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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from sklearn.datasets import make_classification
import medmnist
from medmnist import INFO, Evaluator
from pathlib import Path
import os
if torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
class PerturbationTool():
def __init__(self, seed=0, epsilon=0.03137254901, num_steps=20, step_size=0.00784313725, imgsize=28, patchsize=7):
self.epsilon = epsilon
self.num_steps = num_steps
self.step_size = step_size
self.seed = seed
self.imgsize = imgsize
self.patchsize = patchsize
np.random.seed(seed)
def random_noise(self, noise_shape=[10, 3, 32, 32]):
random_noise = torch.FloatTensor(*noise_shape).uniform_(-self.epsilon, self.epsilon).to(device)
return random_noise
def EM_attack(self, images, labels, model, optimizer, criterion, random_noise=None, sample_wise=False):
if random_noise is None:
random_noise = torch.FloatTensor(*images.shape).uniform_(-self.epsilon, self.epsilon).to(device)
perturb_img = Variable(images.data + random_noise, requires_grad=True)
perturb_img = Variable(torch.clamp(perturb_img, 0, 1), requires_grad=True)
eta = random_noise
for _ in range(self.num_steps):
opt = torch.optim.Adam([perturb_img], lr=1e-3)
opt.zero_grad()
model.zero_grad()
if isinstance(criterion, torch.nn.CrossEntropyLoss):
if hasattr(model, 'classify'):
model.classify = True
logits = model(perturb_img)
loss = criterion(logits, labels)
else:
logits, loss = criterion(model, perturb_img, labels, optimizer)
perturb_img.retain_grad()
loss.backward()
eta = self.step_size * perturb_img.grad.data.sign() * (-1)
perturb_img = Variable(perturb_img.data + eta, requires_grad=True)
eta = torch.clamp(perturb_img.data - images.data, -self.epsilon, self.epsilon)
perturb_img = Variable(images.data + eta, requires_grad=True)
perturb_img = Variable(torch.clamp(perturb_img, 0, 1), requires_grad=True)
return perturb_img, eta
def datacook_v1(self, images, labels, model, optimizer, criterion, random_noise=None, sample_wise=False):
if random_noise is None:
random_noise = torch.FloatTensor(*images.shape).uniform_(-self.epsilon, self.epsilon).to(device)
perturb_img = Variable(images.data + random_noise, requires_grad=True)
perturb_img = Variable(torch.clamp(perturb_img, 0, 1), requires_grad=True)
eta = random_noise
first_outputs = model(perturb_img)
_, first_predictions = torch.max(first_outputs, dim=1)
if(len(first_predictions)==4):
Pseudo = first_predictions.squeeze(0).long()
else:
Pseudo = first_predictions.long()
for _ in range(self.num_steps):
opt = torch.optim.Adam([perturb_img], lr=1e-3)
opt.zero_grad()
model.zero_grad()
if isinstance(criterion, torch.nn.CrossEntropyLoss):
if hasattr(model, 'classify'):
model.classify = True
logits = model(perturb_img)
loss = criterion(logits, Pseudo)
else:
logits, loss = criterion(model, perturb_img, Pseudo, optimizer)
perturb_img.retain_grad()
loss.backward()
eta = self.step_size * perturb_img.grad.data.sign() * (-1)
perturb_img = Variable(perturb_img.data + eta, requires_grad=True)
eta = torch.clamp(perturb_img.data - images.data, -self.epsilon, self.epsilon)
perturb_img = Variable(images.data + eta, requires_grad=True)
perturb_img = Variable(torch.clamp(perturb_img, 0, 1), requires_grad=True)
return perturb_img, eta
def datacook_adv(self, images, labels, model, optimizer, criterion, random_noise=None, sample_wise=False):
if random_noise is None:
random_noise = torch.FloatTensor(*images.shape).uniform_(-self.epsilon, self.epsilon).to(device)
perturb_img = Variable(images.data + random_noise, requires_grad=True)
perturb_img = Variable(torch.clamp(perturb_img, 0, 1), requires_grad=True)
eta = random_noise
first_outputs = model(perturb_img)
_, first_predictions = torch.max(first_outputs, dim=1)
if(len(first_predictions)==4):
Pseudo = first_predictions.squeeze(0).long()
else:
Pseudo = first_predictions.long()
for _ in range(self.num_steps):
# opt = torch.optim.SGD([perturb_img], lr=1e-3)
opt = torch.optim.Adam([perturb_img], lr=1e-3)
opt.zero_grad()
model.zero_grad()
if isinstance(criterion, torch.nn.CrossEntropyLoss):
if hasattr(model, 'classify'):
