def experiment(data_name, filter_list, bit_depth, sigma, kernel_size):
# select data
dc = get_data_container(
data_name,
use_shuffle=True,
use_normalize=True,
)
# train the model
classifier = ExtraTreeClassifier(
criterion='gini',
splitter='random',
)
mc = ModelContainerTree(classifier, dc)
mc.fit()
# no more than 1000 samples are required
x = dc.x_test
y = dc.y_test
if len(x) > 1000:
x = x[:1000]
y = y[:1000]
accuracy = mc.evaluate(x, y)
logger.info('Accuracy on clean: %f', accuracy)
squeezer = FeatureSqueezingTree(
mc,
filter_list,
bit_depth=bit_depth,
sigma=sigma,
kernel_size=kernel_size,
pretrained=True,
)
squeezer.fit()
blocked_indices = squeezer.detect(x)
logger.info('Blocked %d/%d samples on clean',
len(blocked_indices), len(y))
num_blk_clean = len(blocked_indices)
# generate adversarial examples
art_classifier = SklearnClassifier(classifier)
try:
attack = DecisionTreeAttack(art_classifier)
adv = attack.generate(x)
except IndexError as error:
# Output expected IndexErrors.
logger.error(error)
return num_blk_clean, -1
accuracy = mc.evaluate(adv, y)
logger.info('Accuracy on DecisionTreeAttack: %f', accuracy)
blocked_indices = squeezer.detect(adv)
logger.info('Blocked %d/%d samples on DecisionTreeAttack',
len(blocked_indices), len(adv))
num_blk_adv = len(blocked_indices)
return num_blk_clean, num_blk_adv
def experiment(data_name, params):
# select data
dc = get_data_container(
data_name,
use_shuffle=True,
use_normalize=True,
)
# train the model
classifier = ExtraTreeClassifier(
criterion='gini',
splitter='random',
)
mc = ModelContainerTree(classifier, dc)
mc.fit()
# train Applicability Domain
ad = ApplicabilityDomainContainer(
mc, mc.hidden_model, **params)
ad.fit()
# no more than 1000 samples are required
x = dc.x_test
y = dc.y_test
if len(x) > 1000:
x = x[:1000]
y = y[:1000]
accuracy = mc.evaluate(x, y)
logger.info('Accuracy on clean: %f', accuracy)
blocked_indices = ad.detect(x)
logger.info('Blocked %d/%d samples on clean',
len(blocked_indices), len(y))
num_blk_clean = len(blocked_indices)
# generate adversarial examples
art_classifier = SklearnClassifier(classifier)
try:
attack = DecisionTreeAttack(art_classifier)
adv = attack.generate(x)
except IndexError as error:
# Output expected IndexErrors.
logger.error(error)
return num_blk_clean, -1
accuracy = mc.evaluate(adv, y)
logger.info('Accuracy on DecisionTreeAttack: %f', accuracy)
blocked_indices = ad.detect(adv)
logger.info('Blocked %d/%d samples on DecisionTreeAttack',
len(blocked_indices), len(adv))
num_blk_adv = len(blocked_indices)
return num_blk_clean, num_blk_adv
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 attacks')
parser.add_argument(
'-n', '--number', type=int, default=1000,
help='the number of adv. examples want to generate. (if more than test set, it uses all test examples.)')
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(
'-w', '--overwrite', action='store_true', default=False,
help='overwrite the existing file')
parser.add_argument(
'-B', '--bim', action='store_true', default=False,
help='Apply BIM attack')
parser.add_argument(
'-C', '--carlini', action='store_true', default=False,
help='Apply Carlini L2 attack')
parser.add_argument(
'-D', '--deepfool', action='store_true', default=False,
help='Apply DeepFool attack')
parser.add_argument(
'-F', '--fgsm', action='store_true', default=False,
help='Apply FGSM attack')
parser.add_argument(
'-S', '--saliency', action='store_true', default=False,
help='Apply Saliency Map attack')
args = parser.parse_args()
