Source code for mindarmour.defenses.natural_adversarial_defense

# 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.
"""
Natural Adversarial Defense.
"""
from mindarmour.defenses.adversarial_defense import \
    AdversarialDefenseWithAttacks
from mindarmour.attacks.gradient_method import FastGradientSignMethod


[docs]class NaturalAdversarialDefense(AdversarialDefenseWithAttacks): """ Adversarial training based on FGSM. Reference: `A. Kurakin, et al., "Adversarial machine learning at scale," in ICLR, 2017. <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 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 samples. Default: 0.5. eps (float): Step size of the attack method(FGSM). Default: 0.1. Examples: >>> net = Net() >>> adv_defense = NaturalAdversarialDefense(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=0.1): attack = FastGradientSignMethod(network, eps=eps, alpha=None, bounds=bounds) super(NaturalAdversarialDefense, self).__init__( network, [attack], loss_fn=loss_fn, optimizer=optimizer, bounds=bounds, replace_ratio=replace_ratio)