StAdv_attack.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/as791/stAdv-PyTorch/blob/main/StAdv_attack.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UUAq4jOuHzYu"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torchsummary import summary\n",
        "from torchvision import transforms,datasets\n",
        "from scipy import optimize "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YS9WPuyyHuKe"
      },
      "source": [
        "##Train Model "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bEbR0wY8Hl3m"
      },
      "source": [
        "class Net(nn.Module):\n",
        "  def __init__(self):\n",
        "    super(Net, self).__init__()\n",
        "    self.conv1 = nn.Conv2d(1, 32, 3, 1)\n",
        "    self.conv2 = nn.Conv2d(32, 32, 3, 1)\n",
        "    self.conv3= nn.Conv2d(32, 64, 3, 1)\n",
        "    self.conv4= nn.Conv2d(64, 64, 3, 1)\n",
        "    self.dropout1 = nn.Dropout2d(0.5)\n",
        "    self.fc1 = nn.Linear(1024, 200)\n",
        "    self.fc2 = nn.Linear(200, 200)\n",
        "    self.fc3 = nn.Linear(200, 10)\n",
        "\n",
        "  def forward(self, x):\n",
        "    x = self.conv1(x)   \n",
        "    x = F.relu(x)\n",
        "    x = self.conv2(x)   \n",
        "    x = F.relu(x)\n",
        "    x = F.max_pool2d(x, 2)  \n",
        "    x = self.conv3(x)       \n",
        "    x = F.relu(x)\n",
        "    x = self.conv4(x)\n",
        "    x = F.relu(x)\n",
        "    x = F.max_pool2d(x, 2)  \n",
        "    x = torch.flatten(x,1)  \n",
        "    x = self.fc1(x)         \n",
        "    x = F.relu(x)\n",
        "    x = self.dropout1(x)\n",
        "    x = self.fc2(x)\n",
        "    x = F.relu(x)\n",
        "    x = self.fc3(x)        \n",
        "    return x\n",
        "\n",
        "def fit(model,device,train_loader,val_loader,optimizer,criterion,epochs):\n",
        "  data_loader = {'train':train_loader,'val':val_loader}\n",
        "  print(\"Fitting the model...\")\n",
        "  train_loss,val_loss=[],[]\n",
        "  train_acc,val_acc=[],[]\n",
        "  for epoch in range(epochs):\n",
        "    loss_per_epoch,val_loss_per_epoch=0,0\n",
        "    acc_per_epoch,val_acc_per_epoch,total,val_total=0,0,0,0\n",
        "    for phase in ('train','val'):\n",
        "      for i,data in enumerate(data_loader[phase]):\n",
        "        inputs,labels  = data[0].to(device),data[1].to(device)\n",
        "        outputs = model(inputs)\n",
        "        #preding classes of one batch\n",
        "        preds = torch.max(outputs,1)[1]\n",
        "        #calculating loss on the output of one batch\n",
        "        loss = criterion(outputs,labels)\n",
        "        if phase == 'train':\n",
        "          acc_per_epoch+=(labels==preds).sum().item()\n",
        "          total+= labels.size(0)\n",
        "          optimizer.zero_grad()\n",
        "          #grad calc w.r.t Loss func\n",
        "          loss.backward()\n",
        "          #update weights\n",
        "          optimizer.step()\n",
        "          loss_per_epoch+=loss.item()\n",
        "        else:\n",
        "          val_acc_per_epoch+=(labels==preds).sum().item()\n",
        "          val_total+=labels.size(0)\n",
        "          val_loss_per_epoch+=loss.item()\n",