model_file = args.model
attack_param_file = args.param
num_adv = args.number
seed = args.seed
verbose = args.verbose
save_log = args.savelog
overwrite = args.overwrite
# Which attack should apply?
# use binary encoding for attacks
my_attacks = np.zeros(5, dtype=np.int8)
attack_list = np.array(
['FGSM', 'BIM', 'Carlini', 'DeepFool', 'Saliency'])
my_attacks[0] = 1 if args.fgsm else 0
my_attacks[1] = 1 if args.bim else 0
my_attacks[2] = 1 if args.carlini else 0
my_attacks[3] = 1 if args.deepfool else 0
my_attacks[4] = 1 if args.saliency else 0
selected_attacks = attack_list[np.where(my_attacks == 1)[0]]
# check file
for f in [model_file, attack_param_file]:
if not os.path.exists(f):
raise FileNotFoundError('{} does not exist!'.format(f))
dirname = os.path.dirname(model_file)
model_name, dname = parse_model_filename(model_file)
with open(attack_param_file) as param_json:
att_params = json.load(param_json)
# set logging config. Run this before logging anything!
set_logging('attack', dname, verbose, save_log)
# show parameters
print('[attack] Start generating {} adv. samples from {} model...'.format(
num_adv, model_name))
logger.info('Start at : %s', get_time_str())
logger.info('RECEIVED PARAMETERS:')
logger.info('model file :%s', model_file)
logger.info('model :%s', model_name)
logger.info('dataset :%s', dname)
logger.info('params :%s', attack_param_file)
logger.info('num_adv :%r', num_adv)
logger.info('seed :%d', seed)
logger.info('verbose :%r', verbose)
logger.info('save_log :%r', save_log)
logger.info('overwrite :%r', overwrite)
logger.info('dirname :%r', dirname)
logger.info('attacks :%s', ', '.join(selected_attacks))
if len(selected_attacks) == 0:
logger.warning('No attack is selected. Exit.')
sys.exit(0)
# reset seed
master_seed(seed)
# set DataContainer and ModelContainer
dc = get_data_container(dname)
Model = get_model(model_name)
# there models require extra keyword arguments
if dname 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)
run_attacks(mc,
selected_attacks,
att_params,
num_adv,
model_name + '_' + dname,
overwrite)
def main():
parser = ap.ArgumentParser()
parser.add_argument('-d',
'--dataset',
type=str,
required=True,
help='Name of the dataset')
parser.add_argument(
'-p',
'--param',
type=str,
required=True,
help='a JSON config file which contains the parameters for the attacks'
)
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('-F',
'--fgsm',
action='store_true',
default=False,
help='Apply FGSM attack')
parser.add_argument('-B',
'--bim',
action='store_true',
default=False,
help='Apply BIM attack')
parser.add_argument('-D',
'--deepfool',
action='store_true',
default=False,
help='Apply DeepFool attack')
parser.add_argument('-C',
'--carlini',
action='store_true',
default=False,
help='Apply Carlini L2 attack')
args = parser.parse_args()
data_name = args.dataset
param_file = args.param
seed = args.seed
verbose = args.verbose
save_log = args.savelog
# set logging config. Run this before logging anything!
set_logging(LOG_NAME, data_name, verbose, save_log)
# Which attack should apply?
attack_list = []
if args.fgsm:
attack_list.append('FGSM')
if args.bim:
attack_list.append('BIM')
if args.deepfool:
attack_list.append('DeepFool')
if args.carlini:
attack_list.append('Carlini')
# Quit, if there is nothing to do.
if len(attack_list) == 0:
logger.warning('Neither received any filter nor any attack. Exit')
sys.exit(0)
if data_name in ('BankNote', 'HTRU2', 'Iris', 'WheatSeed'):
model_name = 'IrisNN'
if data_name == 'BreastCancerWisconsin':
model_name = 'BCNN'
y_file = os.path.join('save',
f'{model_name}_{data_name}_{attack_list[0]}_y.npy')
attack_files = [
os.path.join('save',
f'{model_name}_{data_name}_{attack_list[0]}_x.npy')
]
for attack_name in attack_list:
attack_files.append(
os.path.join('save',
f'{model_name}_{data_name}_{attack_name}_adv.npy'))
# the 1st file this the clean inputs
attack_list = ['clean'] + attack_list
# load parameters for Applicability Domain
with open(param_file) as param_json:
params = json.load(param_json)
# show parameters
print(f'[{LOG_NAME}] Running tree model...')
