# Copyright 2019 Huawei Technologies Co., Ltd
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# Licensed under the Apache License, Version 2.0 (the "License");
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# http://www.apache.org/licenses/LICENSE-2.0
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""" Iterative gradient method attack. """
from abc import abstractmethod
import numpy as np
from PIL import Image, ImageOps
from mindspore.nn import SoftmaxCrossEntropyWithLogits
from mindspore import Tensor
from mindspore.nn import Cell
from mindarmour.attacks.attack import Attack
from mindarmour.attacks.gradient_method import FastGradientSignMethod
from mindarmour.utils.logger import LogUtil
from mindarmour.utils.util import WithLossCell
from mindarmour.utils.util import GradWrapWithLoss
from mindarmour.utils._check_param import check_pair_numpy_param, \
normalize_value, check_model, check_value_positive, check_int_positive, \
check_param_type, check_norm_level, check_param_multi_types
LOGGER = LogUtil.get_instance()
TAG = 'IterGrad'
def _reshape_l1_projection(values, eps=3):
"""
`Implementation of L1 ball projection from:`_.
.. _`Implementation of L1 ball projection from:`:
https://stanford.edu/~jduchi/projects/DuchiShSiCh08.pdf
Args:
values (numpy.ndarray): Input data reshape into 2-dims.
eps (float): L1 radius. Default: 3.
Returns:
numpy.ndarray, containing the projection.
"""
abs_x = np.abs(values)
abs_x = np.sum(abs_x, axis=1)
indexes_b = (abs_x > eps)
x_b = values[indexes_b]
batch_size_b = x_b.shape[0]
if batch_size_b == 0:
return values
# make the projection on l1 ball for elements outside the ball
b_mu = -np.sort(-np.abs(x_b), axis=1)
b_vv = np.arange(x_b.shape[1]).astype(np.float)
b_st = (np.cumsum(b_mu, axis=1)-eps)/(b_vv+1)
selected = (b_mu - b_st) > 0
rho = np.sum((np.cumsum((1-selected), axis=1) == 0), axis=1)-1
theta = np.take_along_axis(b_st, np.expand_dims(rho, axis=1), axis=1)
proj_x_b = np.maximum(0, np.abs(x_b)-theta)*np.sign(x_b)
# gather all the projected batch
proj_x = np.copy(values)
proj_x[indexes_b] = proj_x_b
return proj_x
def _projection(values, eps, norm_level):
"""
Implementation of values normalization within eps.
Args:
values (numpy.ndarray): Input data.
eps (float): Project radius.
norm_level (Union[int, char, numpy.inf]): Order of the norm. Possible
values: np.inf, 1 or 2.
Returns:
numpy.ndarray, normalized values.
Raises:
NotImplementedError: If the norm_level is not in [1, 2, np.inf, '1',
'2', 'inf'].
"""
if norm_level in (1, '1'):
sample_batch = values.shape[0]
x_flat = values.reshape(sample_batch, -1)
proj_flat = _reshape_l1_projection(x_flat, eps)
return proj_flat.reshape(values.shape)
if norm_level in (2, '2'):
return eps*normalize_value(values, norm_level)
if norm_level in (np.inf, 'inf'):
return eps*np.sign(values)
msg = 'Values of `norm_level` different from 1, 2 and `np.inf` are ' \
'currently not supported.'
LOGGER.error(TAG, msg)
raise NotImplementedError(msg)
[docs]class IterativeGradientMethod(Attack):
"""
Abstract base class for all iterative gradient based attacks.
Args:
network (Cell): Target model.
eps (float): Proportion of adversarial perturbation generated by the
attack to data range. Default: 0.3.
eps_iter (float): Proportion of single-step adversarial perturbation
generated by the attack to data range. Default: 0.1.
bounds (tuple): Upper and lower bounds of data, indicating the data range.
In form of (clip_min, clip_max). Default: (0.0, 1.0).
nb_iter (int): Number of iteration. Default: 5.
loss_fn (Loss): Loss function for optimization. Default: None.
"""
def __init__(self, network, eps=0.3, eps_iter=0.1, bounds=(0.0, 1.0), nb_iter=5,
loss_fn=None):
super(IterativeGradientMethod, self).__init__()
self._network = check_model('network', network, Cell)
self._eps = check_value_positive('eps', eps)
self._eps_iter = check_value_positive('eps_iter', eps_iter)
self._nb_iter = check_int_positive('nb_iter', nb_iter)
self._bounds = check_param_multi_types('bounds', bounds, [list, tuple])
for b in self._bounds:
_ = check_param_multi_types('bound', b, [int, float])
if loss_fn is None:
loss_fn = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=False)
self._loss_grad = GradWrapWithLoss(WithLossCell(self._network, loss_fn))
self._loss_grad.set_train()
[docs] @abstractmethod
def generate(self, inputs, labels):
"""
Generate adversarial examples based on input samples and original/target labels.
