_test, batch_size=512, shuffle=False)
loss = nn.CrossEntropyLoss()
pretrained_path = os.path.join(args.output_path, args.pretrained)
model.load_state_dict(torch.load(pretrained_path))
_, acc_train = validate(model, loader_train, loss, device)
_, acc_test = validate(model, loader_test, loss, device)
print('Accuracy on train set: {:.4f}%'.format(acc_train*100))
print('Accuracy on test set: {:.4f}%'.format(acc_test*100))
if args.data == 'mnist':
detector1 = MagNetDetector(
encoder=Autoencoder1(n_channel=DATA[args.data]['n_features'][0]),
classifier=model,
lr=param['lr'],
batch_size=param['batch_size'],
weight_decay=param['weight_decay'],
x_min=0.0,
x_max=1.0,
noise_strength=param['noise_strength'],
algorithm='error',
p=1,
device=device)
detector1.fit(X_train, y_train, epochs=param['epochs'])
detector2 = MagNetDetector(
encoder=Autoencoder2(n_channel=DATA[args.data]['n_features'][0]),
classifier=model,
lr=param['lr'],
batch_size=param['batch_size'],
weight_decay=param['weight_decay'],
x_min=0.0,
x_max=1.0,
noise_strength=param['noise_strength'],
algorithm='error',
p=2,
device=device)
detector2.fit(X_train, y_train, epochs=param['epochs'])
detectors = [detector1, detector2]
elif args.data == 'cifar10':
autoencoder = Autoencoder2(n_channel=DATA[args.data]['n_features'][0])
detectors = []
detector = MagNetDetector(
encoder=autoencoder,
classifier=model,
lr=param['lr'],
batch_size=param['batch_size'],
weight_decay=param['weight_decay'],
x_min=0.0,
x_max=1.0,
noise_strength=param['noise_strength'],
algorithm='error',
p=2,
device=device)
detector.fit(X_train, y_train, epochs=param['epochs'])
detectors.append(detector)
detectors.append(MagNetDetector(
encoder=autoencoder,
classifier=model,
lr=param['lr'],
batch_size=param['batch_size'],
weight_decay=param['weight_decay'],
x_min=0.0,
x_max=1.0,
noise_strength=param['noise_strength'],
algorithm='prob',
temperature=10,
device=device))
detectors.append(MagNetDetector(
encoder=autoencoder,
classifier=model,
lr=param['lr'],
batch_size=param['batch_size'],
weight_decay=param['weight_decay'],
x_min=0.0,
x_max=1.0,
noise_strength=param['noise_strength'],
algorithm='prob',
temperature=40,
device=device))
else:
raise ValueError('Unsupported dataset.')
# Train autoencoders
for ae in detectors:
mse = ae.score(X_val)
print('MSE training set: {:.6f}, validation set: {:.6f}'.format(
ae.history_train_loss[-1] if len(ae.history_train_loss) > 0 else np.inf,
mse))
ae.search_threshold(X_val, fp=param['fp'], update=True)
print('Threshold:', ae.threshold)
# Save autoencoders
for i, ae in enumerate(detectors, start=1):
encoder_path = os.path.join(
args.output_path,
'autoencoder_{}_{}_{}.pt'.format(args.data, model_name, i))
ae.save(encoder_path)
print('File is saved to:', encoder_path)
def run_attack_untargeted(file_model, X, y, att_name, eps, device):
path = file_model.split('/')[0]
file_str = file_model.split('/')[-1]
name_arr = file_str.split('_')
data = name_arr[0]
model_name = name_arr[1]
file_data = os.path.join(
path, '{}_{}_{}_{}.pt'.format(data, model_name, att_name,
round(eps * 1000)))
if os.path.exists(file_data):
print('Found existing file:', file_data)
obj = torch.load(file_data)
return obj['adv'], obj['X'], obj['y']
if data == 'mnist':
n_features = (1, 28, 28)
n_classes = 10
model = BaseModel(use_prob=False).to(device)
elif data == 'cifar10':
n_features = (3, 32, 32)
n_classes = 10
if model_name == 'resnet':
model = Resnet(use_prob=False).to(device)
elif model_name == 'vgg':
model = Vgg(use_prob=False).to(device)
else:
raise NotImplementedError
else:
raise NotImplementedError
model.load_state_dict(torch.load(file_model, map_location=device))
loss = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(),
lr=0.01,
momentum=0.9,
weight_decay=5e-4)
classifier = PyTorchClassifier(model=model,
loss=loss,
input_shape=n_features,
optimizer=optimizer,
nb_classes=n_classes,
clip_values=(0.0, 1.0),
device_type='gpu')
if att_name == 'apgd':
eps_step = eps / 4. if eps <= 0.2 else 0.1
attack = AutoProjectedGradientDescent(estimator=classifier,
eps=eps,
eps_step=eps_step,
max_iter=1000,
batch_size=BATCH_SIZE,
targeted=False)
elif att_name == 'apgd2':
attack = AutoProjectedGradientDescent(estimator=classifier,
norm=2,
eps=eps,
eps_step=0.1,
max_iter=1000,
batch_size=BATCH_SIZE,
targeted=False)
elif att_name == 'cw2':
# Do not increase the batch_size
attack = CarliniWagnerAttackL2(model=model,
n_classes=n_classes,
confidence=eps,
verbose=True,
check_prob=False,
batch_size=32,
targeted=False)
