ghost_chickens_with_FGSM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import  cv2\n",
    "\n",
    "animals = [\"chicken\", \"pig\", \"panda\", \"terrain\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The idea here is to \"optimize\" an image to belong to a class that it dosn't belong to\n",
    "\n",
    "This is basically the reverse of what we saw in what_on_earth_are_adversarial_attacks.ipynb.\n",
    "\n",
    "## How are the images \"optimized\" using the same FGSM ?\n",
    "* The key here is to understand how FGSM actually worked.\n",
    "> In FGSM, we were tampering with the pixels which has a *positive* gradient and added a certain value `gradient * epsilon` to each of those pixels. This made the image deviate further and further away from the class it actually belongs to and thus maximising loss in the process. Note that this was done with a __positive epsilon__ value\n",
    "\n",
    "But for our current objective, we will try to \"optimize\" the image to a different class. This can be done by:\n",
    "* Doing a forward pass with an image of class `x` and with a label of `y`. Where `y` is the class to which we want to convert our image to. \n",
    "* Performing a backpropagation on the network and extracting the gradients on the input image.\n",
    "* Now instead of trying to maximise loss using the FGSM, we'll reduce the loss with a __negative epsilon__ FGSM.\n",
    "* This will help reduce the loss of the image with respect to the target class `y`, and with a sufficiently  negative epsilon value, the image gets mis-classified as the target class.\n",
    "\n",
    "\n",
    "If you didn't read the boring stuff above, just remember that \n",
    "* A __positive epsilon__ value will __un-optimize__ the image \n",
    "* A __negative epsilon__ value will __optimize__ the image for the given label class\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Net(\n",
       "  (conv1): Conv2d(3, 7, kernel_size=(5, 5), stride=(1, 1))\n",
       "  (pool): MaxPool2d(kernel_size=3, stride=3, padding=0, dilation=1, ceil_mode=False)\n",
       "  (dropout): Dropout(p=0.5, inplace=False)\n",
       "  (conv2): Conv2d(7, 10, kernel_size=(5, 5), stride=(1, 1))\n",
       "  (fc1): Linear(in_features=10, out_features=10, bias=True)\n",
       "  (fc2): Linear(in_features=10, out_features=8, bias=True)\n",
       "  (fc3): Linear(in_features=8, out_features=4, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(3, 7, 5)\n",
    "        self.pool = nn.MaxPool2d(3, 3)\n",
    "        \n",
    "        self.dropout = nn.Dropout(p=0.5)\n",
    "        self.conv2 = nn.Conv2d(7, 10, 5)\n",
    "        \n",
    "        self.fc1 = nn.Linear(10, 10)\n",
    "        self.fc2 = nn.Linear(10, 8)\n",
    "        self.fc3 = nn.Linear(8, 4)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.pool(F.relu(self.conv1(x)))\n",
    "        x = self.pool(F.relu(self.conv2(x)))\n",
    "        x = x.view(x.size(0), -1)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = self.fc3(x)\n",
    "        return x\n",
    "    \n",
    "hunter = Net()\n",
    "hunter.zero_grad()\n",
    "hunter.load_state_dict(torch.load(\"hunter.pt\"))\n",
    "hunter.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining some utils\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def result_to_prob_dist_percentage(output_tensor):\n",
    "    prob_dist = torch.softmax(output_tensor.flatten(), dim = 0)\n",
    "    index = torch.argmax(prob_dist).item()\n",
    "    percentage = prob_dist[index]*100\n",
    "    return index, percentage.item()\n",
    "\n",
    "def tensor_to_image(tensor):\n",
    "    np_arr = tensor.squeeze(0).detach().numpy()\n",
    "    np_arr = np.moveaxis(np_arr, 0,2)\n",
    "    return np_arr.astype(np.uint8)\n",
    "\n",
    "def preprocess_image_for_model(img_path):\n",
    "    im = cv2.imread(img_path)\n",
