Source code for mindarmour.adv_robustness.evaluations.attack_evaluation

# 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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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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"""
Attack evaluation.
"""

import numpy as np

from scipy.ndimage.filters import convolve

from mindarmour.utils.logger import LogUtil
from mindarmour.utils._check_param import check_pair_numpy_param, \
    check_param_type, check_numpy_param, check_equal_shape

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


def _compute_ssim(img_1, img_2, kernel_sigma=1.5, kernel_width=11):
    """
    compute structural similarity.
    Args:
        img_1 (numpy.ndarray): The first image to be compared.
        img_2 (numpy.ndarray): The second image to be compared.
        kernel_sigma (float): Gassian kernel param. Default: 1.5.
        kernel_width (int): Another Gassian kernel param. Default: 11.

    Returns:
        float, structural similarity.
    """
    img_1, img_2 = check_equal_shape('images_1', img_1, 'images_2', img_2)

    if len(img_1.shape) > 2:
        total_ssim = 0
        for i in range(img_1.shape[2]):
            total_ssim += _compute_ssim(img_1[:, :, i], img_2[:, :, i])
        return total_ssim / 3

    # Create gaussian kernel
    gaussian_kernel = np.zeros((kernel_width, kernel_width))
    for i in range(kernel_width):
        for j in range(kernel_width):
            gaussian_kernel[i, j] = (1 / (2*np.pi*(kernel_sigma**2)))*np.exp(
                - (((i - 5)**2) + ((j - 5)**2)) / (2*(kernel_sigma**2)))

    img_1 = img_1.astype(np.float32)
    img_2 = img_2.astype(np.float32)

    img_sq_1 = img_1**2
    img_sq_2 = img_2**2
    img_12 = img_1*img_2

    # Mean
    img_mu_1 = convolve(img_1, gaussian_kernel)
    img_mu_2 = convolve(img_2, gaussian_kernel)

    # Mean square
    img_mu_sq_1 = img_mu_1**2
    img_mu_sq_2 = img_mu_2**2
    img_mu_12 = img_mu_1*img_mu_2

    # Variances
    img_sigma_sq_1 = convolve(img_sq_1, gaussian_kernel)
    img_sigma_sq_2 = convolve(img_sq_2, gaussian_kernel)

    # Covariance
    img_sigma_12 = convolve(img_12, gaussian_kernel)

    # Centered squares of variances
    img_sigma_sq_1 = img_sigma_sq_1 - img_mu_sq_1
    img_sigma_sq_2 = img_sigma_sq_2 - img_mu_sq_2
    img_sigma_12 = img_sigma_12 - img_mu_12

    k_1 = 0.01
    k_2 = 0.03
    c_1 = (k_1*255)**2
    c_2 = (k_2*255)**2

    # Calculate ssim
    num_ssim = (2*img_mu_12 + c_1)*(2*img_sigma_12 + c_2)
    den_ssim = (img_mu_sq_1 + img_mu_sq_2 + c_1)*(img_sigma_sq_1
                                                  + img_sigma_sq_2 + c_2)
    res = np.average(num_ssim / den_ssim)
    return res


