model_inversion_splitNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Inversion on splitNN\n",
    "\n",
    "https://blog.openmined.org/extracting-private-data-from-a-neural-network/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torchvision\n",
    "import torch.nn.functional as F\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define model architecture (CNN classifier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.manual_seed(50)\n",
    "\n",
    "device = \"cpu\"\n",
    "\n",
    "def weights_init(m):\n",
    "    if hasattr(m, \"weight\"):\n",
    "        m.weight.data.uniform_(-0.5, 0.5)\n",
    "    if hasattr(m, \"bias\"):\n",
    "        m.bias.data.uniform_(-0.5, 0.5)\n",
    "\n",
    "n_classes = 10\n",
    "\n",
    "class LeNet(nn.Module):\n",
    "\tdef __init__(self):\n",
    "\t\tsuper(LeNet, self).__init__()\n",
    "\t\tact = nn.Sigmoid\n",
    "\t\tself.body = nn.Sequential(\n",
    "\t\t\tnn.Conv2d(1, 12, kernel_size=5, padding=5//2, stride=2),\n",
    "\t\t\tact(),\n",
    "\t\t\tnn.Conv2d(12, 12, kernel_size=5, padding=5//2, stride=2),\n",
    "\t\t\tact(),\n",
    "\t\t\tnn.Conv2d(12, 12, kernel_size=5, padding=5//2, stride=1),\n",
    "\t\t\tact(),\n",
    "\t\t\tnn.Conv2d(12, 12, kernel_size=5, padding=5//2, stride=1),\n",
    "\t\t\tact(),\n",
    "\t\t)\n",
    "\t\tself.fc = nn.Sequential(\n",
    "\t\t\tnn.Linear(768, n_classes),\n",
    "\t\t)\n",
    "\t\t\n",
    "\tdef forward(self, x):\n",
    "\t\tout = self.body(x)\n",
    "\t\tout = out.view(out.size(0), -1)\n",
    "\t\tout = self.fc(out)\n",
    "\t\treturn out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### define loss for one hot encoded labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cross_entropy_for_onehot(pred, target):\n",
    "\treturn torch.mean(torch.sum(- target * F.log_softmax(pred, dim=-1), 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "net = LeNet().to(device)\n",
    "net.apply(weights_init)\n",
    "\n",
    "criterion = cross_entropy_for_onehot\n",
    "optimizer = optim.Adam(net.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load datset (MNIST)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 64\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(\n",
    "\ttorchvision.datasets.MNIST('/files/', train=True, download=True,\n",
    "\t\t\t\t\t\t\t\ttransform=torchvision.transforms.Compose([\n",
    "\t\t\t\t\t\t\t\ttorchvision.transforms.ToTensor(),\n",
    "\t\t\t\t\t\t\t\ttorchvision.transforms.Normalize(\n",
    "\t\t\t\t\t\t\t\t(0.1307,), (0.3081,)),\n",
    "\t\t\t\t\t\t\t\ttorchvision.transforms.Resize((32, 32))\n",
    "\t\t\t\t\t\t\t\t])),\n",
    "\tbatch_size=batch_size, shuffle=True)\n",
    "\n",
    "test_loader = torch.utils.data.DataLoader(\n",