logger.info('Start at :%s', get_time_str())
logger.info('RECEIVED PARAMETERS:')
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('attacks :%s', ', '.join(attack_list))
logger.debug('params :%s', str(params))
# check files
for file_name in [y_file] + attack_files:
if not os.path.exists(file_name):
logger.error('%s does not exist!', file_name)
raise FileNotFoundError('{} does not exist!'.format(file_name))
# reset seed
master_seed(seed)
# select data
dc = get_data_container(
data_name,
use_shuffle=True,
use_normalize=True,
)
# train the model
classifier = ExtraTreeClassifier(
criterion='gini',
splitter='random',
)
mc = ModelContainerTree(classifier, dc)
mc.fit()
x = np.load(attack_files[0], allow_pickle=False)
art_classifier = SklearnClassifier(classifier)
attack = DecisionTreeAttack(art_classifier)
adv = attack.generate(x)
ad = ApplicabilityDomainContainer(mc, mc.hidden_model, **params)
ad.fit()
# generate adversarial examples
y = np.load(y_file, allow_pickle=False)
accuracy = mc.evaluate(adv, y)
logger.info('Accuracy on DecisionTreeAttack set: %f', accuracy)
blocked_indices = ad.detect(adv)
logger.info('Blocked %d/%d samples on DecisionTreeAttack',
len(blocked_indices), len(adv))
# traverse other attacks
for i in range(len(attack_list)):
adv_file = attack_files[i]
adv_name = attack_list[i]
logger.debug('Load %s...', adv_file)
adv = np.load(adv_file, allow_pickle=False)
accuracy = mc.evaluate(adv, y)
logger.info('Accuracy on %s set: %f', adv_name, accuracy)
blocked_indices = ad.detect(adv, return_passed_x=False)
logger.info('Blocked %d/%d samples on %s', len(blocked_indices),
len(adv), adv_name)
def main():
parser = ap.ArgumentParser()
parser.add_argument(
'-a',
'--adv',
type=str,
required=True,
help=
'file name for adv. examples. The name should in "<model>_<dataset>_<attack>_adv.npy" format'
)
parser.add_argument(
'-p',
'--param',
type=str,
required=True,
help='a JSON config file which contains the parameters for the attacks'
)
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('-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')
args = parser.parse_args()
adv_file = args.adv
param_file = args.param
model_file = args.model
seed = args.seed
verbose = args.verbose
save_log = args.savelog
check_clean = True
# build filenames from the root file
postfix = ['adv', 'pred', 'x', 'y']
data_files = [adv_file.replace('_adv', '_' + s) for s in postfix]
model_name, dname = parse_model_filename(adv_file)
# set logging config. Run this before logging anything!
set_logging('defence_ad', dname, verbose, save_log)
# check adv. examples and parameter config files
for f in data_files[:2] + [param_file]:
if not os.path.exists(f):
logger.warning('%s does not exist. Exit.', f)
sys.exit(0)
# check clean samples
for f in data_files[-2:]:
if not os.path.exists(f):
logger.warning(
'Cannot load files for clean samples. Skip checking clean set.'
)
check_clean = False
with open(param_file) as param_json:
params = json.load(param_json)
# show parameters
print(
'[defend_ad] Running applicability domain on {}...'.format(model_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', dname)
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('check_clean :%r', check_clean)
logger.debug('params : %s', str(params))
# reset seed
master_seed(seed)
# set DataContainer and ModelContainer
dc = get_data_container(dname)
Model = get_model(model_name)
# there models require extra keyword arguments
if dname 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)
# preform defence
ad = ApplicabilityDomainContainer(mc,
hidden_model=model.hidden_model,
**params)
ad.fit()
result_prefix = [model_file] \
+ [adv_file] \
+ [params['k2']] \
+ [params['reliability']] \
+ [params['sample_ratio']] \
+ [params['confidence']] \
+ [params['kappa']] \
+ [params['disable_s2']]
# check clean
if check_clean:
x = np.load(data_files[2], allow_pickle=False)
y = np.load(data_files[3], allow_pickle=False)
x_passed, blk_idx, blocked_counts = detect(ad, 'clean samples', x, y)
result = result_prefix + ['clean'] + blocked_counts
result_clean = '[result]' + ','.join([str(r) for r in result])
# check adversarial examples
adv = np.load(data_files[0], allow_pickle=False)
pred = np.load(data_files[1], allow_pickle=False)
adv_passed, adv_blk_idx, blocked_counts = detect(ad, 'adv. examples', adv,
pred)
result = result_prefix + ['adv'] + blocked_counts
result = '[result]' + ','.join([str(r) for r in result])
if check_clean:
logger.info(result_clean)
logger.info(result)
def experiment(index, dname, mname, max_epochs, adv_file, res_file):
# STEP 1: select data
dc = get_data_container(dname, use_shuffle=True, use_normalize=True)
Model = get_model(mname)
model = Model()
distill_model = Model()
logger.info('Selected %s model', model.__class__.__name__)
# STEP 2: train models
mc = ModelContainerPT(model, dc)
mc.fit(max_epochs=max_epochs, batch_size=BATCH_SIZE)
accuracy = mc.evaluate(dc.x_test, dc.y_test)
logger.info('Accuracy on test set: %f', accuracy)
adv_res = [accuracy]
# STEP 3: generate adversarial examples
# no more than 1000 samples are required
n = 1000 if len(dc.x_test) >= 1000 else len(dc.x_test)
# idx = np.random.choice(len(dc.x_test), n, replace=False)
# x = dc.x_test[idx]
# y = dc.y_test[idx]
x = dc.x_test[:n]
y = dc.y_test[:n]
accuracy = mc.evaluate(x, y)
adv_res.append(accuracy)
advs = np.zeros(
tuple([len(ATTACK_LIST)] + list(x.shape)),
dtype=np.float32)
pred_advs = -np.ones(
(len(ATTACK_LIST), n),
dtype=np.int32) # assign -1 as initial value
pred_clean = mc.predict(x)
advs[0] = x
pred_advs[0] = pred_clean
att_param_json = open(os.path.join(DIR_PATH, 'AttackParams.json'))
att_params = json.load(att_param_json)
for i, att_name in enumerate(ATTACK_LIST):