elif att_name == 'deepfool':
# Do not adjust Epsilon
attack = DeepFool(classifier=classifier, batch_size=BATCH_SIZE)
elif att_name == 'fgsm':
attack = FastGradientMethod(estimator=classifier,
eps=eps,
batch_size=BATCH_SIZE)
elif att_name == 'line':
attack = LineAttack(color=1, thickness=2)
else:
raise NotImplementedError
time_start = time.time()
adv = attack.generate(x=X)
time_elapsed = time.time() - time_start
print('Total run time:', str(datetime.timedelta(seconds=time_elapsed)))
obj = {'X': X, 'y': y, 'adv': adv}
torch.save(obj, file_data)
print('Save data to:', file_data)
return adv, X, y
def main():
with open('data.json') as data_json:
data_params = json.load(data_json)
parser = argparse.ArgumentParser()
parser.add_argument('--data', type=str, required=True)
parser.add_argument('--data_path', type=str, default='data')
parser.add_argument('--output_path', type=str, default='results')
parser.add_argument('--pretrained', type=str, required=True)
parser.add_argument('--adv', type=str, required=True, help="Example: 'mnist_basic_apgd_0.3'")
parser.add_argument('--defence', type=str, required=True, choices=data_params['defences'])
parser.add_argument('--param', type=str, required=True)
parser.add_argument('--suffix', type=str)
parser.add_argument('--random_state', type=int, default=1234)
parser.add_argument('--save', type=int, default=1, choices=[0, 1])
args = parser.parse_args()
print(args)
set_seeds(args.random_state)
if not os.path.exists(args.output_path):
print('Output folder does not exist. Create:', args.output_path)
os.mkdir(args.output_path)
print('Dataset:', args.data)
print('Pretrained model:', args.pretrained)
print('Pretrained samples:', args.adv + '_adv.npy')
print('Defence:', args.defence)
with open(args.param) as param_json:
param = json.load(param_json)
param['n_classes'] = data_params['data'][args.data]['n_classes']
print('Param:', param)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('Device: {}'.format(device))
# Prepare data
transforms = tv.transforms.Compose([tv.transforms.ToTensor()])
if args.data == 'mnist':
dataset_train = datasets.MNIST(args.data_path, train=True, download=True, transform=transforms)
dataset_test = datasets.MNIST(args.data_path, train=False, download=True, transform=transforms)
elif args.data == 'cifar10':
dataset_train = datasets.CIFAR10(args.data_path, train=True, download=True, transform=transforms)
dataset_test = datasets.CIFAR10(args.data_path, train=False, download=True, transform=transforms)
else:
data_path = os.path.join(args.data_path, data_params['data'][args.data]['file_name'])
print('Read file:', data_path)
X, y = load_csv(data_path)
X_train, X_test, y_train, y_test = train_test_split(
X, y,
test_size=data_params['data'][args.data]['n_test'],
random_state=args.random_state)
scaler = MinMaxScaler().fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
dataset_train = TensorDataset(torch.from_numpy(X_train).type(torch.float32), torch.from_numpy(y_train).type(torch.long))
dataset_test = TensorDataset(torch.from_numpy(X_test).type(torch.float32), torch.from_numpy(y_test).type(torch.long))
loader_train = DataLoader(dataset_train, batch_size=512, shuffle=False)
loader_test = DataLoader(dataset_test, batch_size=512, shuffle=False)
shape_train = get_shape(loader_train.dataset)
shape_test = get_shape(loader_test.dataset)
print('Train set:', shape_train)
print('Test set:', shape_test)
use_prob = True
print('Using softmax layer:', use_prob)
# Load model
if args.data == 'mnist':
model = BaseModel(use_prob=use_prob).to(device)
model_name = 'basic'
elif args.data == 'cifar10':
model_name = args.pretrained.split('_')[1]
if model_name == 'resnet':
model = Resnet(use_prob=use_prob).to(device)
elif model_name == 'vgg':
model = Vgg(use_prob=use_prob).to(device)
else:
raise ValueError('Unknown model: {}'.format(model_name))
else:
n_features = data_params['data'][args.data]['n_features']
n_classes = data_params['data'][args.data]['n_classes']
model = NumericModel(n_features, n_hidden=n_features * 4, n_classes=n_classes, use_prob=use_prob).to(device)
model_name = 'basic' + str(n_features * 4)
loss = nn.CrossEntropyLoss()
pretrained_path = os.path.join(args.output_path, args.pretrained)
model.load_state_dict(torch.load(pretrained_path, map_location=device))
_, acc_train = validate(model, loader_train, loss, device)
_, acc_test = validate(model, loader_test, loss, device)
print('Accuracy on train set: {:.4f}%'.format(acc_train * 100))
print('Accuracy on test set: {:.4f}%'.format(acc_test * 100))