    "\ttorchvision.datasets.MNIST('/files/', train=False, download=True,\n",
    "\t\t\t\t\t\t\t\ttransform=torchvision.transforms.Compose([\n",
    "\t\t\t\t\t\t\t\ttorchvision.transforms.ToTensor(),\n",
    "\t\t\t\t\t\t\t\ttorchvision.transforms.Normalize(\n",
    "\t\t\t\t\t\t\t\t\t(0.1307,), (0.3081,)),\n",
    "\t\t\t\t\t\t\t\ttorchvision.transforms.Resize((32, 32))\n",
    "\t\t\t\t\t\t\t\t])),\n",
    "\tbatch_size=batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 1 loss: 9.851\n",
      "epoch 2 loss: 3.062\n",
      "epoch 3 loss: 2.114\n",
      "epoch 4 loss: 1.603\n",
      "epoch 5 loss: 1.320\n",
      "epoch 6 loss: 1.144\n",
      "epoch 7 loss: 1.010\n",
      "epoch 8 loss: 0.918\n",
      "epoch 9 loss: 0.824\n",
      "epoch 10 loss: 0.761\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(10):  # loop over the dataset multiple times\n",
    "\n",
    "\trunning_loss = 0.0\n",
    "\tfor batch_idx, (data, target) in enumerate(train_loader):\n",
    "\n",
    "\t\tlabels = torch.zeros((target.shape[0], 10), dtype=torch.float)\n",
    "\t\tfor i, l in enumerate(target):\n",
    "\t\t\tlabels[i][l] = 1.\n",
    "\n",
    "\t\t# zero the parameter gradients\n",
    "\t\toptimizer.zero_grad()\n",
    "\t\t# forward + backward + optimize\n",
    "\t\toutputs = net(data)\n",
    "\t\tloss = criterion(outputs, labels)\n",
    "\t\tloss.backward()\n",
    "\t\toptimizer.step()\n",
    "\n",
    "\t\t# print statistics\n",
    "\t\trunning_loss += loss.item()\n",
    "\n",
    "\tprint(\"epoch %d loss: %.3f\" % (epoch+1, running_loss / batch_size))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### test model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "expected label 9 / model result 7\n",
      "expected label 7 / model result 3\n",
      "expected label 5 / model result 5\n",
      "expected label 9 / model result 9\n",
      "expected label 3 / model result 3\n",
      "expected label 3 / model result 3\n",
      "expected label 0 / model result 0\n",
      "expected label 7 / model result 7\n",
      "expected label 0 / model result 0\n",
      "expected label 8 / model result 8\n",
      "expected label 8 / model result 8\n",
      "expected label 0 / model result 0\n",
      "expected label 8 / model result 8\n",
      "expected label 3 / model result 3\n",
      "expected label 7 / model result 7\n",
      "expected label 7 / model result 7\n",
      "expected label 4 / model result 4\n",
      "expected label 4 / model result 4\n",
      "expected label 0 / model result 0\n",
      "expected label 8 / model result 8\n",
      "expected label 8 / model result 8\n",
      "expected label 2 / model result 2\n",
      "expected label 3 / model result 3\n",