Args:
inputs (numpy.ndarray): Benign input samples used as references to create
adversarial examples.
labels (numpy.ndarray): Original/target labels.
Raises:
NotImplementedError: This function is not available in
IterativeGradientMethod.
Examples:
>>> adv_x = attack.generate([[0.1, 0.9, 0.6],
>>> [0.3, 0, 0.3]],
>>> [[0, , 1, 0, 0, 0, 0, 0, 0, 0],
>>> [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0]])
"""
msg = 'The function generate() is an abstract method in class ' \
'`IterativeGradientMethod`, and should be implemented ' \
'in child class.'
LOGGER.error(TAG, msg)
raise NotImplementedError(msg)
[docs]class BasicIterativeMethod(IterativeGradientMethod):
"""
The Basic Iterative Method attack, an iterative FGSM method to generate
adversarial examples.
References: `A. Kurakin, I. Goodfellow, and S. Bengio, "Adversarial examples
in the physical world," in ICLR, 2017 <https://arxiv.org/abs/1607.02533>`_
Args:
network (Cell): Target model.
eps (float): Proportion of adversarial perturbation generated by the
attack to data range. Default: 0.3.
eps_iter (float): Proportion of single-step adversarial perturbation
generated by the attack to data range. Default: 0.1.
bounds (tuple): Upper and lower bounds of data, indicating the data range.
In form of (clip_min, clip_max). Default: (0.0, 1.0).
is_targeted (bool): If True, targeted attack. If False, untargeted
attack. Default: False.
nb_iter (int): Number of iteration. Default: 5.
loss_fn (Loss): Loss function for optimization. Default: None.
attack (class): The single step gradient method of each iteration. In
this class, FGSM is used.
Examples:
>>> attack = BasicIterativeMethod(network)
"""
def __init__(self, network, eps=0.3, eps_iter=0.1, bounds=(0.0, 1.0),
is_targeted=False, nb_iter=5, loss_fn=None):
super(BasicIterativeMethod, self).__init__(network,
eps=eps,
eps_iter=eps_iter,
bounds=bounds,
nb_iter=nb_iter,
loss_fn=loss_fn)
self._is_targeted = check_param_type('is_targeted', is_targeted, bool)
self._attack = FastGradientSignMethod(self._network,
eps=self._eps_iter,
bounds=self._bounds,
is_targeted=self._is_targeted,
loss_fn=loss_fn)
[docs] def generate(self, inputs, labels):
"""
Simple iterative FGSM method to generate adversarial examples.
Args:
inputs (numpy.ndarray): Benign input samples used as references to
create adversarial examples.
labels (numpy.ndarray): Original/target labels.
Returns:
numpy.ndarray, generated adversarial examples.
Examples:
>>> adv_x = attack.generate([[0.3, 0.2, 0.6],
>>> [0.3, 0.2, 0.4]],
>>> [[0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
>>> [0, 0, 0, 0, 0, 0, 1, 0, 0, 0]])
"""
inputs, labels = check_pair_numpy_param('inputs', inputs,
'labels', labels)
arr_x = inputs
if self._bounds is not None:
clip_min, clip_max = self._bounds
clip_diff = clip_max - clip_min
for _ in range(self._nb_iter):
if 'self.prob' in globals():
d_inputs = _transform_inputs(inputs, self.prob)
else:
d_inputs = inputs
adv_x = self._attack.generate(d_inputs, labels)
perturs = np.clip(adv_x - arr_x, (0 - self._eps)*clip_diff,
self._eps*clip_diff)
adv_x = arr_x + perturs
inputs = adv_x
else:
for _ in range(self._nb_iter):
if 'self.prob' in globals():
d_inputs = _transform_inputs(inputs, self.prob)
else:
d_inputs = inputs
adv_x = self._attack.generate(d_inputs, labels)
adv_x = np.clip(adv_x, arr_x - self._eps, arr_x + self._eps)
inputs = adv_x
return adv_x
[docs]class MomentumIterativeMethod(IterativeGradientMethod):
"""
The Momentum Iterative Method attack.
