Source code for mindarmour.adv_robustness.defenses.natural_adversarial_defense

# Copyright 2019 Huawei Technologies Co., Ltd
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# 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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# http://www.apache.org/licenses/LICENSE-2.0
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"""
Natural Adversarial Defense.
"""
from ..attacks.gradient_method import FastGradientSignMethod
from .adversarial_defense import AdversarialDefenseWithAttacks


[文档]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: >>> from mindspore.nn.optim.momentum import Momentum >>> import mindspore.ops.operations as P >>> from mindarmour.adv_robustness.defenses import NaturalAdversarialDefense >>> 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) >>> nad = NaturalAdversarialDefense(net, 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 = nad.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, loss_fn=loss_fn) super(NaturalAdversarialDefense, self).__init__( network, [attack], loss_fn=loss_fn, optimizer=optimizer, bounds=bounds, replace_ratio=replace_ratio)