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tensorflow/cleverhans | cleverhans/future/tf2/attacks/fast_gradient_method.py | compute_gradient | def compute_gradient(model_fn, x, y, targeted):
"""
Computes the gradient of the loss with respect to the input tensor.
:param model_fn: a callable that takes an input tensor and returns the model logits.
:param x: input tensor
:param y: Tensor with true labels. If targeted is true, then provide the target la... | python | def compute_gradient(model_fn, x, y, targeted):
"""
Computes the gradient of the loss with respect to the input tensor.
:param model_fn: a callable that takes an input tensor and returns the model logits.
:param x: input tensor
:param y: Tensor with true labels. If targeted is true, then provide the target la... | [
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tensorflow/cleverhans | cleverhans/future/tf2/attacks/fast_gradient_method.py | optimize_linear | def optimize_linear(grad, eps, ord=np.inf):
"""
Solves for the optimal input to a linear function under a norm constraint.
Optimal_perturbation = argmax_{eta, ||eta||_{ord} < eps} dot(eta, grad)
:param grad: tf tensor containing a batch of gradients
:param eps: float scalar specifying size of constraint reg... | python | def optimize_linear(grad, eps, ord=np.inf):
"""
Solves for the optimal input to a linear function under a norm constraint.
Optimal_perturbation = argmax_{eta, ||eta||_{ord} < eps} dot(eta, grad)
:param grad: tf tensor containing a batch of gradients
:param eps: float scalar specifying size of constraint reg... | [
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tensorflow/cleverhans | cleverhans/plot/save_pdf.py | save_pdf | def save_pdf(path):
"""
Saves a pdf of the current matplotlib figure.
:param path: str, filepath to save to
"""
pp = PdfPages(path)
pp.savefig(pyplot.gcf())
pp.close() | python | def save_pdf(path):
"""
Saves a pdf of the current matplotlib figure.
:param path: str, filepath to save to
"""
pp = PdfPages(path)
pp.savefig(pyplot.gcf())
pp.close() | [
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tensorflow/cleverhans | cleverhans/future/tf2/utils_tf.py | clip_eta | def clip_eta(eta, ord, eps):
"""
Helper function to clip the perturbation to epsilon norm ball.
:param eta: A tensor with the current perturbation.
:param ord: Order of the norm (mimics Numpy).
Possible values: np.inf, 1 or 2.
:param eps: Epsilon, bound of the perturbation.
"""
# Clipping p... | python | def clip_eta(eta, ord, eps):
"""
Helper function to clip the perturbation to epsilon norm ball.
:param eta: A tensor with the current perturbation.
:param ord: Order of the norm (mimics Numpy).
Possible values: np.inf, 1 or 2.
:param eps: Epsilon, bound of the perturbation.
"""
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tensorflow/cleverhans | cleverhans_tutorials/mnist_blackbox.py | prep_bbox | def prep_bbox(sess, x, y, x_train, y_train, x_test, y_test,
nb_epochs, batch_size, learning_rate,
rng, nb_classes=10, img_rows=28, img_cols=28, nchannels=1):
"""
Define and train a model that simulates the "remote"
black-box oracle described in the original paper.
:param sess: the TF... | python | def prep_bbox(sess, x, y, x_train, y_train, x_test, y_test,
nb_epochs, batch_size, learning_rate,
rng, nb_classes=10, img_rows=28, img_cols=28, nchannels=1):
"""
Define and train a model that simulates the "remote"
black-box oracle described in the original paper.
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tensorflow/cleverhans | cleverhans_tutorials/mnist_blackbox.py | train_sub | def train_sub(sess, x, y, bbox_preds, x_sub, y_sub, nb_classes,
nb_epochs_s, batch_size, learning_rate, data_aug, lmbda,
aug_batch_size, rng, img_rows=28, img_cols=28,
nchannels=1):
"""
This function creates the substitute by alternatively
augmenting the training data and... | python | def train_sub(sess, x, y, bbox_preds, x_sub, y_sub, nb_classes,
nb_epochs_s, batch_size, learning_rate, data_aug, lmbda,
aug_batch_size, rng, img_rows=28, img_cols=28,
nchannels=1):
"""
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tensorflow/cleverhans | cleverhans_tutorials/mnist_blackbox.py | mnist_blackbox | def mnist_blackbox(train_start=0, train_end=60000, test_start=0,
test_end=10000, nb_classes=NB_CLASSES,
batch_size=BATCH_SIZE, learning_rate=LEARNING_RATE,
nb_epochs=NB_EPOCHS, holdout=HOLDOUT, data_aug=DATA_AUG,
nb_epochs_s=NB_EPOCHS_S, lmbda=... | python | def mnist_blackbox(train_start=0, train_end=60000, test_start=0,
test_end=10000, nb_classes=NB_CLASSES,
batch_size=BATCH_SIZE, learning_rate=LEARNING_RATE,
nb_epochs=NB_EPOCHS, holdout=HOLDOUT, data_aug=DATA_AUG,
nb_epochs_s=NB_EPOCHS_S, lmbda=... | [
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:param train_end: index of last training set example
:param test_start: index of first test set example
:param test_end: index of last test set example
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tensorflow/cleverhans | cleverhans/augmentation.py | random_shift | def random_shift(x, pad=(4, 4), mode='REFLECT'):
"""Pad a single image and then crop to the original size with a random
offset."""
assert mode in 'REFLECT SYMMETRIC CONSTANT'.split()
assert x.get_shape().ndims == 3
xp = tf.pad(x, [[pad[0], pad[0]], [pad[1], pad[1]], [0, 0]], mode)
return tf.random_crop(xp, ... | python | def random_shift(x, pad=(4, 4), mode='REFLECT'):
"""Pad a single image and then crop to the original size with a random
offset."""
assert mode in 'REFLECT SYMMETRIC CONSTANT'.split()
assert x.get_shape().ndims == 3
xp = tf.pad(x, [[pad[0], pad[0]], [pad[1], pad[1]], [0, 0]], mode)
return tf.random_crop(xp, ... | [
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tensorflow/cleverhans | cleverhans/augmentation.py | batch_augment | def batch_augment(x, func, device='/CPU:0'):
"""
Apply dataset augmentation to a batch of exmaples.
:param x: Tensor representing a batch of examples.
:param func: Callable implementing dataset augmentation, operating on
a single image.
:param device: String specifying which device to use.
"""
with tf... | python | def batch_augment(x, func, device='/CPU:0'):
"""
Apply dataset augmentation to a batch of exmaples.
:param x: Tensor representing a batch of examples.
:param func: Callable implementing dataset augmentation, operating on
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:param device: String specifying which device to use.