      "expected label 1 / model result 1\n",
      "expected label 1 / model result 1\n",
      "expected label 1 / model result 1\n",
      "expected label 1 / model result 1\n",
      "expected label 1 / model result 1\n",
      "expected label 0 / model result 0\n",
      "expected label 4 / model result 4\n",
      "expected label 7 / model result 7\n",
      "expected label 3 / model result 3\n",
      "expected label 3 / model result 3\n",
      "expected label 2 / model result 2\n",
      "expected label 1 / model result 1\n",
      "expected label 3 / model result 5\n",
      "expected label 2 / model result 2\n",
      "expected label 1 / model result 1\n",
      "expected label 9 / model result 9\n",
      "expected label 2 / model result 2\n",
      "expected label 7 / model result 7\n",
      "expected label 0 / model result 0\n",
      "expected label 1 / model result 1\n",
      "expected label 6 / model result 6\n",
      "expected label 4 / model result 4\n",
      "expected label 1 / model result 1\n",
      "expected label 9 / model result 9\n",
      "expected label 6 / model result 6\n",
      "expected label 1 / model result 1\n",
      "expected label 3 / model result 3\n",
      "expected label 5 / model result 5\n",
      "expected label 6 / model result 6\n",
      "expected label 9 / model result 9\n",
      "expected label 7 / model result 7\n",
      "expected label 8 / model result 8\n",
      "expected label 8 / model result 8\n",
      "expected label 8 / model result 8\n",
      "expected label 7 / model result 7\n",
      "expected label 0 / model result 0\n",
      "expected label 2 / model result 2\n",
      "expected label 6 / model result 6\n",
      "expected label 8 / model result 8\n",
      "expected label 7 / model result 7\n",
      "expected label 3 / model result 3\n"
     ]
    }
   ],
   "source": [
    "data, label = next(iter(test_loader))\n",
    "for i in range(len(label)):\n",
    "\tpred = net(torch.unsqueeze(data[i], 0))\n",
    "\tprint(\"expected label %d / model result %d\" % (label[i], torch.argmax(pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inversion attack"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We train the attacker model with EMNIST to simulate the fact that we may not know the exact dataset user for target model's training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load EMNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 64\n",
    "\n",
    "attack_train_loader = torch.utils.data.DataLoader(\n",
    "\ttorchvision.datasets.EMNIST('/files/', train=True, download=True, split=\"letters\",\n",
    "\t\t\t\t\t\t\t\ttransform=torchvision.transforms.Compose([\n",
    "\t\t\t\t\t\t\t\ttorchvision.transforms.ToTensor(),\n",
