def main():
master_seed(SEED)
logger.info('Starting %s data container...', NAME)
dc = DataContainer(DATASET_LIST[NAME], get_data_path())
dc(shuffle=True, normalize=True)
num_features = dc.dim_data[0]
num_classes = dc.num_classes
print('Features:', num_features)
print('Classes:', num_classes)
model = BCNN(num_features, num_classes)
filename = get_pt_model_filename(BCNN.__name__, NAME, MAX_EPOCHS)
logger.debug('File name: %s', filename)
mc = ModelContainerPT(model, dc)
file_path = os.path.join('save', filename)
if not os.path.exists(file_path):
logger.debug('Expected initial loss: %f', np.log(dc.num_classes))
mc.fit(max_epochs=MAX_EPOCHS, batch_size=BATCH_SIZE)
mc.save(filename, overwrite=True)
else:
logger.info('Use saved parameters from %s', filename)
mc.load(file_path)
accuracy = mc.evaluate(dc.x_test, dc.y_test)
logger.info('Accuracy on test set: %f', accuracy)
def main():
master_seed(SEED)
dataset = DATASET_LIST[DATA_NAME]
dc = DataContainer(dataset, get_data_path())
dc()
model = MnistCnnCW()
mc = ModelContainerPT(model, dc)
mc.load(MODEL_FILE)
accuracy = mc.evaluate(dc.x_test, dc.y_test)
print('Accuracy on test set: {}'.format(accuracy))
attack = CarliniL2V2Container(mc,
targeted=False,
learning_rate=0.01,
binary_search_steps=9,
max_iter=1000,
confidence=0.0,
initial_const=0.01,
c_range=(0, 1e10),
batch_size=16,
clip_values=(0.0, 1.0))
adv, y_adv, x_clean, y_clean = attack.generate(count=100)
l2 = np.mean(get_l2_norm(adv, x_clean))
print('L2 norm: {}'.format(l2))
not_match = y_adv != y_clean
success_rate = len(not_match[not_match == True]) / len(adv)
print('Success rate: {}'.format(success_rate))
accuracy = mc.evaluate(adv, y_clean)
print('Accuracy on adv. examples: {}'.format(accuracy))
def test_fetch_all(self):
dataname = 'CIFAR10'
data_lookup = DATASET_LIST[dataname]
path = get_data_path()
dc = DataContainer(data_lookup, path)
dc(shuffle=False, normalize=False)
x_train = dc.x_train
y_train = dc.y_train
x_test = dc.x_test
y_test = dc.y_test
x_all = dc.x_all
y_all = dc.y_all
data_shape = list(x_train.shape)
data_shape[0] = len(x_train) + len(x_test)
self.assertTupleEqual(x_all.shape, tuple(data_shape))
label_shape = list(y_train.shape)
label_shape[0] = len(y_train) + len(y_test)
self.assertTupleEqual(y_all.shape, tuple(label_shape))
dc.y_train = onehot_encoding(y_train, dc.num_classes)
dc.y_test = onehot_encoding(y_test, dc.num_classes)
x_train = dc.x_train
y_train = dc.y_train
y_all = dc.y_all
label_shape = list(y_train.shape)
label_shape[0] = len(y_train) + len(y_test)
self.assertTupleEqual(y_all.shape, tuple(label_shape))
def setUpClass(cls):
master_seed(SEED)
Model = get_model('MnistCnnV2')
model = Model()
logger.info('Starting %s data container...', NAME)
dc = DataContainer(DATASET_LIST[NAME], get_data_path())
dc()
mc = ModelContainerPT(model, dc)
mc.load(MODEL_FILE)
accuracy = mc.evaluate(dc.x_test, dc.y_test)
logger.info('Accuracy on test set: %f', accuracy)
cls.distillation = DistillationContainer(mc,
Model(),
temperature=TEMPERATURE,
pretrained=False)
filename = get_pt_model_filename(
model.__class__.__name__, NAME,
str(MAX_EPOCHS) + 't' + str(int(TEMPERATURE * 10)))
filename = os.path.join('test', 'distill_' + filename)
file_path = os.path.join('save', filename)
if not os.path.exists(file_path):
# Expected initial loss = -log(1/num_classes) = 2.3025850929940455'
cls.distillation.fit(max_epochs=MAX_EPOCHS, batch_size=BATCH_SIZE)
cls.distillation.save(filename, overwrite=True)
else:
cls.distillation.load(file_path)
smooth_mc = cls.distillation.get_def_model_container()
accuracy = smooth_mc.evaluate(dc.x_test, dc.y_test)
logger.info('Accuracy on test set: %f', accuracy)
def setUpClass(cls):
master_seed(SEED)
Model = get_model('MnistCnnV2')
model = Model()
logger.info('Starting %s data container...', NAME)
dc = DataContainer(DATASET_LIST[NAME], get_data_path())
dc()
cls.mc = ModelContainerPT(model, dc)
cls.mc.load(MODEL_FILE)