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tensorflow/cleverhans | cleverhans/augmentation.py | random_crop_and_flip | def random_crop_and_flip(x, pad_rows=4, pad_cols=4):
"""Augment a batch by randomly cropping and horizontally flipping it."""
rows = tf.shape(x)[1]
cols = tf.shape(x)[2]
channels = x.get_shape()[3]
def _rand_crop_img(img):
"""Randomly crop an individual image"""
return tf.random_crop(img, [rows, cols... | python | def random_crop_and_flip(x, pad_rows=4, pad_cols=4):
"""Augment a batch by randomly cropping and horizontally flipping it."""
rows = tf.shape(x)[1]
cols = tf.shape(x)[2]
channels = x.get_shape()[3]
def _rand_crop_img(img):
"""Randomly crop an individual image"""
return tf.random_crop(img, [rows, cols... | [
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tensorflow/cleverhans | cleverhans_tutorials/mnist_tutorial_picklable.py | mnist_tutorial | def mnist_tutorial(train_start=0, train_end=60000, test_start=0,
test_end=10000, nb_epochs=NB_EPOCHS, batch_size=BATCH_SIZE,
learning_rate=LEARNING_RATE,
clean_train=CLEAN_TRAIN,
testing=False,
backprop_through_attack=BACKPRO... | python | def mnist_tutorial(train_start=0, train_end=60000, test_start=0,
test_end=10000, nb_epochs=NB_EPOCHS, batch_size=BATCH_SIZE,
learning_rate=LEARNING_RATE,
clean_train=CLEAN_TRAIN,
testing=False,
backprop_through_attack=BACKPRO... | [
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:param train_start: index of first training set example
:param train_end: index of last training set example
:param test_start: index of first test set example
:param test_end: index of last test set example
:param nb_epochs: number of epochs to train model
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tensorflow/cleverhans | cleverhans/attacks/spsa.py | _project_perturbation | def _project_perturbation(perturbation, epsilon, input_image, clip_min=None,
clip_max=None):
"""Project `perturbation` onto L-infinity ball of radius `epsilon`.
Also project into hypercube such that the resulting adversarial example
is between clip_min and clip_max, if applicable.
"""
... | python | def _project_perturbation(perturbation, epsilon, input_image, clip_min=None,
clip_max=None):
"""Project `perturbation` onto L-infinity ball of radius `epsilon`.
Also project into hypercube such that the resulting adversarial example
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"""
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tensorflow/cleverhans | cleverhans/attacks/spsa.py | margin_logit_loss | def margin_logit_loss(model_logits, label, nb_classes=10, num_classes=None):
"""Computes difference between logit for `label` and next highest logit.
The loss is high when `label` is unlikely (targeted by default).
This follows the same interface as `loss_fn` for TensorOptimizer and
projected_optimization, i.e... | python | def margin_logit_loss(model_logits, label, nb_classes=10, num_classes=None):
"""Computes difference between logit for `label` and next highest logit.
The loss is high when `label` is unlikely (targeted by default).
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tensorflow/cleverhans | cleverhans/attacks/spsa.py | spm | def spm(x, model, y=None, n_samples=None, dx_min=-0.1,
dx_max=0.1, n_dxs=5, dy_min=-0.1, dy_max=0.1, n_dys=5,
angle_min=-30, angle_max=30, n_angles=31, black_border_size=0):
"""
TensorFlow implementation of the Spatial Transformation Method.
:return: a tensor for the adversarial example
"""
if... | python | def spm(x, model, y=None, n_samples=None, dx_min=-0.1,
dx_max=0.1, n_dxs=5, dy_min=-0.1, dy_max=0.1, n_dys=5,
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TensorFlow implementation of the Spatial Transformation Method.
:return: a tensor for the adversarial example
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tensorflow/cleverhans | cleverhans/attacks/spsa.py | parallel_apply_transformations | def parallel_apply_transformations(x, transforms, black_border_size=0):
"""
Apply image transformations in parallel.
:param transforms: TODO
:param black_border_size: int, size of black border to apply
Returns:
Transformed images
"""
transforms = tf.convert_to_tensor(transforms, dtype=tf.float32)
x ... | python | def parallel_apply_transformations(x, transforms, black_border_size=0):
"""
Apply image transformations in parallel.
:param transforms: TODO
:param black_border_size: int, size of black border to apply
Returns:
Transformed images
"""
transforms = tf.convert_to_tensor(transforms, dtype=tf.float32)
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tensorflow/cleverhans | cleverhans/attacks/spsa.py | projected_optimization | def projected_optimization(loss_fn,
input_image,
label,
epsilon,
num_steps,
clip_min=None,
clip_max=None,
optimizer=TensorAdam(),
... | python | def projected_optimization(loss_fn,
input_image,
label,
epsilon,
num_steps,
clip_min=None,
clip_max=None,
optimizer=TensorAdam(),
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tensorflow/cleverhans | cleverhans/attacks/spsa.py | SPSA.generate | def generate(self,
x,
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y_target=None,
eps=None,
clip_min=None,
clip_max=None,
nb_iter=None,
is_targeted=None,
early_stop_loss_threshold=None,
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y_target=None,
eps=None,
clip_min=None,
clip_max=None,
nb_iter=None,
is_targeted=None,
early_stop_loss_threshold=None,
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tensorflow/cleverhans | cleverhans/attacks/spsa.py | TensorOptimizer._compute_gradients | def _compute_gradients(self, loss_fn, x, unused_optim_state):
"""Compute a new value of `x` to minimize `loss_fn`.
Args:
loss_fn: a callable that takes `x`, a batch of images, and returns
a batch of loss values. `x` will be optimized to minimize
`loss_fn(x)`.
x: A list o... | python | def _compute_gradients(self, loss_fn, x, unused_optim_state):
"""Compute a new value of `x` to minimize `loss_fn`.
Args:
loss_fn: a callable that takes `x`, a batch of images, and returns
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tensorflow/cleverhans | cleverhans/attacks/spsa.py | TensorOptimizer.minimize | def minimize(self, loss_fn, x, optim_state):
"""
Analogous to tf.Optimizer.minimize
:param loss_fn: tf Tensor, representing the loss to minimize
:param x: list of Tensor, analogous to tf.Optimizer's var_list
:param optim_state: A possibly nested dict, containing any optimizer state.
Returns:
... | python | def minimize(self, loss_fn, x, optim_state):
"""
Analogous to tf.Optimizer.minimize
:param loss_fn: tf Tensor, representing the loss to minimize
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tensorflow/cleverhans | cleverhans/attacks/spsa.py | TensorAdam.init_state | def init_state(self, x):
"""
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"""
optim_state = {}
optim_state["t"] = 0.
optim_state["m"] = [tf.zeros_like(v) for v in x]
optim_state["u"] = [tf.zeros_like(v) for v in x]
return optim_state | python | def init_state(self, x):
"""
Initialize t, m, and u
"""
optim_state = {}
optim_state["t"] = 0.
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tensorflow/cleverhans | cleverhans/attacks/spsa.py | TensorAdam._apply_gradients | def _apply_gradients(self, grads, x, optim_state):
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tensorflow/cleverhans | cleverhans/attacks/spsa.py | SPSAAdam._compute_gradients | def _compute_gradients(self, loss_fn, x, unused_optim_state):
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# Assumes `x` is a list, containing a [1, H, W, C] image
# If static batch dimension is None, tf.reshape to batch size 1
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assert len(x) == 1
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tensorflow/cleverhans | examples/nips17_adversarial_competition/dev_toolkit/run_attacks_and_defenses.py | parse_args | def parse_args():
"""Parses command line arguments."""
parser = argparse.ArgumentParser(
description='Tool to run attacks and defenses.')
parser.add_argument('--attacks_dir', required=True,
help='Location of all attacks.')
parser.add_argument('--targeted_attacks_dir', required=True,
... | python | def parse_args():
"""Parses command line arguments."""
parser = argparse.ArgumentParser(
description='Tool to run attacks and defenses.')
parser.add_argument('--attacks_dir', required=True,
help='Location of all attacks.')