        "    print(\"Epoch: {} Loss: {:0.6f} Acc: {:0.6f} Val_Loss: {:0.6f} Val_Acc: {:0.6f}\".format(epoch+1,loss_per_epoch/len(train_loader),acc_per_epoch/total,val_loss_per_epoch/len(val_loader),val_acc_per_epoch/val_total))\n",
        "    train_loss.append(loss_per_epoch/len(train_loader))\n",
        "    val_loss.append(val_loss_per_epoch/len(val_loader))\n",
        "    train_acc.append(acc_per_epoch/total)\n",
        "    val_acc.append(val_acc_per_epoch/val_total)\n",
        "  return train_loss,val_loss,train_acc,val_acc"
      ],
      "execution_count": null,
      "outputs": []
    },
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        "outputId": "fa9474a7-09d1-4c8e-ffcc-2c3604581146"
      },
      "source": [
        "if __name__=='__main__':\n",
        "  np.random.seed(42)\n",
        "  torch.manual_seed(42)\n",
        "\n",
        "  transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])\n",
        "  dataset = datasets.MNIST(root = './data', train=True, transform = transform, download=True)\n",
        "  train_set, val_set = torch.utils.data.random_split(dataset, [55000, 5000])\n",
        "  train_loader = torch.utils.data.DataLoader(train_set,batch_size=128,shuffle=True)\n",
        "  val_loader = torch.utils.data.DataLoader(val_set,batch_size=128,shuffle=True)\n",
        "  \n",
        "  use_cuda=True\n",
        "  device = torch.device(\"cuda\" if (use_cuda and torch.cuda.is_available()) else \"cpu\")\n",
        "\n",
        "  model = Net().to(device)\n",
        "  summary(model,(1,28,28))\n",
        "\n",
        "  optimizer = optim.SGD(model.parameters(),lr=0.01, momentum=0.9, nesterov=True, weight_decay=1e-6)\n",
        "  criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "  train_loss,val_loss,train_acc,val_acc=fit(model,device,train_loader,val_loader,optimizer,criterion,50)\n",
        "\n",
        "  fig = plt.figure(figsize=(5,5))\n",
        "  plt.plot(np.arange(1,51), train_loss, \"*-\",label=\"Training Loss\")\n",
        "  plt.plot(np.arange(1,51), val_loss,\"o-\",label=\"Val Loss\")\n",
        "  plt.xlabel(\"Num of epochs\")\n",
        "  plt.legend()\n",
        "  plt.show()\n",
        "  # plt.savefig('loss_event.png')\n",
        "\n",
        "  fig = plt.figure(figsize=(5,5))\n",
        "  plt.plot(np.arange(1,51), train_acc, \"*-\",label=\"Training Acc\")\n",
        "  plt.plot(np.arange(1,51), val_acc,\"o-\",label=\"Val Acc\")\n",
        "  plt.xlabel(\"Num of epochs\")\n",
        "  plt.legend()\n",
        "  plt.show()\n",
        "  # plt.savefig('accuracy_event.png')\n",
        "\n",
        "  torch.save(model.state_dict(),'./model.pt')"
      ],
      "execution_count": null,
      "outputs": [
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            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
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            "Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
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            "Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
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            "Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9c2c1c5363fc43d8883c2de292139cb7",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "Processing...\n",
