# Copyright 2019 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Adversarial Defense.
"""
import numpy as np
from mindspore import Tensor
from mindspore.nn import Cell, SoftmaxCrossEntropyWithLogits
from mindspore.nn import WithLossCell, TrainOneStepCell
from mindspore.nn.optim.momentum import Momentum
from mindarmour.utils._check_param import check_pair_numpy_param, check_model, \
check_param_in_range, check_param_type, check_param_multi_types
from .defense import Defense
[文档]class AdversarialDefense(Defense):
"""
Adversarial training using given adversarial examples.
Args:
network (Cell): A MindSpore network to be defensed.
loss_fn (Functions): Loss function. Default: None.
optimizer (Cell): Optimizer used to train the network. Default: None.
Examples:
>>> from mindspore.nn.optim.momentum import Momentum
>>> import mindspore.ops.operations as P
>>> from mindarmour.adv_robustness.defenses import AdversarialDefense
>>> class Net(nn.Cell):
... def __init__(self):
... super(Net, self).__init__()
... self._softmax = P.Softmax()
... self._dense = nn.Dense(10, 10)
... self._squeeze = P.Squeeze(1)
... def construct(self, inputs):
... out = self._softmax(inputs)
... out = self._dense(out)
... out = self._squeeze(out)
... return out
>>> net = Net()
>>> lr = 0.001
>>> momentum = 0.9
>>> batch_size = 16
>>> num_classes = 10
>>> loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=False)
>>> optimizer = Momentum(net.trainable_params(), learning_rate=lr, momentum=momentum)
>>> adv_defense = AdversarialDefense(net, loss_fn, optimizer)
>>> inputs = np.random.rand(batch_size, 1, 10).astype(np.float32)
>>> labels = np.random.randint(10, size=batch_size).astype(np.int32)
>>> labels = np.eye(num_classes)[labels].astype(np.float32)
>>> adv_defense.defense(inputs, labels)
"""
def __init__(self, network, loss_fn=None, optimizer=None):
super(AdversarialDefense, self).__init__(network)
network = check_model('network', network, Cell)
if loss_fn is None:
loss_fn = SoftmaxCrossEntropyWithLogits(sparse=True)
if optimizer is None:
optimizer = Momentum(
params=network.trainable_params(),
learning_rate=0.01,
momentum=0.9)
loss_net = WithLossCell(network, loss_fn)
self._train_net = TrainOneStepCell(loss_net, optimizer)
self._train_net.set_train()
[文档] def defense(self, inputs, labels):
"""
Enhance model via training with input samples.
Args:
inputs (numpy.ndarray): Input samples.
labels (numpy.ndarray): Labels of input samples.
Returns:
numpy.ndarray, loss of defense operation.
"""
inputs, labels = check_pair_numpy_param('inputs', inputs, 'labels',
labels)
loss = self._train_net(Tensor(inputs), Tensor(labels))
return loss.asnumpy()
[文档]class AdversarialDefenseWithAttacks(AdversarialDefense):
"""
Adversarial training using specific attacking method and the given
adversarial examples to enhance model robustness.
Args:
network (Cell): A MindSpore network to be defensed.
attacks (list[Attack]): List of attack method.
loss_fn (Functions): Loss function. Default: None.
optimizer (Cell): Optimizer used to train the network. Default: None.
bounds (tuple): Upper and lower bounds of data. In form of (clip_min,
clip_max). Default: (0.0, 1.0).
replace_ratio (float): Ratio of replacing original samples with
adversarial, which must be between 0 and 1. Default: 0.5.
Raises:
ValueError: If replace_ratio is not between 0 and 1.
