one-pixel-attack.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# One Pixel Attack Tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/pred2.png \"All it takes is one pixel\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook will demonstrate the one pixel attack with a few different convolutional neural network models. By using differential evolution, we find a special pixel that can modify a target image such that the network misclassifies the image (which it previously correctly classified).\n",
    "\n",
    "In theory, we want models that don't get fooled by such tiny changes. Especially in images, it is undesirable to have a small alteration in the input result in a drastic change in the output. However, even the most accurate neural networks are susceptible to this type of attack.\n",
    "\n",
    "Let's get started."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ensure that you have numpy, pandas, scipy, matplotlib, and keras installed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# Python Libraries\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from keras.datasets import cifar10\n",
    "from keras.models import load_model\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "from scipy.optimize import differential_evolution\n",
    "from scipy.stats import truncnorm\n",
    "from IPython.display import clear_output\n",
    "import pickle\n",
    "\n",
    "# Custom Networks\n",
    "from networks.lecun_net import LecunNet\n",
    "from networks.pure_cnn import PureCnn\n",
    "from networks.network_in_network import NetworkInNetwork\n",
    "from networks.resnet import ResNet\n",
    "from networks.densenet import DenseNet\n",
    "from networks.wide_resnet import WideResNet\n",
    "from networks.capsnet import CapsNet\n",
    "\n",
    "# Helper functions\n",
    "from helper import plot_image, plot_images, evaluate_models, visualize_attack, attack_stats\n",
    "\n",
    "matplotlib.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import absolute_import\n",
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "\n",
    "from keras.datasets.cifar import load_batch\n",
    "import keras.backend as K\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "def load_data():\n",
    "    \"\"\"Loads CIFAR10 dataset.\n",
    "    # Returns\n",
    "        Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.\n",
    "    \"\"\"\n",
    "    dirname = 'cifar-10-batches-py'\n",
    "    path = '/input/cifar-10/cifar-10-batches-py'\n",
    "    num_train_samples = 50000\n",
    "\n",
    "    x_train = np.empty((num_train_samples, 3, 32, 32), dtype='uint8')\n",
    "    y_train = np.empty((num_train_samples,), dtype='uint8')\n",
    "\n",
    "    for i in range(1, 6):\n",
    "        fpath = os.path.join(path, 'data_batch_' + str(i))\n",
    "        (x_train[(i - 1) * 10000: i * 10000, :, :, :],\n",
    "         y_train[(i - 1) * 10000: i * 10000]) = load_batch(fpath)\n",
    "\n",
    "    fpath = os.path.join(path, 'test_batch')\n",
    "    x_test, y_test = load_batch(fpath)\n",
    "\n",
    "    y_train = np.reshape(y_train, (len(y_train), 1))\n",
    "    y_test = np.reshape(y_test, (len(y_test), 1))\n",
    "\n",
    "    if K.image_data_format() == 'channels_last':\n",
    "        x_train = x_train.transpose(0, 2, 3, 1)\n",
    "        x_test = x_test.transpose(0, 2, 3, 1)\n",
    "\n",
    "    return (x_train, y_train), (x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this attack, we will use the [Cifar10 dataset](https://www.cs.toronto.edu/~kriz/cifar.html) packaged by Keras. The task of the dataset is to correctly classify a 32x32 pixel image in 1 of 10 categories (e.g., bird, deer, truck).\n",
    "\n",
    "The code below will load the Cifar10 dataset. Keras will need to download the dataset if it is not cached locally already."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "(x_train, y_train), (x_test, y_test) = load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can access and display any image in the dataset by its index. For instance, here is a horse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f97f1a0cc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = 99 # Image index in the test set\n",
    "plot_image(x_test[image])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Image Perturbation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To begin, we need a function to modify one or more pixels in an image. \n",
    "\n",
    "We can define the perturbation of a pixel as a 5-tuple \n",
    "\n",
    "$$\\textbf{x} = (x, y, r, g, b)$$\n",
    "\n",
