from cleverhans_tutorials import check_installation
check_installation(__file__)
cifar10_tutorial(
nb_epochs=FLAGS.nb_epochs,
batch_size=FLAGS.batch_size,
learning_rate=FLAGS.learning_rate,
clean_train=FLAGS.clean_train,
backprop_through_attack=FLAGS.backprop_through_attack,
nb_filters=FLAGS.nb_filters,
)
if __name__ == "__main__":
flags.DEFINE_integer("nb_filters", NB_FILTERS, "Model size multiplier")
flags.DEFINE_integer("nb_epochs", NB_EPOCHS,
"Number of epochs to train model")
flags.DEFINE_integer("batch_size", BATCH_SIZE, "Size of training batches")
flags.DEFINE_float("learning_rate", LEARNING_RATE,
"Learning rate for training")
flags.DEFINE_bool("clean_train", CLEAN_TRAIN, "Train on clean examples")
flags.DEFINE_bool(
"backprop_through_attack",
BACKPROP_THROUGH_ATTACK,
("If True, backprop through adversarial example "
"construction process during adversarial training"),
)
tf.app.run()
return report
def main(argv=None):
"""
Run the tutorial using command line flags
"""
from cleverhans_tutorials import check_installation
check_installation(__file__)
mnist_tutorial(nb_epochs=FLAGS.nb_epochs, batch_size=FLAGS.batch_size,
learning_rate=FLAGS.learning_rate,
clean_train=FLAGS.clean_train,
backprop_through_attack=FLAGS.backprop_through_attack,
nb_filters=FLAGS.nb_filters)
if __name__ == '__main__':
flags.DEFINE_integer('nb_filters', NB_FILTERS, 'Model size multiplier')
flags.DEFINE_integer('nb_epochs', NB_EPOCHS,
'Number of epochs to train model')
flags.DEFINE_integer('batch_size', BATCH_SIZE, 'Size of training batches')
flags.DEFINE_float('learning_rate', LEARNING_RATE,
'Learning rate for training')
flags.DEFINE_bool('clean_train', CLEAN_TRAIN, 'Train on clean examples')
flags.DEFINE_bool('backprop_through_attack', BACKPROP_THROUGH_ATTACK,
('If True, backprop through adversarial example '
'construction process during adversarial training'))
tf.app.run()
report.train_adv_train_adv_eval = accuracy
return report
def main(argv=None):
from cleverhans_tutorials import check_installation
check_installation(__file__)
mnist_tutorial(
nb_epochs=FLAGS.nb_epochs, batch_size=FLAGS.batch_size,
learning_rate=FLAGS.learning_rate, clean_train=FLAGS.clean_train,
backprop_through_attack=FLAGS.backprop_through_attack,
nb_filters=FLAGS.nb_filters, attack_string=FLAGS.attack)
if __name__ == '__main__':
flags.DEFINE_integer('nb_filters', NB_FILTERS,
'Model size multiplier')
flags.DEFINE_integer('nb_epochs', NB_EPOCHS,
'Number of epochs to train model')
flags.DEFINE_integer('batch_size', BATCH_SIZE, 'Size of training batches')
flags.DEFINE_float('learning_rate', LEARNING_RATE,
'Learning rate for training')
flags.DEFINE_bool('clean_train', True, 'Train on clean examples')
flags.DEFINE_bool('backprop_through_attack', False,
('If True, backprop through adversarial example '
'construction process during adversarial training'))
flags.DEFINE_string('attack', 'fgsm',
'Adversarial attack crafted and used for training')
tf.app.run()
check_installation(__file__)
mnist_tutorial(
nb_epochs=FLAGS.nb_epochs,
batch_size=FLAGS.batch_size,
learning_rate=FLAGS.learning_rate,
clean_train=FLAGS.clean_train,
backprop_through_attack=FLAGS.backprop_through_attack,
nb_filters=FLAGS.nb_filters,
attack_string=FLAGS.attack,
)
if __name__ == "__main__":
flags.DEFINE_integer("nb_filters", NB_FILTERS, "Model size multiplier")
flags.DEFINE_integer("nb_epochs", NB_EPOCHS,
"Number of epochs to train model")
flags.DEFINE_integer("batch_size", BATCH_SIZE, "Size of training batches")
flags.DEFINE_float("learning_rate", LEARNING_RATE,
"Learning rate for training")
flags.DEFINE_bool("clean_train", True, "Train on clean examples")
flags.DEFINE_bool(
"backprop_through_attack",
False,
("If True, backprop through adversarial example "
"construction process during adversarial training"),
)
flags.DEFINE_string("attack", "fgsm",
"Adversarial attack crafted and used for training")
tf.app.run()
train_set.discard('white_box')
train_set = train_set - child_set
if not(adv_training or testing):
continue
for tm in train_set:
print('running',tm) #for each threat model availible
report=cifar10_train_on_untargeted(nb_epochs=FLAGS.nb_epochs,
batch_size=FLAGS.batch_size, learning_rate=FLAGS.learning_rate,
testing=testing, adv_training=adv_training,
model_key=FLAGS.model_key, attacker_key=key,threat_model=tm,
backprop_through_attack=FLAGS.backprop_through_attack)
if __name__ == '__main__':
flags.DEFINE_integer('nb_epochs', NB_EPOCHS,
'Number of epochs to train model')
flags.DEFINE_integer('batch_size', BATCH_SIZE,
'Size of training batches' 'Learning rate for training')
flags.DEFINE_bool('backprop_through_attack', BACKPROP_THROUGH_ATTACK,
('If True, backprop through adversarial example '
'construction process during adversarial training'))
flags.DEFINE_bool('adv_training', ADV_TRAINING,
'If True, train the classifier on the adversarial examples.')
flags.DEFINE_bool('testing', TESTING,
'If True, test the trained classifier on the adversarial training examples.')
flags.DEFINE_string('model_key', MODEL_KEY,
'model key for the model to be adversarially trained. See meta.json')
flags.DEFINE_float('learning_rate',LEARNING_RATE,
'The starting learning rate for adversarial training')
flags.DEFINE_list('attacker_keys',ATTACKER_KEYS,'list of attacker keys to train as defined in meta file')
tf.app.run()
sweep_eps=FLAGS.sweep_eps,
model_key=key,
eval_model_keys=eval_model_keys,
attacker_keys=attacker_keys,
threat_model=FLAGS.threat_model,
generate_examples=True)
with open(report_path, 'w') as fp:
deep_update(report_update, {key: report})
json.dump(report_update, fp, indent=4, skipkeys=True)
if FLAGS.create_reports:
create_reports(model['folder_path'], targeted=FLAGS.targeted)
if __name__ == '__main__':
flags.DEFINE_bool('sweep_eps', SWEEP_EPS,
'Flag to sweep epsilon in evaluation')
flags.DEFINE_bool('targeted', TARGETED, 'Flag for targeted attack')
flags.DEFINE_bool(
'reeval', REEVAL,
('Flag for reevaluating all attackers'
'regardless of whether they have been previously computed'))
flags.DEFINE_string('dataset', DATASET,
'flag for dataset to eval. Options are CIFAR10')
flags.DEFINE_bool('create_reports', CREATE_REPORTS,
'Flag whether to create reports')
flags.DEFINE_list(
'attacker_keys', ATTACKER_KEYS,
'list of attacker keys to evaluate as defined in meta file')
flags.DEFINE_list(
'model_keys', MODEL_KEYS,
'list of model keys to evaluate as defined in meta file'
