def run_jsma(weight_fpath):
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,
'Nb of test inputs to attack')
flags.DEFINE_float('learning_rate', LEARNING_RATE,
'Learning rate for training')
mnist_tutorial_jsma(weight_fpath,
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)
def run_black_box(weight_fpath, FGSM):
# 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')
mnist_blackbox(FGSM, weight_fpath, nb_classes=FLAGS.nb_classes, batch_size=FLAGS.batch_size,
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)
#tf.app.run()
#Write about customized weights for paper
#Do we want to perform attacks against samples from y_sub? No right?
#Multi agent attacking
#Corrupt agent follows normal policy - if the state is worth attacking then attack the good agent
#Paper - tactics of adversarial attacks on deep reinforcement learning agents (for white box)
#Assuming black box
#Formulate the problem mathematically
check_installation(__file__)
mnist_tutorial_fgsm(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()
bim_test = np.expand_dims(bim_test, axis=3)
bim_test_decoded = model.predict(bim_test)
eval(bim_test)
eval(bim_test_decoded)
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,
train_dir=FLAGS.train_dir,
filename=FLAGS.filename,
load_model=FLAGS.load_model)
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')
flags.DEFINE_float('learning_rate', LEARNING_RATE,
'Learning rate for training')
flags.DEFINE_string('train_dir', TRAIN_DIR,
'Directory where to save model.')
flags.DEFINE_string('filename', FILENAME, 'Checkpoint filename.')
flags.DEFINE_boolean('load_model', LOAD_MODEL,
'Load saved model or train.')
tf.app.run()
raise ValueError(argv)
make_confidence_report(filepath=filepath,
test_start=FLAGS.test_start,
test_end=FLAGS.test_end,
which_set=FLAGS.which_set,
report_path=FLAGS.report_path,
mc_batch_size=FLAGS.mc_batch_size,
nb_iter=FLAGS.nb_iter,
base_eps_iter=FLAGS.base_eps_iter,
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')
_name_of_script, filepath = argv
except ValueError:
raise ValueError(argv)
make_confidence_report_spsa(filepath=filepath,
test_start=FLAGS.test_start,
test_end=FLAGS.test_end,
which_set=FLAGS.which_set,
report_path=FLAGS.report_path,
nb_iter=FLAGS.nb_iter,
batch_size=FLAGS.batch_size,
spsa_samples=FLAGS.spsa_samples,
spsa_iters=FLAGS.spsa_iters)
if __name__ == '__main__':
flags.DEFINE_integer('spsa_samples', SPSA_SAMPLES,
'Number samples for SPSA')
flags.DEFINE_integer('spsa_iters', SPSA.DEFAULT_SPSA_ITERS,
'Passed to SPSA.generate')
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_SPSA,
return report
def main(argv=None):
from cleverhans_tutorials import check_installation
check_installation(__file__)
mnist_tutorial_jsma(
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,
)
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,
"Nb of test inputs to attack")
flags.DEFINE_float("learning_rate", LEARNING_RATE,
"Learning rate for training")
tf.app.run()
import matplotlib.pyplot as plt
plt.close(figure)
_ = grid_visual(grid_viz_data)
return report
def main(argv=None):
from cleverhans_tutorials import check_installation
check_installation(__file__)
mnist_tutorial_jsma(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)
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,
'Nb of test inputs to attack')
flags.DEFINE_float('learning_rate', LEARNING_RATE,
'Learning rate for training')
tf.app.run()
try:
_name_of_script, filepath = argv
except ValueError:
raise ValueError(argv)
print_accuracies(filepath=filepath,
test_start=FLAGS.test_start,
test_end=FLAGS.test_end,
which_set=FLAGS.which_set,
nb_iter=FLAGS.nb_iter,
base_eps_iter=FLAGS.base_eps_iter,
batch_size=FLAGS.batch_size)
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_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]')
check_installation(__file__)
train_deflecting(dataset_name=FLAGS.dataset,
train_end=FLAGS.train_end,
test_end=FLAGS.test_end,
nb_epochs=FLAGS.nb_epochs,
batch_size=FLAGS.batch_size,
