"""
import copy
import logging
import warnings
import numpy as np
import tensorflow as tf
from src.FGSM.cleverhans.cleverhans.attacks.attack import Attack
from src.FGSM.cleverhans.cleverhans.model import Model, wrapper_warning_logits, CallableModelWrapper
from src.FGSM.cleverhans.cleverhans import utils
from src.FGSM.cleverhans.cleverhans import utils_tf
np_dtype = np.dtype('float32')
_logger = utils.create_logger("cleverhans.attacks.deep_fool")
_logger.setLevel(logging.INFO)
class DeepFool(Attack):
"""
DeepFool is an untargeted & iterative attack which is based on an
iterative linearization of the classifier. The implementation here
is w.r.t. the L2 norm.
Paper link: "https://arxiv.org/pdf/1511.04599.pdf"
:param model: cleverhans.model.Model
:param sess: tf.Session
:param dtypestr: dtype of the data
:param kwargs: passed through to super constructor
"""
"""
The Attack interface.
"""
from abc import ABCMeta
import collections
import warnings
import numpy as np
import tensorflow as tf
from src.FGSM.cleverhans.cleverhans.compat import reduce_max
from src.FGSM.cleverhans.cleverhans.model import Model
from src.FGSM.cleverhans.cleverhans import utils
_logger = utils.create_logger("cleverhans.attacks.attack")
class Attack(object):
"""
Abstract base class for all attack classes.
"""
__metaclass__ = ABCMeta
def __init__(self, model, sess=None, dtypestr='float32', **kwargs):
"""
:param model: An instance of the cleverhans.model.Model class.
:param sess: The (possibly optional) tf.Session to run graphs in.
:param dtypestr: Floating point precision to use (change to float64
to avoid numerical instabilities).
:param back: (deprecated and will be removed on or after 2019-03-26).
"""
# pylint: disable=missing-docstring
import logging
import numpy as np
import tensorflow as tf
from src.FGSM.cleverhans.cleverhans.attacks.attack import Attack
from src.FGSM.cleverhans.cleverhans.compat import reduce_sum, reduce_max
from src.FGSM.cleverhans.cleverhans.model import Model, CallableModelWrapper, wrapper_warning_logits
from src.FGSM.cleverhans.cleverhans import utils
np_dtype = np.dtype('float32')
tf_dtype = tf.as_dtype('float32')
_logger = utils.create_logger("cleverhans.attacks.elastic_net_method")
_logger.setLevel(logging.INFO)
def ZERO():
return np.asarray(0., dtype=np_dtype)
class ElasticNetMethod(Attack):
"""
This attack features L1-oriented adversarial examples and includes
the C&W L2 attack as a special case (when beta is set to 0).
Adversarial examples attain similar performance to those
generated by the C&W L2 attack in the white-box case,
and more importantly, have improved transferability properties
and complement adversarial training.
attack_params = self.attack_params
if attack_params is None:
attack_params = {}
if self.pass_y:
x_adv = self.attack.generate(x_batch, y=y_batch, **attack_params)
else:
# Some code checks the keys of kwargs, rather than checking if
# y is None, so we need to truly not pass y at all, rather than
# just passing a None value for it.
x_adv = self.attack.generate(x_batch, **attack_params)
return (x_batch, y_batch), tuple([x_adv])
_logger = create_logger("cleverhans.evaluation")
# Cache for storing output of `batch_eval_multi_worker`'s calls to
# `graph_factory`, to avoid making the tf graph too big
_batch_eval_multi_worker_cache = {}
def _check_x(x):
"""
Makes sure an `x` argument is a valid numpy dataset.
"""
if not isinstance(x, np.ndarray):
raise TypeError("x must be a numpy array. Typically x contains "
"the entire test set inputs.")
"""
# pylint: disable=missing-docstring
import logging
import numpy as np
import tensorflow as tf
from src.FGSM.cleverhans.cleverhans.attacks.attack import Attack
from src.FGSM.cleverhans.cleverhans.compat import reduce_sum, reduce_max
from src.FGSM.cleverhans.cleverhans.model import CallableModelWrapper, Model, wrapper_warning_logits
from src.FGSM.cleverhans.cleverhans import utils
np_dtype = np.dtype('float32')
tf_dtype = tf.as_dtype('float32')
_logger = utils.create_logger("cleverhans.attacks.carlini_wagner_l2")
_logger.setLevel(logging.INFO)
class CarliniWagnerL2(Attack):
"""
This attack was originally proposed by Carlini and Wagner. It is an
iterative attack that finds adversarial examples on many defenses that
are robust to other attacks.
Paper link: https://arxiv.org/abs/1608.04644
At a high level, this attack is an iterative attack using Adam and
a specially-chosen loss function to find adversarial examples with
lower distortion than other attacks. This comes at the cost of speed,
as this attack is often much slower than others.
import os
import time
import warnings
import numpy as np
import six
from six.moves import xrange
import tensorflow as tf
from src.FGSM.cleverhans.cleverhans.compat import device_lib
from src.FGSM.cleverhans.cleverhans.compat import reduce_sum, reduce_mean
from src.FGSM.cleverhans.cleverhans.compat import reduce_max
from src.FGSM.cleverhans.cleverhans.compat import softmax_cross_entropy_with_logits
from src.FGSM.cleverhans.cleverhans.utils import batch_indices, _ArgsWrapper, create_logger
_logger = create_logger("cleverhans.utils.tf")
_logger.setLevel(logging.INFO)
def model_loss(y, model, mean=True):
"""
Define loss of TF graph
:param y: correct labels
:param model: output of the model
:param mean: boolean indicating whether should return mean of loss
or vector of losses for each input of the batch
:return: return mean of loss if True, otherwise return vector with per
sample loss
"""
warnings.warn("This function is deprecated and will be removed on or after"
" 2019-04-05. Switch to cleverhans.train.train.")
import time
import warnings
import math
import numpy as np
from six.moves import xrange
import tensorflow as tf
from src.FGSM.cleverhans.cleverhans import canary
from src.FGSM.cleverhans.cleverhans.utils import _ArgsWrapper, create_logger
from src.FGSM.cleverhans.cleverhans.utils import safe_zip
from src.FGSM.cleverhans.cleverhans.utils_tf import infer_devices
from src.FGSM.cleverhans.cleverhans.utils_tf import initialize_uninitialized_global_variables
_logger = create_logger("train")
_logger.setLevel(logging.INFO)
def train(sess, loss, x_train, y_train,
init_all=False, evaluate=None, feed=None, args=None,
rng=None, var_list=None, fprop_args=None, optimizer=None,
devices=None, x_batch_preprocessor=None, use_ema=False,
ema_decay=.998, run_canary=None,
loss_threshold=1e5, dataset_train=None, dataset_size=None):
"""
Run (optionally multi-replica, synchronous) training to minimize `loss`
:param sess: TF session to use when training the graph
:param loss: tensor, the loss to minimize
:param x_train: numpy array with training inputs or tf Dataset
:param y_train: numpy array with training outputs or tf Dataset