    "\t\t\t\t\t\t\t\ttorchvision.transforms.Normalize(\n",
    "\t\t\t\t\t\t\t\t(0.1307,), (0.3081,)),\n",
    "\t\t\t\t\t\t\t\ttorchvision.transforms.Resize((32, 32))\n",
    "\t\t\t\t\t\t\t\t])),\n",
    "\tbatch_size=batch_size, shuffle=True)\n",
    "\n",
    "attack_test_loader = torch.utils.data.DataLoader(\n",
    "\ttorchvision.datasets.EMNIST('/files/', train=False, download=True, split=\"letters\",\n",
    "\t\t\t\t\t\t\t\ttransform=torchvision.transforms.Compose([\n",
    "\t\t\t\t\t\t\t\ttorchvision.transforms.ToTensor(),\n",
    "\t\t\t\t\t\t\t\ttorchvision.transforms.Normalize(\n",
    "\t\t\t\t\t\t\t\t\t(0.1307,), (0.3081,)),\n",
    "\t\t\t\t\t\t\t\ttorchvision.transforms.Resize((32, 32))\n",
    "\t\t\t\t\t\t\t\t])),\n",
    "\tbatch_size=batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# attacker is a reversed target's body (only conv2D)\n",
    "class Attacker(nn.Module):\n",
    "\tdef __init__(self):\n",
    "\t\tsuper().__init__()\n",
    "\t\tself.layers= nn.Sequential(\n",
    "\t\t\tnn.ConvTranspose2d(12, 12, kernel_size=7),\n",
    "\t\t\tnn.Sigmoid(),\n",
    "\t\t\tnn.ConvTranspose2d(12, 12, kernel_size=7),\n",
    "\t\t\tnn.Sigmoid(),\n",
    "\t\t\tnn.ConvTranspose2d(12, 12, kernel_size=7),\n",
    "\t\t\tnn.Sigmoid(),\n",
    "\t\t\tnn.ConvTranspose2d(12, 1, kernel_size=7),\n",
    "\t\t)\n",
    "\n",
    "\tdef forward(self, x):\n",
    "\t\treturn self.layers(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "45a537f687434e45b9ec2b3e7e94890e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1950 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "attacker = Attacker()\n",
    "criterion = nn.MSELoss()\n",
    "optimiser = optim.Adam(attacker.parameters(), lr=1e-4)\n",
    "\n",
    "# we will only update the attacker model\n",
    "net.train(False)\n",
    "\n",
    "#for epoch in range(10):\n",
    "for data, labels in tqdm(attack_train_loader):\n",
    "\tfor i in range(len(labels)):\n",
    "\t\t\n",
    "\t\tinputs = torch.unsqueeze(data[i], 0)\n",
    "\n",
    "\t\t# Reset gradients\n",
    "\t\toptimiser.zero_grad()\n",
    "\n",
    "\t\t# First, get outputs from the target model\n",
    "\t\ttarget_outputs = net.body(inputs)\n",
    "\t\t# Next, recreate the data with the attacker\n",
    "\t\tattack_outputs = attacker(target_outputs)\n",
    "\t\t# We want attack outputs to resemble the original data\n",
    "\t\tloss = criterion(attack_outputs, inputs)\n",
    "\n",
    "\t\t# Update the attack model\n",
    "\t\tloss.backward()\n",