    "    im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)  # convert to RGB color space \n",
    "    im_moveaxis =  np.moveaxis(im, 2, 0) \n",
    "    input_tensor =  torch.from_numpy(im_moveaxis).unsqueeze(0).float()\n",
    "    return input_tensor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading up and checking an image of the terrain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction =  terrain 99.9164810180664 %\n"
     ]
    }
   ],
   "source": [
    "input_tensor = preprocess_image_for_model(\"images/terrain.png\")\n",
    "pred = hunter(input_tensor)\n",
    "index, percentage = result_to_prob_dist_percentage(pred)\n",
    "print (\"Prediction = \", animals[index], percentage, \"%\" )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Forward propagation with a manually set label\n",
    "* The manually set label is the target label, i.e the label we want to convert the image into.\n",
    "* In our case the target label is a `[0]` which refers to a chicken"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss =  11.767049789428711\n"
     ]
    }
   ],
   "source": [
    "loss = nn.CrossEntropyLoss()\n",
    "\n",
    "label = torch.tensor([0], dtype=torch.long)  ## manually set the label to zero \n",
    "input_tensor.requires_grad = True  # gradients \n",
    "pred = hunter(input_tensor)\n",
    "loss_val = loss(pred,label)\n",
    "print(\"loss = \",loss_val.item())\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Backpropagation and the extraction of gradients from the input image for the \"reverse\" FGSM to be done\n",
    "* It is kind of justified to call it a form of \"reverse\" FGSM because we are \"optimizing\" the image to minimise loss \n",
    "* But the key is that we're optimizing it to belong to a different class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 25, 25])\n"
     ]
    }
   ],
   "source": [
    "loss_val.backward() #backprop\n",
    "data_grad = input_tensor.grad.data\n",
    "print(data_grad.size())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Note: `torch.clamp()` makes sure that the pixels values which overshoot to a value over 255 after the perturbation are clamped down at a `[0:256]` range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fgsm_attack(image, epsilon, data_grad):\n",
    "    # Collect the element-wise sign of the data gradient\n",
    "    sign_data_grad = data_grad.sign()\n",
    "        # Create the perturbed image by adjusting each pixel of the input image\n",
    "    perturbed_image = image + epsilon*sign_data_grad\n",
    "        \n",
    "    delta = epsilon * sign_data_grad\n",
    "    perturbed_image = torch.clamp(perturbed_image, 0, 255)\n",
    "    \n",
    "    return perturbed_image, delta\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making the model see a chicken where there's none\n",
    "* Notice the use of a __negative epsilon__ on the `fgsm_attack()`\n",
    "* The weird image on the bottom might look like noise, but is actually the visualization of `epsilon*sign(data_grad)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction =  chicken 90.20503997802734 %\n"
     ]
    },
    {
     "data": {
      "image/png": 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oivADRRF+oCjCDxRF+IGiCD9QFOEHivp/t9m2wCVF1nkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bamboozle_tensor , addition= fgsm_attack(input_tensor, -18, data_grad)\n",
    "bamboozle_image = tensor_to_image(bamboozle_tensor)\n",
    "addition = tensor_to_image(addition)\n",
    "\n",
    "plt.imshow(bamboozle_image)\n",
    "plt.show()\n",
    "\n",
    "pred = hunter(bamboozle_tensor)\n",
    "index, percentage = result_to_prob_dist_percentage(pred)\n",
    "print (\"Prediction = \", animals[index], percentage, \"%\" )\n",
    "\n",
    "plt.imshow(addition)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def spawn_ghost_animal_with_visualisation(image_path, model, epsilon, target_label):\n",