# Create a subset which only contains recognisable samples.
# The original train and test sets are no longer needed.
tensor_train_X, tensor_train_y = get_correct_examples(model, dataset_train, device=device, return_tensor=True)
dataset_train = TensorDataset(tensor_train_X, tensor_train_y)
loader_train = DataLoader(dataset_train, batch_size=512, shuffle=True)
_, acc_perfect = validate(model, loader_train, loss, device)
print('Accuracy on {} filtered train set: {:.4f}%'.format(len(dataset_train), acc_perfect * 100))
tensor_test_X, tensor_test_y = get_correct_examples(model, dataset_test, device=device, return_tensor=True)
dataset_test = TensorDataset(tensor_test_X, tensor_test_y)
loader_test = DataLoader(dataset_test, batch_size=512, shuffle=False)
_, acc_perfect = validate(model, loader_test, loss, device)
print('Accuracy on {} filtered test set: {:.4f}%'.format(len(dataset_test), acc_perfect * 100))
# Load pre-trained adversarial examples
path_benign = os.path.join(args.output_path, args.adv + '_x.npy')
path_adv = os.path.join(args.output_path, args.adv + '_adv.npy')
path_y = os.path.join(args.output_path, args.adv + '_y.npy')
X_benign = np.load(path_benign)
adv = np.load(path_adv)
y_true = np.load(path_y)
dataset = TensorDataset(torch.from_numpy(X_benign), torch.from_numpy(y_true))
loader = DataLoader(dataset, batch_size=512, shuffle=False)
_, acc = validate(model, loader, loss, device)
print('Accuracy on {} benign samples: {:.4f}%'.format(len(dataset), acc * 100))
dataset = TensorDataset(torch.from_numpy(adv), torch.from_numpy(y_true))
loader = DataLoader(dataset, batch_size=512, shuffle=False)
_, acc = validate(model, loader, loss, device)
print('Accuracy on {} adversarial examples: {:.4f}%'.format(len(dataset), acc * 100))
# Do NOT shuffle the indices, so different defences can use the same test set.
dataset = TensorDataset(torch.from_numpy(adv))
loader = DataLoader(dataset, batch_size=512, shuffle=False)
pred_adv = predict(model, loader, device).cpu().detach().numpy()
# Find the thresholds using the 2nd half
n = len(X_benign) // 2
# Merge benign samples and adversarial examples into one set.
# This labels indicate a sample is an adversarial example or not.
X_val, labels_val = merge_and_generate_labels(adv[n:], X_benign[n:], flatten=False)
# The predictions for benign samples are exactly same as the true labels.
pred_val = np.concatenate((pred_adv[n:], y_true[n:]))
X_train = tensor_train_X.cpu().detach().numpy()
y_train = tensor_train_y.cpu().detach().numpy()
# Train defence
time_start = time.time()
if args.defence == 'baard':
sequence = param['sequence']
stages = []
if sequence[0]:
stages.append(ApplicabilityStage(n_classes=param['n_classes'], quantile=param['q1']))
if sequence[1]:
stages.append(ReliabilityStage(n_classes=param['n_classes'], k=param['k_re'], quantile=param['q2']))
if sequence[2]:
stages.append(DecidabilityStage(n_classes=param['n_classes'], k=param['k_de'], quantile=param['q3']))
print('BAARD: # of stages:', len(stages))
detector = BAARDOperator(stages=stages)
# Run preprocessing
baard_train_path = os.path.join(args.output_path, '{}_{}_baard_train.pt'.format(args.data, model_name))
obj = torch.load(baard_train_path)
X_baard = obj['X_train']
y_train = obj['y_train']
# Fit the model with the filtered the train set.
detector.stages[0].fit(X_baard, y_train)
detector.stages[1].fit(X_train, y_train)
if len(detector.stages) == 3:
detector.stages[2].fit(X_train, y_train)
detector.search_thresholds(X_val, pred_val, labels_val)
path_baard = os.path.join(args.output_path, 'baard_{}_{}_param.pt'.format(args.data, model_name))
detector.save(path_baard)
elif args.defence == 'fs':
squeezers = []
if args.data == 'mnist':
squeezers.append(DepthSqueezer(x_min=0.0, x_max=1.0, bit_depth=1))
squeezers.append(MedianSqueezer(x_min=0.0, x_max=1.0, kernel_size=2))
elif args.data == 'cifar10':
squeezers.append(DepthSqueezer(x_min=0.0, x_max=1.0, bit_depth=4))
squeezers.append(MedianSqueezer(x_min=0.0, x_max=1.0, kernel_size=2))
squeezers.append(NLMeansColourSqueezer(x_min=0.0, x_max=1.0, h=2, templateWindowsSize=3, searchWindowSize=13))
else:
raise NotImplementedError
print('FS: # of squeezers:', len(squeezers))
detector = FeatureSqueezingTorch(
classifier=model,
lr=0.001,
momentum=0.9,
weight_decay=5e-4,
loss=loss,
batch_size=128,
x_min=0.0,
x_max=1.0,
squeezers=squeezers,
n_classes=param['n_classes'],
device=device)
path_fs = os.path.join(args.output_path, '{}_fs.pt'.format(args.pretrained.split('.')[0]))
detector.load(path_fs)
detector.search_thresholds(X_val, pred_val, labels_val)
elif args.defence == 'lid':