    "\t\toptimiser.step()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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2GiF5cWqbvJJqgXj1gtPPy2lfR/Kj0MyKiuUlKzJ67fbC9mmAI2Iu2sp9hbpT7rDh2FrWJpUgPjbZRBjxBLiqfdpMS0G1w6aTwLRoE2EmnXaEXmXGsBKgV1HQq/xYXnQEy9NW5K2cDETLUzbhTS84IuasUys0vGbFiIqFXeAdOCGEJAoncEIISRRO4IQQkiicwAkhJFEoYl6iLF5hW2TNbpnPbC8c3zIib16ahJUHAUBqWSWtFArLgHtbFWZwAjCCc6FmRUBT+Q5Aa9pmN7amrK2wIVtB0BU/HV+91mJh27BW2cZns2ptnv8Fp+WcrISZmM4Y5xJ62aulJafS4LmlzHbDy0pVe33cO+RA0NVS//fRvAMnhJBE4QROCCGJwgmcEEIShRM4IYQkCkXMS5SakwBWamX/Xk8d67+VE8kiCkggKnpJrYVACNTIzFdxBERP4AtpTdiPeGPKioVe5p80suMKTvakOvt5AqLxq2wHNSbtsZpOsnDLsXmZqiGe+Blms7bHOde6HuzccoROr3TshHW2VcmO80RfK7j67zXvwAkhJFE4gRNCSKL0nMBF5CoR2SMij4vIQRH5cMe+RUQeFpGnOz+79g4kZBxhbJPUibkDbwD4qKreCOA2AH8mIjcC2AXgEVW9DsAjnW1CUoKxTZImpqHDMbSbu0JVF0TkCQBXArgT7W4mAHAfgB8A+NhQvCRrprbZijPT5WzWX/Gkk1n3EmLose1d3lCdcsZ4GZy+IhqUUHXEME8sVK98qUM4ShxxzzuSl1lozunsGCPKdhtnysd6x3e025ZTmta7ZjKZzUotbrSldTFhy+i2Ntn+oK3J3s+OeNfaY01r4CJyDYA3ANgL4PLOBwAAjgO4fC3HImScYGyTFImewEVkA4AHAHxEVTOFALT9LJT7II+I7BSRfSKyr46VgZwlZBjkEdu1htPVnZAhEzWBi0gZ7QD/uqp+u2M+cbGDd+fnSW9fVd2tqnOqOleG/YpByHqSV2xXSlOjcZiQVcR0pRe0G70+oaqfX/VfDwH4AIBPd34+OBQPSV80ttWM7fotJzLbh3TrqNwZS3KP7WCd2ku+CbNc3FVfp72ZacMFoFUJ9o5cQxYnIadQ91qcZWOouBj3DVq9NmvBunhz2ia4eGvzzQm7cN2qOF+IgoQiL9mnZQsuor5iz1mfsdNi8WXZzLjiVNWM8V53c8aOC9fY3bZ3RvPwM5ViMjHfAuD9AH4pIo91bJ9AO7i/KSJ3A/gNgD+MOBYh4wRjmyRNzFMoP0K3PE7g9nzdIWR0MLZJ6jATkxBCEoUTOCGEJAqrEV4CiJNAUK7aVl0bSlbYJDkSipiOGOl03bI4Ypg6LchC0dJLcHFbizkiZnHFVtcrLmXjRRacRyWd1+iuSQVtxKQ2aQ9Vsc7Wp+KqFoav06ti6CXyFOr2WMs1r7Vb1t/SBfuZ88RINykorIAYWZHSg3fghBCSKJzACSEkUTiBE0JIonACJ4SQRKGIeQkg111rbFtnF42tqWyhNlK8dmPFQCBzKvc1q/Zj2ZzwxgXHct7eVtkavaxLr7VYWFVQSo4KaLVyn0CY9bIWY0VYN4wDm7ufYzNVDAE0rL4KCdoRete1uOwIlo5gXArEzujqkw68AyeEkEThBE4IIYnCCZwQQhKFEzghhCQKRcxLgPo2W4t66+QxZyQZKqFQ5/RL0yAzrzlla5zWNzgiZtWKZo3A1opslVZasQKZtKxAKY1stmHRER6laTM4XfE2LCc7ZVMl6zPWh+aEJwT3fp3itarzqtA6tlbJKWsbvE0FR7wtFOPK9BZqgYjplR2O7HbIO3BCCEkUTuCEEJIoPSdwEblKRPaIyOMiclBEPtyxf0pEjorIY51/dwzfXULyg7FNUidmDbwB4KOq+nMRmQHwMxF5uPN/X1DVzw7PPUKGCmObJE1MR55jAI51fl8QkScAXDlsx0g86ohGpDe5x3ZE9pwWg4w+p4SqVy61MelkYgY6oNfz0Wul6Il0Kk7vyWBcacpOF6Y0ahc0OJaXWeqVjvVek1siNxD9CvUot1B02nwWa0653cDmiZNFRxwu1qwaWahlhV+/d2pQmrjLZV7TGriIXAPgDQD2dkwfEpH9InKPiGxey7EIGScY2yRFoidwEdkA4AEAH1HVcwC+BOCVAG5G+y7mc1322yki+0RkXx1xXa0JGSV5xHat4TQ7IGTIRE3gIlJGO8C/rqrfBgBVPaGqTVVtAfgKgFu8fVV1t6rOqepcGbaLBSHrSV6xXSnZZ/EJGTY918BFRAB8FcATqvr5VfbtnTVEAHgvgAPDcZH0ovrMSWN7bn7W2G7b8uvM9k+22r/fM7l5Nf7kGdsqtu2Z1G2SS5iE4lfWi6vKF1bS8xJc3Kp8TlFBz9asZI2lqj2YV8XQW681Lc+can7NSpyW460/ayvY11lW9pJvistOtcAIW+mC00rOWcsuLDuJTgFuYlIhe+276VwxT6G8BcD7AfxSRB7r2D4B4C4RuRltmeQwgA9GHIuQcYKxTZIm5imUH8HvU/q9/N0hZHQwtknqMBOTEEIShRM4IYQkCqsRXgI0j9rKg0vnX2Ns54PMDy/xgORHWIHPxRGnvBZb0nRaeAVP5arTvmuQLnqhSBpWPwSAgqfRRYSV1z7N269Qc2z+ETNbnpDqiZ9eZcbispd8k7V54m2YoNM+p3OBnPfJYF6Avw/vwAkhJFE4gRNCSKJwAieEkEThBE4IIYlCEfMSQBs2xcxkpgE4sjyb2Z48Hdm3ifRGxAhz4ohVhSBbr+hk6nnVAqXptGcrvPg20CXLz9PDnGFhhT9XXPVCKEqj8yrwOeO8jm1e1ccIza9Qd8RJ5z0SZ1z4XrriZEQ1SsDx39lPi05qrAPvwAkhJFE4gRNCSKJwAieEkEThBE4IIYlCEfMlRKOVFUa8zDTSJ6pGaHRbZRUKPcdUztp+YK2yvdcyoqIjRLYcEdMt9+qJna3e2Y2u8BghMrpjPEHRFU4d0c/L7IzYL3yNXcc1w2vhZb164rMVO0NBNCxDvBZ4B04IIYnCCZwQQhKl5wQuIlUR+YmI/EJEDorIX3Xs14rIXhE5JCL/LCKVXsciZJxgbJPUiVkDXwHwDlVd7PQP/JGI/BuAPwfwBVW9X0S+DOButJvBkjFlpryc2a5P27/f1VE5Mx7kFtuitoWal4whtWzSlbd061bSc/qBe8k9dse4L9kxVQvdNXD3YN4aeGBzXPfXzuNsZh3Za0HmHSvWj0Cr8NatvRZ68Nrc9b/kbej57mqbxc5mufNPAbwDwLc69vsAvCc/twgZPoxtkjqxXemLnZ6BJwE8DOAZAPOqevF24giAK4fiISFDhLFNUiZqAlfVpqreDGAHgFsAXB97AhHZKSL7RGRfHc73QELWkbxiu9ZYGpaLhHRlTU+hqOo8gD0A3gxgVkQurqHvAHC0yz67VXVOVefKmBjEV0KGxqCxXSlNj8ZRQlbRU8QUkW0A6qo6LyKTAN4J4DNoB/sfALgfwAcAPDhMRwnJmzxjWwuC5nT2BqWw7FSJrGQ/cq4Q6QiPboW8iOp32oqrOCkxt3KRxSujxciYMbHnDEXGSPE25hoCMLe64lxXrTrTqdc+LWy15xyrVYkQZRH3FMp2APeJSBHtl/FNVf2uiDwO4H4R+RsAjwL4asSxCBknGNskaXpO4Kq6H8AbHPuzaK8ZEpIkjG2SOszEJISQROEETgghiSIau4ifx8lEXgDwGwCXATg1shPnT8r+p+w78OL+X62q20bpzEUY22NByr4DfcT2SCfw/zupyD5VnRv5iXMiZf9T9h0Yf//H3b9epOx/yr4D/fnPJRRCCEkUTuCEEJIo6zWB716n8+ZFyv6n7Dsw/v6Pu3+9SNn/lH0H+vB/XdbACSGEDA6XUAghJFFGPoGLyLtF5MlOt5Ndoz7/WhGRe0TkpIgcWGXbIiIPi8jTnZ+b19PHbojIVSKyR0Qe73Sc+XDHPvb+p9Yth3E9OlKOayDn2FbVkf0DUES73vIrAFQA/ALAjaP0oQ+ffw/AGwEcWGX7OwC7Or/vAvCZ9fazi+/bAbyx8/sMgKcA3JiC/2j3WN/Q+b0MYC+A2wB8E8D7OvYvA/jTMfCVcT1a35ON645vucX2qB1/M4Dvr9r+OICPr/cFjfD7miDQnwSwfVUwPbnePka+jgfRrriXlP8ApgD8HMCtaCc6lLx4Wkf/GNfr+zqSjOuOnwPF9qiXUK4E8Nyq7VS7nVyuqsc6vx8HcPl6OhODiFyDduGmvUjE/4S65TCu14kU4xrIL7YpYg6Itv9cjvWjPCKyAcADAD6iqudW/984+68DdMshgzHOcXGRVOMayC+2Rz2BHwVw1artrt1OxpwTIrIdADo/T66zP13pdFt/AMDXVfXbHXMy/gP9dcsZMYzrEXMpxDUweGyPegL/KYDrOmprBcD7ADw0Yh/y4CG0O7UAY9yNSEQE7WYET6jq51f919j7LyLbRGS28/vFbjlP4P+75QDj4zvjeoSkHNdAzrG9Dov2d6CtGj8D4C/WW0SI8PcbAI4BqKO9LnU3gK0AHgHwNID/BLBlvf3s4vtb0f4auR/AY51/d6TgP4DXod0NZz+AAwD+smN/BYCfADgE4F8ATKy3rx2/GNej8z3ZuO74n1tsMxOTEEIShSImIYQkCidwQghJFE7ghBCSKJzACSEkUTiBE0JIonACJ4SQROEETgghicIJnBBCEuV/Ab+VWx+VzG7FAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3X9NJyKmN2KlgdnVy3Oxa69fUKU7CidPO74wNzyS2N4/ax+9HCjZmn5xdYWwPNq0f04eT/udn7Zh8yqeYZdZpXxeIlo0jR8yY3LAVLHMNOx+kS9FJB+/ACSEkUjiBE0JIpHACJ4SQSOEETgghkUIRswO5c19gbM+es9LYypceSGx/ZotN3Hu8Zqv+eZQkKZ68dWyfGVMUp1Kdw207zza2M7+WFGJkz34zhmSIkwXZGEqKmNWVNuvSy3hU51YrXwvkMEcdE8dWLzvVAlda2+z6pG1utRXk8ifZlMczNjxtbD+x6seJ7TMrT5oxY3krfh4cslmXK4t23N2yObF9ZMhW7Ww6mZLFKXthhxyRV9K0OMs7reryXd4jZ1WNkBBCyGDCCZwQQiKFEzghhEQKJ3BCCImU40/EzFmhIbd1i7Ht/l2bVfWys3Ya29s2fCOx/QpXY/yxZ0xBugxOj4tfsNvYtl/0k4ntU/ZboQfPsEFNVwhsuzQnE7NZSb6n1TE7prrCK/fqnLIRns+O8crE1q0uiNm1VjRrbEimLg6NzZoxp697xtjOX/W4sZ0ztCe5X/EpM2ZlzpaTnS3ZF3VS0WYQrwqEze2jzzNjHi2vN7bCjM2ErRyyn/3h6ROS+1WcD7rTvq45ZG2mfV2Kznid4B04IYRECidwQgiJlAUncBHZJCJ3iMhOEXlQRN7dtq8RkVtF5OH2z469AwkZRBjbJHbS3IHXAbxXVbcCeAmAd4rIVgBXArhdVbcAuL29TUhMMLZJ1KRp6LAPreauUNUJEdkF4GQAl6LVzQQArgPwdQB/uCRe9oCUkyKFnH2aGfP9d1pV599/