forked from BorealisAI/advertorch
/
spatial.py
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/
spatial.py
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# Copyright (c) 2018-present, Royal Bank of Canada.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import numpy as np
import torch
import torch.nn.functional as F
from advertorch.utils import calc_l2distsq
from advertorch.utils import clamp
from advertorch.utils import to_one_hot
from .base import Attack
from .base import LabelMixin
from .utils import is_successful
L2DIST_UPPER = 1e10
TARGET_MULT = 10000.0
INVALID_LABEL = -1
class SpatialTransformAttack(Attack, LabelMixin):
"""
Sptially Transformed Attack
:param predict: forward pass function.
:param num_classes: number of clasess.
:param confidence: confidence of the adversarial examples.
:param initial_const: initial value of the constant c
:param max_iterations: the maximum number of iterations
:param search_steps: number of search times to find the optimum
:param loss_fn: loss function
:param clip_min: mininum value per input dimension.
:param clip_max: maximum value per input dimension.
:param abort_early: if set to true, abort early if getting stuck in local min
:param targeted: if the attack is targeted
"""
def __init__(self, predict, num_classes, confidence=0,
initial_const=1, max_iterations=1000,
search_steps=1, loss_fn=None,
clip_min=0.0, clip_max=1.0,
abort_early=True, targeted=False):
super(SpatialTransformAttack, self).__init__(
predict, loss_fn, clip_min, clip_max)
self.num_classes = num_classes
self.confidence = confidence
self.initial_const = initial_const
self.max_iterations = max_iterations
self.search_steps = search_steps
self.abort_early = abort_early
self.targeted = targeted
def _loss_fn_spatial(self, grid, x, y, const, grid_ori):
imgs = x.clone()
grid = torch.from_numpy(
grid.reshape(grid_ori.shape)).float().to(
x.device).requires_grad_()
delta = grid_ori - grid
adv_img = F.grid_sample(imgs, grid)
output = self.predict(adv_img)
real = (y * output).sum(dim=1)
other = (
(1.0 - y) * output - (y * TARGET_MULT)).max(1)[0]
if self.targeted:
loss1 = clamp(other - real + self.confidence, min=0.)
else:
loss1 = clamp(real - other + self.confidence, min=0.)
loss2 = self.initial_const * (
torch.sqrt((((
delta[:, :, 1:] - delta[:, :, :-1] + 1e-10) ** 2)).view(
delta.shape[0], -1).sum(1)) +
torch.sqrt(((
delta[:, 1:, :] - delta[:, :-1, :] + 1e-10) ** 2).view(
delta.shape[0], -1).sum(1)))
loss = torch.sum(loss1) + torch.sum(loss2)
loss.backward()
grad_ret = grid.grad.data.cpu().numpy().flatten().astype(float)
grid.grad.data.zero_()
return loss.data.cpu().numpy().astype(float), grad_ret
def _update_if_better(
self, adv_img, labs, output, dist, batch_size,
final_l2dists, final_labels, final_advs, step, final_step):
for ii in range(batch_size):
target_label = labs[ii]
output_logits = output[ii]
_, output_label = torch.max(output_logits, 0)
di = dist[ii]
if (di < final_l2dists[ii] and
is_successful(
int(output_label.item()), int(target_label),
self.targeted)):
final_l2dists[ii] = di
final_labels[ii] = output_label
final_advs[ii] = adv_img[ii]
final_step[ii] = step
def perturb(self, x, y=None):
x, y = self._verify_and_process_inputs(x, y)
batch_size = len(x)
loss_coeffs = x.new_ones(batch_size) * self.initial_const
final_l2dists = [L2DIST_UPPER] * batch_size
final_labels = [INVALID_LABEL] * batch_size
final_step = [INVALID_LABEL] * batch_size
final_advs = torch.zeros_like(x)
# TODO: refactor the theta generation
theta = torch.tensor([[[1., 0., 0.],
[0., 1., 0.]]]).to(x.device)
theta = theta.repeat((x.shape[0], 1, 1))
grid = F.affine_grid(theta, x.size())
grid_ori = grid.clone()
y_onehot = to_one_hot(y, self.num_classes).float()
clip_min = np.ones(grid_ori.shape[:]) * -1
clip_max = np.ones(grid_ori.shape[:]) * 1
clip_bound = list(zip(clip_min.flatten(), clip_max.flatten()))
grid_ret = grid.clone().data.cpu().numpy().flatten().astype(float)
from scipy.optimize import fmin_l_bfgs_b
for outer_step in range(self.search_steps):
grid_ret, f, d = fmin_l_bfgs_b(
self._loss_fn_spatial,
grid_ret,
args=(
x.clone().detach(),
y_onehot, loss_coeffs,
grid_ori.clone().detach()),
maxiter=self.max_iterations,
bounds=clip_bound,
iprint=0,
maxls=100,
)
grid = torch.from_numpy(
grid_ret.reshape(grid_ori.shape)).float().to(x.device)
adv_x = F.grid_sample(x.clone(), grid)
l2s = calc_l2distsq(grid.data, grid_ori.data)
output = self.predict(adv_x)
self._update_if_better(
adv_x.data, y, output.data, l2s, batch_size,
final_l2dists, final_labels, final_advs,
outer_step, final_step)
return final_advs