# This batch_size is not same as the mini batch size for the neural network.
before_softmax = args.data == 'cifar10'
detector = LidDetector(
model,
k=param['k'],
batch_size=param['batch_size'],
x_min=0.0,
x_max=1.0,
device=device,
before_softmax=before_softmax)
# LID uses different training set
X_train, y_train = detector.get_train_set(X_benign[n:], adv[n:], std_dominator=param['std_dominator'])
detector.fit(X_train, y_train, verbose=1)
elif args.defence == 'magnet':
magnet_detectors = []
# Different datasets require different autoencoders.
if args.data == 'mnist':
# autoencoder1 and autoencoder2
magnet_detectors.append(MagNetDetector(
encoder=Autoencoder1(n_channel=1),
classifier=model,
lr=param['lr'],
batch_size=param['batch_size'],
weight_decay=param['weight_decay'],
x_min=0.0,
x_max=1.0,
noise_strength=param['noise_strength'],
algorithm='error',
p=1,
device=device))
magnet_detectors.append(MagNetDetector(
encoder=Autoencoder2(n_channel=1),
classifier=model,
lr=param['lr'],
batch_size=param['batch_size'],
weight_decay=param['weight_decay'],
x_min=0.0,
x_max=1.0,
noise_strength=param['noise_strength'],
algorithm='error',
p=2,
device=device))
elif args.data == 'cifar10':
autoencoder = Autoencoder2(
n_channel=data_params['data'][args.data]['n_features'][0])
# There are 3 autoencoder based detectors, but they use the same architecture.
magnet_detectors.append(MagNetDetector(
encoder=autoencoder,
classifier=model,
lr=param['lr'],
batch_size=param['batch_size'],
weight_decay=param['weight_decay'],
x_min=0.0,
x_max=1.0,
noise_strength=param['noise_strength'],
algorithm='error',
p=2,
device=device))
magnet_detectors.append(MagNetDetector(
encoder=autoencoder,
classifier=model,
lr=param['lr'],
batch_size=param['batch_size'],
weight_decay=param['weight_decay'],
x_min=0.0,
x_max=1.0,
noise_strength=param['noise_strength'],
algorithm='prob',
temperature=10,
device=device))
magnet_detectors.append(MagNetDetector(
encoder=autoencoder,
classifier=model,
lr=param['lr'],
batch_size=param['batch_size'],
weight_decay=param['weight_decay'],
x_min=0.0,
x_max=1.0,
noise_strength=param['noise_strength'],
algorithm='prob',
temperature=40,
device=device))
else:
raise ValueError('Magnet requires autoencoder.')
for i, ae in enumerate(magnet_detectors, start=1):
ae_path = os.path.join(args.output_path, 'autoencoder_{}_{}_{}.pt'.format(args.data, model_name, i))
ae.load(ae_path)
tensor_X_test, _ = dataset2tensor(dataset_test)
X_test = tensor_X_test.cpu().detach().numpy()
print('Autoencoder {} MSE training set: {:.6f}, test set: {:.6f}'.format(i, ae.score(X_train), ae.score(X_test)))
print('Autoencoder {} threshold: {}'.format(i, ae.threshold))
reformer = MagNetAutoencoderReformer(
encoder=magnet_detectors[0].encoder,
batch_size=param['batch_size'],
device=device)
detector = MagNetOperator(
classifier=model,
detectors=magnet_detectors,
reformer=reformer,
batch_size=param['batch_size'],
device=device)
elif args.defence == 'rc':
detector = RegionBasedClassifier(
model=model,
r=param['r'],
sample_size=param['sample_size'],
n_classes=param['n_classes'],
x_min=0.0,
x_max=1.0,
batch_size=param['batch_size'],
r0=param['r0'],
step_size=param['step_size'],
stop_value=param['stop_value'],
device=device)
# Region-based classifier only uses benign samples to search threshold.
# The r value is already set to the optimal. We don't need to search it.
# detector.search_thresholds(X_val, pred_val, labels_val, verbose=0)
else:
raise ValueError('{} is not supported!'.format(args.defence))
time_elapsed = time.time() - time_start
print('Total training time:', str(datetime.timedelta(seconds=time_elapsed)))
# Test defence
time_start = time.time()
X_test, labels_test = merge_and_generate_labels(adv[:n], X_benign[:n], flatten=False)
pred_test = np.concatenate((pred_adv[:n], y_true[:n]))
y_test = np.concatenate((y_true[:n], y_true[:n]))
