本文主要是介绍BadNets：基于数据投毒的模型后门攻击代码（Pytorch）以MNIST为例，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
加载数据集
# 载入MNIST训练集和测试集
transform = transforms.Compose([transforms.ToTensor(),])
train_loader = datasets.MNIST(root='data',transform=transform,train=True,download=True)
test_loader = datasets.MNIST(root='data',transform=transform,train=False)
# 可视化样本 大小28×28
plt.imshow(train_loader.data[0].numpy())
plt.show()
在训练集中植入5000个中毒样本
# 在训练集中植入5000个中毒样本
for i in range(5000):train_loader.data[i][26][26] = 255train_loader.data[i][25][25] = 255train_loader.data[i][24][26] = 255train_loader.data[i][26][24] = 255train_loader.targets[i] = 9 # 设置中毒样本的目标标签为9
# 可视化中毒样本
plt.imshow(train_loader.data[0].numpy())
plt.show()
训练模型
data_loader_train = torch.utils.data.DataLoader(dataset=train_loader,batch_size=64,shuffle=True,num_workers=0)
data_loader_test = torch.utils.data.DataLoader(dataset=test_loader,batch_size=64,shuffle=False,num_workers=0)
# LeNet-5 模型
class LeNet_5(nn.Module):def __init__(self):super(LeNet_5, self).__init__()self.conv1 = nn.Conv2d(1, 6, 5, 1)self.conv2 = nn.Conv2d(6, 16, 5, 1)self.fc1 = nn.Linear(16 * 4 * 4, 120)self.fc2 = nn.Linear(120, 84)self.fc3 = nn.Linear(84, 10)def forward(self, x):x = F.max_pool2d(self.conv1(x), 2, 2)x = F.max_pool2d(self.conv2(x), 2, 2)x = x.view(-1, 16 * 4 * 4)x = self.fc1(x)x = self.fc2(x)x = self.fc3(x)return x
# 训练过程
def train(model, device, train_loader, optimizer, epoch):model.train()for idx, (data, target) in enumerate(train_loader):data, target = data.to(device), target.to(device)pred = model(data)loss = F.cross_entropy(pred, target)optimizer.zero_grad()loss.backward()optimizer.step()if idx % 100 == 0:print("Train Epoch: {}, iterantion: {}, Loss: {}".format(epoch, idx, loss.item()))torch.save(model.state_dict(), 'badnets.pth')# 测试过程
def test(model, device, test_loader):model.load_state_dict(torch.load('badnets.pth'))model.eval()total_loss = 0correct = 0with torch.no_grad():for idx, (data, target) in enumerate(test_loader):data, target = data.to(device), target.to(device)output = model(data)total_loss += F.cross_entropy(output, target, reduction="sum").item()pred = output.argmax(dim=1)correct += pred.eq(target.view_as(pred)).sum().item()total_loss /= len(test_loader.dataset)acc = correct / len(test_loader.dataset) * 100print("Test Loss: {}, Accuracy: {}".format(total_loss, acc))
def main():# 超参数num_epochs = 10lr = 0.01momentum = 0.5model = LeNet_5().to(device)optimizer = torch.optim.SGD(model.parameters(),lr=lr,momentum=momentum)# 在干净训练集上训练，在干净测试集上测试# acc=98.29%# 在带后门数据训练集上训练，在干净测试集上测试# acc=98.07%# 说明后门数据并没有破坏正常任务的学习for epoch in range(num_epochs):train(model, device, data_loader_train, optimizer, epoch)test(model, device, data_loader_test)continue
if __name__=='__main__':main()
测试攻击成功率
# 攻击成功率 99.66% 对测试集中所有图像都注入后门for i in range(len(test_loader)):test_loader.data[i][26][26] = 255test_loader.data[i][25][25] = 255test_loader.data[i][24][26] = 255test_loader.data[i][26][24] = 255test_loader.targets[i] = 9data_loader_test2 = torch.utils.data.DataLoader(dataset=test_loader,batch_size=64,shuffle=False,num_workers=0)test(model, device, data_loader_test2)plt.imshow(test_loader.data[0].numpy())plt.show()
可视化中毒样本，成功被预测为特定目标类别“9”，证明攻击成功。
完整代码
from packaging import packaging
from torchvision.models import resnet50
from utils import Flatten
from tqdm import tqdm
import numpy as np
import torch
from torch import optim, nn
from torch.utils.data import DataLoader
import torch.nn.functional as F
import matplotlib.pyplot as plt
from torchvision import datasets, transforms