    "where $x, y$ are the coordinates of the pixel from 0 to 31, and $r,g,b$ are the red, green, and blue values from 0 to 255. Then multiple perturbations can simply be a concatenation of these tuples:\n",
    "\n",
    "$$X = (x_1, y_1, r_1, g_1, b_1, x_2, y_2, r_2, g_2, b_2, ...)$$\n",
    "\n",
    "We could instead use an array of tuples, but the optimization algorithm we will use requires it to be a flat 1-d vector.\n",
    "\n",
    "Then the function to perturb an image can take as an input the image and $\\textbf{X}$, and output a copy of the image with each pixel at $x_i, y_i$ modified to have the color $r_i, g_i, b_i$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def perturb_image(x, img):\n",
    "    # Copy the image to keep the original unchanged\n",
    "    img = np.copy(img) \n",
    "    \n",
    "    # Split into an array of 5-tuples (perturbation pixels)\n",
    "    # Make sure to floor the members of x as int types\n",
    "    pixels = np.split(x.astype(int), len(x) // 5)\n",
    "    \n",
    "    # At each pixel's x,y position, assign its rgb value\n",
    "    for pixel in pixels:\n",
    "        x_pos, y_pos, *rgb = pixel\n",
    "        img[x_pos, y_pos] = rgb\n",
    "    return img"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can modify the pixels of any image we want.\n",
    "\n",
    "Let's modify our horse image by making pixel (16,16) yellow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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p1UEFqL4plSn22jifZuhOv4uIjGO8cAjsJ6/Etkivgxt+XXvmLIxdvbQBY2fXsV0xSt22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\nQuESYhAKlxCDULiEGITCJcQg/wMadBswOfzPHQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f97e67090f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = 99 # Image index in the test set\n",
    "pixel = np.array([16, 16, 255, 255, 0]) # pixel = x,y,r,g,b\n",
    "image_perturbed = perturb_image(pixel, x_test[image])\n",
    "\n",
    "plot_image(image_perturbed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To demonstrate the attack, we need some neural network models trained on the Cifar10 dataset.\n",
    "\n",
    "It is recommended to use Keras with a GPU enabled. The code below can be used to check (if using TensorFlow)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Should output /device:GPU:0\n",
    "\n",
    "```\n",
    "from keras import backend as K\n",
    "K.tensorflow_backend._get_available_gpus()\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now load some pretrained models, which can be found in the `networks/models` directory. \n",
    "\n",
    "There are two models included in this repository, `lecun_net` and `resnet` which will be loaded automatically. Optionally, you may uncomment the lines below to download the larger, more accurate models (e.g., Capsule Network, DenseNet, etc.)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully loaded lecun_net\n",
      "Successfully loaded resnet\n"
     ]
    }
   ],
   "source": [
    "lecun_net = LecunNet()\n",
    "resnet = ResNet()\n",
    "\n",
    "models = [lecun_net, resnet]\n",
    "\n",
    "## Uncomment below to download more models to play with\n",
    "\n",
    "# lecun_net = LecunNet()\n",
    "# pure_cnn = PureCnn()\n",
    "# net_in_net = NetworkInNetwork()\n",
    "# resnet = ResNet()\n",
    "# densenet = DenseNet()\n",
    "# wide_resnet = WideResNet()\n",
    "# capsnet = CapsNet()\n",
    "\n",
    "# models = [lecun_net, pure_cnn, net_in_net, resnet, densenet, wide_resnet, capsnet]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that there are even more networks available in the `networks` directory, but must be trained before loading them here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate Model Accuracies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After loading the models, we would like to evaluate all test images with each model to ensure that we only attack the images which have been classified correctly. The code below will also display the accuracy and number of parameters of each model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating lecun_net\n",
      "Evaluating resnet\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>param_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>lecun_net</td>\n",
       "      <td>0.7488</td>\n",
       "      <td>62006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>resnet</td>\n",
       "      <td>0.9231</td>\n",
       "      <td>470218</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name  accuracy  param_count\n",
       "0  lecun_net    0.7488        62006\n",