    "    \n",
    "    loss = nn.CrossEntropyLoss()\n",
    "    \n",
    "    input_tensor = preprocess_image_for_model(image_path)\n",
    "    input_tensor.requires_grad = True  # gradients \n",
    "    label = torch.tensor([target_label], dtype=torch.long)  \n",
    "    pred = model(input_tensor)\n",
    "    loss_val = loss(pred,label)\n",
    "    loss_val.backward() #backprop\n",
    "    data_grad = input_tensor.grad.data\n",
    "    bamboozle_tensor , addition= fgsm_attack(input_tensor, epsilon, data_grad)\n",
    "    bamboozle_image = tensor_to_image(bamboozle_tensor)\n",
    "    addition = tensor_to_image(addition)\n",
    "    predict = model(bamboozle_tensor)\n",
    "    index, perc = result_to_prob_dist_percentage(predict)\n",
    "#     print (\"Prediction = \", animals[index], perc, \"%\" )\n",
    "    \n",
    "    result = [animals[index], perc]\n",
    "\n",
    "    return bamboozle_image, result , addition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_adversarial_examples(image_path, model, epsilon):\n",
    "    images = []\n",
    "    results = []\n",
    "    deltas = []\n",
    "    for m in range (0,3):\n",
    "\n",
    "        image, res, delta = spawn_ghost_animal_with_visualisation(image_path, model, epsilon, m)\n",
    "        images.append(image)\n",
    "        results.append(res)\n",
    "        deltas.append(delta)\n",
    "\n",
    "    plt.rcParams[\"figure.figsize\"] = (10,10)\n",
    "    fig, axs = plt.subplots(2,3)\n",
    "    axs.flat[0].set(ylabel= \"delta\")\n",
    "    axs.flat[3].set(ylabel= \"perturbed image\")\n",
    "\n",
    "    for i in range(0, 3):\n",
    "        axs[0,i].imshow(deltas[i])\n",
    "        axs[1,i].imshow(images[i])\n",
    "        axs[1,i].set(xlabel = results[i][0] + \"  \"+ str(round(results[i][1],3)) + \"%\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bfO5KPbRRn6B9Vc5Q0QExUWZKoFyXUtMSRcfJj2L5e8w+R0dP0tlBZIaQ7lhqgpEzjAnuhAEAAFSAJAwAAKACJGEAAAAVIAkDAACoAEkYAABABbqzOjLS7EVRyxyrdLVHW7rkBb9TbrHi6LSWrUYr9Tt1QnVrp8qWC5VcZX25xyr7ODSxYri0cL3k+EjRaS0br6V+p06obu1UZWOiRGVieC1kjjOSecTDh7XJ63eXuVBKxUS2EnT5Y8j+ri2KCe6EAQAAVIAkDAAAoAIkYQAAABUgCQMAAKgASRgAAEAFSMIAAAAq0LIpKsxst6T3SNohqSbpKnf/CzMbkfQhSXsk3SPp5e5+rFXjSAoX78yV9rZpDojs6qslDpYr+23iYeo7bOaKyfG4Sy6zvvxN0YFKLlbc0TERnO/cdZ+Ll/g4y++SXX29TE15bsqUJh6mvsMmnqPMuJs+jcByT0TJRe27KiaaOGtL2SfpcBqItsZEmf0tf46Kpk7NIrUsJlp5J2xW0uvd/fGSninpNWb2BElvkHSdu18g6brie6AbEBPAYsQEulrLkjB3P+DuNxZfj0m6Q9IuSZdJurb4sWslvaRVYwA6CTEBLEZMoNu15TNhZrZH0kWSviZpu7sfkOoBKGlbO8YAdBJiAliMmEA3avmyRWa2TtJHJb3O3U+YWaP9rpR0ZSvHBlSBmAAWIybQrVp6J8zM+lUPrPe5+8eK5oNmtrPYvlPSoVRfd7/K3S9294tbOUagnYgJYDFiAt2sZUmY1f+UebekO9z97Qs2fVLSFcXXV0j6RKvGAHQSYgJYjJhAt2vl25F7Jf28pFvN7Kai7Y2S3irpw2b2Kkn3SnpZC8cQKFfQ3RYly/FLLBjfdKNhuXB2XougR4ny+cz+OuQh7+CY6IALKFJy2pZct3YZCadtWX4t/Gjp6Q+i8v5cn7YhJkoNoblTYeSEuysznUNmU5lhj2anRSoxwAoe8pYlYe7+z5KiN/af36rjAp2KmAAWIybQ7ZgxHwAAoAIkYQAAABUgCQMAAKgASRgAAEAFWj5Za2cqUX3ULtmSruYu4Ftqcdjs8Ja/Mmu4Lmt2DJlFW8OOmQXBgy4TE8OZQaw2JapU2yV/McTdShyq2ZVg4bWaqx6LCnzLxJ4yizbnnk+CLsPDE5lBrDYdEBMlFnMvVT3eAZWEI7kF6ku9wC1/poEyoXymMcGdMAAAgAqQhAEAAFSAJAwAAKACJGEAAAAVIAkDAACoAEkYAABABbp0iooSC02XWDw7V1YblrUOl5sWIZxOIVf2W2JLroy4ZB3xsseQFZUed8BMC52txONQ4lLIPQ7lpgSJ+wyPBDGWuU4zEz2EW0azC4w3c6KMkhdxiecnSCs3JmLhy0v2VyoxxUluEKUu4zbFRLm9nRHuhAEAAFSAJAwAAKACJGEAAAAVIAkDAACoAEkYAABABbq0OjItWjhXihealqqpqDhdMxfWze0qt8h5XDmZO7FBc5OrGbtrMe7myT0OuXjphGLU8DEvcSlMZKqWcws6x5WTy1+AOXe+y+iuxbibh5go9pWNiVgnxESpxbhb9BLCnTAAAIAKkIQBAABUgCQMAACgAiRhAAAAFSAJAwAAqABJGAAAQAXM3asew5LM7LCk7xffbpV0pMLhMAbGcJ67n9XmYy5CTDCGDjs+MfFIjKEzxtDRMbEikrCFzOwGd7+YMTCGThlD1TrhHDCGzhhD1cfvFJ1wHhhDZ4yh6uMvhbcjAQAAKkASBgAAUIGVmIRdVfUAxBjmMYbO0AnngDHUVT2Gqo/fKTrhPDCGuqrHUPXxs1bcZ8IAAABWg5V4JwwAAGDFIwkDAACowIpKwszsUjP7jpndaWZvqGgM95jZrWZ2k5nd0KZjXm1mh8zstgVtI2b2RTP7XvH/5jYf/81mdn9xHm4ysxe16vjF8Xab2T+a2R1mdruZ/XrR3rbz0ImICWKCmFiMmCAmVlJMrJgkzMx6Jf2VpJ+Q9ARJl5vZEyoazvPc/cI2zj1yjaRLT2t7g6Tr3P0CSdcV37fz+JL0juI8XOjun