accuracy = cls.mc.evaluate(dc.x_test, dc.y_test)
logger.info('Accuracy on test set: %f', accuracy)
def main():
# load dataset and initial model
Model = get_model(MODEL_NAME)
model = Model()
dc = DataContainer(DATASET_LIST[DATASET], get_data_path())
dc()
mc = ModelContainerPT(model, dc)
mc.load(MODEL_FILE)
accuracy = mc.evaluate(dc.x_test, dc.y_test)
print(f'Accuracy on test set: {accuracy}')
# train or load distillation model
distillation = DistillationContainer(mc,
Model(),
temperature=TEMPERATURE,
pretrained=False)
distill_path = os.path.join('save', DISTILL_FILE)
if not os.path.exists(distill_path):
distillation.fit(max_epochs=MAX_EPOCHS, batch_size=128)
distillation.save(DISTILL_FILE, True)
else:
distillation.load(distill_path)
smooth_mc = distillation.get_def_model_container()
accuracy = smooth_mc.evaluate(dc.x_test, dc.y_test)
print(f'Accuracy on test set: {accuracy}')
# load adversarial examples
adv_list = ['FGSM', 'BIM', 'DeepFool', 'Carlini', 'Saliency']
y_file = os.path.join('save',
f'{MODEL_NAME}_{DATASET}_{adv_list[0]}_y.npy')
x_file = os.path.join('save',
f'{MODEL_NAME}_{DATASET}_{adv_list[0]}_x.npy')
x = np.load(x_file, allow_pickle=False)
y = np.load(y_file, allow_pickle=False)
acc_og = mc.evaluate(x, y)
acc_distill = smooth_mc.evaluate(x, y)
print(f'Accuracy on clean set - OG: {acc_og}, Distill: {acc_distill}')
for adv_name in adv_list:
adv_file = os.path.join(
'save', build_adv_filename(MODEL_NAME, DATASET, adv_name))
adv = np.load(adv_file, allow_pickle=False)
acc_og = mc.evaluate(adv, y)
acc_distill = smooth_mc.evaluate(adv, y)
print(
f'Accuracy on {adv_name} set - OG: {acc_og}, Distill: {acc_distill}'
)
def get_data_container(dname, use_shuffle=True, use_normalize=True):
"""Returns a DataContainer based on given name"""
dataset = DATASET_LIST[dname]
dc = DataContainer(dataset, get_data_path())
if dname in ('MNIST', 'CIFAR10', 'SVHN'):
dc(shuffle=use_shuffle)
elif dname == 'Iris':
dc(shuffle=use_shuffle, normalize=use_normalize, size_train=0.6)
elif dname in ('BankNote', 'BreastCancerWisconsin', 'HTRU2', 'WheatSeed'):
dc(shuffle=use_shuffle, normalize=use_normalize)
elif dname == 'Synthetic':
# No function call for synthetic
return dc
else:
raise AttributeError('Received unknown dataset "{}"'.format(dname))
return dc
def setUpClass(cls):
master_seed(SEED)
logger.info('Starting %s data container...', NAME)
cls.dc = DataContainer(DATASET_LIST[NAME], get_data_path())
cls.dc(shuffle=True)
model = MnistCnnV2()
logger.info('Using model: %s', model.__class__.__name__)
cls.mc = ModelContainerPT(model, cls.dc)
filename = get_pt_model_filename(MnistCnnV2.__name__, NAME, MAX_EPOCHS)
file_path = os.path.join('save', filename)
if not os.path.exists(file_path):
cls.mc.fit(max_epochs=MAX_EPOCHS, batch_size=BATCH_SIZE)
cls.mc.save(filename, overwrite=True)
else:
logger.info('Use saved parameters from %s', filename)
cls.mc.load(file_path)
accuracy = cls.mc.evaluate(cls.dc.x_test, cls.dc.y_test)
logger.info('Accuracy on test set: %f', accuracy)
hidden_model = model.hidden_model
logger.info('sample_ratio: %f', SAMPLE_RATIO)
cls.ad = ApplicabilityDomainContainer(
cls.mc,
hidden_model=hidden_model,
k2=9,
reliability=1.6,
sample_ratio=SAMPLE_RATIO,
kappa=10,
confidence=0.9,
)
cls.ad.fit()
# shuffle the test set
x_test = cls.dc.x_test
y_test = cls.dc.y_test
shuffled_indices = np.random.permutation(len(x_test))[:NUM_ADV]
cls.x = x_test[shuffled_indices]
cls.y = y_test[shuffled_indices]
logger.info('# of test set: %d', len(cls.x))
def setUpClass(cls):
master_seed(SEED)
logger.info('Starting %s data container...', NAME)
cls.dc = DataContainer(DATASET_LIST[NAME], get_data_path())
cls.dc(shuffle=True)
model = CifarCnn()
logger.info('Using model: %s', model.__class__.__name__)
cls.mc = ModelContainerPT(model, cls.dc)
file_path = os.path.join('save', FILE_NAME)
if not os.path.exists(file_path):
raise Exception('Where is the pretrained file?!')