# Clean set is only used in evaluation phase.
if att_name == 'Clean':
continue
logger.debug('[%d]Running %s attack...', i, att_name)
kwargs = att_params[att_name]
logger.debug('%s params: %s', att_name, str(kwargs))
Attack = get_attack(att_name)
attack = Attack(mc, **kwargs)
adv, pred_adv, x_clean, pred_clean_ = attack.generate(
use_testset=False,
x=x)
assert np.all(pred_clean == pred_clean_)
assert np.all(x == x_clean)
logger.info('created %d adv examples using %s from %s',
len(advs[i]),
att_name,
dname)
not_match = pred_adv != pred_clean
success_rate = len(not_match[not_match == True]) / len(pred_clean)
accuracy = mc.evaluate(adv, y)
advs[i] = adv
pred_advs[i] = pred_adv
logger.info('Success rate of %s: %f', att_name, success_rate)
logger.info('Accuracy on %s: %f', att_name, accuracy)
adv_res.append(accuracy)
adv_file.write(','.join([str(r) for r in adv_res]) + '\n')
# STEP 4: train defences
blocked_res = np.zeros(len(TITLE_RESULTS), dtype=np.int32)
blocked_res[0] = index
for def_name in DEFENCE_LIST:
logger.debug('Running %s...', def_name)
if def_name == 'AdvTraining':
attack = BIMContainer(
mc,
eps=0.3,
eps_step=0.1,
max_iter=100,
targeted=False)
defence = AdversarialTraining(mc, [attack])
defence.fit(max_epochs=max_epochs,
batch_size=BATCH_SIZE,
ratio=ADV_TRAIN_RATIO)
block_attack(0, advs, defence, def_name, blocked_res)
elif def_name == 'Destillation':
defence = DistillationContainer(
mc, distill_model, temperature=DISTILL_TEMP, pretrained=False)
defence.fit(max_epochs=max_epochs, batch_size=BATCH_SIZE)
block_attack(1, advs, defence, def_name, blocked_res)
elif def_name == 'Squeezing':
defence = FeatureSqueezing(
mc,
SQUEEZER_FILTER_LIST,
bit_depth=SQUEEZER_DEPTH,
sigma=SQUEEZER_SIGMA,
kernel_size=SQUEEZER_KERNEL,
pretrained=True,
)
defence.fit(max_epochs=max_epochs, batch_size=BATCH_SIZE)
block_attack(2, advs, defence, def_name, blocked_res)
elif def_name == 'AD':
ad_param_file = open(AD_PARAM_FILE)
ad_params = json.load(ad_param_file)
logger.debug('AD params: %s', str(ad_params))
defence = ApplicabilityDomainContainer(
mc,
hidden_model=model.hidden_model,
**ad_params)
defence.fit()
block_attack(3, advs, defence, def_name, blocked_res)
res_file.write(','.join([str(r) for r in blocked_res]) + '\n')
def experiment(index, dname, max_epochs, adv_file, res_file):
# STEP 1: select data
dc = get_data_container(dname, use_shuffle=True, use_normalize=True)
model = None
if dname == 'BreastCancerWisconsin':
model = BCNN()
distill_model = BCNN()
elif dname in ('BankNote', 'HTRU2', 'Iris', 'WheatSeed'):
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
)
distill_model = IrisNN(
num_features=num_features,
hidden_nodes=num_features*4,
num_classes=num_classes
)
if model is None:
logger.error('Unrecognised dataset %s', dname)
logger.info('Selected %s model', model.__class__.__name__)
# STEP 2: train models
mc = ModelContainerPT(model, dc)
mc.fit(max_epochs=max_epochs, batch_size=BATCH_SIZE)
accuracy = mc.evaluate(dc.x_test, dc.y_test)
logger.info('Accuracy on test set: %f', accuracy)
adv_res = [accuracy]
# STEP 3: generate adversarial examples
# no more than 1000 samples are required
x = dc.x_test
y = dc.y_test
if len(x) > 1000:
x = x[:1000]
y = y[:1000]
accuracy = mc.evaluate(x, y)
adv_res.append(accuracy)
advs = np.zeros((len(ATTACK_LIST), x.shape[0], x.shape[1]),
dtype=np.float32)
pred_advs = -np.ones((len(ATTACK_LIST), len(y)),
dtype=np.int32) # assign -1 as initial value
pred_clean = mc.predict(x)
advs[0] = x
pred_advs[0] = pred_clean
att_param_json = open(os.path.join(DIR_PATH, 'AttackParams.json'))
att_params = json.load(att_param_json)
for i, att_name in enumerate(ATTACK_LIST):