model.classify = True
logits = model(perturb_img)
loss = criterion(logits, Pseudo)
else:
logits, loss = criterion(model, perturb_img, Pseudo, optimizer)
perturb_img.retain_grad()
loss.backward()
eta = self.step_size * perturb_img.grad.data.sign()
# eta = self.step_size * perturb_img.grad.data.sign()
perturb_img = Variable(perturb_img.data + eta, requires_grad=True)
eta = torch.clamp(perturb_img.data - images.data, -self.epsilon, self.epsilon)
# eta = perturb_img.data - images.data
perturb_img = Variable(images.data + eta, requires_grad=True)
perturb_img = Variable(torch.clamp(perturb_img, 0, 1), requires_grad=True)
return perturb_img, eta
def Adv_attack(self, images, labels, model, optimizer, criterion, random_noise=None, sample_wise=False):
if random_noise is None:
random_noise = torch.FloatTensor(*images.shape).uniform_(-self.epsilon, self.epsilon).to(device)
labels = torch.squeeze(labels, 1).long().to(device)
perturb_img = Variable(images.data + random_noise, requires_grad=True)
perturb_img = Variable(torch.clamp(perturb_img, 0, 1), requires_grad=True)
eta = random_noise
for _ in range(self.num_steps):
opt = torch.optim.SGD([perturb_img], lr=1e-3)
opt.zero_grad()
model.zero_grad()
if isinstance(criterion, torch.nn.CrossEntropyLoss):
logits = model(perturb_img)
loss = criterion(logits, labels)
else:
logits, loss = criterion(model, perturb_img, labels, optimizer)
perturb_img.retain_grad()
loss.backward()
eta = self.step_size * perturb_img.grad.data.sign()
perturb_img = Variable(perturb_img.data + eta, requires_grad=True)
eta = torch.clamp(perturb_img.data - images.data, -self.epsilon, self.epsilon)
perturb_img = Variable(images.data + eta, requires_grad=True)
perturb_img = Variable(torch.clamp(perturb_img, 0, 1), requires_grad=True)
return perturb_img, eta
def _patch_noise_extend_to_img(self, noise, image_size=[3, 32, 32], patch_location='center'):
c, h, w = image_size[0], image_size[1], image_size[2]
mask = np.zeros((c, h, w), np.float32)
x_len, y_len = noise.shape[1], noise.shape[1]
if patch_location == 'center' or (h == w == x_len == y_len):
x = h // 2
y = w // 2
elif patch_location == 'random':
x = np.random.randint(x_len // 2, w - x_len // 2)
y = np.random.randint(y_len // 2, h - y_len // 2)
else:
raise('Invalid patch location')
x1 = np.clip(x - x_len // 2, 0, h)
x2 = np.clip(x + x_len // 2, 0, h)
y1 = np.clip(y - y_len // 2, 0, w)
y2 = np.clip(y + y_len // 2, 0, w)
if type(noise) is np.ndarray:
pass
else:
mask[:, x1: x2, y1: y2] = noise.cpu().numpy()
return ((x1, x2, y1, y2), torch.from_numpy(mask).to(device))
def _patch_noise_to_3d_img(self, noise, image_size=[3, 32, 32, 32], patch_location='center'):
c, d, h, w = image_size[0], image_size[1], image_size[2], image_size[3]
mask = np.zeros((c, d, h, w), np.float32)
x_len, y_len, z_len = noise.shape[1], noise.shape[2], noise.shape[3] # 假设 noise 是三维的
if patch_location == 'center' or (d == h == w == x_len == y_len == z_len):
x = d // 2
y = h // 2
z = w // 2
elif patch_location == 'random':
x = np.random.randint(x_len // 2, d - x_len // 2)
y = np.random.randint(y_len // 2, h - y_len // 2)
z = np.random.randint(z_len // 2, w - z_len // 2)
else:
raise ValueError('Invalid patch location')
x1 = np.clip(x - x_len // 2, 0, d)
x2 = np.clip(x + x_len // 2, 0, d)
y1 = np.clip(y - y_len // 2, 0, h)
y2 = np.clip(y + y_len // 2, 0, h)
z1 = np.clip(z - z_len // 2, 0, w)
z2 = np.clip(z + z_len // 2, 0, w)
if isinstance(noise, np.ndarray):
mask[:, x1: x2, y1: y2, z1: z2] = noise
else:
mask[:, x1: x2, y1: y2, z1: z2] = noise.cpu().numpy()
return ((x1, x2, y1, y2, z1, z2), torch.from_numpy(mask).to(device))
def comput_l2norm_lim(self, linf=0.03, feature_dim=3072):
return np.sqrt(linf**2 * feature_dim)
def normalize_l2norm(self, data, norm_lim):
n = data.shape[0]
orig_shape = data.shape
flatten_data = data.reshape([n, -1])
norms = np.linalg.norm(flatten_data, axis=1, keepdims=True)
flatten_data = flatten_data/norms
data = flatten_data.reshape(orig_shape)
data = data * norm_lim
return data
def synthetic_perturbations(self, dataset, train_dataset, test_dataset):
"""
Generate synthetic perturbations for training and testing datasets.