2nh8SVpVtLr3f3xkp4p6TXF49/O89BRiAliQsTEIsQEMaEVFhMrJgmTdImkO939LneflvRBSZdVPKa2cPfrJY2e1nyZpGuLr6+V9JI2H7+t3P2Au99YfD0m6Q5Ju9TG89CBiInFiAligphYjJjo8JhYSUnYLkn3Lfh+X9HWbi7pC2b2TTO7soLjz9vu7gek+oUnaVsFY3itmd1S3IZu2+1dM9sj6SJJX1NnnIeqEBOLdcK1QExUi5hYrBOuBWIiYyUlYZZoq2J+jb3u/jTVb3e/xsyeXcEYOsE7JT1a0oWSDkh6WzsOambrJH1U0uvc/UQ7jtnBiInOQkxUj5joLMTEElZSErZP0u4F358jaX+7B+Hu+4v/D0n6uOq3v6tw0Mx2SlLx/6F2HtzdD7r7nLvXJL1LbTgPZtavemC9z90/VjRXeh4qRkwsRkzUERMPIyaICamDY2IlJWHfkHSBmZ1vZgOSXinpk+0cgJmtNbP1819LeqGk2/K9WuaTkq4ovr5C0ifaefD5C7rwUrX4PJiZSXq3pDvc/e0LNlV6HipGTCxGTNQRE8TEPGKirnNjwt1XzD9JL5L0XUn/Lum3Kzj+oyTdXPy7vV1jkPQB1W/lzqj+l96rJG1Rvcrje8X/I20+/nsl3SrpFtUv8J0tPgfPUv1thVsk3VT8e1E7z0Mn/iMmiAli4hHnhZggJlZMTLBsEQAAQAVW0tuRAAAAqwZJGAAAQAVIwgAAACpAEgYAAFABkjAAAIAKkIQBAABUgCQMAACgAiRhAAAAFSAJAwAAqABJGAAAQAVIwgAAACpAEgYAAFABkjAAAIAKkIQBAABUgCQMAACgAiRhAAAAFSAJAwAAqABJGAAAQAVIwgAAACpAEgYAAFABkjAAAIAKkIQBAABUgCQMAACgAiRhAAAAFSAJAwAAqABJGAAAQAVIwgAAACpAEgYAAFABkjAAAIAKkIQBAABUgCQMAACgAiRhAAAAFSAJAwAAqABJGAAAQAVIwgAAACpAEgYAAFABkjAAAIAKkIQBAABUgCQMAACgAiRhAAAAFSAJAwAAqABJGAAAQAVIwgAAACpAEgYAAFABkjAAAIAKkIQBAABUgCQMAACgAiRhAAAAFSAJAwAAqABJGAAAQAVIwgAAACpAEgYAAFABkjAAAIAKkIQBAABUgCQMAACgAiRhAAAAFSAJAwAAqABJGAAAQAVIwgAAACpAEgYAAFABkjAAAIAKkIQBAABUgCQMAACgAiRhAAAAFSAJAwAAqABJGAAAQAVIwgAAACpAEgYAAFABkjAAAIAKkIQBAABUgCQMAACgAiRhAAAAFSAJAwAAqABJGAAAQAVIwgAAACpAEgYAAFABkjAAAIAKkIQBAABUgCQMAACgAiRhAAAAFSAJAwAAqABJGAAAQAVIwgAAACpAEgYAAFABkjAAAIAKkIQBAABUgCQMAACgAiRhAAAAFSAJAwAAqABJGAAAQAVIwgAAACpAEgYAAFABkjAAAIAKkIQBAABUgCQMAACgAiRhAAAAFSAJAwAAqABJGAAAQAVIwgAAACpAEgYAAFABkjAAAIAKkIQBAABUgCQMAACgAiRhAAAAFSAJAwAAqABJGAAAQAVIwgAAACpAEgYAAFABkjAAAIAKkIQBAABUgCQMAACgAiRhAAAAFSAJAwAAqABJGAAAQAVIwgAAACpQSRJmZpea2XfM7E4ze0MVYwA6CTEBLEZMoBuYu7f3gGa9kr4r6QWS9kn6hqTL3f3foj7Da4d8/eb1yW39A2uS7bMzE+EY5mYnk+29vUNhn76+4XCbzJLN03Pp40jSzMmx9K560vuSpNwjZbPpfr3B2CQpeuhrmSP198fnYXjzWcHg4v3VZqfSXaw37JMzEIxvJvdYTNyfbJ/ydJ9Tx2qaPFWLT+wyERN1xERxLGKCmCgQE8WxVnFM9C31Ay1wiaQ73f0uSTKzD0q6TFIYXOs3r9fLX3tZctvZ51ycbD9y+OZwAMeO/nuyfeOGx4Z9RkaeHG7rG+hPtn//+LfDPvv/6Uvpfa2PH5KZWnyRDo6mL8Z1ffETxuxc+vqYmpsJ+2zf9aRw21N/5sr0ht65sM/Y4e8l2/v700+mkmS9ceDt2vnEZPsDx74b9nng334r2f7vU99Jtn/uL0+E+yqJmBAxMY+YkERMSCIm5q3mmKji7chdku5b8P2+og3oVsQEsBgxga5QRRKWSqsfkbqb2ZVmdoOZ3TBxKr5lDKwCxASwGDGBrlBFErZP0u4F358jaf/pP+TuV7n7xe5+8fDazPvswMpHTACLERPoClUkYd+QdIGZnW9mA5JeKemTFYwD6BTEBLAYMYGu0PYP5rv7rJm9VtLnJfVKutrdb8/1mZ0Z15FDNyW3nTyV/pzm5Hj8ocHpyfQ265kO+0zN3htumxg/lj7OdDyG4e2zyfaaxWPoz1TE1AbSH8Y8ceLBsE9PkIIPbkx/gFSSJmbSj4Mk3XzzW5Ptc5nfafpYVP0Tf6hycCge33du25xsnx2M36qYrI0n209qQ7J9TqfCfZVBTNQRE3XEBDExj5ioW80xUUV1pNz9M5I+U8WxgU5ETACLERPoBsyYDwAAUAGSMAAAgAqQhAEAAFSAJAwAAKAClXwwf9lMMkvni2Oj6XWbJk7GFSdrNw4m22eD9akkae7UwXhbsNpCf1+uSiV96mcy1TpzmaqXmdn0IPo2xNUjwyPpeXV8Ll724tRUvLZW7fhdyfZsBdJEetzBwy1JWrMh/fjVB7Ev2Xxyf1x50zOcrqLpWz+SHlvtUHz8diEm6schJiQRE/VBEBMSMTFvpcQEd8IAAAAqQBIGAABQAZIwAACACpCEAQAAVIAkDAAAoAIkYQAAABVYEVNUeE2ankqXqM7V0n36BuKS28mJdCmsxxW3srjqV7PT6UH09C0/x+3JlBfPRb+sJAVj7xuKH+KZyfQ5nZ0Oaqkl9Q/G53V8LL3YrDz+ndasS5cRT08G+5I0MxU/ULXZ9LH6++Jy5eGBgWT71LFgAd+5zMXQJsREHTFRR0wQE/OIibqVEhPcCQMAAKgASRgAAEAFSMIAAAAqQBIGAABQAZIwAACACqyQ6kjX1Hi6CqI3qCzJVb3MTKX31ZNNSTOLos6k9zfYl66mkKTBoBqlbyAeRC1TljM3E1SwzMSVMhMn04uV1mbj40TnW5Lmaul+g5nHwnrT+5udiccwfTJeHDZa0HXN+rjqZfxkekHe6DzkqqPahZioIybqiAliYh4xUbdSYoI7YQAAABUgCQMAAKgASRgAAEAFSMIAAAAqQBIGAABQAZIwAACAClQyRYWZ3SNpTNKcpFl3vzjfIS557elNlwQPrln+r5Zb97SnJ643XbdxKNneNxCPISoVHj+RLgeW8r9TtAisBwvaSgqrqXMLwE4Fi9pKkgW12+OZhV6H1vYn22dn4z5zs/H4ouuhlvmdpoMFamVR6XHz6/GJCWJiHjFRHIqYICYKqzkmqpwn7HnufqTC4wOdhpgAFiMmsKrxdiQAAEAFqkrCXNIXzOybZnZlRWMAOgkxASxGTGDVq+rtyL3uvt/Mtkn6opl9292vX/gDRdBdKUnD69LvBwOrCDEBLEZMYNWr5E6Yu+8v/j8k6eOSLkn8zFXufrG7XzwQrJ8FrBbEBLAYMYFu0PYkzMzWmtn6+a8lvVDSbe0eB9ApiAlgMWIC3aKKPx22S/q4mc0f//3u/rlch76+Hm3etja57cToRLJ9LrO6evQXU4+fCPucnNgWbuvvezDZPjGcHpsk1ZQe3+R0XNpbOxmXz551Mr0C/ZhtDvuM9KTHbT1BTbKksS3xeV07vSbZniunHhs7lWyf6I/7eOaq7R8Iyp+HZsM+vVMDyfbB6DrJnJ+SOjYmHjwan+wdu+J46e/bmmyfPRRfw71z6WPVTsUxMX0k3t/xHemY2NJ3MuwzNpAet62LH/NNp+K/ZafWTibbczHR3z+YbB8Yi8fQn6mGj2KifyJ9fiRpqid9jogJ6cHME9COYWJCIiakxmOi7UmYu98l6antPi7QqYgJYDFiAt2iobcjzew8M/ux4uvh+dvORuJ4AAAgAElEQVTEAAAAKGfJJMzMfknSRyT9r6LpHEl/38pBAQAArHaN3Al7jaS9kk5Ikrt/T1L8ASkAAAAsqZEkbMrdH/rEnJn1ScGnygEAANCQRj6Y/2Uze6OkYTN7gaRflfSp1g5rsZq7JifSlRPRAqezQbsUL945dCquKlljB+P9bdmS7nPgWNjn0Ib0sTbWcouOxjlzb1/6dzKP+xzzkWT7wNDxsM/Aqbh65ORwutLRtsZVIls8vW3fWNhFqsV/A1hPupJHR+MxTKxNXyszJ9K/69xc0yvBlq1dMVHbGF8/+0/FHw3tOfZAsr33rHSsSNKGoErscMmY6C8RE1t8NNk+MHRWfJwNcUz4/emqrsGe9GLOknQ0eNoYyl122ZhIV0Frc/xYDB9IVxP2rUv/rmaZVa3bpG0xMRicT0mTp+Jz+kAzY2JTJiYe7PCYmCgRE8G1P56Jic2lYiLe4fCB9LVypjHRyJ2wN0g6LOlWSa+W9BlJ/6OhvQMAACBpyTth7l6T9K7iHwAAAJpgySTMzG7VIz8DdlzSDZL+wN2PtmJgAAAAq1kjnwn7rKQ5Se8vvn9l8f8JSddI+g/NHxYAAMDq1kgSttfd9y74/lYz+4q77zWzn2vVwAAAAFazRj6Yv87MnjH/jZldImld8W28IB8AAABCjdwJ+0VJV5vZOkmm+tuQv1isbP/HrRzcPK+5pieDBUuDRTIHBuIS2VpQujpqcXlqvT4h2N9MOhftnd0U9rFj6bLf3o3BFAuSfDTdR5KOn5POp3ccj8+DlN7fzPp43MOT8dwRB4Pzt20sM03G1nR59tlHD4V9fC4uPX6g73CyfetcPL+wnQym5NiansKjr4pl70/TrpgYaXpMHAn7aEv6fPcejqcE8JF0H0k660T6unvwrFxMpPc3czAe9/A528Nt64Pzd2J7HBM7HkzPUbF/84awTy4mevZvTLY/OBc/n9iOYPqB4DlotgP+HG9XTMjiKSVyMbF+Jv345WMi3XzW4fjxdotjYqgvHUsP9rYxJo6lr+8TO+LXvh3B47f/3IGwz3ipmIgXYG9VTDRSHfkNSU82s42SzN0XPoofbuwwAAAAWKihv+nN7CclPVHSkBV/2bn777VwXAAAAKtaIwt4/7WkV0j6NdXfjnyZpPNaPC4AAIBVrZEP5v+wu/+CpGPu/hZJPyRpd2uHBQAAsLo1koTNLyI2bmZnS5qRdH7rhgQAALD6NfKZsE+b2SZJfyrpRtVnz/+blo7qNO7SXFBpEBVv9fZnqrqCAppMoZXG+vvDbVt6Tibb58YzFSzBwtW1nnjR79HM+Gx/uvJmemu6WlCSxtekf6eevvh3nZiMz2tvfzqnn8tUiU7el34whjJVRgOD8WV77ly6ymdwc3pxcUlyT1fYjNbSfVzVL1bcrpjIFORqeyYmjq3dkWyf68vFRHqb9cTVaCOj8f6mt6YrbKfvj6uwzl6TvoZ7glipi89rLYyJuLJscjB9fdXmysXE9PnpmFg/vDPsEz0WI5vSfQYG94X7apd2xURQVC5JOrk9vk5sbbp9ri++HjcHC9IcCyq3JWlzLV7E5oHJdHtPTyYmJoOYyFRASvHr2JH+9LFquZiYSb+G1ILrVMrHxLrz0xXxNhHHxOYNwbHOMCYaqY78/eLLj5rZpyUNuXtQ0w8AAIBGNLJ2ZK+kn5S0Z/7nzUzu/vbWDg0AAGD1auTtyE9JmpR0q9QB78MAAACsAo0kYee4+1NaPhIAAIAu0kh15GfN7IUtHwkAAEAXaeRO2L9K+riZ9ag+PYVJcnePFzIDAABAViNJ2NtUn6D1Vo/qlhPM7GpJL5Z0yN2fVLSNSPqQ6h/yv0fSy909rmV9eG+q54CP5LX0x9SmJuLVM3t70/s6NRws0Cmpx+O65NGgInjjyOawj8+lOx3NLDradzK+cTkXlASPnYrLiLdNp/tMDUe12ZIPTIfbpvrTUz1Yply5PvvJI/Vvix+LvpPxYrODa9ILjI+fmIr77EqHwdZj6fL1sut3r8SY2DmcKYT2uEw+uvQPH47L530u3WlztJCypL4dmZiYTU9tsX1nvEjvsf50n6n7MzFxMo6J3RuimIin3TBLl+r3n4gfi761uZhIPwdkY2IifZU/uCUdE3PhnvJWYkwMZmJi5HgcE1G45GLi6MYgJo7kYiKebmJkNt1v8FQmJrYHMXHqYNjHj8bnYe2G9HmdycTEMUs/fv0n4vjvm0ovVi5Jk+ekp5VYqwOZMWxLtkevb3NqbAXvRt6O/J6k25aTgBWukXTpaW1vkHSdu18g6brie6BbXCNiAljoGhET6GKN/FF/QNKXzOyzkh7602mpKSrc/Xoz23Na82WSnlt8fa2kL0n6zcaGCqxsxASwGDGBbtdIEnZ38W+g+Hcmtrv7AUly9wNmwf09oHsQE8BixAS6RiMz5r+lHQM5nZldKelKSRped6a5H7DyERPAYsQEVrowCTOzP3f315nZp1RfL3IRd/+pEsc7aGY7i79udkpKL+5W3/9Vkq6SpE1nrV3u59GAlYKYABYjJtA1cnfC3lv8/2dNPN4nJV0h6a3F/59o4r6BlYiYABYjJtA1wiTM3b9Z/P/lMjs2sw+o/uHKrWa2T9KbVA+qD5vZqyTdK+llje3LZT3pP3Jqs0Hp6kCcX/b0pYtC5w7FpcIaiUtue3uCItNj8SpPh9elp2bY1h+XmttIZmX6Wnp8/evjAtgHLV3KPHbX/rDP0K744xlrxk4m20e3xFPKbTmUflyP9aRL4SXp7IH4d5o8lZ4uYGo8HsOghpLttaDu3r3cJBUrMiaG42lWwrlZlImJYIoDSdr4QHpqhuO5mBiNy9o31dLTyvTPxW9bbbV0WfvY+nS8StLQRFwK37d9R7J95N7w5o4OB5fq1kxMjJeKiXgMg7a8mFCD5finW4kxsTETE3MdHhO1ICZ2r8/ExIPp6/v7PfHr29n9cUys2Z4+1v33xlO92Ib047olOqeShnbEj+3B+x5IH2csvo5HgtN6dFN0PTT2OlF2yqMlufvlwabnt+qYQCcjJoDFiAl0u0bmCQMAAECTkYQBAABUIFcdmayKnFeyOhIAAADKfyZsvirypyXtkPR3xfeXq76eFwAAAErKVUd+WZLM7Pfd/dkLNn3KzK5v+cgWMfUElUEeVIlYppKoNpeu6vCwykGamYyrJravTy9+OrymP+yz5mC6zGh07cb4OANxRcxQT/qhnAt+Vymu1unfGS8A2zsQn9dTPWuS7Z5ZlPzEtvQYRkbjPlN2ON42lF5oeXBN/PfG7MH0ORofSFfQ1CxezLl92hQTx+Nqr5npeNnmsYPpKtpsTJyT3l/fgfjxHmtXTGQqIHuH4jGcujddaexz8WOxLViU+MhIHBM2ET9vTO0LYiJYuF6SZvuCmLh7XbJ9bqoTPtlSfUwcXBfHRP9MOiZ2rRkL+8yuSZ/XvqlM7GViYmsTY2L7RPyY9w7F5/W+eyeT7ZszMdHX7Jg4mY6J2jlxTEyGMZGu6pybis/pQo1Ezllm9qj5b8zsfElnNbR3AAAAJDUyRcV/VX0B77uK7/dIenXLRgQAANAFGlk78nNmdoGkxxVN33b3qdYOCwAAYHVb8u1IM1sj6b9Leq273yzpXDN7cctHBgAAsIo18pmwv5U0LemHiu/3SfqDlo0IAACgCzSShD3a3f9E0owkufuEpLiMAQAAAEtq5IP502Y2rGLiVjN7tKT2fibM4lLigWCR1ZlMCe/k2vRUCj296cVNJWmoP85XT/Ski0WHLV5wu7c//fv0D8blxWOD8dQRNpf+fS96zPPCPk9/wt5k+8mJ74d9Tp2MF3qdqaWndPj67TeEfY4eT/d5cEt8vjcdjRdTPjh1INm+uxaXlY+fSJcymwV/a5Rbq7i5mhwTa6bSZeM9ffFiwLmYGO5JX/tjFu+vTExsH4ynjqgFU6OUi4l4ao1TJ+8Nt83U0lM6fP32D4Z9opjYeiw+30c2pxd6lqT1U2uT7VO1eEqe8RPpaUFscxAT8UPUPm16nRieHA77DGWm6rD16ZgYPRbHxObt6Wu4/2A8NUM+JtKP+UWPeWrYp9kxcbCWntLhztu/EPaJYqLH4vNd81xMpFOYqdrOsM/4ifS4zc/snlQjSdibJH1O0m4ze5+kvZL+8xkdFQAAoMs1Uh35RTO7UdIzVX8b8tfdMykmAAAAltTInTBJeo6kZ6n+lmS/pI+3bEQAAABdoJEpKv6npF+WdKuk2yS92sz+qtUDAwAAWM0auRP2HElPcvf5D+Zfq3pCBgAAgJIaScK+I+lcSfMlc7sl3dKyESWYpKhQbWhtukKjbyC+yTcVzbBxNK5ymN0ULwba2xss1NmfqQQ7K6icnIvHPTMZLwi6ZSRdObl722PDPuNH0yVNBw/G1T+Ki3LUN3BOsn3NybuS7ZI0aofShylZcTI0mb4eDm+JB755Q/pYPVHVkt237HE1W7NjwmaCgueoGk7S7JF4Ye3e3uBY/ZmK4ePB+c5UYbYvJu4P+8jj0sC+gXTV6ZodI2Gf0RNNjolz0tfD0OE4Jo4Gx+rZkn78zOIqw3ZpdkysHQ2qGUfix25jLiYOp481mFnsuieKo+3xdT/zQC4m0vtbu+2Hku1S82NibiBddVomJjaPxufuaFz8G8bEeFBRLUk9feljbQ3a+6KL8fSfizaY2adUf8ndKOkOM/t68f0zJH21ob0DAAAgKXcn7M/aNgoAAIAuEyZh7v5lM+uV9Hl3/7E2jgkAAGDVy1ZHuvucpHEziz8QBQAAgGVr5IP5k5JuNbMvSjo13+ju/6VlowIAAFjlGknC/t/iHwAAAJqkkWWLri2zYzO7WtKLJR1y9ycVbW+W9EuS5ut43+jun1l6X9LAQLrkdW46XY47tCZeXHQw+LW3nRsvTj1xKrOAd396bLkK1bnZ9LhnD8UlzuvPeUy47cXP+j+T7d//Try/D//DNcn289bvCfv0bR8Lt43fN5Rsf+7LXhz2Gbw7Xa58+w3/GvaZ6zkRbptZn17cu3cunhqhNpouS67VgnJlz8zTkdHJMWHD6Zg4MZ2Jiah8XtK2ZsbEhvjTEOsn4zE0NSYu2hP26RvbHm4bH9yXbH/uRZmYGAti4sFMTMxsC7fNHEz/vr298RQ6tZEgJmbS7SVDYkXGxJpMTJwoERPHbDTsUzuUnmdhdjaeEmT92niqhygmvvWdA2GfD//D/5dsz8XEtv74vI6fGk+2Z2OiP4iJu5obE7Wz4hUZfTR9XmfP8HWikRnz7zazu07/18C+r5F0aaL9He5+YfFvycACVpFrREwAC10jYgJdrJG3Iy9e8PWQpJdJilPtgrtfb2Z7yg0LWH2ICWAxYgLdbsk7Ye5+dMG/+939zyX96Bkc87VmdouZXW1m4Zy2Znalmd1gZjdMjs+eweGAjkdMAIsRE+gKjbwd+bQF/y42s1+WtL7k8d4p6dGSLpR0QNLboh9096vc/WJ3v3hoTSM37IAViZgAFiMm0DUauWoXBsCspLslvbzMwdz94PzXZvYuSZ8usx9gtSAmgMWICXSTRpKwV7n7og/im9n5ZQ5mZjvdfb4M46WSbiuzH2C1ICaAxYgJdJNGkrCPSHpaou3puU5m9gFJz5W01cz2SXqTpOea2YWqLwR+j6RXNzRKs3A1+cnxmWR7T29cCz+wPj2VwpHZuKR0yuJtWw6nt92/Ib3yuyTJ07UNvj7us33LznDb3qenV5aqHYxnGDlv4I5k+7MeE49htBaPYWIuvb89ay8M+/Tv+OFk+03TXwn7HFsbbgqnOXBPt0vS7Pp0ufL0zLr0MXrifeV0ckysC0r7N2ViYjgTE4c3prdtPLQh7DPl6evOJ+I+2x+1J9zWzJjY0ROPYWT9feG2WyfSj8WetaeS7ZLU/6wgJj4Wx8SWTExMlImJg+lpQdatn062m2fmHskgJvIxMRrExOaRuM/jHnX6y/XD9r5g+THx3SAmnpCJCc3E1/f9c+nHYkMmJp6+K4iJb2diYircpInZ9FRGs7Px9eAz6WlBxoOYaPR1IkzCzOxxkp4oaaOZ/fSCTRtUr5LMcvfLE83vbmhUwCpETACLERPodrk7YY9VfRK9TZL+w4L2MdUn0gMAAEBJYRLm7p8ws09L+k13/6M2jgkAAGDVy05R4e5zkl7QprEAAAB0jUY+mP9VM/tLSR+S9NAn59z9xpaNCgAAYJVrJAmbL0v4vQVtrjObNX9ZvOaamkjPhmw96Zt5M0ElgyTNBQtuRhU0kjQ0HC9IOjCYrqjoORYvkNu/M115Mz29NewzMxOP766708t5Th1IV25I0mM2pctHBifuD/vsGo0Xet3v6YVZb/vw1WGfgT0vSbZvPe/ssM+JB74dbtvcl35s7XC80pb1HE22z5ydPj/eU3K14iZasTGxJV5Ivf9U+uloevrBsE+pmFgbx0TPpt3J9lxMTIzEbyisGU/HxN/feH3YZ2DPk5LtW2cyMTEbzxZ/NFhI2DbGC0f39KQnqV8/ni7D7PX081k7dUZMxIu5Dwymr/0ei2NicCi9CHXP8fganpk5GG676+50LOViYuum9GOei4meEjFx/T/EMbHjac9Oj219OlYk6Z7BuMp/7Yn0c35tLn797QleQs40JpZMwtz9eQ3tCQAAAA1rZNmi7Wb2bjP7bPH9E8zsVa0fGgAAwOq1ZBIm6RpJn5c0fy/8u5Je16oBAQAAdINGkrCt7v5hSTVJcvdZSfEb6QAAAFhSI0nYKTPbovqH8WVmz5R0vKWjAgAAWOUaqY78b5I+KenRZvYVSWdJ+tmWjgoAAGCVa6Q68kYze47qyxiZpO+4e1yj2yLRpADDa9Jl8h6UF0vSiXseSG/YnC7LllT/zQNHNwfHmog7TU8FpdTBArSSdGIsvgH58av/Pj2Eo/vDPofH0qXC37k3Xki193g8vunhyWT70OHhsM/aXWPpPmPx4rAngpJ7SRqfTF8PW3bHj8XMgfTvtOlo+jh98WwAbdXMmBifG0xvmEuXckuSLJ6iYnNw7qbXtDEmrl9+TBy9v0RMnJWJifuDmNgTx8RTJ85L99meiYmT8eN09jnpx+nYg5mYWJv+nY4Ez3WzvdVP2yJ1ekyk26e3ZBYRP5qeZsEysx/c8/014baP/316uqByMRF/Kqn3rHhamemJ9PiGNsQx0X/H99N91maejKfix2nonPRUImf3xI/Fwan073umMbFkEmZmQ5J+VdKzVL/G/8nM/trd088uAAAAWFIjb0e+R/VFu/+f4vvLJb1X0staNSgAAIDVrpEk7LHu/tQF3/+jmd3cqgEBAAB0g0aqI79VVERKkszsGZK+0rohAQAArH6N3Al7hqRfMLN7i+/PlXSHmd0qyd39KS0bHQAAwCrVSBJ2actHsQTrsbC6ZXAoKBPJFCbMTKYrKub2xUWffY+Oq7BmpzamjzMRV4/01dI3Ift2xNUZkxPxIqtjfekqw8kN8UO8/oH0ueu783DYx47FN09re85Nth996hPDPpsfSC8iPDaZWVy4Lx7DruGBZLsPxxeE7QquoWgImUrZdml2TJw4kr7254YyMTF9Vrhtdip9Dc1YJiY2BzFxsmRM3BPExEQcEwdPpc/dYC4mtiw/JjZPBpV3ku5dl77wpifKxcREEBNDU5mYmAiuoaCAzaz6oOj4mFgbxETmdeJEEBPbTm4J+wxnYuL7J9IxseZQO2MiPQtBqZi4P46JrSPxYtzDQUxEi91L0kBU