Examples:
>>> from mindspore.nn.optim.momentum import Momentum
>>> import mindspore.ops.operations as P
>>> from mindarmour.adv_robustness.attacks import FastGradientSignMethod
>>> from mindarmour.adv_robustness.attacks import ProjectedGradientDescent
>>> from mindarmour.adv_robustness.defenses import AdversarialDefenseWithAttacks
>>> class Net(nn.Cell):
... def __init__(self):
... super(Net, self).__init__()
... self._softmax = P.Softmax()
... self._dense = nn.Dense(10, 10)
... self._squeeze = P.Squeeze(1)
... def construct(self, inputs):
... out = self._softmax(inputs)
... out = self._dense(out)
... out = self._squeeze(out)
... return out
>>> net = Net()
>>> lr = 0.001
>>> momentum = 0.9
>>> batch_size = 16
>>> num_classes = 10
>>> loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=False)
>>> optimizer = Momentum(net.trainable_params(), learning_rate=lr, momentum=momentum)
>>> fgsm = FastGradientSignMethod(net, loss_fn=loss_fn)
>>> pgd = ProjectedGradientDescent(net, loss_fn=loss_fn)
>>> ead = AdversarialDefenseWithAttacks(net, [fgsm, pgd], loss_fn=loss_fn,
... optimizer=optimizer)
>>> inputs = np.random.rand(batch_size, 1, 10).astype(np.float32)
>>> labels = np.random.randint(10, size=batch_size).astype(np.int32)
>>> labels = np.eye(num_classes)[labels].astype(np.float32)
>>> loss = ead.defense(inputs, labels)
"""
def __init__(self, network, attacks, loss_fn=None, optimizer=None,
bounds=(0.0, 1.0), replace_ratio=0.5):
super(AdversarialDefenseWithAttacks, self).__init__(network,
loss_fn,
optimizer)
self._attacks = check_param_type('attacks', attacks, list)
self._bounds = check_param_multi_types('bounds', bounds, [tuple, list])
for elem in self._bounds:
_ = check_param_multi_types('bound', elem, [int, float])
self._replace_ratio = check_param_in_range('replace_ratio',
replace_ratio,
0, 1)
self._graph_initialized = False
self._train_net.set_train()
[文档] def defense(self, inputs, labels):
"""
Enhance model via training with adversarial examples generated from input samples.
Args:
inputs (numpy.ndarray): Input samples.
labels (numpy.ndarray): Labels of input samples.
Returns:
numpy.ndarray, loss of adversarial defense operation.
"""
inputs, labels = check_pair_numpy_param('inputs', inputs, 'labels',
labels)
if not self._graph_initialized:
self._train_net(Tensor(inputs), Tensor(labels))
self._graph_initialized = True
x_len = inputs.shape[0]
n_adv = int(np.ceil(self._replace_ratio*x_len))
n_adv_per_attack = int(n_adv / len(self._attacks))
adv_ids = np.random.choice(x_len, size=n_adv, replace=False)
start = 0
for attack in self._attacks:
idx = adv_ids[start:start + n_adv_per_attack]
inputs[idx] = attack.generate(inputs[idx], labels[idx])
start += n_adv_per_attack
loss = self._train_net(Tensor(inputs), Tensor(labels))
return loss.asnumpy()
[文档]class EnsembleAdversarialDefense(AdversarialDefenseWithAttacks):
"""
Adversarial training using a list of specific attacking methods
and the given adversarial examples to enhance model robustness.
Args:
network (Cell): A MindSpore network to be defensed.
attacks (list[Attack]): List of attack method.
loss_fn (Functions): Loss function. Default: None.
optimizer (Cell): Optimizer used to train the network. Default: None.
bounds (tuple): Upper and lower bounds of data. In form of (clip_min,
clip_max). Default: (0.0, 1.0).
replace_ratio (float): Ratio of replacing original samples with
adversarial, which must be between 0 and 1. Default: 0.5.
Raises:
ValueError: If replace_ratio is not between 0 and 1.
Examples:
>>> from mindspore.nn.optim.momentum import Momentum
>>> import mindspore.ops.operations as P
>>> from mindarmour.adv_robustness.attacks import FastGradientSignMethod
>>> from mindarmour.adv_robustness.attacks import ProjectedGradientDescent
>>> from mindarmour.adv_robustness.defenses import EnsembleAdversarialDefense
>>> class Net(nn.Cell):
... def __init__(self):
... super(Net, self).__init__()
... self._softmax = P.Softmax()
... self._dense = nn.Dense(10, 10)
... self._squeeze = P.Squeeze(1)
... def construct(self, inputs):
... out = self._softmax(inputs)
... out = self._dense(out)
... out = self._squeeze(out)
... return out
>>> net = Net()
>>> lr = 0.001
>>> momentum = 0.9
>>> batch_size = 16
>>> num_classes = 10
>>> loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=False)
>>> optimizer = Momentum(net.trainable_params(), learning_rate=lr, momentum=momentum)
>>> fgsm = FastGradientSignMethod(net, loss_fn=loss_fn)
>>> pgd = ProjectedGradientDescent(net, loss_fn=loss_fn)
>>> ead = EnsembleAdversarialDefense(net, [fgsm, pgd], loss_fn=loss_fn,
... optimizer=optimizer)
>>> inputs = np.random.rand(batch_size, 1, 10).astype(np.float32)
>>> labels = np.random.randint(10, size=batch_size).astype(np.int32)
>>> labels = np.eye(num_classes)[labels].astype(np.float32)
>>> loss = ead.defense(inputs, labels)
"""
def __init__(self, network, attacks, loss_fn=None, optimizer=None,
bounds=(0.0, 1.0), replace_ratio=0.5):
super(EnsembleAdversarialDefense, self).__init__(network,
attacks,
loss_fn,
optimizer,
bounds,
replace_ratio)