References: `Y. Dong, et al., "Boosting adversarial attacks with
momentum," arXiv:1710.06081, 2017 <https://arxiv.org/abs/1710.06081>`_
Args:
network (Cell): Target model.
eps (float): Proportion of adversarial perturbation generated by the
attack to data range. Default: 0.3.
eps_iter (float): Proportion of single-step adversarial perturbation
generated by the attack to data range. Default: 0.1.
bounds (tuple): Upper and lower bounds of data, indicating the data range.
In form of (clip_min, clip_max). Default: (0.0, 1.0).
is_targeted (bool): If True, targeted attack. If False, untargeted
attack. Default: False.
nb_iter (int): Number of iteration. Default: 5.
decay_factor (float): Decay factor in iterations. Default: 1.0.
norm_level (Union[int, numpy.inf]): Order of the norm. Possible values:
np.inf, 1 or 2. Default: 'inf'.
loss_fn (Loss): Loss function for optimization. Default: None.
"""
def __init__(self, network, eps=0.3, eps_iter=0.1, bounds=(0.0, 1.0),
is_targeted=False, nb_iter=5, decay_factor=1.0,
norm_level='inf', loss_fn=None):
super(MomentumIterativeMethod, self).__init__(network,
eps=eps,
eps_iter=eps_iter,
bounds=bounds,
nb_iter=nb_iter,
loss_fn=loss_fn)
self._is_targeted = check_param_type('is_targeted', is_targeted, bool)
self._decay_factor = check_value_positive('decay_factor', decay_factor)
self._norm_level = check_norm_level(norm_level)
[docs] def generate(self, inputs, labels):
"""
Generate adversarial examples based on input data and origin/target labels.
Args:
inputs (numpy.ndarray): Benign input samples used as references to
create adversarial examples.
labels (numpy.ndarray): Original/target labels.
Returns:
numpy.ndarray, generated adversarial examples.
Examples:
>>> adv_x = attack.generate([[0.5, 0.2, 0.6],
>>> [0.3, 0, 0.2]],
>>> [[0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
>>> [0, 0, 0, 0, 0, 1, 0, 0, 0, 0]])
"""
inputs, labels = check_pair_numpy_param('inputs', inputs,
'labels', labels)
arr_x = inputs
momentum = 0
if self._bounds is not None:
clip_min, clip_max = self._bounds
clip_diff = clip_max - clip_min
for _ in range(self._nb_iter):
if 'self.prob' in globals():
d_inputs = _transform_inputs(inputs, self.prob)
else:
d_inputs = inputs
gradient = self._gradient(d_inputs, labels)
momentum = self._decay_factor*momentum + gradient
adv_x = d_inputs + self._eps_iter*np.sign(momentum)
perturs = np.clip(adv_x - arr_x, (0 - self._eps)*clip_diff,
self._eps*clip_diff)
adv_x = arr_x + perturs
adv_x = np.clip(adv_x, clip_min, clip_max)
inputs = adv_x
else:
for _ in range(self._nb_iter):
if 'self.prob' in globals():
d_inputs = _transform_inputs(inputs, self.prob)
else:
d_inputs = inputs
gradient = self._gradient(d_inputs, labels)
momentum = self._decay_factor*momentum + gradient
adv_x = d_inputs + self._eps_iter*np.sign(momentum)
adv_x = np.clip(adv_x, arr_x - self._eps, arr_x + self._eps)
inputs = adv_x
return adv_x
def _gradient(self, inputs, labels):
"""
Calculate the gradient of input samples.
Args:
inputs (numpy.ndarray): Input samples.
labels (numpy.ndarray): Original/target labels.
Returns:
numpy.ndarray, gradient of labels w.r.t inputs.
Examples:
>>> grad = self._gradient([[0.5, 0.3, 0.4]],
>>> [[0, 0, 0, 1, 0, 0, 0, 0, 0, 0])
"""
# get grad of loss over x
out_grad = self._loss_grad(Tensor(inputs), Tensor(labels))
if isinstance(out_grad, tuple):
out_grad = out_grad[0]
gradient = out_grad.asnumpy()
if self._is_targeted:
gradient = -gradient
return normalize_value(gradient, self._norm_level)
[docs]class ProjectedGradientDescent(BasicIterativeMethod):
"""
The Projected Gradient Descent attack is a variant of the Basic Iterative
Method in which, after each iteration, the perturbation is projected on an
lp-ball of specified radius (in addition to clipping the values of the
adversarial sample so that it lies in the permitted data range). This is
the attack proposed by Madry et al. for adversarial training.