num_capsules_output=FLAGS.num_capsules_output,
output_atoms=FLAGS.output_atoms,
num_routing=FLAGS.num_routing,
learning_rate=FLAGS.learning_rate,
nb_filters=FLAGS.nb_filters)
if __name__ == '__main__':
flags.DEFINE_integer('train_end', TRAIN_END,
'Number of training data')
flags.DEFINE_integer('test_end', TEST_END,
'Number of test data')
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_integer('num_capsules_output', NUM_CAPSULES_OUTPUT,
'Number of class capsules and background capsules')
flags.DEFINE_integer('output_atoms', OUTPUT_ATOMS,
'Size of each capsule')
flags.DEFINE_integer('num_routing', NUM_ROUTING,
'Number of routing in capsule layer')
flags.DEFINE_float('learning_rate', LEARNING_RATE,
mnist_tutorial_cw(
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()
trainer = TrainerSingleGPU(hparams)
trainer.model_train()
trainer.eval(inc_epoch=False)
return trainer.finish()
def main(argv=None):
f = {x: flags.FLAGS[x].value for x in dir(flags.FLAGS)}
HParams = namedtuple('HParams', f.keys())
hparams = HParams(**f)
run_trainer(hparams)
if __name__ == '__main__':
flags.DEFINE_integer('train_start', 0,
'Index of first training set example.')
flags.DEFINE_integer('train_end', 60000,
'Index of last training set example.')
flags.DEFINE_integer('test_start', 0, 'Index of first test set example.')
flags.DEFINE_integer('test_end', 10000, 'Index of last test set example.')
flags.DEFINE_integer('nb_epochs', 6, 'Number of epochs to train model.')
flags.DEFINE_integer('batch_size', 128, 'Size of training batches.')
flags.DEFINE_boolean('adv_train', False,
'Whether to do adversarial training.')
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:\
raise ValueError(argv)
print_accuracies(
filepath=filepath,
test_start=FLAGS.test_start,
test_end=FLAGS.test_end,
which_set=FLAGS.which_set,
nb_iter=FLAGS.nb_iter,
base_eps_iter=FLAGS.base_eps_iter,
batch_size=FLAGS.batch_size,
)
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(
try:
_name_of_script, filepath = argv
except ValueError:
raise ValueError(argv)
print(filepath)
make_confidence_report_bundled(filepath=filepath,
test_start=FLAGS.test_start,
test_end=FLAGS.test_end,
which_set=FLAGS.which_set,
recipe=FLAGS.recipe,
report_path=FLAGS.report_path)
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_string('recipe', RECIPE,
'Name of function from attack_bundling'
' to run')
flags.DEFINE_string('which_set', WHICH_SET, '"train" or "test"')
flags.DEFINE_string('report_path', REPORT_PATH, 'Report path')
sess, x, y, preds_2_adv, x_train, y_train, args=eval_params
)
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,
train_dir=FLAGS.train_dir,
filename=FLAGS.filename,
load_model=FLAGS.load_model,
)
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")
flags.DEFINE_float("learning_rate", LEARNING_RATE, "Learning rate for training")
flags.DEFINE_string("train_dir", TRAIN_DIR, "Directory where to save model.")
flags.DEFINE_string("filename", FILENAME, "Checkpoint filename.")
flags.DEFINE_boolean("load_model", LOAD_MODEL, "Load saved model or train.")
tf.app.run()
mnist_blackbox(nb_classes=FLAGS.nb_classes,
batch_size=FLAGS.batch_size,
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')
nb_classes=FLAGS.nb_classes,
batch_size=FLAGS.batch_size,
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"
"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()
print(filepath)
make_confidence_report_bundled(
filepath=filepath,
test_start=FLAGS.test_start,
test_end=FLAGS.test_end,
which_set=FLAGS.which_set,
recipe=FLAGS.recipe,
report_path=FLAGS.report_path,
batch_size=FLAGS.batch_size,
)
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(
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()
test_start=FLAGS.test_start,
test_end=FLAGS.test_end,
which_set=FLAGS.which_set,
report_path=FLAGS.report_path,
mc_batch_size=FLAGS.mc_batch_size,
nb_iter=FLAGS.nb_iter,
base_eps_iter=FLAGS.base_eps_iter,
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,