       "1     resnet    0.9231       470218"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network_stats, correct_imgs = evaluate_models(models, x_test, y_test)\n",
    "correct_imgs = pd.DataFrame(correct_imgs, columns=['name', 'img', 'label', 'confidence', 'pred'])\n",
    "network_stats = pd.DataFrame(network_stats, columns=['name', 'accuracy', 'param_count'])\n",
    "\n",
    "network_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction Function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the black-box attack, all we should care about is the inputs to the model (the images), and the outputs of the model (the prediction probabilities). No special information about the model is required; we could even swap it with a model that is not a neural network.\n",
    "\n",
    "Define a function that runs a perturbed image on a given model and returns the model's confidence (probability output) in a target class. If the target class is the correct class, this will be the function that we want to minimize so that the model will be most confident in another class (which is incorrect). Otherwise, the target is an incorrect class and we will want to maximize it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def predict_class(x, img, target_class, model, minimize=True):\n",
    "    # Perturb the image with the given pixel(s) x and get the prediction of the model\n",
    "    img_perturbed = perturb_image(x, img)\n",
    "    prediction = model.predict_one(img_perturbed)[target_class]\n",
    "    # This function should always be minimized, so return its complement if needed\n",
    "    return prediction if minimize else 1 - prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we can modify a pixel in an image and see how the confidence of the model changes. In almost all cases, the confidence will not change. However, for very special cases it will change drastically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confidence in true class bird is 0.000188875\n",
      "Prior confidence was 0.706618\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f97deed64e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = 384\n",
    "pixel = np.array([16, 13,  25, 48, 156])\n",
    "model = resnet\n",
    "\n",
    "true_class = y_test[image,0]\n",
    "# prior_confidence = model.predict_one(x_test[image])[true_class]\n",
    "prior_confidence = model.predict_one(x_test[image])[true_class]\n",
    "confidence = predict_class(pixel, x_test[image], true_class, model)\n",
    "\n",
    "print('Confidence in true class', class_names[true_class], 'is', confidence)\n",
    "print('Prior confidence was', prior_confidence)\n",
    "plot_image(perturb_image(pixel, x_test[image]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Attack"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we will demonstrate two variants of the one pixel attack: untargeted and targeted."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Targeted vs. Untargeted Attacks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The objective of an untargeted attack is to cause a model to misclassify an image. This means we want to perturb an image as to minimize the confidence probability of the correct classification category and maximize the sum of the probabilities of all other categories.\n",
    "\n",
    "The objective of a targeted attack is to cause a model to classify an image as a given  target class. We want to perturb an image as to maximize the probability of a class of our own choosing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Success Criterion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a function so that whenever a given perturbation is sufficient to fool a model, it returns `True`. This will be called the success criterion. The `targeted_attack` boolean flag will indicate whether success means maximization of the target class or minimization of the correct (target) class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def attack_success(x, img, target_class, model, targeted_attack=False, verbose=False):\n",
    "    # Perturb the image with the given pixel(s) and get the prediction of the model\n",
    "    attack_image = perturb_image(x, x_test[img])\n",
    "\n",
    "    confidence = model.predict_one(attack_image)\n",
    "    predicted_class = np.argmax(confidence)\n",
    "    \n",
    "    # If the prediction is what we want (misclassification or \n",
    "    # targeted classification), return True\n",
    "    if (verbose):\n",
    "        print('Confidence:', confidence[target_class])\n",
    "    if ((targeted_attack and predicted_class == target_class) or\n",
    "        (not targeted_attack and predicted_class != target_class)):\n",