References: `A. Madry, et al., "Towards deep learning models resistant to
adversarial attacks," in ICLR, 2018 <https://arxiv.org/abs/1706.06083>`_
Args:
network (Cell): Target model.
eps (float): Proportion of adversarial perturbation generated by the
attack to data range. Default: 0.3.
eps_iter (float): Proportion of single-step adversarial perturbation
generated by the attack to data range. Default: 0.1.
bounds (tuple): Upper and lower bounds of data, indicating the data range.
In form of (clip_min, clip_max). Default: (0.0, 1.0).
is_targeted (bool): If True, targeted attack. If False, untargeted
attack. Default: False.
nb_iter (int): Number of iteration. Default: 5.
norm_level (Union[int, numpy.inf]): Order of the norm. Possible values:
np.inf, 1 or 2. Default: 'inf'.
loss_fn (Loss): Loss function for optimization. Default: None.
"""
def __init__(self, network, eps=0.3, eps_iter=0.1, bounds=(0.0, 1.0),
is_targeted=False, nb_iter=5, norm_level='inf', loss_fn=None):
super(ProjectedGradientDescent, self).__init__(network,
eps=eps,
eps_iter=eps_iter,
bounds=bounds,
is_targeted=is_targeted,
nb_iter=nb_iter,
loss_fn=loss_fn)
self._norm_level = check_norm_level(norm_level)
[docs] def generate(self, inputs, labels):
"""
Iteratively generate adversarial examples based on BIM method. The
perturbation is normalized by projected method with parameter norm_level .
Args:
inputs (numpy.ndarray): Benign input samples used as references to
create adversarial examples.
labels (numpy.ndarray): Original/target labels.
Returns:
numpy.ndarray, generated adversarial examples.
Examples:
>>> adv_x = attack.generate([[0.6, 0.2, 0.6],
>>> [0.3, 0.3, 0.4]],
>>> [[0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
>>> [1, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
"""
inputs, labels = check_pair_numpy_param('inputs', inputs,
'labels', labels)
arr_x = inputs
if self._bounds is not None:
clip_min, clip_max = self._bounds
clip_diff = clip_max - clip_min
for _ in range(self._nb_iter):
adv_x = self._attack.generate(inputs, labels)
perturs = _projection(adv_x - arr_x,
self._eps,
norm_level=self._norm_level)
perturs = np.clip(perturs, (0 - self._eps)*clip_diff,
self._eps*clip_diff)
adv_x = arr_x + perturs
inputs = adv_x
else:
for _ in range(self._nb_iter):
adv_x = self._attack.generate(inputs, labels)
perturs = _projection(adv_x - arr_x,
self._eps,
norm_level=self._norm_level)
adv_x = arr_x + perturs
adv_x = np.clip(adv_x, arr_x - self._eps, arr_x + self._eps)
inputs = adv_x
return adv_x
def _transform_inputs(inputs, prob, low=29, high=33, full_aug=False):
"""
Inputs data augmentation.
Args:
inputs (Union[np.int8, np.float]): Inputs.
prob (float): The probability of augmentation.
low (int): Lower bound of resize image width. Default: 29.
high (int): Upper bound of resize image height. Default: 33.
full_aug (bool): type of augmentation method, use interpolation and padding
as default. Default: False.
Returns:
numpy.ndarray, the augmentation data.
"""
raw_shape = inputs[0].shape
tran_mask = np.random.uniform(0, 1, size=inputs.shape[0]) < prob
tran_inputs = inputs[tran_mask]
raw_inputs = inputs[tran_mask == 0]
tran_outputs = []
for sample in tran_inputs:
width = np.random.choice(np.arange(low, high))
# resize
sample = (sample*255).astype(np.uint8)
d_image = Image.fromarray(sample, mode='L').resize((width, width), Image.NEAREST)
# pad
left_pad = (raw_shape[0] - width) // 2
right_pad = raw_shape[0] - width - left_pad
top_pad = (raw_shape[1] - width) // 2
bottom_pad = raw_shape[1] - width - top_pad
p_sample = ImageOps.expand(d_image,
border=(left_pad, top_pad, right_pad, bottom_pad))
tran_outputs.append(np.array(p_sample).astype(np.float) / 255)
if full_aug:
# gaussian noise
tran_outputs = np.random.normal(tran_outputs.shape) + tran_outputs
tran_outputs.extend(raw_inputs)
if not np.any(tran_outputs-raw_inputs):
LOGGER.error(TAG, 'the transform function does not take effect.')
return tran_outputs