            "Done!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:469: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\n",
            "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1           [-1, 32, 26, 26]             320\n",
            "            Conv2d-2           [-1, 32, 24, 24]           9,248\n",
            "            Conv2d-3           [-1, 64, 10, 10]          18,496\n",
            "            Conv2d-4             [-1, 64, 8, 8]          36,928\n",
            "            Linear-5                  [-1, 200]         205,000\n",
            "         Dropout2d-6                  [-1, 200]               0\n",
            "            Linear-7                  [-1, 200]          40,200\n",
            "            Linear-8                   [-1, 10]           2,010\n",
            "================================================================\n",
            "Total params: 312,202\n",
            "Trainable params: 312,202\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.00\n",
            "Forward/backward pass size (MB): 0.39\n",
            "Params size (MB): 1.19\n",
            "Estimated Total Size (MB): 1.58\n",
            "----------------------------------------------------------------\n",
            "Fitting the model...\n",
            "Epoch: 1 Loss: 1.472643 Acc: 0.460273 Val_Loss: 0.265602 Val_Acc: 0.916400\n",
            "Epoch: 2 Loss: 0.169875 Acc: 0.948727 Val_Loss: 0.116927 Val_Acc: 0.964800\n",
            "Epoch: 3 Loss: 0.105998 Acc: 0.967945 Val_Loss: 0.102856 Val_Acc: 0.969800\n",
            "Epoch: 4 Loss: 0.084034 Acc: 0.974655 Val_Loss: 0.090810 Val_Acc: 0.972400\n",
            "Epoch: 5 Loss: 0.067883 Acc: 0.978455 Val_Loss: 0.075425 Val_Acc: 0.975400\n",
            "Epoch: 6 Loss: 0.056603 Acc: 0.982055 Val_Loss: 0.069636 Val_Acc: 0.979800\n",
            "Epoch: 7 Loss: 0.049613 Acc: 0.984273 Val_Loss: 0.074366 Val_Acc: 0.979400\n",
            "Epoch: 8 Loss: 0.043806 Acc: 0.986055 Val_Loss: 0.059160 Val_Acc: 0.981600\n",
            "Epoch: 9 Loss: 0.039970 Acc: 0.986982 Val_Loss: 0.058288 Val_Acc: 0.983200\n",
            "Epoch: 10 Loss: 0.034666 Acc: 0.989273 Val_Loss: 0.061891 Val_Acc: 0.985200\n",
            "Epoch: 11 Loss: 0.031844 Acc: 0.989618 Val_Loss: 0.050292 Val_Acc: 0.985000\n",
            "Epoch: 12 Loss: 0.029742 Acc: 0.990255 Val_Loss: 0.062911 Val_Acc: 0.982000\n",
            "Epoch: 13 Loss: 0.026596 Acc: 0.991418 Val_Loss: 0.053053 Val_Acc: 0.984600\n",
            "Epoch: 14 Loss: 0.026365 Acc: 0.991836 Val_Loss: 0.053175 Val_Acc: 0.984400\n",
            "Epoch: 15 Loss: 0.024008 Acc: 0.992109 Val_Loss: 0.039922 Val_Acc: 0.989200\n",
            "Epoch: 16 Loss: 0.023665 Acc: 0.992455 Val_Loss: 0.047416 Val_Acc: 0.986200\n",
            "Epoch: 17 Loss: 0.021272 Acc: 0.992964 Val_Loss: 0.051002 Val_Acc: 0.983400\n",
            "Epoch: 18 Loss: 0.019701 Acc: 0.993655 Val_Loss: 0.077054 Val_Acc: 0.986400\n",
            "Epoch: 19 Loss: 0.017795 Acc: 0.994255 Val_Loss: 0.058756 Val_Acc: 0.984600\n",
            "Epoch: 20 Loss: 0.017708 Acc: 0.994273 Val_Loss: 0.052982 Val_Acc: 0.985800\n",
            "Epoch: 21 Loss: 0.017668 Acc: 0.993945 Val_Loss: 0.048424 Val_Acc: 0.988000\n",
            "Epoch: 22 Loss: 0.015387 Acc: 0.994527 Val_Loss: 0.086491 Val_Acc: 0.986800\n",
            "Epoch: 23 Loss: 0.014592 Acc: 0.995382 Val_Loss: 0.052220 Val_Acc: 0.988200\n",