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tensorflow/cleverhans | examples/nips17_adversarial_competition/dev_toolkit/run_attacks_and_defenses.py | read_submissions_from_directory | def read_submissions_from_directory(dirname, use_gpu):
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Args:
dirname: directory to scan.
use_gpu: whether submissions should use GPU. This argument is
used to pick proper Docker container for each submission and create
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"""Scans directory and read all submissions.
Args:
dirname: directory to scan.
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tensorflow/cleverhans | examples/nips17_adversarial_competition/dev_toolkit/run_attacks_and_defenses.py | load_defense_output | def load_defense_output(filename):
"""Loads output of defense from given file."""
result = {}
with open(filename) as f:
for row in csv.reader(f):
try:
image_filename = row[0]
if image_filename.endswith('.png') or image_filename.endswith('.jpg'):
image_filename = image_filename[... | python | def load_defense_output(filename):
"""Loads output of defense from given file."""
result = {}
with open(filename) as f:
for row in csv.reader(f):
try:
image_filename = row[0]
if image_filename.endswith('.png') or image_filename.endswith('.jpg'):
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tensorflow/cleverhans | examples/nips17_adversarial_competition/dev_toolkit/run_attacks_and_defenses.py | compute_and_save_scores_and_ranking | def compute_and_save_scores_and_ranking(attacks_output,
defenses_output,
dataset_meta,
output_dir,
save_all_classification=False):
"""Computes scores and rank... | python | def compute_and_save_scores_and_ranking(attacks_output,
defenses_output,
dataset_meta,
output_dir,
save_all_classification=False):
"""Computes scores and rank... | [
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tensorflow/cleverhans | examples/nips17_adversarial_competition/dev_toolkit/run_attacks_and_defenses.py | main | def main():
"""Run all attacks against all defenses and compute results.
"""
args = parse_args()
attacks_output_dir = os.path.join(args.intermediate_results_dir,
'attacks_output')
targeted_attacks_output_dir = os.path.join(args.intermediate_results_dir,
... | python | def main():
"""Run all attacks against all defenses and compute results.
"""
args = parse_args()
attacks_output_dir = os.path.join(args.intermediate_results_dir,
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targeted_attacks_output_dir = os.path.join(args.intermediate_results_dir,
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tensorflow/cleverhans | examples/nips17_adversarial_competition/dev_toolkit/run_attacks_and_defenses.py | Attack.run | def run(self, input_dir, output_dir, epsilon):
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input_dir: directory with input (dataset).
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tensorflow/cleverhans | examples/nips17_adversarial_competition/dev_toolkit/run_attacks_and_defenses.py | AttacksOutput._load_dataset_clipping | def _load_dataset_clipping(self, dataset_dir, epsilon):
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Args:
dataset_dir: location of the dataset.
epsilon: maximum allowed size of adversarial perturbation.
"""
self.dataset_max_clip = {}
self.dataset_min_clip = {}
... | python | def _load_dataset_clipping(self, dataset_dir, epsilon):
"""Helper method which loads dataset and determines clipping range.
Args:
dataset_dir: location of the dataset.
epsilon: maximum allowed size of adversarial perturbation.
"""
self.dataset_max_clip = {}
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tensorflow/cleverhans | examples/nips17_adversarial_competition/dev_toolkit/run_attacks_and_defenses.py | AttacksOutput.clip_and_copy_attack_outputs | def clip_and_copy_attack_outputs(self, attack_name, is_targeted):
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attack_name: name of the attack.
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"""Clips results of attack and copy it to directory with all images.
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attack_name: name of the attack.
is_targeted: if True then attack is targeted, otherwise non-targeted.
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tensorflow/cleverhans | examples/nips17_adversarial_competition/dev_toolkit/run_attacks_and_defenses.py | DatasetMetadata.save_target_classes | def save_target_classes(self, filename):
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tensorflow/cleverhans | cleverhans/attack_bundling.py | single_run_max_confidence_recipe | def single_run_max_confidence_recipe(sess, model, x, y, nb_classes, eps,
clip_min, clip_max, eps_iter, nb_iter,
report_path,
batch_size=BATCH_SIZE,
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clip_min, clip_max, eps_iter, nb_iter,
report_path,
batch_size=BATCH_SIZE,
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tensorflow/cleverhans | cleverhans/attack_bundling.py | random_search_max_confidence_recipe | def random_search_max_confidence_recipe(sess, model, x, y, eps,
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report_path, batch_size=BATCH_SIZE,
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tensorflow/cleverhans | cleverhans/attack_bundling.py | bundle_attacks | def bundle_attacks(sess, model, x, y, attack_configs, goals, report_path,
attack_batch_size=BATCH_SIZE, eval_batch_size=BATCH_SIZE):
"""
Runs attack bundling.
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tensorflow/cleverhans | cleverhans/attack_bundling.py | bundle_attacks_with_goal | def bundle_attacks_with_goal(sess, model, x, y, adv_x, attack_configs,
run_counts,
goal, report, report_path,
attack_batch_size=BATCH_SIZE, eval_batch_size=BATCH_SIZE):
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goal, report, report_path,
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tensorflow/cleverhans | cleverhans/attack_bundling.py | run_batch_with_goal | def run_batch_with_goal(sess, model, x, y, adv_x_val, criteria, attack_configs,
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attack_batch_size=BATCH_SIZE):
"""
Runs attack bundling on one batch of data.
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tensorflow/cleverhans | cleverhans/attack_bundling.py | save | def save(criteria, report, report_path, adv_x_val):
"""
Saves the report and adversarial examples.
:param criteria: dict, of the form returned by AttackGoal.get_criteria
:param report: dict containing a confidence report
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tensorflow/cleverhans | cleverhans/attack_bundling.py | unfinished_attack_configs | def unfinished_attack_configs(new_work_goal, work_before, run_counts,
log=False):
"""
Returns a list of attack configs that have not yet been run the desired
number of times.
:param new_work_goal: dict mapping attacks to desired number of times to run
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tensorflow/cleverhans | cleverhans/attack_bundling.py | bundle_examples_with_goal | def bundle_examples_with_goal(sess, model, adv_x_list, y, goal,
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"""
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:param adv_x_list: list of numpy arrays
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tensorflow/cleverhans | cleverhans/attack_bundling.py | spsa_max_confidence_recipe | def spsa_max_confidence_recipe(sess, model, x, y, nb_classes, eps,
clip_min, clip_max, nb_iter,
report_path,
spsa_samples=SPSA.DEFAULT_SPSA_SAMPLES,
spsa_iters=SPSA.DEFAULT_SPSA_ITERS,
... | python | def spsa_max_confidence_recipe(sess, model, x, y, nb_classes, eps,
clip_min, clip_max, nb_iter,
report_path,
spsa_samples=SPSA.DEFAULT_SPSA_SAMPLES,
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tensorflow/cleverhans | cleverhans/attack_bundling.py | AttackGoal.get_criteria | def get_criteria(self, sess, model, advx, y, batch_size=BATCH_SIZE):
"""
Returns a dictionary mapping the name of each criterion to a NumPy
array containing the value of that criterion for each adversarial
example.
