import torch
import torch.nn as nn
import numpy as np
from torchvision.transforms.functional import normalize
from ares.utils.loss import loss_adv
from ares.utils.registry import registry
[docs]@registry.register_attack('si_ni_fgsm')
class SI_NI_FGSM(object):
''' Nesterov Accelerated Gradient and Scale Invariance with FGSM. A black-box attack method.
Example:
>>> from ares.utils.registry import registry
>>> attacker_cls = registry.get_attack('si_ni_fgsm')
>>> attacker = attacker_cls(model)
>>> adv_images = attacker(images, labels, target_labels)
- Supported distance metric: 1, 2, np.inf.
- References: https://arxiv.org/abs/1908.06281.
'''
[docs] def __init__(self, model, device='cuda', norm=np.inf, eps=4/255, stepsize=1/255, steps=20,
scale_factor=1, decay_factor=1.0, loss='ce', target=False):
'''The initialize function for PGD.
Args:
model (torch.nn.Module): The target model to be attacked.
device (torch.device): The device to perform autoattack. Defaults to 'cuda'.
norm (float): The norm of distance calculation for adversarial constraint. Defaults to np.inf.
eps (float): The maximum perturbation range epsilon.
stepsize (float): The attack range for each step.
steps (int): The number of attack iteration.
scale_factor (float): The scale factor.
decay_factor (float): The decay factor.
loss (str): The loss function.
target (bool): Conduct target/untarget attack. Defaults to False.
'''
self.epsilon = eps
self.p = norm
self.net = model
self.scale_factor = scale_factor
self.decay_factor = decay_factor
self.stepsize = stepsize
self.target = target
self.steps = steps
self.loss = loss
self.device = device
def __call__(self, images=None, labels=None, target_labels=None):
'''This function perform attack on target images with corresponding labels
and target labels for target attack.
Args:
images (torch.Tensor): The images to be attacked. The images should be torch.Tensor with shape [N, C, H, W] and range [0, 1].
labels (torch.Tensor): The corresponding labels of the images. The labels should be torch.Tensor with shape [N, ]
target_labels (torch.Tensor): The target labels for target attack. The labels should be torch.Tensor with shape [N, ]
Returns:
torch.Tensor: Adversarial images with value range [0,1].
'''
images, labels = images.to(self.device), labels.to(self.device)
if target_labels is not None:
target_labels = target_labels.to(self.device)
batchsize = images.shape[0]
advimage = images
# PGD to get adversarial example
momentum = torch.zeros_like(images).detach()
for i in range(self.steps):
advimage_nes = advimage + self.decay_factor * self.stepsize * momentum
grads = torch.zeros_like(images).to(self.device)
for j in range(self.scale_factor):
x_s = (advimage_nes / 2**(j)).requires_grad_(True)
netOut = self.net(x_s)
loss = loss_adv(self.loss, netOut, labels, target_labels, self.target, self.device)
loss.backward(retain_graph=True)
grads += torch.autograd.grad(loss, [x_s])[0].detach()
grads_norm = torch.norm(nn.Flatten()(grads), p=1, dim=1)
grads = grads / grads_norm.view([-1]+[1]*(len(grads.shape)-1))
grads = self.decay_factor * momentum + grads
momentum = grads
if self.p==np.inf:
updates = grads.sign()
else:
normVal = torch.norm(grads.view(batchsize, -1), self.p, 1)
updates = grads/normVal.view(batchsize, 1, 1, 1)
updates = updates*self.stepsize
advimage = advimage + updates
# project the disturbed image to feasible set if needed
delta = advimage-images
if self.p==np.inf:
delta = torch.clamp(delta, -self.epsilon, self.epsilon)
else:
normVal = torch.norm(delta.view(batchsize, -1), self.p, 1)
mask = normVal<=self.epsilon
scaling = self.epsilon/normVal
scaling[mask] = 1
delta = delta*scaling.view(batchsize, 1, 1, 1)
advimage = images+delta
advimage= torch.clamp(advimage, 0, 1)#cifar10(-1,1)
return advimage
[docs] def attack_detection_forward(self, batch_data, excluded_losses, scale_factor=255.0,
object_vanish_only=False):
"""This function is used to attack object detection models.
Args:
batch_data (dict): {'inputs': torch.Tensor with shape [N,C,H,W] and value range [0, 1], 'data_samples': list of mmdet.structures.DetDataSample}.
excluded_losses (list): List of losses not used to compute the attack loss.
scale_factor (float): Factor used to scale adv images.
object_vanish_only (bool): When True, just make objects vanish only.
Returns:
torch.Tensor: Adversarial images with value range [0,1].
"""
images = batch_data['inputs']
batchsize = len(images)
advimages = images
# PGD to get adversarial example
momentum = torch.zeros_like(images).detach()
for i in range(self.steps):
advimage_nes = advimages + self.decay_factor * self.stepsize * momentum
grads = torch.zeros_like(images).to(self.device)
for j in range(self.scale_factor):
x_s = (advimage_nes / 2 ** (j)).requires_grad_(True)
# normalize images for detector inputs
normed_x_s = normalize(x_s * scale_factor, self.net.data_preprocessor.mean,
self.net.data_preprocessor.std)
losses = self.net.loss(normed_x_s, batch_data['data_samples'])
loss = []
for key in losses.keys():
if isinstance(losses[key], list):
losses[key] = torch.stack(losses[key]).mean()
kept = True
for excluded_loss in excluded_losses:
if excluded_loss in key:
kept = False
continue
if kept and 'loss' in key:
loss.append(losses[key].mean().unsqueeze(0))
if object_vanish_only:
loss = - torch.stack(loss).mean()
else:
loss = torch.stack((loss)).mean()
loss.backward(retain_graph=True)
grads += x_s.grad.detach()
grads_norm = torch.norm(nn.Flatten()(grads), p=1, dim=1)
grads = grads / grads_norm.view([-1] + [1] * (len(grads.shape) - 1))
grads = self.decay_factor * momentum + grads
momentum = grads
if self.p == np.inf:
updates = grads.sign()
else:
normVal = torch.norm(grads.view(batchsize, -1), self.p, 1)
updates = grads / normVal.view(batchsize, 1, 1, 1)
updates = updates * self.stepsize
advimages = advimages + updates
# project the disturbed image to feasible set if needed
delta = advimages - images
if self.p == np.inf:
delta = torch.clamp(delta, -self.epsilon, self.epsilon)
else:
normVal = torch.norm(delta.view(batchsize, -1), self.p, 1)
mask = normVal <= self.epsilon
scaling = self.epsilon / normVal
scaling[mask] = 1
delta = delta * scaling.view(batchsize, 1, 1, 1)
advimages = images + delta
advimages = torch.clamp(advimages, 0, 1)
return advimages