else:
logger.info('Use saved parameters from %s', FILE_NAME)
cls.mc.load(file_path)
accuracy = cls.mc.evaluate(cls.dc.x_test, cls.dc.y_test)
logger.info('Accuracy on test set: %f', accuracy)
hidden_model = model.hidden_model
logger.info('sample_ratio: %f', SAMPLE_RATIO)
cls.ad = ApplicabilityDomainContainer(
cls.mc,
hidden_model=hidden_model,
k2=9,
reliability=1.6,
sample_ratio=SAMPLE_RATIO,
kappa=10,
confidence=0.7,
)
cls.ad.fit()
# shuffle the test set
x_test = cls.dc.x_test
y_test = cls.dc.y_test
shuffled_indices = np.random.permutation(len(x_test))[:NUM_ADV]
cls.x = x_test[shuffled_indices]
cls.y = y_test[shuffled_indices]
logger.info('# of test set: %d', len(cls.x))
def main():
master_seed(SEED)
dataset = DATASET_LIST[DATA_NAME]
dc = DataContainer(dataset, get_data_path())
dc(shuffle=True, normalize=True)
print('# of trainset: {}, # of testset: {}'.format(len(dc.x_train),
len(dc.x_test)))
num_classes = dc.num_classes
num_features = dc.dim_data[0]
model = IrisNN(num_features=num_features,
hidden_nodes=num_features * 4,
num_classes=num_classes)
mc = ModelContainerPT(model, dc)
mc.load(MODEL_FILE)
accuracy = mc.evaluate(dc.x_test, dc.y_test)
print('Accuracy on test set: {}'.format(accuracy))
clip_values = get_range(dc.x_train, is_image=False)
print('clip_values', clip_values)
attack = CarliniL2V2Container(mc,
targeted=False,
learning_rate=0.01,
binary_search_steps=9,
max_iter=1000,
confidence=0.0,
initial_const=0.01,
c_range=(0, 1e4),
batch_size=16,
clip_values=clip_values)
adv, y_adv, x_clean, y_clean = attack.generate(count=100)
l2 = np.mean(get_l2_norm(adv, x_clean))
print('L2 norm: {}'.format(l2))
not_match = y_adv != y_clean
success_rate = len(not_match[not_match == True]) / len(adv)
print('Success rate: {}'.format(success_rate))
accuracy = mc.evaluate(adv, y_clean)
print('Accuracy on adv. examples: {}'.format(accuracy))
def main():
Model = get_model('MnistCnnV2')
classifier = Model()
dc = DataContainer(DATASET_LIST['MNIST'], get_data_path())
dc()
classifier_mc = ModelContainerPT(classifier, dc)
classifier_mc.load(MODEL_FILE)
accuracy = classifier_mc.evaluate(dc.x_test, dc.y_test)
print(f'Accuracy on test set: {accuracy}')
attack = BIMContainer(classifier_mc,
eps=0.3,
eps_step=0.1,
max_iter=100,
targeted=False)
adv_trainer = AdversarialTraining(classifier_mc, [attack])
adv_trainer.fit(max_epochs=5, batch_size=128, ratio=0.1)
discriminator = adv_trainer.get_def_model_container()
print(discriminator.accuracy_test)
def setUpClass(cls):
master_seed(SEED)
logger.info('Starting %s data container...', NAME)
cls.dc = DataContainer(DATASET_LIST[NAME], get_data_path())
cls.dc(shuffle=False)
model = MnistCnnCW()
logger.info('Using model: %s', model.__class__.__name__)
cls.mc = ModelContainerPT(model, cls.dc)
file_path = os.path.join('save', FILE_NAME)
if not os.path.exists(file_path):
cls.mc.fit(max_epochs=MAX_EPOCHS, batch_size=BATCH_SIZE)
cls.mc.save(FILE_NAME, overwrite=True)
else:
logger.info('Use saved parameters from %s', FILE_NAME)
cls.mc.load(file_path)
accuracy = cls.mc.evaluate(cls.dc.x_test, cls.dc.y_test)
logger.info('Accuracy on test set: %f', accuracy)
def setUpClass(cls):
master_seed(SEED)
# generating synthetic data
x, y = make_classification(
n_samples=SAMPLE_SIZE,
n_features=NUM_FEATURES,
n_informative=NUM_CLASSES,
n_redundant=0,
n_classes=NUM_CLASSES,
n_clusters_per_class=1,
)
x_max = np.max(x, axis=0)
x_min = np.min(x, axis=0)
x = scale_normalize(x, x_min, x_max)
n_train = int(np.floor(SAMPLE_SIZE * 0.8))
x_train = np.array(x[:n_train], dtype=np.float32)
y_train = np.array(y[:n_train], dtype=np.long)
x_test = np.array(x[n_train:], dtype=np.float32)
y_test = np.array(y[n_train:], dtype=np.long)
data_dict = get_synthetic_dataset_dict(SAMPLE_SIZE, NUM_CLASSES,
NUM_FEATURES)
dc = DataContainer(data_dict, get_data_path())
dc.x_train = x_train
dc.y_train = y_train
dc.x_test = x_test
dc.y_test = y_test
# training Extra Tree classifier
classifier = ExtraTreeClassifier(
criterion='gini',
splitter='random',
)
cls.mc = ModelContainerTree(classifier, dc)
cls.mc.fit()
accuracy = cls.mc.evaluate(dc.x_test, dc.y_test)
logger.info('Accuracy on test set: %f', accuracy)
def setUpClass(cls):
master_seed(SEED)
logger.info('Starting %s data container...', NAME)
cls.dc = DataContainer(DATASET_LIST[NAME], get_data_path())