# Clean set is only used in evaluation phase.
if att_name == 'Clean':
continue
logger.debug('[%d]Running %s attack...', i, att_name)
kwargs = att_params[att_name]
logger.debug('%s params: %s', att_name, str(kwargs))
Attack = get_attack(att_name)
attack = Attack(mc, **kwargs)
adv, pred_adv, x_clean, pred_clean_ = attack.generate(
use_testset=False,
x=x)
assert np.all(pred_clean == pred_clean_)
assert np.all(x == x_clean)
logger.info('created %d adv examples using %s from %s',
len(advs[i]),
att_name,
dname)
not_match = pred_adv != pred_clean
success_rate = len(not_match[not_match == True]) / len(pred_clean)
accuracy = mc.evaluate(adv, y)
advs[i] = adv
pred_advs[i] = pred_adv
logger.info('Success rate of %s: %f', att_name, success_rate)
logger.info('Accuracy on %s: %f', att_name, accuracy)
adv_res.append(accuracy)
adv_file.write(','.join([str(r) for r in adv_res]) + '\n')
# STEP 4: train defences
blocked_res = np.zeros(len(TITLE_RESULTS), dtype=np.int32)
blocked_res[0] = index
for def_name in DEFENCE_LIST:
logger.debug('Running %s...', def_name)
if def_name == 'AdvTraining':
attack = BIMContainer(
mc,
eps=0.3,
eps_step=0.1,
max_iter=100,
targeted=False)
defence = AdversarialTraining(mc, [attack])
defence.fit(max_epochs=max_epochs,
batch_size=BATCH_SIZE,
ratio=ADV_TRAIN_RATIO)
block_attack(0, advs, defence, def_name, blocked_res)
elif def_name == 'Destillation':
if dname == 'Iris':
temp = 10
elif dname == 'BreastCancerWisconsin':
temp = 2
else:
temp = 20
defence = DistillationContainer(
mc, distill_model, temperature=temp, pretrained=False)
defence.fit(max_epochs=max_epochs, batch_size=BATCH_SIZE)
block_attack(1, advs, defence, def_name, blocked_res)
elif def_name == 'Squeezing':
defence = FeatureSqueezing(
mc,
SQUEEZER_FILTER_LIST,
bit_depth=SQUEEZER_DEPTH,
sigma=SQUEEZER_SIGMA,
pretrained=True,
)
defence.fit(max_epochs=max_epochs, batch_size=BATCH_SIZE)
block_attack(2, advs, defence, def_name, blocked_res)
elif def_name == 'AD':
ad_param_file = open(AD_PARAM_FILE)
# BreastCancer uses a different set of parameters
if dname == 'BreastCancerWisconsin':
param_file = os.path.join(DIR_PATH, 'AdParamsBC.json')
ad_param_file = open(param_file)
ad_params = json.load(ad_param_file)
logger.debug('AD params: %s', str(ad_params))
defence = ApplicabilityDomainContainer(
mc,
hidden_model=model.hidden_model,
**ad_params)
defence.fit()
block_attack(3, advs, defence, def_name, blocked_res)
res_file.write(','.join([str(r) for r in blocked_res]) + '\n')
def main():
parser = ap.ArgumentParser()
parser.add_argument('-d',
'--dataset',
type=str,
required=True,
choices=get_dataset_list(),
help='the dataset you want to train')
parser.add_argument(
'-o',
'--ofile',
type=str,
help='the filename will be used to store model parameters')
parser.add_argument('-e',
'--epoch',
type=int,
default=5,
help='the number of max epochs for training')
parser.add_argument('-b',
'--batchsize',
type=int,
default=128,
help='batch size')
parser.add_argument('-s',
'--seed',
type=int,
default=4096,
help='the seed for random number generator')
parser.add_argument('-H',
'--shuffle',
type=bool,
default=True,
help='shuffle the dataset')
parser.add_argument(
'-n',
'--normalize',
type=bool,
default=True,
help=
'apply zero mean and scaling to the dataset (for numeral dataset only)'
)
parser.add_argument('-m',
'--model',
type=str,
choices=AVALIABLE_MODELS,
help='select a model to train the data')
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()
