GMMClassifier_Torch_Attack.ipynb

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        "# Overview\n",
        "\n",
        "> Gaussian Mixture Model (GMM) is often categorized as a clustering algorithm (similar to KMean) – i.e. the number of categories for the Z latent variable has been initialized as the number of clustered in question (which is equivalent to the number of Gaussian in the GMM model). For this project, GMM is used as a probability density estimation. It is a generative probabilistic model that is used to describe the distribution of the given sample data.\n",
        "\n",
        "> ## Clustering\n",
        "\n",
        ">> When GMM is used as clustering, then it needs to be initialized to the number of clusters. The EM algorithm will try to learn the “pattern” within the sample data, group similar data, and categorize them into the different clusters. The limitation of this is that each data point either belongs to a cluster completely or not. However, some datasets may not be well organized into clusters. so “naïve” clustering algorithm may not be able to cluster them correctly and accurately. For example, “Moon Shape”, there are 2 clusters/Gaussian. \n",
        "\n",
        ">> ![GMM_Clusters.png](data:image/png;base64,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)\n",
        "\n",
        "> ## Probability Density Estimation\n",
        "\n",
        ">> The dataset is separated into k different subsets, one for each class. A GMM model is created for each subset. The model will estimate the probability distribution using the given training data. During prediction, each of the GMM models will provide a likelihood score. The maximum likelihood will be chosen. This is more flexible than clustering because each individual GMM model can contain many Gaussian (i.e. the latent variable Z is not limited to the number of classes anymore) to represent individual data subset.\n",
        "\n",
        ">> 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)\n",
        "\n",
        "# Experiment\n",
        "\n",
        "> For this experiment, two GMM classifiers will be created. One is created using the sklearn GaussianMixture model. The other torch-based GMM classifier first uses the sklearn GMM Classifier to learn the GMM model parameters using the training data (fit). The torch-based GMM classifier will then create the MixtureSameFamily (Categorical + MultivariateNormal) using these parameters. The reason of this is to allow later interation with the torch base attack framework that bases on PyTorchModel.\n",
        "\n",
        "# References\n",
        "\n",
        "> [In Depth: Gaussian Mixture Models](https://jakevdp.github.io/PythonDataScienceHandbook/05.12-gaussian-mixtures.html)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f-M5-Gy7SGCs"
      },
      "source": [
        "# (1) Load the required packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3pzu-bS4Kro2",
        "outputId": "992294ad-5593-4039-a901-9e3e4bba1060"
      },
      "source": [
        "!pip install foolbox\n",
        "# !pip install git+https://github.com/nottombrown/imagenet_stubs"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting foolbox\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/60/ff/1ba817ad9aa7c2ca28fb809d64939bee7311e3e62fdd87c4011232c4640e/foolbox-3.3.1-py3-none-any.whl (1.7MB)\n",
            "\u001b[K     |████████████████████████████████| 1.7MB 7.6MB/s \n",
            "\u001b[?25hRequirement already satisfied: typing-extensions>=3.7.4.1 in /usr/local/lib/python3.7/dist-packages (from foolbox) (3.7.4.3)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from foolbox) (1.4.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from foolbox) (54.2.0)\n",
            "Collecting requests>=2.24.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/29/c1/24814557f1d22c56d50280771a17307e6bf87b70727d975fd6b2ce6b014a/requests-2.25.1-py2.py3-none-any.whl (61kB)\n",
            "\u001b[K     |████████████████████████████████| 61kB 5.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from foolbox) (1.19.5)\n",
            "Collecting eagerpy==0.29.0\n",
            "  Downloading https://files.pythonhosted.org/packages/e1/07/54994565da4fc5a4840d3a434fb9bf3835b4a4e68c931ccfcc327d568f95/eagerpy-0.29.0-py3-none-any.whl\n",
            "Collecting GitPython>=3.0.7\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a6/99/98019716955ba243657daedd1de8f3a88ca1f5b75057c38e959db22fb87b/GitPython-3.1.14-py3-none-any.whl (159kB)\n",
            "\u001b[K     |████████████████████████████████| 163kB 29.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.24.0->foolbox) (2020.12.5)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.24.0->foolbox) (2.10)\n",