    "        return True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we demonstrate the usage of the success criterion function. It's nearly identical to `predict_class()` as before, but also decides the success of the attack. For purposes of demonstration we assume an untargeted attack."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confidence: 0.0746009\n",
      "Prior confidence 0.500542\n",
      "Attack success: True\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f97deedc198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = 541\n",
    "pixel = np.array([17, 18, 185, 36, 215])\n",
    "model = resnet\n",
    "\n",
    "true_class = y_test[image,0]\n",
    "prior_confidence = model.predict_one(x_test[image])[true_class]\n",
    "success = attack_success(pixel, image, true_class, model, verbose=True)\n",
    "\n",
    "print('Prior confidence', prior_confidence)\n",
    "print('Attack success:', success == True)\n",
    "plot_image(perturb_image(pixel, x_test[image]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Attack Function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we arrive at the attack itself: how do we find the pixels that will result in a successful attack? First, formulate it as an optimization problem: in an untargeted attack, minimize the confidence of the correct class, and in a targeted attack, maximize the confidence of a target class. This is precisely our `predict_class` function.\n",
    "\n",
    "When performing black-box optimizations such as the one pixel attack, it can be very difficult to find an efficient gradient-based optimization that will work for the problem. It would be nice to use an optimization algorithm that can find good solutions without relying on the smoothness of the function. In our case, we have discrete integer positions ranging from 0 to 31 and color intensities from 0 to 255, so the function is expected to be spiky.\n",
    "\n",
    "For that, we use an algorithm called [differential evolution](https://en.wikipedia.org/wiki/Differential_evolution). Here's an example of differential evolution optimizing the [Ackley function](https://en.wikipedia.org/wiki/Ackley_function):\n",
    "\n",
    "<br>\n",
    "\n",
    "![Ackley GIF](images/Ackley.gif)\n",
    "\n",
    "<br>\n",
    "\n",
    "Differential evolution is a type of evolutionary algorithm where a population of candidate solutions generate offspring which compete with the rest of the population each generation according to their fitness. Each candidate solution is represented by a vector of real numbers which are the inputs to the function we would like to minimize. The lower the output of this function, the better the fitness. The algorithm works by initializing a (usually random) population of vectors, generating new offspring vectors by combining (mutating) individuals in the population, and replacing worse-performing individuals with better candidates.\n",
    "\n",
    "In the context of the one pixel attack, our input will be a flat vector of pixel values:\n",
    "\n",
    "$$X = (x_1, y_1, r_1, g_1, b_1, x_2, y_2, r_2, g_2, b_2, ...)$$\n",
    "\n",
    "These will be encoded as floating-point values, but will be floored back into integers to calculate image perturbations. First we generate a random population of $n$ perturbations\n",
    "\n",
    "$$\\textbf{P} = (X_1, X_2, \\dots, X_n)$$\n",
    "\n",
    "Then, on each iteration we calculate $n$ new mutant children using the formula\n",
    "\n",
    "$$X_i = X_{r1} + F (X_{r2} - X_{r3})$$\n",
    "\n",
    "such that\n",
    "\n",
    "$$r1 \\neq r2 \\neq r3$$\n",
    "\n",
    "where $r1,r2,r3$ are random indices into our population $\\textbf{P}$, and $F = 0.5$ is a mutation parameter. Basically, we pick 3 random individuals from the previous generation and recombine them to make a new candidate solution. If this candidate $X_i$ gives a lower minimum at position $i$ (i.e., the attack is closer to success), replace the old $X_i$ with this new one. This process repeats for several iterations until our stopping criterion, `attack_success`, which is when we find an image that successfully completes the attack.\n",
    "\n",
    "<br>\n",
    "\n",
    "See [this excellent tutorial post](https://pablormier.github.io/2017/09/05/a-tutorial-on-differential-evolution-with-python/) on how differential evolution works in greater detail. \n",
    "\n",
    "We will use [Scipy's implementation of differential evolution](https://docs.scipy.org/doc/scipy-0.17.0/reference/generated/scipy.optimize.differential_evolution.html). For demonstration purposes it's more than sufficient (however, it can be optimized further).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def attack(img, model, target=None, pixel_count=1, \n",