            "Epoch: 24 Loss: 0.014615 Acc: 0.994927 Val_Loss: 0.047964 Val_Acc: 0.986600\n",
            "Epoch: 25 Loss: 0.012900 Acc: 0.995673 Val_Loss: 0.051500 Val_Acc: 0.988600\n",
            "Epoch: 26 Loss: 0.012767 Acc: 0.995673 Val_Loss: 0.048978 Val_Acc: 0.987400\n",
            "Epoch: 27 Loss: 0.013146 Acc: 0.995764 Val_Loss: 0.069179 Val_Acc: 0.984800\n",
            "Epoch: 28 Loss: 0.011747 Acc: 0.996218 Val_Loss: 0.051985 Val_Acc: 0.988000\n",
            "Epoch: 29 Loss: 0.011133 Acc: 0.996182 Val_Loss: 0.051931 Val_Acc: 0.988800\n",
            "Epoch: 30 Loss: 0.011567 Acc: 0.995873 Val_Loss: 0.063120 Val_Acc: 0.985800\n",
            "Epoch: 31 Loss: 0.010249 Acc: 0.996291 Val_Loss: 0.057894 Val_Acc: 0.987200\n",
            "Epoch: 32 Loss: 0.009403 Acc: 0.996655 Val_Loss: 0.050393 Val_Acc: 0.990400\n",
            "Epoch: 33 Loss: 0.009335 Acc: 0.996818 Val_Loss: 0.048494 Val_Acc: 0.989000\n",
            "Epoch: 34 Loss: 0.006783 Acc: 0.997691 Val_Loss: 0.060511 Val_Acc: 0.988400\n",
            "Epoch: 35 Loss: 0.007457 Acc: 0.997655 Val_Loss: 0.059045 Val_Acc: 0.987000\n",
            "Epoch: 36 Loss: 0.007300 Acc: 0.997527 Val_Loss: 0.056964 Val_Acc: 0.988800\n",
            "Epoch: 37 Loss: 0.007875 Acc: 0.997364 Val_Loss: 0.049360 Val_Acc: 0.988600\n",
            "Epoch: 38 Loss: 0.008268 Acc: 0.997200 Val_Loss: 0.049651 Val_Acc: 0.988400\n",
            "Epoch: 39 Loss: 0.008157 Acc: 0.997291 Val_Loss: 0.041614 Val_Acc: 0.988600\n",
            "Epoch: 40 Loss: 0.006921 Acc: 0.997782 Val_Loss: 0.047100 Val_Acc: 0.988800\n",
            "Epoch: 41 Loss: 0.008065 Acc: 0.997018 Val_Loss: 0.053047 Val_Acc: 0.988000\n",
            "Epoch: 42 Loss: 0.007576 Acc: 0.997327 Val_Loss: 0.048302 Val_Acc: 0.989400\n",
            "Epoch: 43 Loss: 0.006725 Acc: 0.997564 Val_Loss: 0.043413 Val_Acc: 0.990200\n",
            "Epoch: 44 Loss: 0.007075 Acc: 0.997745 Val_Loss: 0.053848 Val_Acc: 0.988600\n",
            "Epoch: 45 Loss: 0.006586 Acc: 0.997582 Val_Loss: 0.046619 Val_Acc: 0.989400\n",
            "Epoch: 46 Loss: 0.006155 Acc: 0.997764 Val_Loss: 0.054634 Val_Acc: 0.989200\n",
            "Epoch: 47 Loss: 0.005016 Acc: 0.998291 Val_Loss: 0.054638 Val_Acc: 0.988000\n",
            "Epoch: 48 Loss: 0.005117 Acc: 0.998327 Val_Loss: 0.046499 Val_Acc: 0.990000\n",
            "Epoch: 49 Loss: 0.006215 Acc: 0.998036 Val_Loss: 0.049788 Val_Acc: 0.989600\n",
            "Epoch: 50 Loss: 0.006280 Acc: 0.997764 Val_Loss: 0.058732 Val_Acc: 0.988600\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nLHN6HmWH6kA"
      },
      "source": [
        "##stAdv Attack "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gdAXxnSMIbtQ"
      },
      "source": [
        "#https://github.com/rakutentech/stAdv - refered code\n",
        "def flow_st(images,flows):\n",
        "  images_shape = images.size()\n",
        "  flows_shape = flows.size()\n",
        "  batch_size = images_shape[0]\n",
        "  H = images_shape[2]\n",
        "  W = images_shape[3]\n",
        "  basegrid = torch.stack(torch.meshgrid(torch.arange(0,H), torch.arange(0,W))) #(2,H,W)\n",
        "  sampling_grid = basegrid.unsqueeze(0).type(torch.float32).cuda() + flows.cuda()\n",
        "  sampling_grid_x = torch.clamp(sampling_grid[:,1],0.0,W-1.0).type(torch.float32)\n",