# ordered by labels, it requires shuffle!
cls.dc(shuffle=True, normalize=True)
num_features = cls.dc.dim_data[0]
num_classes = cls.dc.num_classes
model = BCNN(num_features, num_classes)
logger.info('Using model: %s', model.__class__.__name__)
cls.mc = ModelContainerPT(model, cls.dc)
filename = get_pt_model_filename(BCNN.__name__, NAME, MAX_EPOCHS)
file_path = os.path.join('save', filename)
if not os.path.exists(file_path):
cls.mc.fit(max_epochs=MAX_EPOCHS, batch_size=BATCH_SIZE)
cls.mc.save(filename, overwrite=True)
else:
logger.info('Use saved parameters from %s', filename)
cls.mc.load(file_path)
accuracy = cls.mc.evaluate(cls.dc.x_test, cls.dc.y_test)
logger.info('Accuracy on test set: %f', accuracy)
hidden_model = model.hidden_model
cls.ad = ApplicabilityDomainContainer(
cls.mc,
hidden_model=hidden_model,
k2=6,
reliability=1.6,
sample_ratio=SAMPLE_RATIO,
kappa=10,
confidence=0.9,
)
cls.ad.fit()
def setUpClass(cls):
master_seed(SEED)
if not os.path.exists(os.path.join('save', 'test')):
os.makedirs(os.path.join('save', 'test'))
NAME = 'Iris'
logger.info('Starting %s data container...', NAME)
cls.dc = DataContainer(DATASET_LIST[NAME], get_data_path())
cls.dc(shuffle=True)
model = IrisNN()
model_name = model.__class__.__name__
logger.info('Using model: %s', model_name)
cls.mc = ModelContainerPT(model, cls.dc)
cls.mc.fit(max_epochs=100, batch_size=BATCH_SIZE)
# for comparison
model2 = IrisNN()
cls.mc2 = ModelContainerPT(model2, cls.dc)
# inputs for testing
cls.x = np.copy(cls.dc.x_test[:5])
cls.y = np.copy(cls.dc.y_test[:5])
def main():
dc = DataContainer(DATASET_LIST[NAME], get_data_path())
dc()
model = MnistCnnV2()
mc = ModelContainerPT(model, dc)
mc.load(MODEL_FILE)
accuracy = mc.evaluate(dc.x_test, dc.y_test)
print(f'Accuracy on test set: {accuracy}')
carlini_attack = attacks.CarliniL2V2Container(
mc,
learning_rate=0.01,
binary_search_steps=9,
max_iter=1000,
confidence=0.0,
initial_const=0.01,
c_range=(0, 1e10),
batch_size=BATCH_SIZE,
clip_values=(0.0, 1.0),
)
# adv, y_adv, x_clean, y_clean = carlini_attack.generate(count=1000)
# carlini_attack.save_attack(
# 'MnistCnnV2_MNIST_Carlini',
# adv,
# y_adv,
# x_clean,
# y_clean,
# True,
# )
# use pre-trained adversarial examples
adv, y_adv, x_clean, y_clean = carlini_attack.load_adv_examples(
CARLINI_FILE)
accuracy = mc.evaluate(adv, y_clean)
print(f'Accuracy on adv. examples: {accuracy}')
bim_attack = attacks.BIMContainer(
mc,
eps=0.3,
eps_step=0.1,
max_iter=100,
targeted=False,
)
k2, zeta, kappa, gamma = cross_validation(dc, model, bim_attack)
# use pre-defined parameters
# k2, zeta, kappa, gamma, score = K2, RELIABILITY, KAPPA, CONFIDENCE, 0
ad = ApplicabilityDomainContainer(
mc,
hidden_model=model.hidden_model,
k2=k2,
reliability=zeta,
sample_ratio=SAMPLE_RATIO,
kappa=kappa,
confidence=gamma,
)
print(ad.params)
ad.fit()
blocked_indices, x_passed = ad.detect(adv, y_adv, return_passed_x=True)
print('After update parameters, blocked {}/{} samples from adv. examples'.
format(len(blocked_indices), len(adv)))
def main():
parser = ap.ArgumentParser()
parser.add_argument('-s',
'--size',
type=int,
required=True,
help='the number of sample size')
parser.add_argument('-f',
'--features',
type=int,
required=True,
help='the number of features')
parser.add_argument('-c',
'--classes',
type=int,
default=2,
help='the number of classes')
parser.add_argument(
'-i',
'--iteration',
type=int,
default=MAX_ITERATIONS,
help='the number of iterations that the experiment will repeat')
parser.add_argument('-e',
'--epoch',
type=int,
required=True,
help='the number of max epochs for training')
parser.add_argument('-v',
'--verbose',
action='store_true',
default=False,
help='set logger level to debug')
parser.add_argument('-l',
'--savelog',
action='store_true',
default=False,
help='save logging file')
parser.add_argument('-w',
'--overwrite',
action='store_true',
default=False,
help='overwrite the existing file')
args = parser.parse_args()