    "           maxiter=30, popsize=30, verbose=False):\n",
    "    # Change the target class based on whether this is a targeted attack or not\n",
    "    targeted_attack = target is not None\n",
    "    target_class = target if targeted_attack else y_test[img,0]\n",
    "    \n",
    "    # Define bounds for a flat vector of x,y,r,g,b values\n",
    "    # For more pixels, repeat this layout\n",
    "    bounds = [(0,32), (0,32), (0,256), (0,256), (0,256)] * pixel_count\n",
    "    \n",
    "    # Format the predict/callback functions for the differential evolution algorithm\n",
    "    predict_fn = lambda x: predict_class(\n",
    "        x, x_test[img], target_class, model, target is None)\n",
    "    callback_fn = lambda x, convergence: attack_success(\n",
    "        x, img, target_class, model, targeted_attack, verbose)\n",
    "    \n",
    "    # Call Scipy's Implementation of Differential Evolution\n",
    "    attack_result = differential_evolution(\n",
    "        predict_fn, bounds, maxiter=maxiter, popsize=max(1, popsize // pixel_count),\n",
    "        recombination=1, atol=-1, callback=callback_fn)\n",
    "\n",
    "    # Calculate some useful statistics to return from this function\n",
    "    attack_image = perturb_image(attack_result.x, x_test[img])\n",
    "    prior_probs = model.predict_one(x_test[img])\n",
    "    predicted_probs = model.predict_one(attack_image)\n",
    "    predicted_class = np.argmax(predicted_probs)\n",
    "    actual_class = y_test[img,0]\n",
    "    success = predicted_class != actual_class\n",
    "    cdiff = prior_probs[actual_class] - predicted_probs[actual_class]\n",
    "\n",
    "    # Show the best attempt at a solution (successful or not)\n",
    "    plot_image(attack_image, actual_class, class_names, predicted_class)\n",
    "\n",
    "    return [model.name, pixel_count, img, actual_class, predicted_class, success, cdiff, prior_probs, predicted_probs, attack_image]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Untargeted Attack"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at one iteration of the untargeted attack. Here we will demonstrate a successful attack an image of a frog with the `resnet` model. We should see the confidence in the true class drop after several iterations.\n",
    "\n",
    "Try to see if you can successfully attack other images/models. The more pixels we are allowed to modify, the more likely it is we are to find a solution for any given image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confidence: 0.998371\n",
      "Confidence: 0.998371\n",
      "Confidence: 0.998371\n",
      "Confidence: 0.982149\n",
      "Confidence: 0.982149\n",
      "Confidence: 0.982149\n",
      "Confidence: 0.942167\n",
      "Confidence: 0.942167\n",
      "Confidence: 0.942167\n",
      "Confidence: 0.942167\n",
      "Confidence: 0.942167\n",
      "Confidence: 0.211713\n"
     ]
    },
    {
     "data": {
      "image/png": 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jxw5s2rQJtm1j4sSJ+NrXvgYA+OY3v4nNmzdj+fLlmDRpEm677bYBifAPP/ww\n7rnnHmXZ2BkzZmD+/Pn44Q9/CNu2sXTpUrzyyiv98QceeAA1NTVYtWoVgsEg5s2bh7lz5w54v488\n8ggikQjuvPNO1NXV9ZeqJYODzyqTj5Xa2lr8+te/xi9/+cuPeypGwV9z5IqSSqVw5MgROI6DtrY2\n/P73vx9gpZHBwRWXXFGSySTWrl2L8+fPIxgMYubMmVi2bFl/rWYyOChcQgyEX5UJMRAKlxADoXAJ\nMRAKlxADoXAJMRAKlxAD+f8yiJVHFGiRfgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f97bfff35f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = 102\n",
    "pixels = 2 # Number of pixels to attack\n",
    "model = resnet\n",
    "\n",
    "_ = attack(image, model, pixel_count=pixels, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Targeted Attack"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the targeted attack, we can choose which class we want a model to classify an image as. The task is much harder for the targeted attack, as we constrain the misclassification to a given class rather than any class that's not the correct one. We should see the confidence in the target class rise after several iterations.\n",
    "\n",
    "Below we try to cause the `lecun_net` to classify an image of a `ship` as an `automobile`. Try to change the parameters and see what happens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attacking with target automobile\n",
      "Confidence: 0.0169682\n",
      "Confidence: 0.0263455\n",
      "Confidence: 0.029639\n",
      "Confidence: 0.0955807\n",
      "Confidence: 0.0955807\n",
      "Confidence: 0.0955807\n",
