# 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.
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Natural Adversarial Defense.
from mindarmour.defenses.adversarial_defense import \
from mindarmour.attacks.gradient_method import FastGradientSignMethod
Adversarial training based on FGSM.
Reference: `A. Kurakin, et al., "Adversarial machine learning at scale," in
ICLR, 2017. <https://arxiv.org/abs/1611.01236>`_
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.
>>> 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,