sample_size = args.size
num_features = args.features
num_classes = args.classes
max_iterations = args.iteration
max_epochs = args.epoch
verbose = args.verbose
save_log = args.savelog
overwrite = args.overwrite
# set logging config. Run this before logging anything!
dname = f'SyntheticS{sample_size}F{num_features}C{num_classes}'
set_logging(LOG_NAME, dname, verbose, save_log)
print('[{}] Start experiment on {}...'.format(LOG_NAME, dname))
logger.info('Start at :%s', get_time_str())
logger.info('RECEIVED PARAMETERS:')
logger.info('dataset :%s', dname)
logger.info('train size :%d', sample_size)
logger.info('num features:%d', num_features)
logger.info('num classes :%d', num_classes)
logger.info('iterations :%d', max_iterations)
logger.info('max_epochs :%d', max_epochs)
logger.info('verbose :%r', verbose)
logger.info('save_log :%r', save_log)
logger.info('overwrite :%r', overwrite)
result_file = name_handler(os.path.join(
'save', f'{LOG_NAME}_{dname}_i{max_iterations}'),
'csv',
overwrite=overwrite)
adv_file = name_handler(os.path.join('save',
f'{LOG_NAME}_{dname}_AdvExamples'),
'csv',
overwrite=overwrite)
adv_file = open(adv_file, 'w')
adv_file.write(','.join(TITLE_ADV) + '\n')
res_file = open(result_file, 'w')
res_file.write(','.join(TITLE_RESULTS) + '\n')
for i in range(max_iterations):
since = time.time()
# generate synthetic data
x, y = make_classification(
n_samples=sample_size + 1000,
n_features=num_features,
n_informative=num_classes,
n_redundant=0,
n_classes=num_classes,
n_clusters_per_class=1,
)
# normalize data
x_max = np.max(x, axis=0)
x_min = np.min(x, axis=0)
# NOTE: Carlini attack expects the data in range [0, 1]
# x_mean = np.mean(x, axis=0)
# x = scale_normalize(x, x_min, x_max, x_mean)
x = scale_normalize(x, x_min, x_max)
# training/test split
# NOTE: test set has fixed size
x_train = np.array(x[:-1000], dtype=np.float32)
y_train = np.array(y[:-1000], dtype=np.long)
x_test = np.array(x[-1000:], dtype=np.float32)
y_test = np.array(y[-1000:], dtype=np.long)
# create data container
data_dict = get_synthetic_dataset_dict(sample_size + 1000, num_classes,
num_features)
dc = DataContainer(data_dict, get_data_path())
# assign data manually
dc.x_train = x_train
dc.y_train = y_train
dc.x_test = x_test
dc.y_test = y_test
experiment(i, dc, max_epochs, adv_file, res_file)
time_elapsed = time.time() - since
print('Completed {} [{}/{}]: {:d}m {:2.1f}s'.format(
dname, i + 1, max_iterations, int(time_elapsed // 60),
time_elapsed % 60))
adv_file.close()
res_file.close()
def main():
dc = DataContainer(DATASET_LIST[NAME], get_data_path())
dc(shuffle=True, normalize=True, size_train=0.8)
print('Sample size: {}'.format(len(dc)))
num_classes = dc.num_classes
num_features = dc.dim_data[0]
model = IrisNN(num_features=num_features,
hidden_nodes=num_features * 4,
num_classes=num_classes)
mc = ModelContainerPT(model, dc)
# mc.fit(max_epochs=MAX_EPOCHS, batch_size=BATCH_SIZE, early_stop=False)
# filename = get_pt_model_filename('IrisNN', NAME, MAX_EPOCHS)
# mc.save(filename, overwrite=True)
mc.load(MODEL_FILE)
accuracy = mc.evaluate(dc.x_test, dc.y_test)
print(f'Accuracy on test set: {accuracy}')
carlini_attack = attacks.CarliniL2V2Container(
mc,
learning_rate=0.01,
binary_search_steps=9,
max_iter=1000,
confidence=0.0,
initial_const=0.01,
c_range=(0, 1e4),
batch_size=BATCH_SIZE,
clip_values=(0.0, 1.0),
)
# we need more than 30 adv. examples
# x_all = dc.x_all
# y_all = dc.x_all
# indices = np.random.permutation(np.arange(len(dc)))[:100]
# x = x_all[indices]
# y = y_all[indices]
# adv, pred_adv, x_clean, pred_clean = carlini_attack.generate(use_testset=False, x=x)
# carlini_attack.save_attack(
# 'IrisNN_Iris_Carlini',
# adv, pred_adv,
# x_clean, pred_clean,
# True,
# )
# use pre-trained adversarial examples
adv, y_adv, x_clean, pred_clean = carlini_attack.load_adv_examples(
CARLINI_FILE)
accuracy = mc.evaluate(adv, pred_clean)
print(f'Accuracy on adv. examples: {accuracy}')
bim_attack = attacks.BIMContainer(
mc,
eps=0.3,
eps_step=0.1,
max_iter=100,
targeted=False,
)
k2, zeta, kappa, gamma = cross_validation(dc, model, bim_attack)
ad = ApplicabilityDomainContainer(
mc,
hidden_model=model.hidden_model,
k2=k2,
reliability=zeta,
sample_ratio=SAMPLE_RATIO,
kappa=kappa,
confidence=gamma,
)
print(ad.params)
ad.fit()
blocked_indices, x_passed = ad.detect(adv, y_adv, return_passed_x=True)
print('After update parameters, blocked {}/{} samples from adv. examples'.