      "Confidence: 0.0955807\n",
      "Confidence: 0.0955807\n",
      "Confidence: 0.131502\n",
      "Confidence: 0.131502\n",
      "Confidence: 0.131502\n",
      "Confidence: 0.131502\n",
      "Confidence: 0.131502\n",
      "Confidence: 0.131502\n",
      "Confidence: 0.164266\n",
      "Confidence: 0.164266\n",
      "Confidence: 0.164266\n",
      "Confidence: 0.266853\n",
      "Confidence: 0.266853\n",
      "Confidence: 0.266853\n",
      "Confidence: 0.266853\n",
      "Confidence: 0.31181\n",
      "Confidence: 0.415371\n",
      "Confidence: 0.415371\n",
      "Confidence: 0.415371\n",
      "Confidence: 0.415371\n",
      "Confidence: 0.452527\n",
      "Confidence: 0.613849\n"
     ]
    },
    {
     "data": {
      "image/png": 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//AFz585FU1MTPB4PGhoaUFdXZ/zb119/HRdeeCEuuugieL1eXHDBBRg/fjx2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f97bffe9780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = 108\n",
    "target_class = 1 # Integer in range 0-9\n",
    "pixels = 3\n",
    "model = lecun_net\n",
    "\n",
    "print('Attacking with target', class_names[target_class])\n",
    "_ = attack(image, model, target_class, pixel_count=pixels, popsize=50, maxiter=40, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Collect Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Armed with all the necessary tools to conduct a one pixel attack, the final step is to collect relevant statistics on the targeted and untargeted attack. The relevant data points are what percentage of images were we able to successfully attack for a given model, and how the number of pixels affect this percentage.\n",
    "\n",
    "We will loop through every combination of all models, perturbations of 1-3 pixels, images, and target classes (for the targeted attack). This will take a lot of computational resources and time, so [skip to the statistics section](#Attack-Statistics) if that's not your idea of fun."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def attack_all(models, samples=100, pixels=(1,2,3), targeted=False, verbose=False):\n",
    "    results = []\n",
    "    for model in models:\n",
    "        model_results = []\n",
    "        valid_imgs = correct_imgs[correct_imgs.name == model.name].img\n",
    "        img_samples = np.random.choice(valid_imgs, samples)\n",
    "        for pixel_count in pixels:\n",
    "            for i,img in enumerate(img_samples):\n",
    "                print(model.name, '- image', img, '-', i+1, '/', len(img_samples))\n",
    "                targets = [None] if not targeted else range(10)\n",
    "                \n",
    "                for target in targets:\n",
    "                    if (targeted):\n",
    "                        print('Attacking with target', class_names[target])\n",
    "                        if (target == y_test[img,0]):\n",
    "                            continue\n",
    "                    result = attack(img, model, target, pixel_count, verbose=verbose)\n",
    "                    model_results.append(result)\n",
    "        results += model_results\n",
    "    return results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "```\n",
    "untargeted = attack_all(models, samples=100, targeted=False)\n",
    "targeted = attack_all(models, samples=10, targeted=True)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Attack Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Save/load our results\n",
    "# There are precalculated results we can load in this repo\n",
    "save = False\n",
    "load = True\n",
    "\n",
    "if save:\n",
    "    with open('networks/results/untargeted_results.pkl', 'wb') as file:\n",
    "        pickle.dump(untargeted, file)\n",
    "    with open('networks/results/targeted_results.pkl', 'wb') as file:\n",
    "        pickle.dump(targeted, file)\n",
    "if load:\n",
    "    with open('networks/results/untargeted_results.pkl', 'rb') as file:\n",
    "        untargeted = pickle.load(file)\n",
    "    with open('networks/results/targeted_results.pkl', 'rb') as file:\n",
    "        targeted = pickle.load(file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the final results! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "columns = ['model', 'pixels', 'image', 'true', 'predicted', 'success', 'cdiff', 'prior_probs', 'predicted_probs', 'attack_image']\n",
    "untargeted_results = pd.DataFrame(untargeted, columns=columns)\n",
    "targeted_results = pd.DataFrame(targeted, columns=columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Untargeted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>attack_success_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>lecun_net</td>\n",