Subclasses can add extra criteria by implementing the `extra_criteria`
method.
... | python | def get_criteria(self, sess, model, advx, y, batch_size=BATCH_SIZE):
"""
Returns a dictionary mapping the name of each criterion to a NumPy
array containing the value of that criterion for each adversarial
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Subclasses can add extra criteria by implementing the `extra_criteria`
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tensorflow/cleverhans | cleverhans/attack_bundling.py | AttackGoal.request_examples | def request_examples(self, attack_config, criteria, run_counts, batch_size):
"""
Returns a numpy array of integer example indices to run in the next batch.
"""
raise NotImplementedError(str(type(self)) +
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"""
Returns a numpy array of integer example indices to run in the next batch.
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tensorflow/cleverhans | cleverhans/attack_bundling.py | AttackGoal.new_wins | def new_wins(self, orig_criteria, orig_idx, new_criteria, new_idx):
"""
Returns a bool indicating whether a new adversarial example is better
than the pre-existing one for the same clean example.
:param orig_criteria: dict mapping names of criteria to their value
for each example in the whole data... | python | def new_wins(self, orig_criteria, orig_idx, new_criteria, new_idx):
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tensorflow/cleverhans | cleverhans/attack_bundling.py | Misclassify.filter | def filter(self, run_counts, criteria):
"""
Return run counts only for examples that are still correctly classified
"""
correctness = criteria['correctness']
assert correctness.dtype == np.bool
filtered_counts = deep_copy(run_counts)
for key in filtered_counts:
filtered_counts[key] = f... | python | def filter(self, run_counts, criteria):
"""
Return run counts only for examples that are still correctly classified
"""
correctness = criteria['correctness']
assert correctness.dtype == np.bool
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tensorflow/cleverhans | cleverhans/attack_bundling.py | MaxConfidence.filter | def filter(self, run_counts, criteria):
"""
Return the counts for only those examples that are below the threshold
"""
wrong_confidence = criteria['wrong_confidence']
below_t = wrong_confidence <= self.t
filtered_counts = deep_copy(run_counts)
for key in filtered_counts:
filtered_count... | python | def filter(self, run_counts, criteria):
"""
Return the counts for only those examples that are below the threshold
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tensorflow/cleverhans | cleverhans/future/tf2/attacks/projected_gradient_descent.py | projected_gradient_descent | def projected_gradient_descent(model_fn, x, eps, eps_iter, nb_iter, ord,
clip_min=None, clip_max=None, y=None, targeted=False,
rand_init=None, rand_minmax=0.3, sanity_checks=True):
"""
This class implements either the Basic Iterative Method
(Kurakin et... | python | def projected_gradient_descent(model_fn, x, eps, eps_iter, nb_iter, ord,
clip_min=None, clip_max=None, y=None, targeted=False,
rand_init=None, rand_minmax=0.3, sanity_checks=True):
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tensorflow/cleverhans | cleverhans/attacks/bapp.py | clip_image | def clip_image(image, clip_min, clip_max):
""" Clip an image, or an image batch, with upper and lower threshold. """
return np.minimum(np.maximum(clip_min, image), clip_max) | python | def clip_image(image, clip_min, clip_max):
""" Clip an image, or an image batch, with upper and lower threshold. """
return np.minimum(np.maximum(clip_min, image), clip_max) | [
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tensorflow/cleverhans | cleverhans/attacks/bapp.py | compute_distance | def compute_distance(x_ori, x_pert, constraint='l2'):
""" Compute the distance between two images. """
if constraint == 'l2':
dist = np.linalg.norm(x_ori - x_pert)
elif constraint == 'linf':
dist = np.max(abs(x_ori - x_pert))
return dist | python | def compute_distance(x_ori, x_pert, constraint='l2'):
""" Compute the distance between two images. """
if constraint == 'l2':
dist = np.linalg.norm(x_ori - x_pert)
elif constraint == 'linf':
dist = np.max(abs(x_ori - x_pert))
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tensorflow/cleverhans | cleverhans/attacks/bapp.py | approximate_gradient | def approximate_gradient(decision_function, sample, num_evals,
delta, constraint, shape, clip_min, clip_max):
""" Gradient direction estimation """
# Generate random vectors.
noise_shape = [num_evals] + list(shape)
if constraint == 'l2':
rv = np.random.randn(*noise_shape)
elif con... | python | def approximate_gradient(decision_function, sample, num_evals,
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""" Gradient direction estimation """
# Generate random vectors.
noise_shape = [num_evals] + list(shape)
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tensorflow/cleverhans | cleverhans/attacks/bapp.py | project | def project(original_image, perturbed_images, alphas, shape, constraint):
""" Projection onto given l2 / linf balls in a batch. """
alphas_shape = [len(alphas)] + [1] * len(shape)
alphas = alphas.reshape(alphas_shape)
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""" Projection onto given l2 / linf balls in a batch. """
alphas_shape = [len(alphas)] + [1] * len(shape)
alphas = alphas.reshape(alphas_shape)
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tensorflow/cleverhans | cleverhans/attacks/bapp.py | binary_search_batch | def binary_search_batch(original_image, perturbed_images, decision_function,
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""" Binary search to approach the boundary. """
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dists_post_update = np.array([
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""" Binary search to approach the boundary. """
# Compute distance between each of perturbed image and original image.
dists_post_update = np.array([
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tensorflow/cleverhans | cleverhans/attacks/bapp.py | initialize | def initialize(decision_function, sample, shape, clip_min, clip_max):
"""
Efficient Implementation of BlendedUniformNoiseAttack in Foolbox.
"""
success = 0
num_evals = 0
# Find a misclassified random noise.
while True:
random_noise = np.random.uniform(clip_min, clip_max, size=shape)
success = dec... | python | def initialize(decision_function, sample, shape, clip_min, clip_max):
"""
Efficient Implementation of BlendedUniformNoiseAttack in Foolbox.
"""
success = 0
num_evals = 0
# Find a misclassified random noise.
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tensorflow/cleverhans | cleverhans/attacks/bapp.py | geometric_progression_for_stepsize | def geometric_progression_for_stepsize(x, update, dist, decision_function,
current_iteration):
""" Geometric progression to search for stepsize.
Keep decreasing stepsize by half until reaching
the desired side of the boundary.
"""
epsilon = dist / np.sqrt(current... | python | def geometric_progression_for_stepsize(x, update, dist, decision_function,
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tensorflow/cleverhans | cleverhans/attacks/bapp.py | select_delta | def select_delta(dist_post_update, current_iteration,
clip_max, clip_min, d, theta, constraint):
"""
Choose the delta at the scale of distance
between x and perturbed sample.
"""
if current_iteration == 1:
delta = 0.1 * (clip_max - clip_min)
else:
if constraint == 'l2':
delta... | python | def select_delta(dist_post_update, current_iteration,
clip_max, clip_min, d, theta, constraint):
"""
Choose the delta at the scale of distance
between x and perturbed sample.
"""
if current_iteration == 1:
delta = 0.1 * (clip_max - clip_min)
else:
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delta... | [
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tensorflow/cleverhans | cleverhans/attacks/bapp.py | BoundaryAttackPlusPlus.generate | def generate(self, x, **kwargs):
"""
Return a tensor that constructs adversarial examples for the given
input. Generate uses tf.py_func in order to operate over tensors.