format(len(blocked_indices), len(adv)))
def main():
# load dataset and initial model
model = MnistCnnV2()
dc = DataContainer(DATASET_LIST[DATASET], get_data_path())
dc(shuffle=True, normalize=True)
mc = ModelContainerPT(model, dc)
mc.load(MODEL_FILE)
accuracy = mc.evaluate(dc.x_test, dc.y_test)
print(f'Accuracy on test set: {accuracy}')
# train or load pretrained parameters
squeezer = FeatureSqueezing(mc, ['median', 'normal', 'binary'],
bit_depth=BIT_DEPTH,
sigma=SIGMA,
kernel_size=KERNEL_SIZE,
pretrained=True)
x_test = dc.x_test
y_test = dc.y_test
mc_binary = squeezer.get_def_model_container('binary')
mc_median = squeezer.get_def_model_container('median')
mc_normal = squeezer.get_def_model_container('normal')
print('before fit')
acc_bin = mc_binary.evaluate(squeezer.apply_binary_transform(x_test),
y_test)
print(f'Accuracy of binary squeezer: {acc_bin}')
acc_med = mc_median.evaluate(squeezer.apply_median_transform(x_test),
y_test)
print(f'Accuracy of median squeezer: {acc_med}')
acc_nor = mc_normal.evaluate(squeezer.apply_normal_transform(x_test),
y_test)
print(f'Accuracy of normal squeezer: {acc_nor}')
if not squeezer.does_pretrained_exist(MODEL_FILE):
squeezer.fit(max_epochs=MAX_EPOCHS, batch_size=128)
print('after fit')
acc_bin = mc_binary.evaluate(squeezer.apply_binary_transform(x_test),
y_test)
print(f'Accuracy of binary squeezer: {acc_bin}')
acc_med = mc_median.evaluate(squeezer.apply_median_transform(x_test),
y_test)
print(f'Accuracy of median squeezer: {acc_med}')
acc_nor = mc_normal.evaluate(squeezer.apply_normal_transform(x_test),
y_test)
print(f'Accuracy of normal squeezer: {acc_nor}')
squeezer.save(MODEL_FILE, True)
print('after load')
squeezer.load(MODEL_FILE)
acc_bin = mc_binary.evaluate(squeezer.apply_binary_transform(x_test),
y_test)
print(f'Accuracy of binary squeezer: {acc_bin}')
acc_med = mc_median.evaluate(squeezer.apply_median_transform(x_test),
y_test)
print(f'Accuracy of median squeezer: {acc_med}')
acc_nor = mc_normal.evaluate(squeezer.apply_normal_transform(x_test),
y_test)
print(f'Accuracy of normal squeezer: {acc_nor}')
# load adversarial examples
adv_list = ['FGSM', 'BIM', 'DeepFool', 'Carlini', 'Saliency']
y_file = os.path.join('save',
f'{MODEL_NAME}_{DATASET}_{adv_list[0]}_y.npy')
x_file = os.path.join('save',
f'{MODEL_NAME}_{DATASET}_{adv_list[0]}_x.npy')
x = np.load(x_file, allow_pickle=False)
y = np.load(y_file, allow_pickle=False)
acc_og = mc.evaluate(x, y)
acc_squeezer = squeezer.evaluate(x, y)
print(f'Accuracy on clean set - OG: {acc_og}, Squeezer: {acc_squeezer}')
for adv_name in adv_list:
adv_file = os.path.join(
'save', build_adv_filename(MODEL_NAME, DATASET, adv_name))
adv = np.load(adv_file, allow_pickle=False)
acc_og = mc.evaluate(adv, y)
acc_squeezer = squeezer.evaluate(adv, y)
print(
f'Accuracy on {adv_name} set - OG: {acc_og}, Squeezer: {acc_squeezer}'
)
def init_datacontainer(cls, name):
x = DATASET_LIST[name]
path = get_data_path()
dc = DataContainer(x, path)
dc(shuffle=False, normalize=True, size_train=0.5)
return dc
def main():
data_name = 'Iris'
set_logging('advTraining', data_name, True, True)
dc = DataContainer(DATASET_LIST[data_name], get_data_path())
dc()
model_file = os.path.join('save', 'IrisNN_Iris_e200.pt')
num_features = dc.dim_data[0]
num_classes = dc.num_classes
classifier = IrisNN(
num_features=num_features,
hidden_nodes=num_features*4,
num_classes=num_classes,
)
classifier_mc = ModelContainerPT(classifier, dc)
classifier_mc.load(model_file)
accuracy = classifier_mc.evaluate(dc.x_test, dc.y_test)
logger.info('Accuracy on test set: %f', accuracy)
attack = BIMContainer(
classifier_mc,
eps=0.3,
eps_step=0.1,
max_iter=100,
targeted=False)
adv_trainer = AdversarialTraining(classifier_mc, [attack])
# adv_trainer.fit(max_epochs=100, batch_size=64, ratio=1)
# adv_trainer.save('AdvTrain_IrisNN_Iris', overwrite=True)
file_name = os.path.join('save', 'AdvTrain_IrisNN_Iris.pt')
adv_trainer.load(file_name)
x = np.load(os.path.join('save', 'IrisNN_Iris_BIM_x.npy'),
allow_pickle=False)
y = np.load(os.path.join('save', 'IrisNN_Iris_BIM_y.npy'),
allow_pickle=False)
blocked_indices = adv_trainer.detect(x, return_passed_x=False)
logger.info('Blocked %d/%d samples on %s',
len(blocked_indices), len(x), 'clean')
adv = np.load(os.path.join(
'save', 'IrisNN_Iris_BIM_adv.npy'), allow_pickle=False)
accuracy = classifier_mc.evaluate(adv, y)
logger.info('Accuracy on %s set: %f', 'BIM', accuracy)
blocked_indices = adv_trainer.detect(adv, return_passed_x=False)
logger.info('Blocked %d/%d samples on %s',
len(blocked_indices), len(adv), 'BIM')
adv = np.load(os.path.join(
'save', 'IrisNN_Iris_Carlini_adv.npy'), allow_pickle=False)
accuracy = classifier_mc.evaluate(adv, y)
logger.info('Accuracy on %s set: %f', 'Carlini', accuracy)