# Only MegNet uses reformer.
X_reformed = None
if args.defence == 'magnet':
X_reformed, res_test = detector.detect(X_test, pred_test)
y_pred = predict_numpy(model, X_reformed, device)
elif args.defence == 'rc':
y_pred = detector.detect(X_test, pred_test)
res_test = np.zeros_like(y_pred)
else:
res_test = detector.detect(X_test, pred_test)
y_pred = pred_test
acc = acc_on_adv(y_pred[:n], y_test[:n], res_test[:n])
if args.defence == 'rc':
fpr = np.mean(y_pred[n:] != y_test[n:])
else:
fpr = np.mean(res_test[n:])
print('Acc_on_adv:', acc)
print('FPR:', fpr)
time_elapsed = time.time() - time_start
print('Total test time:', str(datetime.timedelta(seconds=time_elapsed)))
# Save results
suffix = '_' + args.suffix if args.suffix is not None else ''
if args.save:
path_result = os.path.join(args.output_path, '{}_{}{}.pt'.format(args.adv, args.defence, suffix))
torch.save({
'X_val': X_val,
'y_val': np.concatenate((y_true[n:], y_true[n:])),
'labels_val': labels_val,
'X_test': X_test,
'y_test': y_test,
'labels_test': labels_test,
'res_test': y_pred if args.defence == 'rc' else res_test,
'X_reformed': X_reformed,
'param': param}, path_result)
print('Saved to:', path_result)
else:
print('No file is save!')
print()
def main():
with open('data.json') as data_json:
data_params = json.load(data_json)
parser = argparse.ArgumentParser()
parser.add_argument('--data', type=str, required=True)
parser.add_argument('--data_path', type=str, default='data')
parser.add_argument('--output_path', type=str, default='results')
parser.add_argument('--pretrained', type=str, required=True)
parser.add_argument('--adv',
type=str,
required=True,
help="Example: 'mnist_basic_apgd_0.3'")
parser.add_argument('--random_state', type=int, default=1234)
args = parser.parse_args()
print(args)
set_seeds(args.random_state)
print('Dataset:', args.data)
print('Pretrained model:', args.pretrained)
print('Pretrained samples:', args.adv + '_adv.npy')
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('Device: {}'.format(device))
# Prepare data
transforms = tv.transforms.Compose([tv.transforms.ToTensor()])
if args.data == 'mnist':
dataset_train = datasets.MNIST(args.data_path,
train=True,
download=True,
transform=transforms)
dataset_test = datasets.MNIST(args.data_path,
train=False,
download=True,
transform=transforms)
elif args.data == 'cifar10':
dataset_train = datasets.CIFAR10(args.data_path,
train=True,
download=True,
transform=transforms)
dataset_test = datasets.CIFAR10(args.data_path,
train=False,
download=True,
transform=transforms)
else:
data_path = os.path.join(args.data_path,
data_params['data'][args.data]['file_name'])
print('Read file:', data_path)
X, y = load_csv(data_path)
X_train, X_test, y_train, y_test = train_test_split(
X,
y,
test_size=data_params['data'][args.data]['n_test'],
random_state=args.random_state)
scaler = MinMaxScaler().fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
dataset_train = TensorDataset(
torch.from_numpy(X_train).type(torch.float32),
torch.from_numpy(y_train).type(torch.long))
dataset_test = TensorDataset(
torch.from_numpy(X_test).type(torch.float32),
torch.from_numpy(y_test).type(torch.long))
# Note: Train set alway shuffle!
loader_train = DataLoader(dataset_train, batch_size=512, shuffle=True)
loader_test = DataLoader(dataset_test, batch_size=512, shuffle=False)
shape_train = get_shape(loader_train.dataset)
shape_test = get_shape(loader_test.dataset)
print('Train set:', shape_train)
print('Test set:', shape_test)
use_prob = True
print('Using softmax layer:', use_prob)
n_classes = data_params['data'][args.data]['n_classes']
# Load model
if args.data == 'mnist':
model = BaseModel(use_prob=use_prob).to(device)
model_name = 'basic'
elif args.data == 'cifar10':
model_name = args.pretrained.split('_')[1]
if model_name == 'resnet':
model = Resnet(use_prob=use_prob).to(device)
elif model_name == 'vgg':
model = Vgg(use_prob=use_prob).to(device)
else:
raise ValueError('Unknown model: {}'.format(model_name))
else:
n_features = data_params['data'][args.data]['n_features']
model = NumericModel(n_features,
n_hidden=n_features * 4,
n_classes=n_classes,
use_prob=use_prob).to(device)
model_name = 'basic' + str(n_features * 4)
loss = nn.CrossEntropyLoss()
pretrained_path = os.path.join(args.output_path, args.pretrained)
model.load_state_dict(torch.load(pretrained_path))
_, acc_train = validate(model, loader_train, loss, device)
_, acc_test = validate(model, loader_test, loss, device)
print('Accuracy on train set: {:.4f}%'.format(acc_train * 100))
print('Accuracy on test set: {:.4f}%'.format(acc_test * 100))