        "  sampling_grid_y = torch.clamp(sampling_grid[:,0],0.0,H-1.0).type(torch.float32)\n",
        "\n",
        "  x0 = torch.floor(sampling_grid_x).type(torch.int64)\n",
        "  x1 = x0 + 1\n",
        "  y0 = torch.floor(sampling_grid_y).type(torch.int64)\n",
        "  y1 = y0 + 1\n",
        "\n",
        "  x0 = torch.clamp(x0, 0, W - 2)\n",
        "  x1 = torch.clamp(x1, 0, W - 1)\n",
        "  y0 = torch.clamp(y0, 0, H - 2)\n",
        "  y1 = torch.clamp(y1, 0, H - 1)\n",
        "  \n",
        "  Ia = images[:,:,y0[0,:,:], x0[0,:,:]]\n",
        "  Ib = images[:,:,y1[0,:,:], x0[0,:,:]]\n",
        "  Ic = images[:,:,y0[0,:,:], x1[0,:,:]]\n",
        "  Id = images[:,:,y1[0,:,:], x1[0,:,:]]\n",
        "\n",
        "  x0 = x0.type(torch.float32)\n",
        "  x1 = x1.type(torch.float32)\n",
        "  y0 = y0.type(torch.float32)\n",
        "  y1 = y1.type(torch.float32)\n",
        "  \n",
        "  wa = (x1 - sampling_grid_x) * (y1 - sampling_grid_y)\n",
        "  wb = (x1 - sampling_grid_x) * (sampling_grid_y - y0)\n",
        "  wc = (sampling_grid_x - x0) * (y1 - sampling_grid_y)\n",
        "  wd = (sampling_grid_x - x0) * (sampling_grid_y - y0)\n",
        "  \n",
        "  perturbed_image = wa.unsqueeze(0)*Ia+wb.unsqueeze(0)*Ib+wc.unsqueeze(0)*Ic+wd.unsqueeze(0)*Id\n",
        "\n",
        "  return perturbed_image.type(torch.float32).cuda()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PsXXKPSYFzQ1"
      },
      "source": [
        "def flow_loss(flows,padding_mode='constant', epsilon=1e-8):\n",
        "  paddings = (1,1,1,1)\n",
        "  padded_flows = F.pad(flows,paddings,mode=padding_mode,value=0)\n",
        "  shifted_flows = [\n",
        "    padded_flows[:, :, 2:, 2:],  # bottom right (+1,+1)\n",
        "    padded_flows[:, :, 2:, :-2],  # bottom left (+1,-1)\n",
        "    padded_flows[:, :, :-2, 2:],  # top right (-1,+1)\n",
        "    padded_flows[:, :, :-2, :-2]  # top left (-1,-1)\n",
        "  ]\n",
        "  #||\\Delta u^{(p)} - \\Delta u^{(q)}||_2^2 + # ||\\Delta v^{(p)} - \\Delta v^{(q)}||_2^2 \n",
        "  loss=0\n",
        "  for shifted_flow in shifted_flows:\n",
        "    loss += torch.sum(torch.square(flows[:, 1] - shifted_flow[:, 1]) + torch.square(flows[:, 0] - shifted_flow[:, 0]) + epsilon).cuda()\n",
        "  return loss.type(torch.float32)\n",
        "\n",
        "def adv_loss(logits,targets,confidence=0.0):\n",
        "  confidence=torch.tensor(confidence).cuda()\n",
        "  real = torch.sum(logits*targets,-1)\n",
        "  other = torch.max((1-targets)*logits-(targets*10000),-1)[0]\n",
        "  return torch.max(other-real,confidence)[0].type(torch.float32)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cUO-YVefL2Hc"
      },
      "source": [
        "def func(flows,input,target,model,const=0.05):\n",
        "  input = torch.from_numpy(input).cuda()\n",
        "  target = torch.from_numpy(target).cuda()\n",
        "  flows = torch.from_numpy(flows).view((1,2,)+input.size()[2:]).cuda()\n",
        "  flows.requires_grad=True\n",
        "  pert_out = flow_st(input,flows)\n",
        "  output = model(pert_out)\n",
        "  L_flow = flow_loss(flows)\n",
        "  L_adv = adv_loss(output,target)\n",
        "  L_final = L_adv+const*L_flow\n",
        "  model.zero_grad()\n",
        "  L_final.backward()\n",