blocked_indices = adv_trainer.detect(adv, return_passed_x=False)
logger.info('Blocked %d/%d samples on %s',
len(blocked_indices), len(adv), 'Carlini')
adv = np.load(os.path.join(
'save', 'IrisNN_Iris_DeepFool_adv.npy'), allow_pickle=False)
accuracy = classifier_mc.evaluate(adv, y)
logger.info('Accuracy on %s set: %f', 'DeepFool', accuracy)
blocked_indices = adv_trainer.detect(adv, return_passed_x=False)
logger.info('Blocked %d/%d samples on %s',
len(blocked_indices), len(adv), 'DeepFool')
adv = np.load(os.path.join(
'save', 'IrisNN_Iris_FGSM_adv.npy'), allow_pickle=False)
accuracy = classifier_mc.evaluate(adv, y)
logger.info('Accuracy on %s set: %f', 'FGSM', accuracy)
blocked_indices = adv_trainer.detect(adv, return_passed_x=False)
logger.info('Blocked %d/%d samples on %s',
len(blocked_indices), len(adv), 'FGSM')
def test_file_exist(self):
self.assertTrue(os.path.exists(get_data_path()))
def main():
data_name = 'MNIST'
set_logging('advTraining', data_name, True, True)
model_file = os.path.join('save', 'MnistCnnV2_MNIST_e50.pt')
Model = get_model('MnistCnnV2')
classifier = Model()
dc = DataContainer(DATASET_LIST[data_name], get_data_path())
dc()
classifier_mc = ModelContainerPT(classifier, dc)
classifier_mc.load(model_file)
accuracy = classifier_mc.evaluate(dc.x_test, dc.y_test)
logger.info('Accuracy on test set: %f', accuracy)
attack = BIMContainer(classifier_mc,
eps=0.3,
eps_step=0.1,
max_iter=100,
targeted=False)
adv_trainer = AdversarialTraining(classifier_mc, [attack])
# adv_trainer.fit(max_epochs=30, batch_size=128, ratio=0.1)
# adv_trainer.save('AdvTrain_MnistCnnV2_MNIST', overwrite=True)
file_name = os.path.join('save', 'AdvTrain_MnistCnnV2_MNIST.pt')
adv_trainer.load(file_name)
x = np.load(os.path.join('save', 'MnistCnnV2_MNIST_BIM_x.npy'),
allow_pickle=False)
y = np.load(os.path.join('save', 'MnistCnnV2_MNIST_BIM_y.npy'),
allow_pickle=False)
blocked_indices = adv_trainer.detect(x, return_passed_x=False)
logger.info('Blocked %d/%d samples on %s', len(blocked_indices), len(x),
'clean')
adv = np.load(os.path.join('save', 'MnistCnnV2_MNIST_BIM_adv.npy'),
allow_pickle=False)
accuracy = classifier_mc.evaluate(adv, y)
logger.info('Accuracy on %s set: %f', 'BIM', accuracy)
blocked_indices = adv_trainer.detect(adv, return_passed_x=False)
logger.info('Blocked %d/%d samples on %s', len(blocked_indices), len(adv),
'BIM')
adv = np.load(os.path.join('save', 'MnistCnnV2_MNIST_Carlini_adv.npy'),
allow_pickle=False)
accuracy = classifier_mc.evaluate(adv, y)
logger.info('Accuracy on %s set: %f', 'Carlini', accuracy)
blocked_indices = adv_trainer.detect(adv, return_passed_x=False)
logger.info('Blocked %d/%d samples on %s', len(blocked_indices), len(adv),
'Carlini')
adv = np.load(os.path.join('save', 'MnistCnnV2_MNIST_DeepFool_adv.npy'),
allow_pickle=False)
accuracy = classifier_mc.evaluate(adv, y)
logger.info('Accuracy on %s set: %f', 'DeepFool', accuracy)
blocked_indices = adv_trainer.detect(adv, return_passed_x=False)
logger.info('Blocked %d/%d samples on %s', len(blocked_indices), len(adv),
'DeepFool')
adv = np.load(os.path.join('save', 'MnistCnnV2_MNIST_FGSM_adv.npy'),
allow_pickle=False)
accuracy = classifier_mc.evaluate(adv, y)
logger.info('Accuracy on %s set: %f', 'FGSM', accuracy)
blocked_indices = adv_trainer.detect(adv, return_passed_x=False)
logger.info('Blocked %d/%d samples on %s', len(blocked_indices), len(adv),
'FGSM')
adv = np.load(os.path.join('save', 'MnistCnnV2_MNIST_Saliency_adv.npy'),
allow_pickle=False)
accuracy = classifier_mc.evaluate(adv, y)
logger.info('Accuracy on %s set: %f', 'Saliency', accuracy)
blocked_indices = adv_trainer.detect(adv, return_passed_x=False)
logger.info('Blocked %d/%d samples on %s', len(blocked_indices), len(adv),
'Saliency')
def main():
parser = ap.ArgumentParser()
parser.add_argument(
'-m', '--model', type=str, required=True,
help='a file which contains a pretrained model. The filename should in "<model>_<dataset>_e<max epochs>[_<date>].pt" format')
parser.add_argument(
'-p', '--param', type=str, required=True,
help='a JSON config file which contains the parameters for the cross validation')
parser.add_argument(
'-a', '--adv', type=str,
help='file name of adv. examples for testing. If it\'s none, the program will ignore testing. The name should in "<model>_<dataset>_<attack>_adv.npy" format')
parser.add_argument(
'-s', '--seed', type=int, default=4096,
help='the seed for random number generator')
parser.add_argument(
'-v', '--verbose', action='store_true', default=False,
help='set logger level to debug')
parser.add_argument(
'-l', '--savelog', action='store_true', default=False,
help='save logging file')
parser.add_argument(
'-i', '--ignore', action='store_true', default=False,
help='Ignore saving the results. Only returns the results from terminal.')