# Create a subset which only contains recognisable samples.
# The original train and test sets are no longer needed.
tensor_train_X, tensor_train_y = get_correct_examples(model,
dataset_train,
device=device,
return_tensor=True)
dataset_train = TensorDataset(tensor_train_X, tensor_train_y)
loader_train = DataLoader(dataset_train, batch_size=512, shuffle=True)
_, acc_perfect = validate(model, loader_train, loss, device)
print('Accuracy on {} filtered train set: {:.4f}%'.format(
len(dataset_train), acc_perfect * 100))
tensor_test_X, tensor_test_y = get_correct_examples(model,
dataset_test,
device=device,
return_tensor=True)
dataset_test = TensorDataset(tensor_test_X, tensor_test_y)
loader_test = DataLoader(dataset_test, batch_size=512, shuffle=False)
_, acc_perfect = validate(model, loader_test, loss, device)
print('Accuracy on {} filtered test set: {:.4f}%'.format(
len(dataset_test), acc_perfect * 100))
# Load pre-trained adversarial examples
path_benign = os.path.join(args.output_path, args.adv + '_x.npy')
path_adv = os.path.join(args.output_path, args.adv + '_adv.npy')
path_y = os.path.join(args.output_path, args.adv + '_y.npy')
X_benign = np.load(path_benign)
adv = np.load(path_adv)
y_true = np.load(path_y)
dataset = TensorDataset(torch.from_numpy(X_benign),
torch.from_numpy(y_true))
loader = DataLoader(dataset, batch_size=512, shuffle=False)
_, acc = validate(model, loader, loss, device)
print('Accuracy on {} benign samples: {:.4f}%'.format(
len(dataset), acc * 100))
dataset = TensorDataset(torch.from_numpy(adv), torch.from_numpy(y_true))
loader = DataLoader(dataset, batch_size=512, shuffle=False)
_, acc = validate(model, loader, loss, device)
print('Accuracy on {} adversarial examples: {:.4f}%'.format(
len(dataset), acc * 100))
# Do NOT shuffle the indices, so different defences can use the same test set.
dataset = TensorDataset(torch.from_numpy(adv))
loader = DataLoader(dataset, batch_size=512, shuffle=False)
pred_adv = predict(model, loader, device).cpu().detach().numpy()
# Find the thresholds using the 2nd half
n = len(X_benign) // 2
# Merge benign samples and adversarial examples into one set.
# This labels indicate a sample is an adversarial example or not.
X_val, labels_val = merge_and_generate_labels(adv[n:],
X_benign[n:],
flatten=False)
# The predictions for benign samples are exactly same as the true labels.
pred_val = np.concatenate((pred_adv[n:], y_true[n:]))
X_train = tensor_train_X.cpu().detach().numpy()
y_train = tensor_train_y.cpu().detach().numpy()
# Train defence
time_start = time.time()
detector = RegionBasedClassifier(model=model,
r=0.2,
sample_size=1000,
n_classes=n_classes,
x_min=0.0,
x_max=1.0,
batch_size=512,
r0=0.0,
step_size=0.02,
stop_value=0.4,
device=device)
r_best = detector.search_thresholds(X_val, pred_val, labels_val, verbose=0)
time_elapsed = time.time() - time_start
print('Total training time:',
str(datetime.timedelta(seconds=time_elapsed)))
param = {
"r": r_best,
"sample_size": 1000,
"batch_size": 512,
"r0": 0,
"step_size": 0.02,
"stop_value": 0.40
}
path_json = os.path.join(
'params', 'rc_param_{}_{}.json'.format(args.data, args.model))
with open(path_json, 'w') as f:
json.dump(param, f)
print('Save to:', path_json)
print()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--data', type=str, required=True, choices=['mnist', 'cifar10'])
parser.add_argument('--data_path', type=str, default='data')
parser.add_argument('--output_path', type=str, default='results')
parser.add_argument('--pretrained', type=str, required=True)
parser.add_argument('--param', type=str, required=True)
parser.add_argument('--random_state', type=int, default=1234)
args = parser.parse_args()
print(args)
set_seeds(args.random_state)
if not os.path.exists(args.output_path):
print('Output folder does not exist. Create:', args.output_path)
os.mkdir(args.output_path)
print('Dataset:', args.data)
print('Pretrained model:', args.pretrained)
with open(args.param) as param_json:
param = json.load(param_json)
param['n_classes'] = 10
print('Param:', param)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('Device: {}'.format(device))
# Prepare data
transforms = tv.transforms.Compose([tv.transforms.ToTensor()])
if args.data == 'mnist':
dataset_train = datasets.MNIST(args.data_path, train=True, download=True, transform=transforms)
dataset_test = datasets.MNIST(args.data_path, train=False, download=True, transform=transforms)
elif args.data == 'cifar10':
dataset_train = datasets.CIFAR10(args.data_path, train=True, download=True, transform=transforms)
dataset_test = datasets.CIFAR10(args.data_path, train=False, download=True, transform=transforms)
else:
raise ValueError('{} is not supported.'.format(args.data))