:param x: A tensor with the inputs.
:param kwargs: See `parse_params`
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:param kwargs: See `parse_params`
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tensorflow/cleverhans | cleverhans/attacks/bapp.py | BoundaryAttackPlusPlus.generate_np | def generate_np(self, x, **kwargs):
"""
Generate adversarial images in a for loop.
:param y: An array of shape (n, nb_classes) for true labels.
:param y_target: An array of shape (n, nb_classes) for target labels.
Required for targeted attack.
:param image_target: An array of shape (n, **image ... | python | def generate_np(self, x, **kwargs):
"""
Generate adversarial images in a for loop.
:param y: An array of shape (n, nb_classes) for true labels.
:param y_target: An array of shape (n, nb_classes) for target labels.
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tensorflow/cleverhans | cleverhans/attacks/bapp.py | BoundaryAttackPlusPlus.parse_params | def parse_params(self,
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image_target=None,
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max_num_evals=10000,
stepsize_search='grid_search',
num_iterations=64,
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tensorflow/cleverhans | cleverhans/attacks/bapp.py | BoundaryAttackPlusPlus._bapp | def _bapp(self, sample, target_label, target_image):
"""
Main algorithm for Boundary Attack ++.
Return a tensor that constructs adversarial examples for the given
input. Generate uses tf.py_func in order to operate over tensors.
:param sample: input image. Without the batchsize dimension.
:par... | python | def _bapp(self, sample, target_label, target_image):
"""
Main algorithm for Boundary Attack ++.
Return a tensor that constructs adversarial examples for the given
input. Generate uses tf.py_func in order to operate over tensors.
:param sample: input image. Without the batchsize dimension.
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tensorflow/cleverhans | cleverhans/attacks/fast_feature_adversaries.py | FastFeatureAdversaries.parse_params | def parse_params(self,
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eps_iter=0.05,
nb_iter=10,
ord=np.inf,
clip_min=None,
clip_max=None,
**kwargs):
"""
Take in a dictionary of paramete... | python | def parse_params(self,
layer=None,
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eps_iter=0.05,
nb_iter=10,
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tensorflow/cleverhans | cleverhans/attacks/fast_feature_adversaries.py | FastFeatureAdversaries.attack_single_step | def attack_single_step(self, x, eta, g_feat):
"""
TensorFlow implementation of the Fast Feature Gradient. This is a
single step attack similar to Fast Gradient Method that attacks an
internal representation.
:param x: the input placeholder
:param eta: A tensor the same shape as x that holds the... | python | def attack_single_step(self, x, eta, g_feat):
"""
TensorFlow implementation of the Fast Feature Gradient. This is a
single step attack similar to Fast Gradient Method that attacks an
internal representation.
:param x: the input placeholder
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tensorflow/cleverhans | cleverhans/attacks/fast_feature_adversaries.py | FastFeatureAdversaries.generate | def generate(self, x, g, **kwargs):
"""
Generate symbolic graph for adversarial examples and return.
:param x: The model's symbolic inputs.
:param g: The target value of the symbolic representation
:param kwargs: See `parse_params`
"""
# Parse and save attack-specific parameters
assert... | python | def generate(self, x, g, **kwargs):
"""
Generate symbolic graph for adversarial examples and return.
:param x: The model's symbolic inputs.
:param g: The target value of the symbolic representation
:param kwargs: See `parse_params`
"""
# Parse and save attack-specific parameters
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tensorflow/cleverhans | scripts/make_confidence_report_bundled.py | main | def main(argv=None):
"""
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"""
try:
_name_of_script, filepath = argv
except ValueError:
raise ValueError(argv)
print(filepath)
make_confidence_report_bundled(filepath=filepath,
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... | python | def main(argv=None):
"""
Make a confidence report and save it to disk.
"""
try:
_name_of_script, filepath = argv
except ValueError:
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print(filepath)
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tensorflow/cleverhans | examples/nips17_adversarial_competition/dev_toolkit/sample_defenses/ens_adv_inception_resnet_v2/inception_resnet_v2.py | block35 | def block35(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
"""Builds the 35x35 resnet block."""
with tf.variable_scope(scope, 'Block35', [net], reuse=reuse):
with tf.variable_scope('Branch_0'):
tower_conv = slim.conv2d(net, 32, 1, scope='Conv2d_1x1')
with tf.variable_scope('Branch_... | python | def block35(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
"""Builds the 35x35 resnet block."""
with tf.variable_scope(scope, 'Block35', [net], reuse=reuse):
with tf.variable_scope('Branch_0'):
tower_conv = slim.conv2d(net, 32, 1, scope='Conv2d_1x1')
with tf.variable_scope('Branch_... | [
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tensorflow/cleverhans | examples/nips17_adversarial_competition/dev_toolkit/sample_defenses/ens_adv_inception_resnet_v2/inception_resnet_v2.py | block17 | def block17(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
"""Builds the 17x17 resnet block."""
with tf.variable_scope(scope, 'Block17', [net], reuse=reuse):
with tf.variable_scope('Branch_0'):
tower_conv = slim.conv2d(net, 192, 1, scope='Conv2d_1x1')
with tf.variable_scope('Branch... | python | def block17(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
"""Builds the 17x17 resnet block."""
with tf.variable_scope(scope, 'Block17', [net], reuse=reuse):
with tf.variable_scope('Branch_0'):
tower_conv = slim.conv2d(net, 192, 1, scope='Conv2d_1x1')
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tensorflow/cleverhans | examples/nips17_adversarial_competition/dev_toolkit/sample_defenses/ens_adv_inception_resnet_v2/inception_resnet_v2.py | inception_resnet_v2_base | def inception_resnet_v2_base(inputs,
final_endpoint='Conv2d_7b_1x1',
output_stride=16,
align_feature_maps=False,
scope=None):
"""Inception model from http://arxiv.org/abs/1602.07261.
Constructs an I... | python | def inception_resnet_v2_base(inputs,
final_endpoint='Conv2d_7b_1x1',
output_stride=16,
align_feature_maps=False,
scope=None):
"""Inception model from http://arxiv.org/abs/1602.07261.
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tensorflow/cleverhans | examples/nips17_adversarial_competition/dev_toolkit/sample_defenses/ens_adv_inception_resnet_v2/inception_resnet_v2.py | inception_resnet_v2 | def inception_resnet_v2(inputs, nb_classes=1001, is_training=True,
dropout_keep_prob=0.8,
reuse=None,
scope='InceptionResnetV2',
create_aux_logits=True,
num_classes=None):
"""Creates the Inception R... | python | def inception_resnet_v2(inputs, nb_classes=1001, is_training=True,
dropout_keep_prob=0.8,
reuse=None,
scope='InceptionResnetV2',
create_aux_logits=True,
num_classes=None):
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is_training: whether is training or not.
dropout_keep_prob: float, the fraction to keep before final layer.
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tensorflow/cleverhans | examples/nips17_adversarial_competition/dev_toolkit/sample_defenses/ens_adv_inception_resnet_v2/inception_resnet_v2.py | inception_resnet_v2_arg_scope | def inception_resnet_v2_arg_scope(weight_decay=0.00004,
batch_norm_decay=0.9997,
batch_norm_epsilon=0.001):
"""Returns the scope with the default parameters for inception_resnet_v2.