            "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.24.0->foolbox) (3.0.4)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.24.0->foolbox) (1.24.3)\n",
            "Collecting gitdb<5,>=4.0.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ea/e8/f414d1a4f0bbc668ed441f74f44c116d9816833a48bf81d22b697090dba8/gitdb-4.0.7-py3-none-any.whl (63kB)\n",
            "\u001b[K     |████████████████████████████████| 71kB 6.5MB/s \n",
            "\u001b[?25hCollecting smmap<5,>=3.0.1\n",
            "  Downloading https://files.pythonhosted.org/packages/68/ee/d540eb5e5996eb81c26ceffac6ee49041d473bc5125f2aa995cf51ec1cf1/smmap-4.0.0-py2.py3-none-any.whl\n",
            "\u001b[31mERROR: google-colab 1.0.0 has requirement requests~=2.23.0, but you'll have requests 2.25.1 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Installing collected packages: requests, eagerpy, smmap, gitdb, GitPython, foolbox\n",
            "  Found existing installation: requests 2.23.0\n",
            "    Uninstalling requests-2.23.0:\n",
            "      Successfully uninstalled requests-2.23.0\n",
            "Successfully installed GitPython-3.1.14 eagerpy-0.29.0 foolbox-3.3.1 gitdb-4.0.7 requests-2.25.1 smmap-4.0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GIIphg1NKrvW"
      },
      "source": [
        "import time, torch\n",
        "import torch.nn as nn\n",
        "from torch.utils.data import DataLoader\n",
        "from torchvision import transforms\n",
        "from torchvision.datasets import MNIST\n",
        "import numpy as np\n",
        "from foolbox import PyTorchModel, accuracy, samples, plot, criteria, distances\n",
        "import foolbox.attacks as fa\n",
        "import torchvision.models as models\n",
        "import eagerpy as ep\n",
        "# from imagenet_stubs.imagenet_2012_labels import label_to_name\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.mixture import GaussianMixture\n",
        "from sklearn.base import BaseEstimator, ClassifierMixin\n",
        "from numpy.random import permutation\n",
        "\n",
        "import torch.distributions as D\n",
        "\n",
        "import foolbox as fb"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M5PIo2jSA1K4"
      },
      "source": [
        "if torch.cuda.is_available():\n",
        "    device = 'cuda'\n",
        "else:\n",
        "    device = 'cpu'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wZVlYT0HOcAb"
      },
      "source": [
        "# (2) Setup the GMM Classifier\n",
        "\n",
        "*   GMMSKlearnClassifier - This classifier uses the GaussianMixture model provided by sklearn to build the GMM Classifier \n",
        "*   GMMTorchClassifier - The GMMSKlearnClassifier is embedded inside this torch-based classifier. GMMTorchClassifier first trains the GMMSKlearnClassifier using the training data. The trained parameters are passed to torch MixtureSameFamily (Categorical + MultivariateNormal) to build a torch-based GMM Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xu0IOjqpOlnG"
      },
      "source": [
        "class GMMSKlearnClassifier(BaseEstimator, ClassifierMixin):\n",
        "    \n",
        "    def __init__(self, n_components, n_features, covariance_type='spherical', max_iter=1000):\n",
        "        self.n_component = n_components\n",
        "        self.n_features = n_features\n",
        "        self.covariance_type = covariance_type\n",
        "        self.max_iter = max_iter\n",
        "        \n",
        "    def initialize_gmm(self, k, y, weights, means, Sigmas):\n",
        "        \n",
        "        self.y_classes_ = np.unique(y)\n",
        "\n",
        "        self.model_list_ = np.asarray([GaussianMixture(self.n_component, \n",
        "                                                       covariance_type=self.covariance_type, \n",
        "                                                       max_iter=self.max_iter, \n",
        "                                                       means_init=means[i],\n",
        "                                                       weights_init=weights[i],\n",
        "                                                       precisions_init=Sigmas[i],\n",
        "                                                       random_state=0) \n",
        "                                     for i in range(k)])\n",
        "        \n",
        "    def fit(self, X, y):\n",
        "        self.y_classes_ = np.unique(y)\n",
        "        \n",
        "        # Create a Subset list. Each X Subset for each Y Class\n",
