learning_rate=FLAGS.learning_rate,
nb_epochs=FLAGS.nb_epochs,
holdout=FLAGS.holdout,
data_aug=FLAGS.data_aug,
nb_epochs_s=FLAGS.nb_epochs_s,
lmbda=FLAGS.lmbda,
aug_batch_size=FLAGS.data_aug_batch_size,
)
if __name__ == "__main__":
# General flags
flags.DEFINE_integer("nb_classes", NB_CLASSES, "Number of classes in problem")
flags.DEFINE_integer("batch_size", BATCH_SIZE, "Size of training batches")
flags.DEFINE_float("learning_rate", LEARNING_RATE, "Learning rate for training")
# Flags related to oracle
flags.DEFINE_integer("nb_epochs", NB_EPOCHS, "Number of epochs to train model")
# Flags related to substitute
flags.DEFINE_integer("holdout", HOLDOUT, "Test set holdout for adversary")
flags.DEFINE_integer(
"data_aug", DATA_AUG, "Number of substitute data augmentations"
)
flags.DEFINE_integer("nb_epochs_s", NB_EPOCHS_S, "Training epochs for substitute")
flags.DEFINE_float("lmbda", LMBDA, "Lambda from arxiv.org/abs/1602.02697")
flags.DEFINE_integer(
"data_aug_batch_size", AUG_BATCH_SIZE, "Batch size for augmentation"
)
learning_rate=FLAGS.learning_rate,
nb_epochs=FLAGS.nb_epochs,
holdout=FLAGS.holdout,
data_aug=FLAGS.data_aug,
nb_epochs_s=FLAGS.nb_epochs_s,
lmbda=FLAGS.lmbda,
aug_batch_size=FLAGS.data_aug_batch_size)
if __name__ == '__main__':
# General flags
flags.DEFINE_integer('nb_classes', NB_CLASSES,
'Number of classes in problem')
flags.DEFINE_integer('batch_size', BATCH_SIZE, 'Size of training batches')
flags.DEFINE_float('learning_rate', LEARNING_RATE,
'Learning rate for training')
# Flags related to oracle
flags.DEFINE_integer('nb_epochs', NB_EPOCHS,
'Number of epochs to train model')
# Flags related to substitute
flags.DEFINE_integer('holdout', HOLDOUT, 'Test set holdout for adversary')
flags.DEFINE_integer('data_aug', DATA_AUG,
'Number of substitute data augmentations')
flags.DEFINE_integer('nb_epochs_s', NB_EPOCHS_S,
'Training epochs for substitute')
flags.DEFINE_float('lmbda', LMBDA, 'Lambda from arxiv.org/abs/1602.02697')
flags.DEFINE_integer('data_aug_batch_size', AUG_BATCH_SIZE,
'Batch size for augmentation')
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__)
cifar10_tutorial_bim(viz_enabled=FLAGS.viz_enabled,
nb_epochs=FLAGS.nb_epochs,
batch_size=FLAGS.batch_size,
source_samples=FLAGS.source_samples,
learning_rate=FLAGS.learning_rate,
attack_iterations=FLAGS.attack_iterations,
model_path=FLAGS.model_path,
targeted=FLAGS.targeted)
if __name__ == '__main__':
flags.DEFINE_boolean('viz_enabled', VIZ_ENABLED,
'Visualize adversarial ex.')
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_integer('source_samples', SOURCE_SAMPLES,
'Number of test inputs to attack')
flags.DEFINE_float('learning_rate', LEARNING_RATE,
'Learning rate for training')
flags.DEFINE_string('model_path', MODEL_PATH,
'Path to save or load the model file')
flags.DEFINE_integer('attack_iterations', ATTACK_ITERATIONS,
'Number of iterations to run attack; 1000 is good')
flags.DEFINE_boolean('targeted', TARGETED,
'Run the tutorial in targeted mode?')
tf.app.run()
batch_size=FLAGS.batch_size,
save_advx=FLAGS.save_advx)
if __name__ == '__main__':
flags.DEFINE_integer(
'train_start', TRAIN_START, 'Starting point (inclusive)'
'of range of train examples to use')
flags.DEFINE_integer(
'train_end', TRAIN_END, 'Ending point (non-inclusive) '
'of range of train examples to use')
flags.DEFINE_integer(
'test_start', TEST_START, 'Starting point (inclusive) '
'of range of test examples to use')
flags.DEFINE_integer(
'test_end', TEST_END, 'End point (non-inclusive) of '
'range of test examples to use')
flags.DEFINE_integer('nb_iter', NB_ITER, 'Number of iterations of PGD')
flags.DEFINE_string('which_set', WHICH_SET, '"train" or "test"')
flags.DEFINE_string('report_path', REPORT_PATH, 'Path to save to')
flags.DEFINE_integer('mc_batch_size', MC_BATCH_SIZE,
'Batch size for MaxConfidence')
flags.DEFINE_integer('batch_size', BATCH_SIZE, 'Batch size for most jobs')
flags.DEFINE_float('base_eps_iter', BASE_EPS_ITER,
'epsilon per iteration, if data were in [0, 1]')
flags.DEFINE_integer(
'save_advx', SAVE_ADVX,
'If True, saves the adversarial examples to the '
'filesystem.')