# Note: Train set alway shuffle!
loader_train = DataLoader(dataset_train, batch_size=512, shuffle=True)
loader_test = DataLoader(dataset_test, batch_size=512, shuffle=False)
shape_train = get_shape(loader_train.dataset)
shape_test = get_shape(loader_test.dataset)
print('Train set:', shape_train)
print('Test set:', shape_test)
use_prob = True
print('Using softmax layer:', use_prob)
# Load model
if args.data == 'mnist':
model = BaseModel(use_prob=use_prob).to(device)
model_name = 'basic'
else: # args.data == 'cifar10':
model_name = args.pretrained.split('_')[1]
if model_name == 'resnet':
model = Resnet(use_prob=use_prob).to(device)
elif model_name == 'vgg':
model = Vgg(use_prob=use_prob).to(device)
else:
raise ValueError('Unknown model: {}'.format(model_name))
loss = nn.CrossEntropyLoss()
pretrained_path = os.path.join(args.output_path, args.pretrained)
model.load_state_dict(torch.load(pretrained_path))
_, acc_train = validate(model, loader_train, loss, device)
_, acc_test = validate(model, loader_test, loss, device)
print('Accuracy on train set: {:.4f}%'.format(acc_train * 100))
print('Accuracy on test set: {:.4f}%'.format(acc_test * 100))
tensor_train_X, tensor_train_y = dataset2tensor(dataset_train)
X_train = tensor_train_X.cpu().detach().numpy()
y_train = tensor_train_y.cpu().detach().numpy()
# Train defence
squeezers = []
if args.data == 'mnist':
squeezers.append(DepthSqueezer(x_min=0.0, x_max=1.0, bit_depth=1))
squeezers.append(MedianSqueezer(x_min=0.0, x_max=1.0, kernel_size=2))
else:
# CIFAR10
squeezers.append(DepthSqueezer(x_min=0.0, x_max=1.0, bit_depth=4))
squeezers.append(MedianSqueezer(x_min=0.0, x_max=1.0, kernel_size=2))
squeezers.append(NLMeansColourSqueezer(x_min=0.0, x_max=1.0, h=2, templateWindowsSize=3, searchWindowSize=13))
print('FS: # of squeezers:', len(squeezers))
detector = FeatureSqueezingTorch(
classifier=model,
lr=0.001,
momentum=0.9,
weight_decay=5e-4,
loss=loss,
batch_size=128,
x_min=0.0,
x_max=1.0,
squeezers=squeezers,
n_classes=param['n_classes'],
device=device)
detector.fit(X_train, y_train, epochs=param['epochs'], verbose=1)
path_fs = os.path.join(args.output_path, '{}_fs.pt'.format(args.pretrained.split('.')[0]))
detector.save(path_fs)
print('Saved fs to:', path_fs)
print()
def main(seed, dataset_name, clf_name, detector_name, epsilon_lst, input_shape,
json_param, path):
set_seeds(SEEDS[seed])
device = device = torch.device(
'cuda' if torch.cuda.is_available() else 'cpu')
print('device:', device)
# Load classifier
print("load the classifier")
file_model = os.path.join(
'result_{:}'.format(seed),
'{:}_{:}_model.pt'.format(dataset_name, clf_name))
if clf_name == 'dnn':
model = BaseModel(use_prob=False).to(device)
elif clf_name == 'resnet':
model = Resnet(use_prob=False).to(device)
else:
raise ValueError("model idx unknown")
model.load_state_dict(torch.load(file_model, map_location=device))
file_data = os.path.join(
'result_{:}'.format(seed),
'{:}_{:}_apgd2_2000.pt'.format(dataset_name, clf_name))
obj = torch.load(file_data)
X = obj['X']
y = obj['y']
adv = obj['adv']
print("undefended model acc")
pred = predict_numpy(model, X, device)
print('Acc on clean:', np.mean(pred == y))
# Split data
X_att_test = X[2000:3000].astype(np.float32)
y_att_test = y[2000:3000].astype(np.float32)
print("x attr shape ", X_att_test.shape)
#################################################################
print("Load Magnet")
with open(json_param) as j:
param = json.load(j)
print("before load magnet")
model_with_reformer_nn_module, detector_nn_module, full_magnet_orig = \
loadmagnet(dataset_name, clf_name,param, device,path, model)
print("Magnet loaded")
loss = torch.nn.CrossEntropyLoss()
# this one return the logits
art_classifier = PyTorchClassifier(model=model_with_reformer_nn_module,
loss=loss,
input_shape=input_shape,
nb_classes=10,
optimizer=None)
# y_pred = model_with_reformer_nn_module(X)
# print("model_with_reformer_nn_module", y_pred.shape)
y_pred = art_classifier.predict(X)
print("art_classifier", y_pred.shape)
print("check full magnet ")
_, y_pred = full_magnet_orig.detect(X)
print("full magnet", y_pred.shape)
print("check detector nn module")
# correcly return an array with the logits
y_pred = detector_nn_module(X)
print("y pred ", y_pred)
print("detector_nn_module", y_pred.shape)
print("create pytorch detector")
# must be only the detector
art_detector = PyTorchClassifier(model=detector_nn_module,
loss=loss,
input_shape=input_shape,
nb_classes=2,
optimizer=None)
print("check art detector")
y_pred = art_detector.predict(X + 1000)
print("detector_nn_module", y_pred.shape)
print("art detector ok")
print("y pred ", y_pred)
print("detected by detector used by attack ",
np.mean(y_pred.argmax(axis=1) == 1))