dname = args.dataset
filename = args.ofile
max_epochs = args.epoch
batch_size = args.batchsize
seed = args.seed
use_shuffle = args.shuffle
use_normalize = args.normalize
model_name = args.model
verbose = args.verbose
save_log = args.savelog
overwrite = args.overwrite
# set logging config. Run this before logging anything!
set_logging('train', dname, verbose, save_log)
# show parameters
print('[train] Start training {} model...'.format(model_name))
logger.info('Start at : %s', get_time_str())
logger.info('RECEIVED PARAMETERS:')
logger.info('dataset :%s', dname)
logger.info('filename :%s', filename)
logger.info('max_epochs :%d', max_epochs)
logger.info('batch_size :%d', batch_size)
logger.info('seed :%d', seed)
logger.info('use_shuffle :%r', use_shuffle)
logger.info('use_normalize :%r', use_normalize)
logger.info('model_name :%s', model_name)
logger.info('verbose :%r', verbose)
logger.info('save_log :%r', save_log)
logger.info('overwrite :%r', overwrite)
master_seed(seed)
# set DataContainer
dc = get_data_container(
dname,
use_shuffle=use_shuffle,
use_normalize=use_normalize,
)
# select a model
model = None
if model_name is not None:
Model = models.get_model(model_name)
model = Model()
else:
if dname == 'MNIST':
model = models.MnistCnnV2()
elif dname == 'CIFAR10':
model = models.CifarCnn()
elif dname == 'BreastCancerWisconsin':
model = models.BCNN()
elif dname in ('BankNote', 'HTRU2', 'Iris', 'WheatSeed'):
num_classes = dc.num_classes
num_features = dc.dim_data[0]
model = models.IrisNN(num_features=num_features,
hidden_nodes=num_features * 4,
num_classes=num_classes)
if model is None:
raise AttributeError('Cannot find model!')
modelname = model.__class__.__name__
logger.info('Selected %s model', modelname)
# set ModelContainer and train the model
mc = models.ModelContainerPT(model, dc)
mc.fit(max_epochs=max_epochs, batch_size=batch_size)
# save
if not os.path.exists('save'):
os.makedirs('save')
if filename is None:
filename = get_pt_model_filename(modelname, dname, max_epochs)
logger.debug('File name: %s', filename)
mc.save(filename, overwrite=overwrite)
# test result
file_path = os.path.join('save', filename)
logger.debug('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():
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(
'-e', '--epoch', type=int, required=True,
help='the number of max epochs for training')
parser.add_argument(
'-r', '--ratio', type=float, required=True,
help='the percentage of adversarial examples mix to the training set.')
parser.add_argument(
'-b', '--batchsize', type=int, default=128, help='batch size')
parser.add_argument(
'-t', '--train', action='store_true', default=False,
help='Force the model to retrain without searching existing pretrained file')
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(
'-B', '--bim', action='store_true', default=False,
help='Apply BIM attack')
parser.add_argument(
'-C', '--carlini', action='store_true', default=False,
help='Apply Carlini L2 attack')
parser.add_argument(
'-D', '--deepfool', action='store_true', default=False,
help='Apply DeepFool attack')
parser.add_argument(
'-F', '--fgsm', action='store_true', default=False,
help='Apply FGSM attack')
parser.add_argument(
'-S', '--saliency', action='store_true', default=False,
help='Apply Saliency Map attack')
args = parser.parse_args()
model_file = args.model
max_epochs = args.epoch
ratio = args.ratio
batch_size = args.batchsize
seed = args.seed
verbose = args.verbose
save_log = args.savelog
need_train = args.train
model_name, data_name = parse_model_filename(model_file)
# Which attack should apply?
attack_list = []
if args.bim:
attack_list.append('BIM')
if args.carlini:
attack_list.append('Carlini')
if args.deepfool:
attack_list.append('DeepFool')
if args.fgsm:
attack_list.append('FGSM')
if args.saliency:
attack_list.append('Saliency')