The method first determines the number of samples required based on the dataset type.
It then generates synthetic feature data using sklearn's `make_classification`, reshaping
and repeating them to form image-like patches. The generated synthetic data is projected
into a small L2-norm ball to control perturbation magnitude.
Synthetic noise is then added to the original training and testing images, preserving
class-wise alignment between synthetic data and original samples. Finally, the synthetic
perturbations are converted to PyTorch tensors with proper shape for further use.
Adapted from https://github.com/dayu11/Availability-Attacks-Create-Shortcuts
"""
if dataset in ['PathMNIST', 'DermaMNIST', 'OCTMNIST', 'PneumoniaMNIST', 'RetinaMNIST', 'BreastMNIST', 'BloodMNIST', 'TissueMNIST', 'OrganAMNIST', 'OrganCMNIST', 'OrganSMNIST']:
n = len(train_dataset)
n *= 7
else:
n = train_dataset.data.shape[0]
img_size = self.imgsize
noise_frame_size = self.patchsize
info = INFO[dataset.lower()]
num_classes = len(info['label'])
min_val = np.min(train_dataset.imgs)
max_val = np.max(train_dataset.imgs)
is_even = img_size % noise_frame_size
num_patch = img_size//noise_frame_size
if(is_even > 0):
num_patch += 1
if(len(train_dataset.imgs.shape)==4):
n_random_fea = int((img_size/noise_frame_size)**2 * 3)
# generate initial data points
simple_data, simple_label = make_classification(n_samples=n, n_features=n_random_fea, n_classes=num_classes, n_informative=n_random_fea, n_redundant=0, n_repeated=0, class_sep=10., flip_y=0., n_clusters_per_class=1)
simple_data = simple_data.reshape([simple_data.shape[0], num_patch, num_patch, 3])
simple_data = simple_data.astype(np.float32)
# duplicate each dimension to get 2-D patches
simple_images = np.repeat(simple_data, noise_frame_size, 2)
simple_images = np.repeat(simple_images, noise_frame_size, 1)
simple_data = simple_images[:, 0:img_size, 0:img_size, :]
else:
n_random_fea = int((img_size/noise_frame_size)**2)
# generate initial data points
simple_data, simple_label = make_classification(n_samples=n, n_features=n_random_fea, n_classes=num_classes, n_informative=n_random_fea, n_redundant=0, n_repeated=0, class_sep=10., flip_y=0., n_clusters_per_class=1)
simple_data = simple_data.reshape([simple_data.shape[0], num_patch, num_patch])
simple_data = simple_data.astype(np.float32)
# duplicate each dimension to get 2-D patches
simple_images = np.repeat(simple_data, noise_frame_size, 2)
simple_images = np.repeat(simple_images, noise_frame_size, 1)
simple_data = simple_images[:, 0:img_size, 0:img_size]
# project the synthetic images into a small L2 ball
linf = self.epsilon
feature_dim = img_size**2 * 3
l2norm_lim = self.comput_l2norm_lim(linf, feature_dim)
simple_data = self.normalize_l2norm(simple_data, l2norm_lim)
imgs_copy = train_dataset.imgs.copy()
test_imgs_copy = test_dataset.imgs.copy()
if imgs_copy.dtype != np.float32:
imgs_copy = imgs_copy.astype(np.float32) / 255.0
test_imgs_copy = test_imgs_copy.astype(np.float32) / 255.0
arr_target = np.array(train_dataset.labels)
arr_test_target = np.array(test_dataset.labels)
perturb_img = imgs_copy.copy()
perturb_test_img = test_imgs_copy.copy()
# add synthetic noises to original examples
for label in range(num_classes):
orig_data_idx = arr_target == label
simple_data_idx = simple_label == label
mini_simple_data_for_train = simple_data[simple_data_idx][0:int(sum(orig_data_idx))]
mini_simple_data_for_test = simple_data[simple_data_idx][int(sum(orig_data_idx)):int(sum(orig_data_idx)) + int(sum(arr_test_target == label))]
orig_data_idx = np.squeeze(orig_data_idx, 1)