Args:
weight_decay: the weight decay for weights variables.
... | python | def inception_resnet_v2_arg_scope(weight_decay=0.00004,
batch_norm_decay=0.9997,
batch_norm_epsilon=0.001):
"""Returns the scope with the default parameters for inception_resnet_v2.
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weight_decay: the weight decay for weights variables.
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tensorflow/cleverhans | tutorials/future/tf2/mnist_tutorial.py | ld_mnist | def ld_mnist():
"""Load training and test data."""
def convert_types(image, label):
image = tf.cast(image, tf.float32)
image /= 255
return image, label
dataset, info = tfds.load('mnist', data_dir='gs://tfds-data/datasets', with_info=True,
as_supervised=True)
mnist_train... | python | def ld_mnist():
"""Load training and test data."""
def convert_types(image, label):
image = tf.cast(image, tf.float32)
image /= 255
return image, label
dataset, info = tfds.load('mnist', data_dir='gs://tfds-data/datasets', with_info=True,
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tensorflow/cleverhans | cleverhans_tutorials/mnist_tutorial_keras.py | mnist_tutorial | def mnist_tutorial(train_start=0, train_end=60000, test_start=0,
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"""
MNIST CleverHans tutorial
:param train_start: index of first t... | python | def mnist_tutorial(train_start=0, train_end=60000, test_start=0,
test_end=10000, nb_epochs=NB_EPOCHS, batch_size=BATCH_SIZE,
learning_rate=LEARNING_RATE, testing=False,
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/validation_tool/validate_and_copy_submissions.py | main | def main(args):
"""Validate all submissions and copy them into place"""
random.seed()
temp_dir = tempfile.mkdtemp()
logging.info('Created temporary directory: %s', temp_dir)
validator = SubmissionValidator(
source_dir=args.source_dir,
target_dir=args.target_dir,
temp_dir=temp_dir,
do_c... | python | def main(args):
"""Validate all submissions and copy them into place"""
random.seed()
temp_dir = tempfile.mkdtemp()
logging.info('Created temporary directory: %s', temp_dir)
validator = SubmissionValidator(
source_dir=args.source_dir,
target_dir=args.target_dir,
temp_dir=temp_dir,
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/validation_tool/validate_and_copy_submissions.py | ValidationStats._update_stat | def _update_stat(self, submission_type, increase_success, increase_fail):
"""Common method to update submission statistics."""
stat = self.stats.get(submission_type, (0, 0))
stat = (stat[0] + increase_success, stat[1] + increase_fail)
self.stats[submission_type] = stat | python | def _update_stat(self, submission_type, increase_success, increase_fail):
"""Common method to update submission statistics."""
stat = self.stats.get(submission_type, (0, 0))
stat = (stat[0] + increase_success, stat[1] + increase_fail)
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/validation_tool/validate_and_copy_submissions.py | ValidationStats.log_stats | def log_stats(self):
"""Print statistics into log."""
logging.info('Validation statistics: ')
for k, v in iteritems(self.stats):
logging.info('%s - %d valid out of %d total submissions',
k, v[0], v[0] + v[1]) | python | def log_stats(self):
"""Print statistics into log."""
logging.info('Validation statistics: ')
for k, v in iteritems(self.stats):
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/validation_tool/validate_and_copy_submissions.py | SubmissionValidator.copy_submission_locally | def copy_submission_locally(self, cloud_path):
"""Copies submission from Google Cloud Storage to local directory.
Args:
cloud_path: path of the submission in Google Cloud Storage
Returns:
name of the local file where submission is copied to
"""
local_path = os.path.join(self.download_d... | python | def copy_submission_locally(self, cloud_path):
"""Copies submission from Google Cloud Storage to local directory.
Args:
cloud_path: path of the submission in Google Cloud Storage
Returns:
name of the local file where submission is copied to
"""
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/validation_tool/validate_and_copy_submissions.py | SubmissionValidator.copy_submission_to_destination | def copy_submission_to_destination(self, src_filename, dst_subdir,
submission_id):
"""Copies submission to target directory.
Args:
src_filename: source filename of the submission
dst_subdir: subdirectory of the target directory where submission should
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submission_id):
"""Copies submission to target directory.
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src_filename: source filename of the submission
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/validation_tool/validate_and_copy_submissions.py | SubmissionValidator.validate_and_copy_one_submission | def validate_and_copy_one_submission(self, submission_path):
"""Validates one submission and copies it to target directory.
Args:
submission_path: path in Google Cloud Storage of the submission file
"""
if os.path.exists(self.download_dir):
shutil.rmtree(self.download_dir)
os.makedirs(s... | python | def validate_and_copy_one_submission(self, submission_path):
"""Validates one submission and copies it to target directory.
Args:
submission_path: path in Google Cloud Storage of the submission file
"""
if os.path.exists(self.download_dir):
shutil.rmtree(self.download_dir)
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/validation_tool/validate_and_copy_submissions.py | SubmissionValidator.save_id_to_path_mapping | def save_id_to_path_mapping(self):
"""Saves mapping from submission IDs to original filenames.
This mapping is saved as CSV file into target directory.
"""
if not self.id_to_path_mapping:
return
with open(self.local_id_to_path_mapping_file, 'w') as f:
writer = csv.writer(f)
writer... | python | def save_id_to_path_mapping(self):
"""Saves mapping from submission IDs to original filenames.
This mapping is saved as CSV file into target directory.
"""
if not self.id_to_path_mapping:
return
with open(self.local_id_to_path_mapping_file, 'w') as f:
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/validation_tool/validate_and_copy_submissions.py | SubmissionValidator.run | def run(self):
"""Runs validation of all submissions."""
cmd = ['gsutil', 'ls', os.path.join(self.source_dir, '**')]
try:
files_list = subprocess.check_output(cmd).split('\n')
except subprocess.CalledProcessError:
logging.error('Can''t read source directory')
all_submissions = [
... | python | def run(self):
"""Runs validation of all submissions."""
cmd = ['gsutil', 'ls', os.path.join(self.source_dir, '**')]
try:
files_list = subprocess.check_output(cmd).split('\n')
except subprocess.CalledProcessError:
logging.error('Can''t read source directory')
all_submissions = [
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tensorflow/cleverhans | scripts/plot_success_fail_curve.py | main | def main(argv=None):
"""Takes the path to a directory with reports and renders success fail plots."""
report_paths = argv[1:]
fail_names = FLAGS.fail_names.split(',')
for report_path in report_paths:
plot_report_from_path(report_path, label=report_path, fail_names=fail_names)
pyplot.legend()
pyplot.x... | python | def main(argv=None):
"""Takes the path to a directory with reports and renders success fail plots."""
report_paths = argv[1:]
fail_names = FLAGS.fail_names.split(',')
for report_path in report_paths:
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/work_data.py | is_unclaimed | def is_unclaimed(work):
"""Returns True if work piece is unclaimed."""
if work['is_completed']:
return False
cutoff_time = time.time() - MAX_PROCESSING_TIME
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"""Returns True if work piece is unclaimed."""
if work['is_completed']:
return False
cutoff_time = time.time() - MAX_PROCESSING_TIME
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/work_data.py | WorkPiecesBase.write_all_to_datastore | def write_all_to_datastore(self):
"""Writes all work pieces into datastore.
