add source for adversarial

official/adversarial/src/.gitignore

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+.vscode/
+__pycache__/
+data/
+sample.png

official/adversarial/src/Dockerfile

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+FROM ubuntu:artful
+MAINTAINER SIYUAN-ZHUANG USTC-1411
+ENV LC_ALL=C.UTF-8
+
+RUN apt-get update
+RUN apt-get install --yes python3.6 python3.6-dev python3-pip python3-openssl
+COPY ./webapp /webapp
+WORKDIR /webapp
+RUN pip3 install -r requirements.txt
+ENTRYPOINT ["python3"]
+CMD ["webapp.py"]

official/adversarial/src/docker-compose.yml

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+version: '3'
+services:
+  web:
+    build: .
+    ports:
+     - "12004:5000"
+    restart: always

official/adversarial/src/solution/Dockerfile

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+FROM ubuntu:artful
+MAINTAINER SIYUAN-ZHUANG USTC-1411
+ENV LC_ALL=C.UTF-8
+
+RUN apt-get update
+RUN apt-get install --yes python3.6 python3.6-dev python3-pip python3-openssl
+COPY . /solution
+WORKDIR /solution
+RUN pip3 install -r requirements.txt
+ENTRYPOINT ["python3"]
+CMD ["solution.py"]

official/adversarial/src/solution/README.md

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+这个目录内部包括参考答案 `solution.py` 和其它依赖文件。
+目录同时包含了 `Dockerfile`, 用户可以用以下方式检验答案：
+
+```bash
+docker build -t solution .
+```
+
+```bash
+docker run -it solution
+```

official/adversarial/src/solution/main.py

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+"""
+
+mmmummm  mmmm  mmsmm          mmmm  mmmmmm   mtm  mmcmm  mmmmmmmmmmmmm
+   #    m"  "m #   "#        #"   " #      m"   " #   "# #        #
+   #    #    # #mmm#"        "#mmm  #mmmmm #      #mmmm" #mmmmm   #
+   #    #    # #                 "# #      #      #   "m #        #
+   #     #mm#  #             "mmm#" #mmmmm  "mmm" #    " #mmmmm   #
+   
+"""
+
+
+from __future__ import print_function
+import argparse
+import os
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+
+from torchvision import datasets, transforms
+
+
+class Net(nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.conv1 = nn.Conv2d(1, 10, kernel_size=7)
+        self.bn1 = nn.BatchNorm2d(10)
+        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
+        self.bn2 = nn.BatchNorm2d(20)
+        self.conv2_drop = nn.Dropout2d()
+        self.fc1 = nn.Linear(320, 50)
+        self.bn3 = nn.BatchNorm1d(50)
+        self.fc2 = nn.Linear(50, 10)
+
+    def forward(self, x):
+        x = F.relu(F.max_pool2d(self.bn1(self.conv1(x)), 2))
+        x = F.relu(F.max_pool2d(self.bn2(self.conv2(x)), 2))
+        x = self.conv2_drop(x)
+        x = x.view(-1, 320)
+        x = F.relu(self.bn3(self.fc1(x)))
+        x = F.dropout(x, training=self.training)
+        x = self.fc2(x)
+        return F.log_softmax(x, dim=1)
+
+
+def train(args, model, device, train_loader, optimizer, epoch):
+    model.train()
+    for batch_idx, (data, target) in enumerate(train_loader):
+        data, target = data.to(device), target.to(device)
+        optimizer.zero_grad()
+        output = model(data)
+        loss = F.nll_loss(output, target)
+        loss.backward()
+        optimizer.step()
+        if batch_idx % args.log_interval == 0:
+            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
+                epoch, batch_idx * len(data), len(train_loader.dataset),
+                100. * batch_idx / len(train_loader), loss.item()))
+
+def test(args, model, device, test_loader):
+    model.eval()
+    test_loss = 0
+    correct = 0
+    with torch.no_grad():
+        for data, target in test_loader:
+            data, target = data.to(device), target.to(device)
+            output = model(data)
+            test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss
+            pred = output.max(1, keepdim=True)[1] # get the index of the max log-probability
+            correct += pred.eq(target.view_as(pred)).sum().item()
+
+    test_loss /= len(test_loader.dataset)
+    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
+        test_loss, correct, len(test_loader.dataset),
+        100. * correct / len(test_loader.dataset)))
+
+def main():
+    # Training settings
+    parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
+    parser.add_argument('--batch-size', type=int, default=64, metavar='N',
+                        help='input batch size for training (default: 64)')
+    parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
+                        help='input batch size for testing (default: 1000)')
+    parser.add_argument('--epochs', type=int, default=2, metavar='N',
+                        help='number of epochs to train (default: 2)')
+    parser.add_argument('--lr', type=float, default=0.02, metavar='LR',
+                        help='learning rate (default: 0.02)')
+    parser.add_argument('--no-cuda', action='store_true', default=False,
+                        help='disables CUDA training')
+    parser.add_argument('--seed', type=int, default=1, metavar='S',
+                        help='random seed (default: 1)')
+    parser.add_argument('--log-interval', type=int, default=10, metavar='N',
+                        help='how many batches to wait before logging training status')
+    args = parser.parse_args()
+    use_cuda = not args.no_cuda and torch.cuda.is_available()
+
+    torch.manual_seed(args.seed)
+
+    device = torch.device("cuda" if use_cuda else "cpu")
+
+    kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
+    train_loader = torch.utils.data.DataLoader(
+        datasets.MNIST('data', train=True, download=True,
+                       transform=transforms.Compose([
+                           transforms.Resize((30, 30)),
+                           transforms.ToTensor(),
+                           transforms.Normalize((0.1307,), (0.3081,)),
+                       ])),
+        batch_size=args.batch_size, shuffle=True, **kwargs)
+    test_loader = torch.utils.data.DataLoader(
+        datasets.MNIST('data', train=False, transform=transforms.Compose([
+                           transforms.Resize((30, 30)),
+                           transforms.ToTensor(),
+                           transforms.Normalize((0.1307,), (0.3081,)),
+                       ])),
+        batch_size=args.test_batch_size, shuffle=True, **kwargs)
+
+
+    model = Net().to(device)
+    optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=1e-5)
+
+    for epoch in range(1, args.epochs + 1):
+        train(args, model, device, train_loader, optimizer, epoch)
+        test(args, model, device, test_loader)
+
+    with open('model.pth', 'wb') as f:
+        torch.save(model.to(torch.device("cpu")).state_dict(), f)
+
+
+if __name__ == '__main__':
+    assert not os.path.exists('model.pth'), "There already exists a model file. DO NOT OVERRIDE IT UNLESS YOU KNOW WHAT YOU ARE DOING."
+    main()