        "        X_train_subsets = np.asarray([X[y==self.y_classes_[i]] for i in range(self.y_classes_.shape[0])])\n",
        "        \n",
        "        # Create a GMM list. Each GMM for each X Subset for each Y Class\n",
        "        # Train the GMM model with the X subset\n",
        "        # The model will learn and estimate the density distribution (Gaussian - mu and Sigma)\n",
        "        self.model_list_ = np.asarray([GaussianMixture(self.n_component, \n",
        "                                                     covariance_type=self.covariance_type, \n",
        "                                                     max_iter=self.max_iter, \n",
        "                                                     random_state=0).fit(X_train_subsets[i]) \n",
        "                                     for i in range(self.y_classes_.shape[0])])\n",
        "        \n",
        "        # Prior list which is the X Subset ratio based on the Sample data\n",
        "        self.logprior_list_ = np.asarray([\n",
        "            np.log(X_train_subsets[i].shape[0] / X.shape[0]) for i in range(self.y_classes_.shape[0])\n",
        "        ])\n",
        "        \n",
        "        return self\n",
        "    \n",
        "    def predict(self, X, method='mle'):\n",
        "        \n",
        "        result = None\n",
        "        if method == 'mle':\n",
        "            # use np.argmax to find the max for each N\n",
        "            # predict_proba return (k x N), so axis=0\n",
        "            result = self.y_classes_[np.argmax(self.predict_proba_likelihood(X), axis=0)]\n",
        "        elif method == 'post':\n",
        "            result = self.y_classes_[np.argmax(self.predict_proba_posterior(X), axis=0)]\n",
        "        else:\n",
        "            print(f'Unsupported classification method')\n",
        "    \n",
        "        return result\n",
        "    \n",
        "    def predict_proba_likelihood(self, X):\n",
        "        # (k x N)\n",
        "        loglikelihood_list = np.asarray([self.model_list_[i].score_samples(X) for i in range(self.y_classes_.shape[0])])       \n",
        "        likelihood_list = np.exp(loglikelihood_list)\n",
        "        \n",
        "        # Sum the likelihood_list over the k dimension, keep the number of N\n",
        "        # (k x N) / (1 x N)\n",
        "        normalized = likelihood_list / likelihood_list.sum(0, keepdims=True)\n",
        "        \n",
        "        # return (k x N) where the sum of the probabilities for each N is 1\n",
        "        return normalized\n",
        "    \n",
        "    def predict_proba_posterior(self, X):\n",
        "        # (k x N)\n",
        "        loglikelihood_list = np.asarray([self.model_list_[i].score_samples(X) for i in range(self.y_classes_.shape[0])])\n",
        "        # Need to change logprior_list from (k, ) to (k, 1)\n",
        "        # (k x N) + (k, 1)\n",
        "        posterior_list = np.exp(loglikelihood_list + self.logprior_list_[:, np.newaxis])\n",
        "        \n",
        "        # Sum the posterior_list over the k dimension, keep the number of N\n",
        "        # (k x N) / (1 x N)\n",
        "        normalized = posterior_list / posterior_list.sum(0, keepdims=True)\n",
        "        \n",
        "        # return (k x N) where the sum of the probabilities for each N is 1\n",
        "        return normalized\n",
        "    \n",
        "    def get_gmm_params(self):\n",
        "        means_list = []\n",
        "        weights_list = []\n",
        "        covariances_list = []\n",
        "        for i in range(self.y_classes_.shape[0]):\n",
        "            means_list.append(self.model_list_[i].means_)\n",
        "            weights_list.append(self.model_list_[i].weights_)\n",
        "            covariances_list.append(self.model_list_[i].covariances_)\n",
        "            \n",
        "        return np.asarray(means_list), np.asarray(weights_list), np.asarray(covariances_list)\n",
        "\n",
        "\n",
        "class GMMTorchClassifier(torch.nn.Module):\n",
        "    \n",
        "    def __init__(self, n_components, n_features, covariance_type='spherical', max_iter=1000):\n",
        "        super().__init__()\n",
        "        self.n_components = n_components\n",
        "        self.n_features = n_features\n",
        "        self.covariance_type = covariance_type\n",
        "        self.max_iter = max_iter\n",
        "\n",
        "    def fit(self, X, y):\n",
        "        \n",
        "        # 1. Use sklearn to train and get the means, weights, and variances parameters\n",