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work pieces which IDs are stored in this class. For examples, if this class
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... | python | def write_all_to_datastore(self):
"""Writes all work pieces into datastore.
Each work piece is identified by ID. This method writes/updates only those
work pieces which IDs are stored in this class. For examples, if this class
has only work pieces with IDs '1' ... '100' and datastore already contains
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/work_data.py | WorkPiecesBase.read_all_from_datastore | def read_all_from_datastore(self):
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for entity in client.query_fetch(kind=KIND_WORK, ancestor=parent_key):
work_id = entity.key.flat... | python | def read_all_from_datastore(self):
"""Reads all work pieces from the datastore."""
self._work = {}
client = self._datastore_client
parent_key = client.key(KIND_WORK_TYPE, self._work_type_entity_id)
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/work_data.py | WorkPiecesBase._read_undone_shard_from_datastore | def _read_undone_shard_from_datastore(self, shard_id=None):
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client = self._datastore_client
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filters = [('is_completed', '=', False)]
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"""Reads undone worke pieces which are assigned to shard with given id."""
self._work = {}
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/work_data.py | WorkPiecesBase.read_undone_from_datastore | def read_undone_from_datastore(self, shard_id=None, num_shards=None):
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If shard_id and num_shards are specified then this method will attempt
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"""Reads undone work from the datastore.
If shard_id and num_shards are specified then this method will attempt
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/work_data.py | WorkPiecesBase.try_pick_piece_of_work | def try_pick_piece_of_work(self, worker_id, submission_id=None):
"""Tries pick next unclaimed piece of work to do.
Attempt to claim work piece is done using Cloud Datastore transaction, so
only one worker can claim any work piece at a time.
Args:
worker_id: ID of current worker
submission_... | python | def try_pick_piece_of_work(self, worker_id, submission_id=None):
"""Tries pick next unclaimed piece of work to do.
Attempt to claim work piece is done using Cloud Datastore transaction, so
only one worker can claim any work piece at a time.
Args:
worker_id: ID of current worker
submission_... | [
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/work_data.py | WorkPiecesBase.update_work_as_completed | def update_work_as_completed(self, worker_id, work_id, other_values=None,
error=None):
"""Updates work piece in datastore as completed.
Args:
worker_id: ID of the worker which did the work
work_id: ID of the work which was done
other_values: dictionary with addi... | python | def update_work_as_completed(self, worker_id, work_id, other_values=None,
error=None):
"""Updates work piece in datastore as completed.
Args:
worker_id: ID of the worker which did the work
work_id: ID of the work which was done
other_values: dictionary with addi... | [
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/work_data.py | WorkPiecesBase.compute_work_statistics | def compute_work_statistics(self):
"""Computes statistics from all work pieces stored in this class."""
result = {}
for v in itervalues(self.work):
submission_id = v['submission_id']
if submission_id not in result:
result[submission_id] = {
'completed': 0,
'num_er... | python | def compute_work_statistics(self):
"""Computes statistics from all work pieces stored in this class."""
result = {}
for v in itervalues(self.work):
submission_id = v['submission_id']
if submission_id not in result:
result[submission_id] = {
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/work_data.py | AttackWorkPieces.init_from_adversarial_batches | def init_from_adversarial_batches(self, adv_batches):
"""Initializes work pieces from adversarial batches.
Args:
adv_batches: dict with adversarial batches,
could be obtained as AversarialBatches.data
"""
for idx, (adv_batch_id, adv_batch_val) in enumerate(iteritems(adv_batches)):
w... | python | def init_from_adversarial_batches(self, adv_batches):
"""Initializes work pieces from adversarial batches.
Args:
adv_batches: dict with adversarial batches,
could be obtained as AversarialBatches.data
"""
for idx, (adv_batch_id, adv_batch_val) in enumerate(iteritems(adv_batches)):
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/work_data.py | DefenseWorkPieces.init_from_class_batches | def init_from_class_batches(self, class_batches, num_shards=None):
"""Initializes work pieces from classification batches.
Args:
class_batches: dict with classification batches, could be obtained
as ClassificationBatches.data
num_shards: number of shards to split data into,
if None ... | python | def init_from_class_batches(self, class_batches, num_shards=None):
"""Initializes work pieces from classification batches.
Args:
class_batches: dict with classification batches, could be obtained
as ClassificationBatches.data
num_shards: number of shards to split data into,
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tensorflow/cleverhans | scripts/make_confidence_report_bundle_examples.py | main | def main(argv=None):
"""
Make a confidence report and save it to disk.
"""
assert len(argv) >= 3
_name_of_script = argv[0]
model_filepath = argv[1]
adv_x_filepaths = argv[2:]
sess = tf.Session()
with sess.as_default():
model = serial.load(model_filepath)
factory = model.dataset_factory
facto... | python | def main(argv=None):
"""
Make a confidence report and save it to disk.
"""
assert len(argv) >= 3
_name_of_script = argv[0]
model_filepath = argv[1]
adv_x_filepaths = argv[2:]
sess = tf.Session()
with sess.as_default():
model = serial.load(model_filepath)
factory = model.dataset_factory
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tensorflow/cleverhans | cleverhans/attacks/fast_gradient_method.py | fgm | def fgm(x,
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ord=np.inf,
clip_min=None,
clip_max=None,
targeted=False,
sanity_checks=True):
"""
TensorFlow implementation of the Fast Gradient Method.
:param x: the input placeholder
:param logits: output of model.get_logits
... | python | def fgm(x,
logits,
y=None,
eps=0.3,
ord=np.inf,
clip_min=None,
clip_max=None,
targeted=False,
sanity_checks=True):
"""
TensorFlow implementation of the Fast Gradient Method.
:param x: the input placeholder
:param logits: output of model.get_logits
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tensorflow/cleverhans | cleverhans/attacks/fast_gradient_method.py | optimize_linear | def optimize_linear(grad, eps, ord=np.inf):
"""
Solves for the optimal input to a linear function under a norm constraint.
Optimal_perturbation = argmax_{eta, ||eta||_{ord} < eps} dot(eta, grad)
:param grad: tf tensor containing a batch of gradients
:param eps: float scalar specifying size of constraint reg... | python | def optimize_linear(grad, eps, ord=np.inf):
"""
Solves for the optimal input to a linear function under a norm constraint.
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:param grad: tf tensor containing a batch of gradients
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tensorflow/cleverhans | cleverhans/attacks/fast_gradient_method.py | FastGradientMethod.generate | def generate(self, x, **kwargs):
"""
Returns the graph for Fast Gradient Method adversarial examples.
:param x: The model's symbolic inputs.
:param kwargs: See `parse_params`
"""
# Parse and save attack-specific parameters
assert self.parse_params(**kwargs)
labels, _nb_classes = self.g... | python | def generate(self, x, **kwargs):
"""
Returns the graph for Fast Gradient Method adversarial examples.
:param x: The model's symbolic inputs.
:param kwargs: See `parse_params`
"""
# Parse and save attack-specific parameters
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tensorflow/cleverhans | cleverhans/experimental/certification/nn.py | load_network_from_checkpoint | def load_network_from_checkpoint(checkpoint, model_json, input_shape=None):
"""Function to read the weights from checkpoint based on json description.