clip_fun = None
#################################################################
pred_folder = 'result_{:}/predictions_wb_eval/{:}_{:}_{:}'.format(
seed, dataset_name, clf_name, detector_name)
print("compute prediction for samples at epsilon 0")
x = X_att_test[:10]
y = y_att_test[:10]
# compute and save predictions
cmpt_and_save_predictions(art_classifier, full_magnet_orig, art_detector,
device, x, y, pred_folder, 0)
for eps in epsilon_lst:
print("epsilon ", eps)
print("detector threshold ", detector_nn_module.detector.threshold)
attack = AutoProjectedGradientDescentDetectorsMagnet(
estimator=art_classifier,
detector=art_detector,
detector_th=0,
detector_clip_fun=clip_fun,
loss_type='logits_difference',
batch_size=128,
norm=2,
eps=eps,
eps_step=0.9,
beta=1.0,
max_iter=100)
adv_x = attack.generate(x=x, y=None)
# compute and save predictions
cmpt_and_save_predictions(art_classifier, full_magnet_orig,
art_detector, device, adv_x, y, pred_folder,
eps)
def main(seed, dataset_name, clf_name, detector_name, epsilon_lst,input_shape):
set_seeds(SEEDS[seed])
device = device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('device:', device)
# Load classifier
print("load the classifier")
file_model = os.path.join('result_{:}'.format(seed),
'{:}_{:}_model.pt'.format(dataset_name,
clf_name))
if clf_name == 'dnn':
model = BaseModel(use_prob=False).to(device)
elif clf_name == 'resnet':
model = Resnet(use_prob=False).to(device)
else:
raise ValueError("model idx unknown")
model.load_state_dict(torch.load(file_model, map_location=device))
file_data = os.path.join('result_{:}'.format(seed),
'{:}_{:}_apgd2_2000.pt'.format(dataset_name,
clf_name))
obj = torch.load(file_data)
X = obj['X']
y = obj['y']
adv = obj['adv']
pred = predict_numpy(model, X, device)
print('Acc on clean:', np.mean(pred == y))
pred = predict_numpy(model, adv, device)
print('Acc on adv (epsilon 2):', np.mean(pred == y))
# Split data
X_att_test = X[2000:3000]
y_att_test = y[2000:3000]
print("x attr shape ", X_att_test.shape)
#########################################################################
# Load baard
print("Load baard")
file_baard_train = os.path.join(
'result_{:}'.format(seed), '{:}_{:}_baard_s1_train_data.pt'.format(
dataset_name,
clf_name))
obj = torch.load(file_baard_train)
X_baard_train_s1 = obj['X_s1']
X_baard_train = obj['X']
y_baard_train = obj['y']
stages = []
stages.append(ApplicabilityStage(n_classes=10, quantile=1., verbose=False))
stages.append(ReliabilityStage(n_classes=10, k=10, quantile=1., verbose=False))
stages.append(DecidabilityStage(n_classes=10, k=100, quantile=1., verbose=False))
detector = BAARDOperator(stages=stages)
detector.stages[0].fit(X_baard_train_s1, y_baard_train)
for stage in detector.stages[1:]:
stage.fit(X_baard_train, y_baard_train)
print("load baard's thresholds")
file_baard_threshold = os.path.join(
'result_{:}'.format(seed), '{:}_{:}_baard_threshold.pt'.format(
dataset_name,
clf_name))
thresholds = torch.load(file_baard_threshold)['thresholds']
detector.load(file_baard_threshold)
print("load the surrogate")
file_surro = os.path.join('result_{:}'.format(seed),
'{:}_{:}_baard_surrogate.pt'.format(
dataset_name,
clf_name))
surrogate = get_pretrained_surrogate(file_surro, device)
loss = torch.nn.CrossEntropyLoss()
optimizer_clf = torch.optim.SGD(
model.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)
art_classifier = PyTorchClassifier(
model=model,
loss=loss,
input_shape=input_shape,
nb_classes=10,
optimizer=optimizer_clf
)
optimizer_sur = torch.optim.SGD(
surrogate.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)
art_detector = PyTorchClassifier(
model=surrogate,
loss=loss,
input_shape=input_shape,
nb_classes=2,
optimizer=optimizer_sur
)
clip_fun = BAARD_Clipper(detector)
#########################################################################
pred_folder = 'result_{:}/predictions_wb_eval/{:}_{:}_{:}'.format(seed,
dataset_name,
clf_name, detector_name)
print("compute prediction for samples at epsilon 0")
x = X_att_test[:10]
y = y_att_test[:10]
# compute and save predictions
cmpt_and_save_predictions(model, art_detector, detector, device, x, y,
pred_folder, 0)
for eps in epsilon_lst:
print("epsilon ", eps)
if dataset_name == 'mnist':
loss_multiplier = 1. / 36.
else:
loss_multiplier = 0.1
attack = AutoProjectedGradientDescentDetectors(
estimator=art_classifier,
detector=art_detector,
detector_th=0,
detector_clip_fun=clip_fun,
loss_type='logits_difference',
batch_size=128,
norm=2,
eps=eps,
eps_step=0.9,
beta=0.5,
max_iter=100)
adv_x = attack.generate(x=x, y=None)
# compute and save predictions
cmpt_and_save_predictions(model, art_detector, detector, device, adv_x,
y, pred_folder, eps)