       "      <td>0.7488</td>\n",
       "      <td>0.726667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>resnet</td>\n",
       "      <td>0.9231</td>\n",
       "      <td>0.533333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       model  accuracy  attack_success_rate\n",
       "0  lecun_net    0.7488             0.726667\n",
       "1     resnet    0.9231             0.533333"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attack_stats(untargeted_results, models, network_stats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Targeted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>attack_success_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>lecun_net</td>\n",
       "      <td>0.7488</td>\n",
       "      <td>0.344444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>resnet</td>\n",
       "      <td>0.9231</td>\n",
       "      <td>0.233333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       model  accuracy  attack_success_rate\n",
       "0  lecun_net    0.7488             0.344444\n",
       "1     resnet    0.9231             0.233333"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attack_stats(targeted_results, models, network_stats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show some successful attacks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot 9 random successful attack images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Untargeted Attack\n"
     ]
    },
    {
     "data": {
      "image/png": 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3ezJyU67Zl8gQei5ZTCgq8iBU1fE4kefj5yn1AC5RLM1lT/13qmGy3z5//QTR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3AAAg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\nViumTp16V20abEa864GSlJSE6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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f97bfedce48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Untargeted Attack')\n",
    "visualize_attack(untargeted_results, class_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Targeted Attack\n"
     ]
    },
    {
     "data": {
      "image/png": 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gCIIgaNL0XA/11uDzuAiq/O8MlWaB+Yt4LPasLOqT5DkJAODESeqX7Naa+n/j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wjlkGdfvr+5T5544omhdZLJpHz+85+XO++8U4IgkI9+9KMgTZxzzjlSKBTku9/9rnR0dMi4cePk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\nWHkqFAnuJAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f97b71e90b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Targeted Attack')\n",
    "visualize_attack(targeted_results, class_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It appears that the accuracy of a model is not strongly correlated with the chance of performing a successful attack on an image. Perhaps surprisingly, the purely convolutional model is the most resistant to these types of attacks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These results achieve success rates much lower than described in the academic paper due to a couple reasons:\n",
    "\n",
    "1. The authors do not provide any source code, so there is no baseline to compare to.\n",
    "2. This implementation calculates a far smaller population size and uses fewer iterations in the differential evolution algorithm to keep it fast. This is because the differential evolution algorithm is inefficient and could be improved. The `differential_evolution()` function calculates predictions individually instead of batching inputs together. As there's no option to do this in `Scipy`'s implementation, it will need to be rewritten."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TODO: more data necessary for more conclusions!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Credits\n",
    " -  This implemenation is based off of the original paper describing the one pixel attack: https://arxiv.org/abs/1710.08864\n",
    " - Base code for iPython notebook: https://github.com/09rohanchopra/cifar10\n",
    " - Keras Cifar10 models: https://github.com/BIGBALLON/cifar-10-cnn\n",
    " - Scipy's differential evolution implementation: https://docs.scipy.org/doc/scipy-0.17.0/reference/generated/scipy.optimize.differential_evolution.html\n",
    " - State of the art: https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems\n",
    " - CapsNet Keras: https://github.com/XifengGuo/CapsNet-Keras\n",
    " - CapsNet with Cifar: https://github.com/theblackcat102/dynamic-routing-capsule-cifar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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