Args:
checkpoint: tensorflow checkpoint with trained model to
verify
model_json: path of json file with model description of
the netwo... | python | def load_network_from_checkpoint(checkpoint, model_json, input_shape=None):
"""Function to read the weights from checkpoint based on json description.
Args:
checkpoint: tensorflow checkpoint with trained model to
verify
model_json: path of json file with model description of
the netwo... | [
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] | 97488e215760547b81afc53f5e5de8ba7da5bd98 | https://github.com/tensorflow/cleverhans/blob/97488e215760547b81afc53f5e5de8ba7da5bd98/cleverhans/experimental/certification/nn.py#L161-L226 | train |
tensorflow/cleverhans | cleverhans/experimental/certification/nn.py | NeuralNetwork.forward_pass | def forward_pass(self, vector, layer_index, is_transpose=False, is_abs=False):
"""Performs forward pass through the layer weights at layer_index.
Args:
vector: vector that has to be passed through in forward pass
layer_index: index of the layer
is_transpose: whether the weights of the layer h... | python | def forward_pass(self, vector, layer_index, is_transpose=False, is_abs=False):
"""Performs forward pass through the layer weights at layer_index.
Args:
vector: vector that has to be passed through in forward pass
layer_index: index of the layer
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is_transpose: whether the weights of the layer have to be transposed
is_abs: whether to take the absolute value of the weights
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] | 97488e215760547b81afc53f5e5de8ba7da5bd98 | https://github.com/tensorflow/cleverhans/blob/97488e215760547b81afc53f5e5de8ba7da5bd98/cleverhans/experimental/certification/nn.py#L111-L159 | train |
tensorflow/cleverhans | cleverhans/devtools/version.py | dev_version | def dev_version():
"""
Returns a hexdigest of all the python files in the module.
"""
md5_hash = hashlib.md5()
py_files = sorted(list_files(suffix=".py"))
if not py_files:
return ''
for filename in py_files:
with open(filename, 'rb') as fobj:
content = fobj.read()
md5_hash.update(conten... | python | def dev_version():
"""
Returns a hexdigest of all the python files in the module.
"""
md5_hash = hashlib.md5()
py_files = sorted(list_files(suffix=".py"))
if not py_files:
return ''
for filename in py_files:
with open(filename, 'rb') as fobj:
content = fobj.read()
md5_hash.update(conten... | [
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tensorflow/cleverhans | scripts/print_report.py | current | def current(report):
"""
The current implementation of report printing.
:param report: ConfidenceReport
"""
if hasattr(report, "completed"):
if report.completed:
print("Report completed")
else:
print("REPORT NOT COMPLETED")
else:
warnings.warn("This report does not indicate whether i... | python | def current(report):
"""
The current implementation of report printing.
:param report: ConfidenceReport
"""
if hasattr(report, "completed"):
if report.completed:
print("Report completed")
else:
print("REPORT NOT COMPLETED")
else:
warnings.warn("This report does not indicate whether i... | [
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:param report: ConfidenceReport | [
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] | 97488e215760547b81afc53f5e5de8ba7da5bd98 | https://github.com/tensorflow/cleverhans/blob/97488e215760547b81afc53f5e5de8ba7da5bd98/scripts/print_report.py#L23-L41 | train |
tensorflow/cleverhans | scripts/print_report.py | deprecated | def deprecated(report):
"""
The deprecated implementation of report printing.
:param report: dict
"""
warnings.warn("Printing dict-based reports is deprecated. This function "
"is included only to support a private development branch "
"and may be removed without warning.")
... | python | def deprecated(report):
"""
The deprecated implementation of report printing.
:param report: dict
"""
warnings.warn("Printing dict-based reports is deprecated. This function "
"is included only to support a private development branch "
"and may be removed without warning.")
... | [
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:param report: dict | [
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] | 97488e215760547b81afc53f5e5de8ba7da5bd98 | https://github.com/tensorflow/cleverhans/blob/97488e215760547b81afc53f5e5de8ba7da5bd98/scripts/print_report.py#L43-L65 | train |
tensorflow/cleverhans | cleverhans/model_zoo/soft_nearest_neighbor_loss/SNNL_regularized_train.py | SNNL_example | def SNNL_example(train_start=0, train_end=60000, test_start=0,
test_end=10000, nb_epochs=NB_EPOCHS, batch_size=BATCH_SIZE,
learning_rate=LEARNING_RATE,
nb_filters=NB_FILTERS,
SNNL_factor=SNNL_FACTOR,
output_dir=OUTPUT_DIR):
"""
A s... | python | def SNNL_example(train_start=0, train_end=60000, test_start=0,
test_end=10000, nb_epochs=NB_EPOCHS, batch_size=BATCH_SIZE,
learning_rate=LEARNING_RATE,
nb_filters=NB_FILTERS,
SNNL_factor=SNNL_FACTOR,
output_dir=OUTPUT_DIR):
"""
A s... | [
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Neighbor Loss at each internal layer. This outputs a TSNE of the sign of
the adversarial gradients of a trained model. A model with a negative
SNNL_factor will show little or no class clusters, while a model with a
0 SNNL_factor will hav... | [
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tensorflow/cleverhans | cleverhans/attacks/projected_gradient_descent.py | ProjectedGradientDescent.generate | def generate(self, x, **kwargs):
"""
Generate symbolic graph for adversarial examples and return.
:param x: The model's symbolic inputs.
:param kwargs: See `parse_params`
"""
# Parse and save attack-specific parameters
assert self.parse_params(**kwargs)
asserts = []
# If a data ra... | python | def generate(self, x, **kwargs):
"""
Generate symbolic graph for adversarial examples and return.
:param x: The model's symbolic inputs.
:param kwargs: See `parse_params`
"""
# Parse and save attack-specific parameters
assert self.parse_params(**kwargs)
asserts = []
# If a data ra... | [
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] | 97488e215760547b81afc53f5e5de8ba7da5bd98 | https://github.com/tensorflow/cleverhans/blob/97488e215760547b81afc53f5e5de8ba7da5bd98/cleverhans/attacks/projected_gradient_descent.py#L48-L166 | train |
tensorflow/cleverhans | cleverhans/attacks/projected_gradient_descent.py | ProjectedGradientDescent.parse_params | def parse_params(self,
eps=0.3,
eps_iter=0.05,
nb_iter=10,
y=None,
ord=np.inf,
clip_min=None,
clip_max=None,
y_target=None,
rand_init=None,
... | python | def parse_params(self,
eps=0.3,
eps_iter=0.05,
nb_iter=10,
y=None,
ord=np.inf,
clip_min=None,
clip_max=None,
y_target=None,
rand_init=None,
... | [
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before saving them as attributes.
Attack-specific parameters:
:param eps: (optional float) maximum distortion of adversarial example
compared to original input
:param eps_iter: (optional float) step size for each att... | [
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] | 97488e215760547b81afc53f5e5de8ba7da5bd98 | https://github.com/tensorflow/cleverhans/blob/97488e215760547b81afc53f5e5de8ba7da5bd98/cleverhans/attacks/projected_gradient_descent.py#L168-L237 | train |
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