Source code for mindarmour.adv_robustness.detectors.spatial_smoothing

# 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
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"""
Spatial-Smoothing detector.
"""
import numpy as np
from scipy import ndimage

from mindspore import Model
from mindspore import Tensor

from mindarmour.utils.logger import LogUtil
from mindarmour.utils._check_param import check_model, check_numpy_param, \
    check_pair_numpy_param, check_int_positive, check_param_type, \
    check_param_in_range, check_equal_shape, check_value_positive
from .detector import Detector

LOGGER = LogUtil.get_instance()
TAG = 'SpatialSmoothing'


def _median_filter_np(inputs, size=2):
    """median filter using numpy"""
    return ndimage.filters.median_filter(inputs, size=size, mode='reflect')


[docs]class SpatialSmoothing(Detector): """ Detect method based on spatial smoothing. Args: model (Model): Target model. ksize (int): Smooth window size. Default: 3. is_local_smooth (bool): If True, trigger local smooth. If False, none local smooth. Default: True. metric (str): Distance method. Default: 'l1'. false_positive_ratio (float): False positive rate over benign samples. Default: 0.05. Examples: >>> detector = SpatialSmoothing(model) >>> detector.fit(ori, labels) >>> adv_ids = detector.detect(adv) """ def __init__(self, model, ksize=3, is_local_smooth=True, metric='l1', false_positive_ratio=0.05): super(SpatialSmoothing, self).__init__() self._ksize = check_int_positive('ksize', ksize) self._is_local_smooth = check_param_type('is_local_smooth', is_local_smooth, bool) self._model = check_model('model', model, Model) self._metric = metric self._fpr = check_param_in_range('false_positive_ratio', false_positive_ratio, 0, 1) self._threshold = None
[docs] def fit(self, inputs, labels=None): """ Train detector to decide the threshold. The proper threshold make sure the actual false positive rate over benign sample is less than the given value. Args: inputs (numpy.ndarray): Benign samples. labels (numpy.ndarray): Default None. Returns: float, threshold, distance larger than which is reported as positive, i.e. adversarial. """ inputs = check_numpy_param('inputs', inputs) raw_pred = self._model.predict(Tensor(inputs)).asnumpy() smoothing_pred = self._model.predict(Tensor(self.transform(inputs))).asnumpy() dist = self._dist(raw_pred, smoothing_pred) index = int(len(dist)*(1 - self._fpr)) threshold = np.sort(dist, axis=None)[index] self._threshold = threshold return self._threshold
[docs] def detect(self, inputs): """ Detect if an input sample is an adversarial example. Args: inputs (numpy.ndarray): Suspicious samples to be judged. Returns: list[int], whether a sample is adversarial. if res[i]=1, then the input sample with index i is adversarial. """ inputs = check_numpy_param('inputs', inputs) raw_pred = self._model.predict(Tensor(inputs)).asnumpy() smoothing_pred = self._model.predict(Tensor(self.transform(inputs))).asnumpy() dist = self._dist(raw_pred, smoothing_pred) res = [0]*len(dist) for i, elem in enumerate(dist): if elem > self._threshold: res[i] = 1 return res
[docs] def detect_diff(self, inputs): """ Return the raw distance value (before apply the threshold) between the input sample and its smoothed counterpart. Args: inputs (numpy.ndarray): Suspicious samples to be judged. Returns: float, distance. """ inputs = check_numpy_param('inputs', inputs) raw_pred = self._model.predict(Tensor(inputs)).asnumpy() smoothing_pred = self._model.predict(Tensor(self.transform(inputs))).asnumpy() dist = self._dist(raw_pred, smoothing_pred) return dist
def transform(self, inputs): inputs = check_numpy_param('inputs', inputs) return _median_filter_np(inputs, self._ksize)
[docs] def set_threshold(self, threshold): """ Set the parameters threshold. Args: threshold (float): Detection threshold. Default: None. """ self._threshold = check_value_positive('threshold', threshold)
def _dist(self, before, after): """ Calculate the distance between the model outputs of a raw sample and its smoothed counterpart. Args: before (numpy.ndarray): Model output of raw samples. after (numpy.ndarray): Model output of smoothed counterparts. Returns: float, distance based on specified norm. """ before, after = check_pair_numpy_param('before', before, 'after', after) before, after = check_equal_shape('before', before, 'after', after) res = [] diff = after - before for _, elem in enumerate(diff): if self._metric == 'l1': res.append(np.linalg.norm(elem, ord=1)) elif self._metric == 'l2': res.append(np.linalg.norm(elem, ord=2)) else: res.append(np.linalg.norm(elem, ord=1)) return res