# Quit, if there is nothing to do.
if len(attack_list) == 0:
logger.warning('Neither received any filter nor any attack. Exit')
sys.exit(0)
y_file = os.path.join(
'save', f'{model_name}_{data_name}_{attack_list[0]}_y.npy')
attack_files = [
os.path.join(
'save', f'{model_name}_{data_name}_{attack_list[0]}_x.npy')
]
for attack_name in attack_list:
attack_files.append(os.path.join(
'save', f'{model_name}_{data_name}_{attack_name}_adv.npy'))
# the 1st file this the clean inputs
attack_list = ['clean'] + attack_list
# Do I need train the discriminator?
pretrain_file = f'AdvTrain_{model_name}_{data_name}.pt'
if not os.path.exists(os.path.join('save', pretrain_file)):
need_train = True
# set logging config. Run this before logging anything!
set_logging(LOG_NAME, data_name, verbose, save_log)
# show parameters
print(f'[{LOG_NAME}] Running adversarial training on {model_name}...')
logger.info('Start at : %s', get_time_str())
logger.info('RECEIVED PARAMETERS:')
logger.info('model file :%s', model_file)
logger.info('model :%s', model_name)
logger.info('dataset :%s', data_name)
logger.info('max_epochs :%d', max_epochs)
logger.info('ratio :%d', ratio)
logger.info('batch_size :%d', batch_size)
logger.info('seed :%d', seed)
logger.info('verbose :%r', verbose)
logger.info('save_log :%r', save_log)
logger.info('need train :%r', need_train)
logger.info('attacks :%s', ', '.join(attack_list))
# check files
for file_name in [model_file, y_file] + attack_files:
if not os.path.exists(file_name):
logger.error('%s does not exist!', file_name)
raise FileNotFoundError('{} does not exist!'.format(file_name))
# reset seed
master_seed(seed)
# select data
dc = get_data_container(
data_name,
use_shuffle=True,
use_normalize=True,
)
# select a model
Model = get_model(model_name)
model = Model()
if data_name in ('BankNote', 'HTRU2', 'Iris', 'WheatSeed'):
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)
classifier_mc = ModelContainerPT(model, 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])
if need_train:
adv_trainer.fit(max_epochs=max_epochs,
batch_size=batch_size, ratio=ratio)
adv_trainer.save(pretrain_file, overwrite=True)
else:
adv_trainer.load(os.path.join('save', pretrain_file))
y = np.load(y_file, allow_pickle=False)
for i in range(len(attack_list)):
adv_file = attack_files[i]
adv_name = attack_list[i]
logger.debug('Load %s...', adv_file)
adv = np.load(adv_file, allow_pickle=False)
accuracy = classifier_mc.evaluate(adv, y)
logger.info('Accuracy on %s set: %f', adv_name, accuracy)
blocked_indices = adv_trainer.detect(adv, return_passed_x=False)
logger.info('Blocked %d/%d samples on %s',
len(blocked_indices), len(adv), adv_name)
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('-e',
'--epoch',
type=int,
required=True,
help='the number of max epochs for training')
parser.add_argument(
'-d',
'--depth',
type=int,
default=0,
help=
'The image color depth for input images. Apply Binary-Depth filter when receives a parameter'
)
parser.add_argument(
'--sigma',
type=float,
default=0,
help=
'The Standard Deviation of Normal distribution. Apply Gaussian Noise filter when receives a parameter'
)
parser.add_argument(
'-k',
'--kernelsize',
type=int,
default=0,
help=
'The kernel size for Median filter. Apply median filter when receives a parameter'
)
parser.add_argument('-b',
'--batchsize',
type=int,
default=128,
help='batch size')
parser.add_argument(
'-T',
'--train',
action='store_true',
default=False,
help=
'Force the model to retrain without searching existing pretrained file'
)
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('-B',
'--bim',
action='store_true',
default=False,
help='Apply BIM attack')
parser.add_argument('-C',
'--carlini',
action='store_true',
default=False,
help='Apply Carlini L2 attack')
parser.add_argument('-D',
'--deepfool',
action='store_true',
default=False,
help='Apply DeepFool attack')
parser.add_argument('-F',
'--fgsm',
action='store_true',
default=False,
help='Apply FGSM attack')
parser.add_argument('-S',
'--saliency',
action='store_true',
default=False,
help='Apply Saliency Map attack')
args = parser.parse_args()
model_file = args.model
max_epochs = args.epoch
bit_depth = args.depth
sigma = args.sigma
kernel_size = args.kernelsize
batch_size = args.batchsize
seed = args.seed
verbose = args.verbose
save_log = args.savelog
need_train = args.train
model_name, data_name = parse_model_filename(model_file)
