Source code for mindarmour.defenses.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.
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# http://www.apache.org/licenses/LICENSE-2.0
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"""
Adversarial Defense.
"""
import numpy as np

from mindspore import Tensor
from mindspore.nn import Cell
from mindspore.nn.optim.momentum import Momentum
from mindspore.nn import SoftmaxCrossEntropyWithLogits
from mindspore.nn import WithLossCell, TrainOneStepCell

from mindarmour.utils._check_param import check_pair_numpy_param, check_model, \
     check_param_in_range, check_param_type, check_param_multi_types
from mindarmour.defenses.defense import Defense


[docs]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: >>> class Net(Cell): >>> def __init__(self): >>> super(Net, self).__init__() >>> self._reshape = P.Reshape() >>> self._full_con_1 = Dense(28*28, 120) >>> self._full_con_2 = Dense(120, 84) >>> self._full_con_3 = Dense(84, 10) >>> self._relu = ReLU() >>> >>> def construct(self, x): >>> out = self._reshape(x, (-1, 28*28)) >>> out = self._full_con_1(out) >>> out = self.relu(out) >>> out = self._full_con_2(out) >>> out = self.relu(out) >>> out = self._full_con_3(out) >>> return out >>> >>> net = Net() >>> lr = 0.0001 >>> momentum = 0.9 >>> loss_fn = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True) >>> optimizer = Momentum(net.trainable_params(), lr, momentum) >>> adv_defense = AdversarialDefense(net, loss_fn, optimizer) >>> inputs = np.random.rand(32, 1, 28, 28).astype(np.float32) >>> labels = np.random.randint(0, 10).astype(np.int32) >>> 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(is_grad=False, 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()
[docs] 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()
[docs]class AdversarialDefenseWithAttacks(AdversarialDefense): """ Adversarial defense with attacks. 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: >>> net = Net() >>> fgsm = FastGradientSignMethod(net) >>> pgd = ProjectedGradientDescent(net) >>> ead = AdversarialDefenseWithAttacks(net, [fgsm, pgd]) >>> 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)
[docs] 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) 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()
EnsembleAdversarialDefense = AdversarialDefenseWithAttacks