[docs]class AttackEvaluate: """ Evaluation metrics of attack methods. Args: inputs (numpy.ndarray): Original samples. labels (numpy.ndarray): Original samples' label by one-hot format. adv_inputs (numpy.ndarray): Adversarial samples generated from original samples. adv_preds (numpy.ndarray): Probability of all output classes of adversarial examples. targeted (bool): If True, it is a targeted attack. If False, it is an untargeted attack. Default: False. target_label (numpy.ndarray): Targeted classes of adversarial examples, which is one dimension whose size is adv_inputs.shape[0]. Default: None. Raises: ValueError: If target_label is None when targeted is True. Examples: >>> x = np.random.normal(size=(3, 512, 512, 3)) >>> adv_x = np.random.normal(size=(3, 512, 512, 3)) >>> y = np.array([[0.1, 0.1, 0.2, 0.6], >>> [0.1, 0.7, 0.0, 0.2], >>> [0.8, 0.1, 0.0, 0.1]]) >>> adv_y = np.array([[0.1, 0.1, 0.2, 0.6], >>> [0.1, 0.0, 0.8, 0.1], >>> [0.0, 0.9, 0.1, 0.0]]) >>> attack_eval = AttackEvaluate(x, y, adv_x, adv_y) >>> mr = attack_eval.mis_classification_rate() """ def __init__(self, inputs, labels, adv_inputs, adv_preds, targeted=False, target_label=None): self._inputs, self._labels = check_pair_numpy_param('inputs', inputs, 'labels', labels) self._adv_inputs, self._adv_preds = check_pair_numpy_param('adv_inputs', adv_inputs, 'adv_preds', adv_preds) targeted = check_param_type('targeted', targeted, bool) self._targeted = targeted if target_label is not None: target_label = check_numpy_param('target_label', target_label) self._target_label = target_label self._true_label = np.argmax(self._labels, axis=1) self._adv_label = np.argmax(self._adv_preds, axis=1) idxes = np.arange(self._adv_preds.shape[0]) if self._targeted: if target_label is None: msg = 'targeted attack need target_label, but got None.' LOGGER.error(TAG, msg) raise ValueError(msg) self._adv_preds, self._target_label = check_pair_numpy_param('adv_pred', self._adv_preds, 'target_label', target_label) self._success_idxes = idxes[self._adv_label == self._target_label] else: self._success_idxes = idxes[self._adv_label != self._true_label]
[docs] def mis_classification_rate(self): """ Calculate misclassification rate(MR). Returns: float, ranges between (0, 1). The higher, the more successful the attack is. """ return self._success_idxes.shape[0]*1.0 / self._inputs.shape[0]
[docs] def avg_conf_adv_class(self): """ Calculate average confidence of adversarial class (ACAC). Returns: float, ranges between (0, 1). The higher, the more successful the attack is. """ idxes = self._success_idxes success_num = idxes.shape[0] if success_num == 0: return 0 if self._targeted: return np.mean(self._adv_preds[idxes, self._target_label[idxes]]) return np.mean(self._adv_preds[idxes, self._adv_label[idxes]])
[docs] def avg_conf_true_class(self): """ Calculate average confidence of true class (ACTC). Returns: float, ranges between (0, 1). The lower, the more successful the attack is. """ idxes = self._success_idxes success_num = idxes.shape[0] if success_num == 0: return 0 return np.mean(self._adv_preds[idxes, self._true_label[idxes]])
[docs] def avg_lp_distance(self): """ Calculate average lp distance (lp-dist). Returns: - float, return average l0, l2, or linf distance of all success adversarial examples, return value includes following cases. - If return value :math:`>=` 0, average lp distance. The lower, the more successful the attack is. - If return value is -1, there is no success adversarial examples. """ idxes = self._success_idxes success_num = idxes.shape[0] if success_num == 0: return -1, -1, -1 l0_dist = 0 l2_dist = 0 linf_dist = 0 avoid_zero_div = 1e-14 for i in idxes: diff = (self._adv_inputs[i] - self._inputs[i]).flatten() data = self._inputs[i].flatten() l0_dist += np.linalg.norm(diff, ord=0) \ / (np.linalg.norm(data, ord=0) + avoid_zero_div) l2_dist += np.linalg.norm(diff, ord=2) \ / (np.linalg.norm(data, ord=2) + avoid_zero_div) linf_dist += np.linalg.norm(diff, ord=np.inf) \ / (np.linalg.norm(data, ord=np.inf) + avoid_zero_div) return l0_dist / success_num, l2_dist / success_num, \ linf_dist / success_num
[docs] def avg_ssim(self): """ Calculate average structural similarity (ASS). Returns: - float, average structural similarity. - If return value ranges between (0, 1), the higher, the more successful the attack is. - If return value is -1: there is no success adversarial examples. """ success_num = self._success_idxes.shape[0] if success_num == 0: return -1 total_ssim = 0.0 for _, i in enumerate(self._success_idxes): total_ssim += _compute_ssim(self._adv_inputs[i], self._inputs[i]) return total_ssim / success_num
[docs] def nte(self): """ Calculate noise tolerance estimation (NTE). References: `Towards Imperceptible and Robust Adversarial Example Attacks against Neural Networks <https://arxiv.org/abs/1801.04693>`_ Returns: float, ranges between (0, 1). The higher, the more successful the attack is. """ idxes = self._success_idxes success_num = idxes.shape[0] adv_y = self._adv_preds[idxes] adv_y_2 = np.copy(adv_y) adv_y_2[range(success_num), np.argmax(adv_y_2, axis=1)] = 0 net = np.mean(np.abs(np.max(adv_y_2, axis=1) - np.max(adv_y, axis=1))) return net