# Which filter should apply?
filter_list = []
if bit_depth > 0:
filter_list.append('binary')
if sigma > 0:
filter_list.append('normal')
if kernel_size > 0:
filter_list.append('median')
# Which attack should apply?
attack_list = []
if args.fgsm:
attack_list.append('FGSM')
if args.bim:
attack_list.append('BIM')
if args.deepfool:
attack_list.append('DeepFool')
if args.carlini:
attack_list.append('Carlini')
if args.saliency:
attack_list.append('Saliency')
# Quit, if there is nothing to do.
if len(filter_list) == 0 or len(attack_list) == 0:
logger.warning('Neither received any filter nor any attack. Exit')
sys.exit(0)
y_file = os.path.join('save',
f'{model_name}_{data_name}_{attack_list[0]}_y.npy')
attack_files = [
os.path.join('save',
f'{model_name}_{data_name}_{attack_list[0]}_x.npy')
]
for attack_name in attack_list:
attack_files.append(
os.path.join('save',
f'{model_name}_{data_name}_{attack_name}_adv.npy'))
# the 1st file this the clean inputs
attack_list = ['clean'] + attack_list
# Do I need train the distillation network?
pretrain_files = []
for fname in filter_list:
pretrain_file = build_squeezer_filename(model_name, data_name,
max_epochs, fname)
pretrain_files.append(pretrain_file)
if not os.path.exists(os.path.join('save', pretrain_file)):
need_train = True
# set logging config. Run this before logging anything!
set_logging(LOG_NAME, data_name, verbose, save_log)
# show parameters
print(f'[{LOG_NAME}] Running feature squeezing on {model_name}...')
logger.info('Start at : %s', get_time_str())
logger.info('RECEIVED PARAMETERS:')
logger.info('model file :%s', model_file)
logger.info('model :%s', model_name)
logger.info('dataset :%s', data_name)
logger.info('max_epochs :%d', max_epochs)
logger.info('bit_depth :%d', bit_depth)
logger.info('sigma :%f', sigma)
logger.info('kernel_size :%d', kernel_size)
logger.info('batch_size :%d', batch_size)
logger.info('seed :%d', seed)
logger.info('verbose :%r', verbose)
logger.info('save_log :%r', save_log)
logger.info('need train :%r', need_train)
logger.info('filters :%s', ', '.join(filter_list))
logger.info('attacks :%s', ', '.join(attack_list))
logger.info('pretrained :%s', ', '.join(pretrain_files))
# check files
for file_name in [model_file, y_file] + attack_files:
if not os.path.exists(file_name):
logger.error('%s does not exist!', file_name)
raise FileNotFoundError('{} does not exist!'.format(file_name))
# reset seed
master_seed(seed)
# select data
dc = get_data_container(
data_name,
use_shuffle=True,
use_normalize=True,
)
# select a model
Model = get_model(model_name)
model = Model()
if data_name in ('BankNote', 'HTRU2', 'Iris', 'WheatSeed'):
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)
classifier_mc = ModelContainerPT(model, dc)
classifier_mc.load(model_file)
accuracy = classifier_mc.evaluate(dc.x_test, dc.y_test)
logger.info('Accuracy on test set: %f', accuracy)
# initialize Squeezer
squeezer = FeatureSqueezing(
classifier_mc,
filter_list,
bit_depth=bit_depth,
sigma=sigma,
kernel_size=kernel_size,
pretrained=True,
)
# train or load parameters for Squeezer
if need_train:
squeezer.fit(max_epochs=max_epochs, batch_size=batch_size)
squeezer.save(model_file, True)
else:
squeezer.load(model_file)
# traverse all attacks
y = np.load(y_file, allow_pickle=False)
for i in range(len(attack_list)):
adv_file = attack_files[i]
adv_name = attack_list[i]
logger.debug('Load %s...', adv_file)
adv = np.load(adv_file, allow_pickle=False)
acc_og = classifier_mc.evaluate(adv, y)
acc_squeezer = squeezer.evaluate(adv, y)
logger.info('Accuracy on %s set - OG: %f, Squeezer: %f', adv_name,
acc_og, acc_squeezer)
blocked_indices = squeezer.detect(adv, return_passed_x=False)
logger.info('Blocked %d/%d samples on %s', len(blocked_indices),
len(adv), adv_name)