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('--batch_size', type=int, default=128)
parser.add_argument('--epochs', type=int, default=5)
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)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('Device: {}'.format(device))
# Prepare data
data_path = os.path.join(args.data_path,
data_params['data'][args.data]['file_name'])
print('Read file: {}'.format(data_path))
X, y = load_csv(data_path)
# Normalize data
scaler = MinMaxScaler().fit(X)
X = scaler.transform(X)
n_test = data_params['data'][args.data]['n_test']
random_state = args.random_state
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=n_test, random_state=random_state)
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, args.batch_size, shuffle=True)
dataloader_test = DataLoader(dataset_test, args.batch_size, shuffle=False)
print('Train set: {}, Test set: {}'.format(X_train.shape, X_test.shape))
# Prepare model
n_features = data_params['data'][args.data]['n_features']
n_classes = data_params['data'][args.data]['n_classes']
print('n_features: {}, n_classes: {}'.format(n_features, n_classes))
model = NumericModel(n_features,
n_hidden=n_features * 4,
n_classes=n_classes).to(device)
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,
eta_min=1e-7)
# 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
if epoch % 10 == 0:
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,
'{}_{}.pt'.format(args.data, 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_path', type=str, default='data')
parser.add_argument('--output_path', type=str, default='results')
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()
print(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
transforms = tv.transforms.Compose([tv.transforms.ToTensor()])
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)
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
model = BaseModel().to(device)
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, map_location=device))
# 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, 'mnist_{}.pt'.format(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():
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():
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 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()