perturb_img[orig_data_idx] += mini_simple_data_for_train
test_data_idx = arr_test_target == label
test_data_idx = np.squeeze(test_data_idx, 1)
perturb_test_img[test_data_idx] += mini_simple_data_for_test
random_noise = perturb_img - imgs_copy
random_test_noise = perturb_test_img - test_imgs_copy
if(len(imgs_copy.shape)==3):
random_noise = np.stack((random_noise,)*3, axis=-1)
random_test_noise = np.stack((random_test_noise,)*3, axis=-1)
random_noise = torch.tensor(random_noise, dtype=torch.float32)
random_test_noise = torch.tensor(random_test_noise, dtype=torch.float32)
random_noise = random_noise.permute(0, 3, 1, 2)
random_test_noise = random_test_noise.permute(0, 3, 1, 2)
return random_noise, random_test_noise
def synthetic_3dperturbations(self, dataset, train_dataset, test_dataset):
n = len(train_dataset)
n *= 7
img_size = self.imgsize
depth_size = self.imgsize
noise_frame_size = self.patchsize
info = INFO[dataset.lower()]
num_classes = len(info['label'])
min_val = np.min(train_dataset.imgs)
max_val = np.max(train_dataset.imgs)
is_even = img_size % noise_frame_size
num_patch = img_size // noise_frame_size
num_depth_patch = depth_size // noise_frame_size
if is_even > 0:
num_patch += 1
num_depth_patch += 1
if len(train_dataset.imgs.shape) == 4:
n_random_fea = int((img_size / noise_frame_size) ** 2 * (depth_size / noise_frame_size))
simple_data, simple_label = make_classification(
n_samples=n, n_features=n_random_fea, n_classes=num_classes,
n_informative=n_random_fea, n_redundant=0, n_repeated=0,
class_sep=10., flip_y=0., n_clusters_per_class=1
)
simple_data = simple_data.reshape([simple_data.shape[0], num_depth_patch, num_patch, num_patch])
simple_data = simple_data.astype(np.float32)
simple_images = np.repeat(simple_data, noise_frame_size, axis=2)
simple_images = np.repeat(simple_images, noise_frame_size, axis=3)
simple_images = np.repeat(simple_images, noise_frame_size, axis=1)
simple_data = simple_images[:, 0:depth_size, 0:img_size, 0:img_size]
else:
raise ValueError("输入数据不是 3D 图像格式")
linf = self.epsilon
feature_dim = depth_size * img_size ** 2
l2norm_lim = self.comput_l2norm_lim(linf, feature_dim)
simple_data = self.normalize_l2norm(simple_data, l2norm_lim)
imgs_copy = train_dataset.imgs.copy()
test_imgs_copy = test_dataset.imgs.copy()
if imgs_copy.dtype != np.float32:
imgs_copy = imgs_copy.astype(np.float32) / 255.0
test_imgs_copy = test_imgs_copy.astype(np.float32) / 255.0
arr_target = np.array(train_dataset.labels)
arr_test_target = np.array(test_dataset.labels)
perturb_img = imgs_copy.copy()
perturb_test_img = test_imgs_copy.copy()
for label in range(num_classes):
orig_data_idx = arr_target == label
simple_data_idx = simple_label == label
mini_simple_data_for_train = simple_data[simple_data_idx][0:int(sum(orig_data_idx))]
、
mini_simple_data_for_test = simple_data[simple_data_idx][int(sum(orig_data_idx)):int(sum(orig_data_idx)) + int(sum(arr_test_target == label))]
orig_data_idx = np.squeeze(orig_data_idx, 1)
perturb_img[orig_data_idx] += mini_simple_data_for_train
test_data_idx = arr_test_target == label
test_data_idx = np.squeeze(test_data_idx, 1)
perturb_test_img[test_data_idx] += mini_simple_data_for_test
random_noise = perturb_img - imgs_copy
random_test_noise = perturb_test_img - test_imgs_copy
random_noise = torch.tensor(random_noise, dtype=torch.float32)
random_test_noise = torch.tensor(random_test_noise, dtype=torch.float32)
random_noise = random_noise.permute(0, 1, 2, 3)
random_test_noise = random_test_noise.permute(0, 1, 2, 3)
return random_noise, random_test_noise
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