# 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.
"""
Projected Adversarial Defense.
"""
from ..attacks.iterative_gradient_method import ProjectedGradientDescent
from .adversarial_defense import AdversarialDefenseWithAttacks
[docs]class ProjectedAdversarialDefense(AdversarialDefenseWithAttacks):
"""
Adversarial training based on PGD.
Reference: `A. Madry, et al., "Towards deep learning models resistant to
adversarial attacks," in ICLR, 2018. <https://arxiv.org/abs/1611.01236>`_
Args:
network (Cell): A MindSpore network to be defensed.
loss_fn (Functions): Loss function. Default: None.
optimizer (Cell): Optimizer used to train the nerwork. Default: None.
bounds (tuple): Upper and lower bounds of input data. In form of
(clip_min, clip_max). Default: (0.0, 1.0).
replace_ratio (float): Ratio of replacing original samples with
adversarial samples. Default: 0.5.
eps (float): PGD attack parameters, epsilon. Default: 0.3.
eps_iter (int): PGD attack parameters, inner loop epsilon.
Default:0.1.
nb_iter (int): PGD attack parameters, number of iteration.
Default: 5.
norm_level (str): Norm type. 'inf' or 'l2'. Default: 'inf'.
Examples:
>>> net = Net()
>>> adv_defense = ProjectedAdversarialDefense(net)
>>> adv_defense.defense(inputs, labels)
"""
def __init__(self,
network,
loss_fn=None,
optimizer=None,
bounds=(0.0, 1.0),
replace_ratio=0.5,
eps=0.3,
eps_iter=0.1,
nb_iter=5,
norm_level='inf'):
attack = ProjectedGradientDescent(network,
eps=eps,
eps_iter=eps_iter,
nb_iter=nb_iter,
bounds=bounds,
norm_level=norm_level,
loss_fn=loss_fn)
super(ProjectedAdversarialDefense, self).__init__(
network, [attack], loss_fn=loss_fn, optimizer=optimizer,
bounds=bounds, replace_ratio=replace_ratio)