use_cuda = True
device = torch.device("cuda" if (use_cuda and torch.cuda.is_available()) else "cpu")# 载入MNIST训练集和测试集
transform = transforms.Compose([transforms.ToTensor(),])
train_loader = datasets.MNIST(root='data',transform=transform,train=True,download=True)
test_loader = datasets.MNIST(root='data',transform=transform,train=False)
# 可视化样本 大小28×28
# plt.imshow(train_loader.data[0].numpy())
# plt.show()# 训练集样本数据
print(len(train_loader))# 在训练集中植入5000个中毒样本
''' '''
for i in range(5000):train_loader.data[i][26][26] = 255train_loader.data[i][25][25] = 255train_loader.data[i][24][26] = 255train_loader.data[i][26][24] = 255train_loader.targets[i] = 9 # 设置中毒样本的目标标签为9
# 可视化中毒样本
plt.imshow(train_loader.data[0].numpy())
plt.show()data_loader_train = torch.utils.data.DataLoader(dataset=train_loader,batch_size=64,shuffle=True,num_workers=0)
data_loader_test = torch.utils.data.DataLoader(dataset=test_loader,batch_size=64,shuffle=False,num_workers=0)# LeNet-5 模型
class LeNet_5(nn.Module):def __init__(self):super(LeNet_5, self).__init__()self.conv1 = nn.Conv2d(1, 6, 5, 1)self.conv2 = nn.Conv2d(6, 16, 5, 1)self.fc1 = nn.Linear(16 * 4 * 4, 120)self.fc2 = nn.Linear(120, 84)self.fc3 = nn.Linear(84, 10)def forward(self, x):x = F.max_pool2d(self.conv1(x), 2, 2)x = F.max_pool2d(self.conv2(x), 2, 2)x = x.view(-1, 16 * 4 * 4)x = self.fc1(x)x = self.fc2(x)x = self.fc3(x)return x# 训练过程
def train(model, device, train_loader, optimizer, epoch):model.train()for idx, (data, target) in enumerate(train_loader):data, target = data.to(device), target.to(device)pred = model(data)loss = F.cross_entropy(pred, target)optimizer.zero_grad()loss.backward()optimizer.step()if idx % 100 == 0:print("Train Epoch: {}, iterantion: {}, Loss: {}".format(epoch, idx, loss.item()))torch.save(model.state_dict(), 'badnets.pth')# 测试过程
def test(model, device, test_loader):model.load_state_dict(torch.load('badnets.pth'))model.eval()total_loss = 0correct = 0with torch.no_grad():for idx, (data, target) in enumerate(test_loader):data, target = data.to(device), target.to(device)output = model(data)total_loss += F.cross_entropy(output, target, reduction="sum").item()pred = output.argmax(dim=1)correct += pred.eq(target.view_as(pred)).sum().item()total_loss /= len(test_loader.dataset)acc = correct / len(test_loader.dataset) * 100print("Test Loss: {}, Accuracy: {}".format(total_loss, acc))def main():# 超参数num_epochs = 10lr = 0.01momentum = 0.5model = LeNet_5().to(device)optimizer = torch.optim.SGD(model.parameters(),lr=lr,momentum=momentum)# 在干净训练集上训练，在干净测试集上测试# acc=98.29%# 在带后门数据训练集上训练，在干净测试集上测试# acc=98.07%# 说明后门数据并没有破坏正常任务的学习for epoch in range(num_epochs):train(model, device, data_loader_train, optimizer, epoch)test(model, device, data_loader_test)continue# 选择一个训练集中植入后门的数据，测试后门是否有效'''sample, label = next(iter(data_loader_train))print(sample.size()) # [64, 1, 28, 28]print(label[0])# 可视化plt.imshow(sample[0][0])plt.show()model.load_state_dict(torch.load('badnets.pth'))model.eval()sample = sample.to(device)output = model(sample)print(output[0])pred = output.argmax(dim=1)print(pred[0])'''# 攻击成功率 99.66%for i in range(len(test_loader)):test_loader.data[i][26][26] = 255test_loader.data[i][25][25] = 255test_loader.data[i][24][26] = 255test_loader.data[i][26][24] = 255test_loader.targets[i] = 9data_loader_test2 = torch.utils.data.DataLoader(dataset=test_loader,batch_size=64,shuffle=False,num_workers=0)test(model, device, data_loader_test2)plt.imshow(test_loader.data[0].numpy())plt.show()if __name__=='__main__':main()
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