parser.add_argument(
'-w', '--overwrite', action='store_true', default=False,
help='overwrite the existing file')
args = parser.parse_args()
model_file = args.model
param_file = args.param
adv_file = args.adv
seed = args.seed
verbose = args.verbose
save_log = args.savelog
does_ignore = args.ignore
overwrite = args.overwrite
model_name, data_name = parse_model_filename(model_file)
# set logging config. Run this before logging anything!
set_logging('cross_validation', data_name, verbose, save_log)
# check files
for file_path in [model_file, param_file]:
if not os.path.exists(file_path):
logger.warning('%s does not exist. Exit.', file_path)
sys.exit(0)
if adv_file is not None and not os.path.exists(adv_file):
logger.warning('%s does not exist. Exit.', adv_file)
sys.exit(0)
# read parameters
with open(param_file) as param_json:
params = json.load(param_json)
# show parameters
print('[cv] Running cross validation on {} with {}...'.format(
model_file, data_name))
logger.info('Start at : %s', get_time_str())
logger.info('RECEIVED PARAMETERS:')
logger.info('model file :%s', model_file)
logger.info('adv file :%s', adv_file)
logger.info('model :%s', model_name)
logger.info('dataset :%s', data_name)
logger.info('param file :%s', param_file)
logger.info('seed :%d', seed)
logger.info('verbose :%r', verbose)
logger.info('save_log :%r', save_log)
logger.info('Ignore saving :%r', does_ignore)
logger.info('overwrite :%r', overwrite)
logger.debug('params :%s', str(params))
# load parameters
k_range = params['k_range']
z_range = params['z_range']
kappa_range = params['kappa_range']
gamma_range = params['gamma_range']
epsilon = params['epsilon']
num_folds = params['num_folds']
batch_size = params['batch_size']
sample_ratio = params['sample_ratio']
logger.info('k_range :%s', str(k_range))
logger.info('z_range :%s', str(z_range))
logger.info('kappa_range :%s', str(kappa_range))
logger.info('gamma_range :%s', str(gamma_range))
logger.info('epsilon :%.1f', epsilon)
logger.info('num_folds :%d', num_folds)
logger.info('batch_size :%d', batch_size)
logger.info('sample_ratio :%.1f', sample_ratio)
# reset seed
master_seed(seed)
dc = DataContainer(DATASET_LIST[data_name], get_data_path())
dc(shuffle=True, normalize=True, size_train=0.8)
logger.info('Sample size: %d', len(dc))
Model = get_model(model_name)
# there models require extra keyword arguments
if data_name in ('BankNote', 'HTRU2', 'Iris', 'WheatSeed'):
num_classes = dc.num_classes
num_features = dc.dim_data[0]
kwargs = {
'num_features': num_features,
'hidden_nodes': num_features*4,
'num_classes': num_classes,
}
model = Model(**kwargs)
else:
model = Model()
logger.info('Use %s model', model.__class__.__name__)
mc = ModelContainerPT(model, dc)
mc.load(model_file)
accuracy = mc.evaluate(dc.x_test, dc.y_test)
logger.info('Accuracy on test set: %f', accuracy)
ad = ApplicabilityDomainContainer(
mc, hidden_model=model.hidden_model, sample_ratio=sample_ratio)
cross_validation = CrossValidation(
ad,
num_folds=num_folds,
k_range=k_range,
z_range=z_range,
kappa_range=kappa_range,
gamma_range=gamma_range,
epsilon=epsilon,
)
bim_attack = BIMContainer(
mc,
eps=0.3,
eps_step=0.1,
max_iter=100,
targeted=False,
)
cross_validation.fit(bim_attack)
# test optimal parameters
if adv_file is not None:
postfix = ['adv', 'pred', 'x', 'y']
data_files = [adv_file.replace('_adv', '_' + s) for s in postfix]
adv = np.load(data_files[0], allow_pickle=False)
pred_adv = np.load(data_files[1], allow_pickle=False)
x = np.load(data_files[2], allow_pickle=False)
pred = np.load(data_files[3], allow_pickle=False)
# fetch optimal parameters
ad = ApplicabilityDomainContainer(
mc,
hidden_model=model.hidden_model,
k2=cross_validation.k2,
reliability=cross_validation.reliability,
sample_ratio=sample_ratio,
kappa=cross_validation.kappa,
confidence=cross_validation.confidence,
)
logger.info('Params: %s', str(ad.params))
ad.fit()
blocked_indices = ad.detect(x, pred, return_passed_x=False)
logger.info('Blocked %d/%d on clean data',
len(blocked_indices), len(x))
blocked_indices = ad.detect(adv, pred_adv, return_passed_x=False)
logger.info('Blocked %d/%d on adv. examples.',
len(blocked_indices), len(adv))
# save results
if not does_ignore:
file_name = name_handler(
model_name + '_' + data_name, 'csv', overwrite=overwrite)
cross_validation.save(file_name)