8HmGMdHIFBXp2lAAAACU1shnwgAAANBkJGEAAAAVIAkDAACoAEkYAABABUjCAAAAKtDIFBWlmNlu1Zc82iGpJukqd/8LMxuR9CFJeyTdI+nl7n4suzOX5ubSpaNzs7VgAPHu1j5qZ7J93WR6sVRJmhjYEW6bGk8vozm7PlOOHyyQ2/NgvPBpbVNcPjsxtTu9vxMTYZ+56V3J9l133xP26dsUnG9JtfNekGz/zkh6Cg9JGlW6+Hbi5L+Hfc7KLOg8cTJdnj3bEy/m2juWvljmNqR/V+8pt1hxR8dELb1A9QNT6YVuJWnzunjalqnx9BhmD5aIiV1xqXntSHxtTUydTO9vW/q6l6TzJ9PnwXIxcWcmJp5zfrJ9rhaPe/++dEz0boofwOOZmPAoJo6npwqQpN7twbV1IIiJGWJCWiImDgcxMRnHxEgYE5kpIDLX1pr96ZiwbEw8Od3n7njO9nxMPD3ZfrgWn4fJfempNXo3xZfEccXPG6NRTByKr+Pe7cHrxBnGRCvvhM1Ker27P17SMyW9xsyeIOkNkq5z9wskXVd8D3QDYgJYjJhAV2tZEubuB9z9xuLrMUl3SNol6TJJ1xY/dq2kl7RqDEAnISaAxYgJdLu2fCbMzPZIukjS1yRtd/cDUj0AJW0L+lxpZjeY2Q2TE/EMxcBKREwAixET6EYtT8LMbJ2kj0p6nbufaLSfu1/l7he7+8VDw+mlKICViJgAFiMm0K1amoSZWb/qgfU+d/9Y0XzQzHYW23dKij8ND6wyxASwGDGBbtayJMzqq1e+W9Id7v72BZs+KemK4usrJH2iVWMAOgkxASxGTKDbtWyKCkl7Jf28pFvN7Kai7Y2S3irpw2b2Kkn3SnrZUjuquWtqMv1+f60WlP1OxeWuvVtnk+3rNwfLoUs6ORGXm/b1p1eZ97hKV9NBWbIPbwr7zB2Kc+aNaw8k2zfU1od9+nedl2yfeMlZYZ91Zw+F26Z3p0uC16WroiVJs1PpcW+qxefBt8SX7ez04XSfo/HjVwvK+3v70u31141SOjYmNvWlH7v1m9PXtiTNTaTLxqUmx8T9R+IxnDUabjt8KvkxIm2oxdPNRDExkIuJ8Tgmzt16bnpsJ+8M+9Sm1ibbj9XSU1dIUt+xXEykY8lHMtMIzC0vJnJTPSyBmMjFxJogJiYyMXE8jom5ueXHxFgUE88u9zpx7u50TEzcG8fE+NRUsv3Ykfh1oq83Pg+z0+lpPHwk8zrRophoWRLm7v+cGcbzW3VcoFMRE8BixAS6HTPmAwAAVIAkDAAAoAIkYQAAABUgCQMAAKhAK6sjm8cl1dJVC1FlydR4ugJSknpm0pURR3rjOQInTqSrMyRpNlg0trc3Lo+ItgyOxwvAjgdVU5J0dF26KmhyOr1QqSSd1Zd++PsfFy9Q21uLF/3tX3dOsn3Hybj8556BdLWOb44rwfpPpRfWlaQHetPj6+uLz+tZ29J/i4wdH0i212rlS8GapskxMTeYrird1Jt+fCTpRLti4uy4Cmv8ZLq6VpI2Wzom1mZi4ngUE32ZmLgwExNTk8n2HTvSsSJJk99LV8TddPTW+Di7d4bb1t2dPkd9ffF5PX4iHRP9/cGi6JYp8WuX1RgTQYHf4KMyMTEbj6EveJ2YnX4g7DPdl/59s68TmzIxsXZNsv0JP/CUsE8UEw9O3h8fZ2d8jqbvTp+jbX1x5WurYoI7YQAAABUgCQMAAKgASRgAAEAFSMIAAAAqQBIGAABQAZIwAACACqyIKSrcXdPBQqvRYso1xQtx9vY+mGwfH9sQ9pncH5f9btmSHsOxw3Hp8foN6dL1dZvT5buSVJuZCLdNj6YXjvWRzFQd/f3J9gNT94Z9dtfODrdtuj9d3vvdB28I+xzsOZpsH7F4SolT8Uwiqs2lz9+mHXG58Mx0eltPz2DQo/opKpodE9abXqx4fCwuXZ9cE19bx46lx7BlY3zuZmbTj13/dDpWJGn81HC4rX8kiIlD8fW4OYiJ6f643P3g7ePhtt1PflGy/btHMzHx4NeS7WbxGE5lpkbo35R+3I8cyZXQp7cNro0eP2JC6pSYiLf1r0tv89G7wj6b++9Ltk+fzMTE/fEC3ruf/Mxke+514r4wJuKpMHIxUYtiIljoXZIUvE6caUxwJwwAAKACJGEAAAAVIAkDAACoAEkYAABABUjCAAAAKrAiqiOluLpl/FR6QVKzuOqlx9K55/r9cWXL9M54Yc/RoGqivy+ujhhck67Cqs3mxh1ukg2lV3rtiwtEtCZYiHp4Jn1OJengyUPxGHak+/nAPWGf9fenq9uOxIU32aKTnmAx3KnJePHz4HKQV1/wldXcmEjv62Cm2mvz0TgmRoLqur5MTPQMNDcmTgRFi/1r4yrjgf509djRdKGcJGnXyXh837zjE8n2BzIxNrE+fc7PiguG84VYQUysXRM/OYQxkTlMJyAmVkJMHEy252JipskxMXo8iIn1mZhYl24/05jgThgAAEAFSMIAAAAqQBIGAABQAZIwAACACpCEAQAAVIAkDAAAoALm3ulFx5KZHZb0/eLbrZKOVDgcxsAYznP33CQaLUdMMIYOOz4x8UiMoTPG0NExsSKSsIXM7AZ3v5gxMIZOGUPVOuEcMIbOGEPVx+8UnXAeGENnjKHq4y+FtyMBAAAqQBIGAABQgZWYhF1V9QDEGOYxhs7QCeeAMdRVPYaqj98pOuE8MIa6qsdQ9fGzVtxnwgAAAFaDlXgnDAAAYMVbUUmYmV1qZt8xszvN7A0VjeEeM7vVzG4ysxvadMyrzeyQmd22oG3EzL5oZt8r/t/c5uO/2czuL87DTWb2olYdvzjebjP7RzO7w8xuN7NfL9rbdh46ETFBTBATixETxMRKiokVk4SZWa+kv5L0E5KeIOlyM3tCRcN5nrtf2May12skXXpa2xskXefuF0i6rvi+nceXpHcU5+FCd/9MC48vSbOSXu/uj5f0TEmvKR7/dp6HjkJMEBMiJhYhJogJrbCYWDFJmKRLJN3p7ne5+7SkD0q6rOIxtYW7Xy9p9LTmyyRdW3x9raSXtPn4beXuB9z9xuLrMUl3SNqlNp6HDkRMLEZMEBPExGLERIfHxEpKwnZJum/B9/uKtnZzSV8ws2+a2ZUVHH/ednc/INUvPEnbKhjDa83sluI2dNtu75rZHkkXSfqaOuM8VIWYWPnAOc4AAAwySURBVKwTrgViolrExGKdcC0QExkrKQmzRFsVpZ173f1pqt/ufo2ZPbuCMXSCd0p6tKQLJR2Q9LZ2HNTM1kn6qKTXufuJdhyzgxETnYWYqB4x0VmIiSWspCRsn6TdC74/R9L+dg/C3fcX/x+S9HHVb39X4aCZ7ZSk4v9D7Ty4ux909zl3r0l6l9pwHsysX/XAep+7f6xorvQ8VIyYWIyYqCMmHkZMEBNSB8fESkrCviHpAjM738wGJL1S0ifbOQAzW2tm6+e/lvRCSbfle7XMJyVdUXx9haRPtPPg8xd04aVq8XkwM5P0bkl3uPvbF2yq9DxUjJhYjJioIyaIiXnERF3nxoS7r5h/kl4k6buS/l3Sb1dw/EdJurn4d3u7xiDpA6rfyp1R/S+9V0naonqVx/eK/0fafPz3SrpV0i2qX+A7W3wOnqX62wq3SLqp+Peidp6HTvxHTBATxMQjzgsxQUysmJhgxnwAAIAKrKS3IwEAAFYNkjAAAIAKkIQBAABUgCQMAACgAiRhAAAAFSAJazEzu8bMfjbRfraZfWSJvveY2dYWjm1LseL8STP7y9O2Pd3MbjWzO83s/y7mX2l4NXozu6L4me+Z2RVF26CZfc7MbjOzX13ws1eZ2UWt+j2x8pjZ35RdeNnMBszsb4vr92Yze+6Cba8ollC53cz+JOjfb2bXFv3vMLPfKtp3F/FyR9H/1xf0ebOZ3W9mNxX/XlS07y2O9w0