        "  gradient = flows.grad.data.view(-1).detach().cpu().numpy()\n",
        "  return L_final.item(),gradient"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q-UHbZxi3Ujs"
      },
      "source": [
        "def attack(input,target,model):\n",
        "  init_flows = np.zeros((1,2,)+input.size()[2:]).reshape(-1)\n",
        "  results = optimize.fmin_l_bfgs_b(func,init_flows,args=(input.cpu().numpy(),target.cpu().numpy(),model))\n",
        "  if b'CONVERGENCE' in results[2]['task']:\n",
        "    flows = torch.from_numpy(results[0]).view((1,2,)+input.size()[2:])\n",
        "    pert_out = flow_st(input,flows)\n",
        "  else:\n",
        "    return None\n",
        "  return pert_out"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BeDjzbFkSBHm"
      },
      "source": [
        "if __name__=='__main__':\n",
        "  np.random.seed(42)\n",
        "  torch.manual_seed(42)\n",
        "\n",
        "  transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])\n",
        "  testset = datasets.MNIST(root = './data', train=False, transform = transform, download=True)\n",
        "  test_loader = torch.utils.data.DataLoader(testset,batch_size=1,shuffle=True)\n",
        "\n",
        "  use_cuda=True\n",
        "  device = torch.device(\"cuda\" if (use_cuda and torch.cuda.is_available()) else \"cpu\")\n",
        "  model = Net().to(device) \n",
        "  model.load_state_dict(torch.load('./model.pt'))\n",
        "  model.eval()\n",
        "\n",
        "  adv=[]\n",
        "  adv_label=[]\n",
        "  correct_label=[]\n",
        "  sample=100\n",
        "  success=0\n",
        "  for i,data in enumerate(test_loader):\n",
        "    if i<sample:\n",
        "      input,label = data[0].to(device),data[1].to(device)\n",
        "      random_target = np.random.choice([num for num in range(10) if num != label.item()])\n",
        "      target = torch.from_numpy(np.expand_dims(np.eye(10, dtype=np.float32)[random_target],0)).cuda()\n",
        "      pert_out = attack(input,target,model)\n",
        "      if pert_out!=None:\n",
        "        output =  model(pert_out) \n",
        "        adv.append(pert_out.squeeze().detach().cpu().numpy())\n",
        "        correct_label.append(label.item())\n",
        "        adv_label.append(torch.argmax(output).item())\n",
        "        if label.item()!=torch.argmax(output).item():\n",
        "          success+=1\n",
        "    else:\n",
        "      break"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zl8Npntlf6ui",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 637
        },
        "outputId": "91d96a44-6e4b-429b-8588-33cba862bf70"
      },
      "source": [
        "print(\"Success Rate: {:.4f} \".format(success/sample))\n",
        "cnt = 0\n",
        "plt.figure(figsize=(8,10))\n",
        "for i in range(len(adv)):\n",
        "  cnt += 1\n",
        "  plt.subplot(8,10,cnt)\n",
        "  plt.xticks([], [])\n",
        "  plt.yticks([], [])\n",
        "  orig,adv_,ex = correct_label[i],adv_label[i],adv[i]\n",
        "  plt.title(\"{} -> {}\".format(orig, adv_))\n",
        "  plt.imshow(ex, cmap=\"gray\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Success Rate: 0.3800 \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x720 with 66 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}