tf.compat.v1.app.run()
fontsize=42,
)
imscatter(X_embedded, x_test[:batch_size], zoom=2, cmap="Purples")
plt.savefig(
output_dir + "adversarial_gradients_SNNL_factor_" + str(SNNL_factor) + ".png"
)
def main(argv=None):
SNNL_example(
nb_epochs=FLAGS.nb_epochs,
batch_size=FLAGS.batch_size,
learning_rate=FLAGS.learning_rate,
nb_filters=FLAGS.nb_filters,
SNNL_factor=FLAGS.SNNL_factor,
output_dir=FLAGS.output_dir,
)
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(
"SNNL_factor", SNNL_FACTOR, "Multiplier for Soft Nearest Neighbor Loss"
)
flags.DEFINE_float("learning_rate", LEARNING_RATE, "Learning rate for training")
flags.DEFINE_string("output_dir", OUTPUT_DIR, "output directory for saving figures")
tf.app.run()
generate_adv_images(gpu=FLAGS.gpus,
attack_algo=FLAGS.attack,
dataset=FLAGS.dataset,
source_data_dir=DATASET_SOURCE_PATH[FLAGS.dataset],
nb_epochs=FLAGS.nb_epochs,
batch_size=FLAGS.batch_size,
learning_rate=FLAGS.learning_rate,
testing=True,
args=FLAGS)
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', config.BATCH_SIZE,
'Size of training batches')
flags.DEFINE_float('learning_rate', 1e-3, 'Learning rate for training')
flags.DEFINE_string('gpus', "0", 'GPU for training')
flags.DEFINE_enum("attack", "FGSM", META_ATTACKER_INDEX,
"the attack method")
flags.DEFINE_enum("dataset", "CIFAR10", [
"CIFAR10", "CIFAR100", "TinyImageNet", "CIFAR100_coarse_label",
"MNIST", "FashionMNIST", "ImageNet", "SVHN", "AWA2", "CUB"
], "the dataset we want to generate")
flags.DEFINE_enum("arch", "conv4", [
"conv10", "conv4", "vgg16", "vgg16small", "resnet10", "resnet18",
"resnet50", "resnet101"
], "the network be used to generate adversarial examples")
tf.app.run()
plt.title("TSNE of Sign of Adv Gradients, SNNLCrossEntropy Model, factor:" +
str(FLAGS.SNNL_factor), fontsize=42)
imscatter(X_embedded, x_test[:batch_size], zoom=2, cmap="Purples")
plt.savefig(output_dir + 'adversarial_gradients_SNNL_factor_' +
str(SNNL_factor) + '.png')
def main(argv=None):
SNNL_example(nb_epochs=FLAGS.nb_epochs, batch_size=FLAGS.batch_size,
learning_rate=FLAGS.learning_rate,
nb_filters=FLAGS.nb_filters,
SNNL_factor=FLAGS.SNNL_factor,
output_dir=FLAGS.output_dir)
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('SNNL_factor', SNNL_FACTOR,
'Multiplier for Soft Nearest Neighbor Loss')
flags.DEFINE_float('learning_rate', LEARNING_RATE,
'Learning rate for training')
flags.DEFINE_string('output_dir', OUTPUT_DIR,
'output directory for saving figures')
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()
flags.DEFINE_boolean('save', True, 'Whether to save from a checkpoint.')
flags.DEFINE_string('save_dir', 'runs/X', 'Location to store logs/model.')
flags.DEFINE_string('model_type', 'madry',
'Model type: basic|madry|resnet_tf.')
flags.DEFINE_string(
'attack_type_train', 'MadryEtAl_y_multigpu',
'Attack type for adversarial training:\
FGSM|MadryEtAl{,_y}{,_multigpu}.')
flags.DEFINE_string('attack_type_test', 'FGSM',
'Attack type for test: FGSM|MadryEtAl{,_y}.')
flags.DEFINE_string('dataset', 'mnist', 'Dataset mnist|cifar10.')
flags.DEFINE_boolean(
'only_adv_train', False,
'Do not train with clean examples when adv training.')
flags.DEFINE_integer('save_steps', 50, 'Save model per X steps.')
flags.DEFINE_integer('attack_nb_iter_train', None,
'Number of iterations of training attack.')
flags.DEFINE_integer('eval_iters', 1, 'Evaluate every X steps.')
flags.DEFINE_integer('lrn_step', 30000, 'Step to decrease learning rate'
'for ResNet.')
flags.DEFINE_float('adam_lrn', 0.001, 'Learning rate for Adam Optimizer.')
flags.DEFINE_float('mom_lrn', 0.1, 'Learning rate for Momentum Optimizer.')
flags.DEFINE_integer('ngpu', 1, 'Number of gpus.')
flags.DEFINE_integer('sync_step', 1, 'Sync params frequency.')
flags.DEFINE_boolean('fast_tests', False, 'Fast tests against attacks.')
flags.DEFINE_string(
'data_path', './datasets/', 'Path to datasets.'
'Each dataset should be in a subdirectory.')
app.run()
flags.DEFINE_integer(
"train_start",
TRAIN_START,
"Starting point (inclusive)"
"of range of train examples to use",
)
flags.DEFINE_integer(
"train_end",
TRAIN_END,
"Ending point (non-inclusive) "
"of range of train examples to use",
)
flags.DEFINE_integer(
"test_start",
TEST_START,
"Starting point (inclusive) "
"of range of test examples to use",
)
flags.DEFINE_integer(
"test_end",
TEST_END,
"End point (non-inclusive) of "
"range of test examples to use",
)
flags.DEFINE_integer("nb_iter", NB_ITER, "Number of iterations of PGD")
flags.DEFINE_string("which_set", WHICH_SET, '"train" or "test"')
flags.DEFINE_integer("batch_size", BATCH_SIZE, "Batch size for most jobs")
flags.DEFINE_float("base_eps_iter", BASE_EPS_ITER,
"epsilon per iteration, if data were in [0, 1]")
tf.app.run()