2pZtPd8RKQaVq593p7H99W8mhaNrT7jYjNn8JXuzKf+zIzO/BomsY9sIhk7vyXol7DNpx1SdrMiGE3oaaNyNIStE1oec3oqrbM/HdesmjO2Fa5PZkutKtg/kliGbzfvC8h5jOy1oUrkub3t8FsUKijVtGNtI7oCxVSSpBK4r2tdzR/4sY9s1c4qx5Wo2O7ZeWZM830H7AIDUnZ6kTgwUpoJeplVbvhb2Zbssag1cRDYDeBGAewBsaH8AAOBJABsWcyxCBgnGNomR1BO4iIwC+AKA96hqopq5qio61CkXkW0isl1EtteQsqI6IX0ki9iuNmx9DkKWmlQTuIgU0Qrw61X1prZ5/9EO3u2f9nsNAFW9SlXHVXW8CPsViZDlJKvYLjlLAoQsNWm60gtajV53qeon5v3XLQAuB/Dh9s+bl8TDHsmtSLZk2vsqu8b7zUs+amwbC062wxITrvddP7HRjLlp/4uN7ZwVNlHoravvMbYPrEsm94z/0qNmzNvX/oaxbX30RGOrP+lUMgwqqOXXrrFDZu23sObUlD1WH8g6tiVoaebetge3TGEbPQCoOzkitRV2LbsxmrTlV9i17Q2r7Vrw2avt36Nzx54wtq3lvYntipNosz5nqxGuyllfi0HyymTTxkEuTHABkIe1VZxxJxaSr7NZsevwz64aMbYnNq4ytsmqbeNWH0oKDuVn7b1v+bB9x8tH7GJ2fnrhzJ0wljqRJhPzpQB+DcD3RGRH2/YBtIL78yJyBYAfAXhzqjMSMjgwtknUpHkK5U7A+TPY4tXZukNI/2Bsk9hhJiYhhEQKJ3BCCImUY74aoYwmK4sdOcs+NF+R/v8dm25awelrM0nR7yM3vsmMOe2zjxnb11/+U8Z21xWnGts1Z16f2P7ZYSvUfurifzK2D+z6LWNbd9/JxpZrJMWruVEbXsMPO9UOH/qBtR0LOG2xNKxY6AhyzZJTGXDEaWcWiJarV1hB8fSVtlrglmErYp5YOGz9CO7vppv2KbL9TgnEHzfsa5poJp/SmXKOFSbjAMD6wpFU4/KBZLzKEVdPLtsqhieusCLvD0+wWVMz+aS/jYpTKdRZjCvMpZhbGk6yVUp4B04IIZHCCZwQQiKFEzghhEQKJ3BCCImUY17E7DcNtYLEkaZtRXXTpK2K+LF/fWNie/NHd5gx9WkrzozdaDMxp+oXGtuvvu3XE9s3bP2cGfMzQ1Yget0ffMbY0nDG9e8wtjMf7upQceJoU2GGXa7hZNx5SXidnlafx1zdCmt7JlcZ28E52zLs7pwVvZua4qQO9aa9L5yuJYXBmjOmUrAPGKwu23hf69jWlJLZvGsKNrt3X3WlsU3MOeU9nNetheSb0iwuLFADgDjvr8mybDqBEgrgHRIzeQdOCCGRwgmcEEIihRM4IYRECidwQgiJFIqYGfP1WVsf9G23vd3YXvBHtpTr5ukdie2mI1imZfTm7xpb7rZkNtwv/9zvmzGve//Xje2P132/az+OJ8KWauJkYuaCtlt5q2+jMO20VJu091oNJIXBiUkbexOw2bZSc+7bnBZeuWrSj1zd+pWzCcVmPwCQ4PhOAicODtvr9bhjazpt4qSSPEGhYsX4piOcNo7YrMvcjB1XnEm+pvIh+xrLR5wyupP2wuZmgpZqzMQkhJDjD07ghBASKQtO4CKySUTuEJGdIvKgiLy7bf9TEdkrIjva/1679O4Skh2MbRI7adbA6wDeq6rfEZExAPeKyK3t//ukqn5s6dwjZElhbJOoSdORZx+Afe3fJ0RkFwBbS/Q44PqJtcb2x998fWL7xK9ZIWnr3fuMrf60LfOZJVqz6lLjUFB+9Cu7zJjbDr3c2D53+QXG9gtbHjC2ci6ZSXfCvY4A9SPbq3C5yDq2TU/Mms0szE8nbaUpmz1Zm7A2zdkvy82pwCaOaFpLJzzmbYtKFKf0ObcBoDThlLmdc0TGepjJaP2qj9jXXR21rzvsTwkA9UpyKmuWbYall1jqve5QcG2NS/pfmLHXovKM3bEw4VzsucBW9xTk0Fk/FXNRa+AishnAiwAc7Zj7LhG5X0SuERHbLZiQSGBskxhJPYGLyCiALwB4j6oeAfD3AE4HcB5adzEf77DfNhHZLiLba3D+3BGyzGQR29VG9498EtItqSZwESmiFeDXq+pNAKCq+1W1oapNAJ8FYL9nt8ZdparjqjpehFM4hpBlJKvYLuVtgShClpoF18BFRABcDWCXqn5inn1jew0RAN4AwC6KDgDN/U8lts+8zlYku+Su99r9nI5J3oP6Zz06mdjO/fARM6Z+yLarGgQajl9D//2gsZ36zOnGdu+68+0Bg2W71d95zAypzw3Ot7BsY1ughSBo3ESeZAwVJ21MeUki3lp2sxAkDnlLqTXrQ9FZv/XWdEtHkuv1xSN2PTc3Zd9PmXXWfYOKe1q0U48O2aSaxoi96WsM2X3rw/lg296bemvgXjVIcfJqwuvorfMXJp1Wb5Mp4j2MG8DGklPpEEj3FMpLAfwagO+JyI627QMALhOR89BaXX8MgE03JGSwYWyTqEnzFMqd8KsRfzl7dwjpH4xtEjvMxCSEkEjhBE4IIZFyzFcjNBX97r7fjFl1d/fHDyUQR0eKCrcC4re+Z0xpnieyaSzHMuqKliG52eRVKTiJPBVHQC9OL3yv5QmWuaojts16FfLsu5WbDarmTTuCpZOsBK+6XnBtxEle8Y7lnbPgCKClcjKBrllyLqJJjvHx2qBZ/51kpapzLapW2DSv3RMoU8QSwDtwQgiJFk7ghBASKZzACSEkUjiBE0JIpBzzIiYh/UGsGNV0hK6ZZJZiwRGrpGEzEj2hK2zFJTVHGPQyDeessAZPQAxtXtW8RkrZvkMm4ULHEi+R0anMKEHmojhCp4tzrDQCotsGLaXwaHDiBPl099a8AyeEkEjhBE4IIZHCCZwQQiKFEzghhEQKRUxCMkGNGOVlFoYyl1StuFfwMvo8ETA4viuseXjCY3Ph7ENXbEuJptnX9SHdOaVZDLZTCoopxcJ0TjjvkSf8htfVE1zTiL7gHTghhEQLJ3BCCImUBSdwEamIyLdE5D4ReVBE/qxtP1VE7hGRR0TkX0XEeXiVkMGFsU1iJ80a+ByAi1V1st0/8E4R+QqA3wPwSVW9UUT+AcAVaDWDJSQWsottVbvm7SWcBGM0RYJOR7xKgCHeGq+3PpxmLThtcoyXnBQa0ibCpE2OCX3zEnTS4p0zRUs4N0HKaZfmroun8cFhwVepLY42fiy2/ymAiwH8e9t+HYDXpzojIQMCY5vETtqu9Pl2z8ADAG4F8AMAh1T16C3AHgAnL4mHhCwhjG0SM6kmcFVtqOp5AE4BcAGAs9OeQES2ich2Edlew+B0JCcEyC62q42ZpXKRkI4saqFIVQ8BuAPARQBWicjRhaBTAOztsM9VqjququPFVH1cCOk/vcZ2KT/UH0cJmceCqoSIrAdQU9VDIjIE4DUAPoJWsL8JwI0ALgdw81I6SkjWZBrbuRx0JDmJy2zVDNNSMTBYsUodQdFrQWZahKVM/nCFtRT7asqWZB4msabLKoCdMNcs7bXwBFdPQw789YRmHbI3qN77ZsZ5SU7l4MGnDq8njay8EcB1IpJH647986r6JRHZCeBGEflLAN8FcHWKYxEySDC2SdQsOIGr6v0AXuTYH0VrzZCQKGFsk9hhJiYhhEQKJ3BCCIkU0R6Eg0WfTOQpAD8CsA7A0307cfbE7H/MvgPP7f/zVXV9P505CmN7IIjZd6CL2O7rBP5/JxXZrqrjfT9xRsTsf8y+A4Pv/6D7txAx+x+z70B3/nMJhRBCIoUTOCGERMpyTeBXLdN5syJm/2P2HRh8/wfdv4WI2f+YfQe68H9Z1sAJIYT0DpdQCCEkUvo+gYvIJSKyu93t5Mp+n3+xiMg1InJARB6YZ1sjIreKyMPtn6uX08dOiMgmEblDRHa2O868u20feP9j65bDuO4fMcc1kHFsq2rf/gHIo1Vv+TQAJQD3AdjaTx+68PkVAF4M4IF5to8CuLL9+5UAPrLcfnbwfSOAF7d/HwPwEICtMfiPVhOX0fbvRQD3AHgJgM8DeEvb/g8A3jEAvjKu++t7tHHd9i2z2O634xcB+Oq87fcDeP9yX9AUfm8OAn03gI3zgmn3cvuY8nXcjFbFvaj8BzAM4DsALkQr0aHgxdMy+se4Xt7XEWVct/3sKbb7vYRyMoAn5m3H2u1kg6rua//+JIANy+lMGkRkM1qFm+5BJP5H1C2Hcb1MxBjXQHaxTRGzR7T153KgH+URkVEAXwDwHlU9Mv//Btl/7aFbDumNQY6Lo8Qa10B2sd3vCXwvgE3ztjt2Oxlw9ovIRgBo/zywzP50pN1t/QsArlfVm9rmaPwHuuuW02cY133mWIhroPfY7vcE/m0AW9pqawnAWwDc0mcfsuAWtDq1AAPcjUhEBK1mBLtU9RPz/mvg/ReR9SKyqv370W45u/D/3XKAwfGdcd1HYo5rIOPYXoZF+9eipRr/AMAfLbeIkMLfGwDsA1BDa13qCgBrAdwO4GEAtwFYs9x+dvD9ZWh9jbwfwI72v9fG4D+An0SrG879AB4A8MG2/TQA3wLwCIB/A1Bebl/bfjGu++d7tHHd9j+z2GYmJiGERApFTEIIiRRO4IQQEimcwAkhJFI4gRNCSKRwAieEkEjhBE4IIZHCCZwQQiKFEzghhETK/wJulMzhxdvVcAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 0\n",
    "for data, targets in test_loader:\n",
    "\tfor i in range(len(targets)):\n",
    "\t\ttarget_outputs = net.body(torch.unsqueeze(data[i], 0))\n",
    "\t\trecreated_data = attacker(target_outputs)\n",
    "\n",
    "\t\tfig, axes = plt.subplots(1, 2)\n",
    "\t\taxes[0].imshow(data[i].detach().numpy()[0].reshape((32, 32, 1)))\n",
    "\t\taxes[1].imshow(recreated_data.detach().numpy()[0].reshape((32, 32, 1)))\n",
    "\t\tplt.show()\n",
    "\t\ti += 1\n",
    "\t\tif i == 10: break\n",
    "\tbreak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.4 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "8a27c3155a6d0a7d96b28712a017d32793704b7d92013c85497e0832d2f2fb12"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}