# 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.
"""
Base Class of Attack.
"""
from abc import abstractmethod
import numpy as np
from mindarmour.utils._check_param import check_pair_numpy_param, \
check_int_positive
from mindarmour.utils.logger import LogUtil
LOGGER = LogUtil.get_instance()
TAG = 'Attack'
[docs]class Attack:
"""
The abstract base class for all attack classes creating adversarial examples.
"""
def __init__(self):
pass
[docs] def batch_generate(self, inputs, labels, batch_size=64):
"""
Generate adversarial examples in batch, based on input samples and
their labels.
Args:
inputs (numpy.ndarray): Samples based on which adversarial
examples are generated.
labels (numpy.ndarray): Labels of samples, whose values determined
by specific attacks.
batch_size (int): The number of samples in one batch.
Returns:
numpy.ndarray, generated adversarial examples
Examples:
>>> inputs = np.array([[0.2, 0.4, 0.5, 0.2], [0.7, 0.2, 0.4, 0.3]])
>>> labels = np.array([3, 0])
>>> advs = attack.batch_generate(inputs, labels, batch_size=2)
"""
arr_x, arr_y = check_pair_numpy_param('inputs', inputs, 'labels', labels)
len_x = arr_x.shape[0]
batch_size = check_int_positive('batch_size', batch_size)
batchs = int(len_x / batch_size)
rest = len_x - batchs*batch_size
res = []
for i in range(batchs):
x_batch = arr_x[i*batch_size: (i + 1)*batch_size]
y_batch = arr_y[i*batch_size: (i + 1)*batch_size]
adv_x = self.generate(x_batch, y_batch)
# Black-attack methods will return 3 values, just get the second.
res.append(adv_x[1] if isinstance(adv_x, tuple) else adv_x)
if rest != 0:
x_batch = arr_x[batchs*batch_size:]
y_batch = arr_y[batchs*batch_size:]
adv_x = self.generate(x_batch, y_batch)
# Black-attack methods will return 3 values, just get the second.
res.append(adv_x[1] if isinstance(adv_x, tuple) else adv_x)
adv_x = np.concatenate(res, axis=0)
return adv_x
[docs] @abstractmethod
def generate(self, inputs, labels):
"""
Generate adversarial examples based on normal samples and their labels.
Args:
inputs (numpy.ndarray): Samples based on which adversarial
examples are generated.
labels (numpy.ndarray): Labels of samples, whose values determined
by specific attacks.
Raises:
NotImplementedError: It is an abstract method.
"""
msg = 'The function generate() is an abstract function in class ' \
'`Attack` and should be implemented in child class.'
LOGGER.error(TAG, msg)
raise NotImplementedError(msg)