z+4GibZOZfX4+poBmMLMvmVmpxcLN7Dwzu664Vr9kZucs2PZ/Fc/Zt5nZK4L+5xax8a1iHy9KbD9pZr9RfD9kZl8v4vN2M3vLgp99X7GPP1rQ9jtmtirXACUJq4i773f3RyRnbTYp6Xck/UZi2zslXSnpguLfpUX7kqvRm9mIpDdJeobqMyS/qUjWflzSNyU9pdi3zOypknrc/VvN+7Ww0rn7L7r7v5Xs/kvFPp4s6QWS3mZmPWa2RdKfSnq+uz9R0nYze36i/8skDRb9ny7p1VZfh25W0uvd/fGSnqn6cjQLE8V3uPuFxb/PFG2vl/Qzkt4o6VeKtt+R9EfO/EDoHH8m6T3u/hRJvyfpjyXJzH5S0tNUX3boGZL+u5ltSPT/H5I+7O4XqT5B7v88bfs7JH12wfdTkn7U3Z9a7PtSM3ummT1Fkopx/IiZbSwmfL3E3TtngtUmIglrIjP7hSKDv9nM3rtg07PN7Ktmdtf8XTEz22NmtxVf95rZnxV/ed9iZr922n6HiztIv1R8/3PFXxE3mdn/MrPeov2kmf1hcfx/NbPtufG6+yl3/2fVk7GFx9spaYO7/0vxQvEePbzqfCOr0f+4pC+6+6i7H5P0RdWTuBlJw5L6Fvzs70v63dw4sToVMfDt4q7TLWb2ETNbU2x76K96M3uVmX23aHuXnXbXNuEJqv+BIK8vG/OgpItVn0Tzu+5+uPi5f1A9QTqdS1prZn2qX6/Tkk64+wF3v7HY75ikO7T04tDz1/waSTNm9mhJu9z9y0v0wyq0xDX/u8Ud09us/u7A/LsPXyruRn29iIMfKdqHzeyDxX4+pPp1Nn+cd5rZDaffZcp4KGYk/aPqz/Pz7V9291l3P6X6BLSXJvq7pPnkbKMWLBVlZi+RdJfqE9fWf7juZPFtf/HPVcSLmfVIGpA0p3pSuGpfI0jCmsTMnijpt/Vwdv/rCzbvVH0m3xdLemui+5WSzpd0UfEXwPsWbFsn6VOS3u/u7zKzx0t6heoLxF6o+kX6n4qfXSvpX4vjX6/ijkAJu1Sf8XjePj38YtPIavS7JN2X6P9FSTtUX9X+T8zspyR904t11tCVHivpquK6PyHpVxduNLOzVb9z9EzV72o9roF93izpMjPrM7PzVb+btVvSnZIeV7wQ9qn+B8TuRP+PSDql+uzf90r6M3cfPW1ceyRdpPq1PO+1xQvi1fbw2/R/LOkqSa+T9JeS/rD4fdC9omv+L939B939SaonVC9e0KfP3S9R/Tp6U9H2K5LGi/38oerX+bzfdveLVX/X4Tnzd5gybtbDf5C8VNL64s7xzZJ+wszWWP2jMc9TOmbeLOnnzGyfpM9I+jXpoWWbflPSIxLB4ubDTaqv4/hFd/+au9+heszdKOnDkn5Akq3md0pIwprnRyV9xN2PSNJpT9p/7+614u2V1N2pH5P01+4+m+j7CUl/6+7vKb5/vurB9o3iAn6+6n/hS/W/2D9dfP1NSXtK/i6pz6os562TZP/ir6n/WNyy/t+qP6G8zczeXvxF+FNlBosV7T53/0rx9d+p/sfKQpeo/pf4qLvPqH7dLOVq1RP/GyT9uaSvSpot7sr+iqQPSfonSfeo/hbj6S5R/Y+bs1X/4+j1ZjYfYzKzdaovEPw6dz9RNL9T0qNVf2vlgKS3SZK73+Tuz3T356kep/vru7APmdnfLXW3GqtSdM0/z8y+Zma3qv568sQFfeYXol74vP7sor/c/RbVl+qZ93Izu1HSt4r9LPX5yt9QPVn7lqTnSLpf9Zj5gupJ1VdVX5boX5SOmcslXePu56i+TNB7i7tZb1H9bfqTp3fw+sLeF6q+yPolZvakov11xVv6b1PxTomZ/baZfXj+3aDVpG/pH0GDTHGiMnXazy2n71dU/0vk/cVbgybpWnf/rcTPziz4nMmcyj+++1QPjHnn6OHbywfNbKe7H7B4Nfp9kp57Wv8vnfYzv6r625k/pHry+ArVA7yti+2icqdf96d/v+wPrxd/zPzXh3Zg9lXV14yTu39K9TvLMrMrVY+T0/1HSZ8rkr5DZvYV1d/OvMvM+lVPwN7n7vMvjHL3gwuO9y49/MfQfJup/rmZV6h+R+xNqr+Y/hfV76CjezzimjezIdU/R3Wxu99nZm+WNLTgZ+ZfQ05/Xn/E60Zx9/c3JP2gux8zs2tO29cjB1R/N+Kni/7rJP2Mux8vtv2h6nfaZGbvVxFLp3mVircp3f1fit9nq+qfI/tZqxfBbJJUM7NJd3/oIwXu/qCZfano/9AC31b/IP4Nqr/D8yR3f7mZXW9m73P38dzvs5JwJ6x5rlP9r48t0kMfTm/UFyT9cvEWyel9f1fSUT38QcfrVL+ot83/rJmdd6aDX6h4m3HM6h+UNEm/oIdXnW9kNfrPS3qhmW0u3pZ5YdGmYsybVb/V/h7VPytTU/3JJPtEgVXpXDP7oeLryyX982nbv676X+ibi/hIfYZrkeKtk7XF1y9Q/S/6fyu+n4+bzar/IfA3iV3cK+lHrW6t6m+FfruIhXdLusPd3/7/t2/3IFlFcRzHv7/AKIhoqMCtqTEaHAuagsaGtkKhocKllqCICHvFIAgKC0NrbSmiF3KraAgbSnujJQuaisgIhyT/Df9z68FMH0S6mb/PpD7PPfdeONd7zv9l0jlbG37dSsPLpGgHbpVoXDXnJ8rPtrBMNeer/30fyyKomaat+5RSlBJFqlKOy8l0+miJtG6ZaSBJK0vkCuAAGU2uUobVO21dOcfAFEO8I7MylJKZJcCHiNgYEWsiYg0ZlT4REeckrZK0onx/KZkNetVwPS1kSc9p8hmpFptVrdh/w5GwORIRzyUdB+5J+k6GgTuaPPwSsBYYkjQO9JK75cpeoE9Sd0Tsl3QIGCgPzTjQCbydzXVLGiEf2sWlgHJzeWHtAS6TtQl3+NXZcgq4Kmkn+eBtK+O0AbtLV9snSUeBwXJM16QU62HgWESEpLvl+oeBC7O5B5vXXgLtki6SO+yexg8j4r2yVf0RGY19AYzOMOZq4K6kCTKtsqPhs7PKjlzIefkaoKTC2yLiMHAe6CcXUiLLAYYkbShjDZdSAICDpROyW9J68mUxAuyqTqgsvG4nNyMAZ8ho2jfyJWwLy29zPiLGSgR1mJw/g9McX+kB+iUNAU/IDQsR8bSkFZ+TBfEP/zzET5uAk5KCXNx1lr+3AA9y/8EXYHtVNiOpC3gcETfILuBeSfvIZ6Bjhu7fVuCKsqlsEdlZ2Rg97iQzPmPl/lTStLcj4nMT9zNvyF3SZlYHZXH7zVKIPN33lkXE1xIJuwb0RcS1v3CJZnOq2TlvC4fTkWb2rztSIk/PgDfA9Zqvx8xsTjgSZmZmZlYDR8LMzMzMauBFmJmZmVkNvAgzMzMzq4EXYWZmZmY18CLMzMzMrAZehJmZmZnV4Ae+laSOE2CuZAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "generate_adversarial_examples(image_path = \"images/chicken.png\", model = hunter, epsilon = -9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "generate_adversarial_examples(image_path = \"images/pig.png\", model = hunter, epsilon = -20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "generate_adversarial_examples(image_path = \"images/terrain.png\", model = hunter, epsilon = -10)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}