official/adversarial/src/solution/pick.py

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+# 这个文件和答案无关，只是展示获取 target.png 的方式
+from __future__ import print_function
+import torch
+from torchvision import datasets, transforms
+import numpy as np
+from PIL import Image
+
+test_loader = torch.utils.data.DataLoader(
+        datasets.MNIST('data', train=False, transform=transforms.Compose([
+            transforms.Resize((30, 30)),
+            transforms.ToTensor(),
+            # transforms.Normalize((0.1307,), (0.3081,)),
+])), batch_size=1, shuffle=False)
+
+for i, (data, target) in enumerate(test_loader):
+    if i != 534:
+        continue
+    else:
+        data = (np.array(data).reshape(30, 30) * 255).astype(np.uint8)
+        img = Image.fromarray(data, mode='L')
+        img = img.resize((600, 600), resample=Image.BILINEAR)
+        img.save('target.png')
+        break

official/adversarial/src/solution/requirements.txt

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+http://download.pytorch.org/whl/cpu/torch-0.4.1-cp36-cp36m-linux_x86_64.whl
+torchvision==0.2.1

official/adversarial/src/solution/solution.py

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+"""
+
+mmmummm  mmmm  mmsmm          mmmm  mmmmmm   mtm  mmcmm  mmmmmmmmmmmmm
+   #    m"  "m #   "#        #"   " #      m"   " #   "# #        #
+   #    #    # #mmm#"        "#mmm  #mmmmm #      #mmmm" #mmmmm   #
+   #    #    # #                 "# #      #      #   "m #        #
+   #     #mm#  #             "mmm#" #mmmmm  "mmm" #    " #mmmmm   #
+   
+"""
+
+from __future__ import print_function, division
+import torch
+import numpy as np
+from PIL import Image
+import torch.nn.functional as F
+
+from main import Net
+
+def solve():
+    model = load_model()
+    image = Image.open('target.png')
+    image = convert2tensor(image)
+    inputs = preprocess_image(image)
+    label = torch.tensor([6], dtype=torch.long)
+
+    # A solution for part 1
+    inputs.requires_grad = True
+    x = (inputs - 0.1307) / 0.3081  # scale mean & std 
+    output = model(x)
+    loss = F.nll_loss(output, label)
+    loss.backward()
+    x_grad = torch.sign(inputs.grad.data)
+    epsilon = 0.18
+    inputs = torch.clamp(inputs + epsilon * x_grad, 0, 1)
+
+    inputs = inputs.reshape(30, 30)
+
+    for i in range(30):
+        for j in range(30):
+            image[int((i + 0.5) * 20), int((j + 0.5) * 20)] = inputs[i, j]
+
+    return image
+
+def convert2tensor(image):
+    return torch.from_numpy(np.array(image).astype('f') / 255.)
+
+def convert2image(arr):
+    return Image.fromarray((arr.detach().numpy() * 255).astype(np.uint8), mode='L')
+
+def preprocess_image(arr):
+    image = convert2image(arr)
+    image = image.resize((30, 30), resample=Image.NEAREST)
+    return convert2tensor(image).reshape(1, 1, 30 ,30)
+
+def load_model():
+    model = Net()
+    model.load_state_dict(torch.load('model.pth'))
+    model.eval()
+    return model
+
+def check():
+    model = load_model()
+
+    target = convert2tensor(Image.open('target.png'))
+    sample = convert2tensor(Image.open('sample.png'))
+    assert torch.sum(target != sample) < 600 * 600 * 0.002, 'Input hacking is detected! Game over.'
+    assert F.l1_loss(target, sample) < 0.001, 'Input hacking is detected! Game over.'
+    assert (torch.max(torch.abs(target - sample))) < 0.2, 'Input hacking is detected! Game over.'
+    target = preprocess_image(target)
+    sample = preprocess_image(sample)
+    # A solution for part 2
+    target = (target - 0.1307) / 0.3081 
+    sample = (sample - 0.1307) / 0.3081
+
+    truth = torch.argmax(model(target))
+    hack = torch.argmax(model(sample))
+    assert int(truth) != int(hack), 'Fail to hack. Game over.'
+    print('Cong! You win!!!')
+
+
+if __name__ == '__main__':
+    output = solve()
+    convert2image(output).save('sample.png')
+    # You have to pass this check!
+    check()

official/adversarial/src/webapp/README.md

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+这个目录内部包括网页交互相关的代码。