        "        #\n",
        "        X_np = X.detach().cpu().numpy()\n",
        "        y_np = y.detach().cpu().numpy()\n",
        "        \n",
        "        sklearn_gmm_clf = GMMSKlearnClassifier(\n",
        "            n_components=self.n_components,\n",
        "            n_features=self.n_features,\n",
        "            covariance_type=self.covariance_type,\n",
        "            max_iter=self.max_iter,\n",
        "        ).fit(X_np, y_np)\n",
        "        \n",
        "        means_list_np, weights_list_np, variances_list_np = sklearn_gmm_clf.get_gmm_params()\n",
        "        \n",
        "        self.y_classes_ = torch.unique(y).to(device=device)\n",
        "\n",
        "        # Create a Subset list. Each X Subset for each Y Class\n",
        "        X_train_subsets = [X[y==self.y_classes_[i]] for i in range(self.y_classes_.shape[0])]\n",
        "        \n",
        "        self.model_list_ = []\n",
        "        for i in range(self.y_classes_.shape[0]):\n",
        "            weights = torch.from_numpy(weights_list_np[i]).float().to(device=device)  # n_components\n",
        "            means = torch.from_numpy(means_list_np[i]).float().to(device=device)      # n_components x n_features\n",
        "            variances = torch.from_numpy(variances_list_np[i]).float().to(device=device) # n_components\n",
        "            \n",
        "            \n",
        "            # covar = k x D x D \n",
        "            covariances_list = torch.empty((variances.shape[0], self.n_features, self.n_features)).to(device=device)\n",
        "            for i in range(variances.shape[0]):\n",
        "                covariances_list[i] = torch.eye(self.n_features).to(device=device) * variances[i]\n",
        "    \n",
        "            mix = D.Categorical(weights)\n",
        "            comp = D.MultivariateNormal(means, scale_tril=torch.cholesky(covariances_list))\n",
        "            gmm = D.MixtureSameFamily(mix, comp)\n",
        "            self.model_list_.append(gmm)\n",
        "\n",
        "    def forward(self, X):\n",
        "\n",
        "        X = X.reshape(X.shape[0], -1)\n",
        "\n",
        "        likelihood_list = self.predict_proba_likelihood(X)\n",
        "        softmax = torch.nn.Softmax(dim=0)\n",
        "        softmax_list = softmax(likelihood_list)\n",
        "\n",
        "        return softmax_list.T\n",
        "\n",
        "    def predict(self, X):\n",
        "        \n",
        "        likelihood_list = self.predict_proba_likelihood(X)\n",
        "        softmax = torch.nn.Softmax(dim=0)\n",
        "        softmax_list = softmax(likelihood_list)\n",
        "        \n",
        "        result = self.y_classes_[torch.argmax(softmax_list, axis=0)]\n",
        "        return result\n",
        "\n",
        "    def predict_proba_likelihood(self, X):\n",
        "        # (k x N)\n",
        "        loglikelihood_list = torch.empty((self.y_classes_.shape[0], X.shape[0])).to(device=device)\n",
        "        for i in range(self.y_classes_.shape[0]):\n",
        "            loglikelihood_list[i] = self.model_list_[i].log_prob(X)\n",
        "\n",
        "        return loglikelihood_list\n",
        "        \n",
        "        "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IT5t889iO8ej"
      },
      "source": [
        "# (3) Train the GMM model before the attack"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lM6baxWaLK6I"
      },
      "source": [
        "## (3a) Prepare the MNIST dataset for training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W_kcxzTQA1K4",
        "outputId": "e147849d-5035-4832-be9a-8a74cb744686"
      },
      "source": [
        "from keras.datasets import mnist\n",
        "\n",
        "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
        "\n",
        "print('Training Data: {}'.format(X_train.shape))\n",
        "print('Training Labels: {}'.format(y_train.shape))\n",
        "print('Testing Data: {}'.format(X_test.shape))\n",
        "print('Testing Labels: {}'.format(y_test.shape))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n",
            "Training Data: (60000, 28, 28)\n",
            "Training Labels: (60000,)\n",
            "Testing Data: (10000, 28, 28)\n",
            "Testing Labels: (10000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bSnz7JF4LUKN"
      },
      "source": [
        "## (3b) Data Preprocessing\n",
        "\n",
        "*   Flatten each image from 2-D (28, 28) into 1-D (784)\n",
        "*   Normalize the data to 0 - 1\n",
        "*   Also find the number of class label (i.e. for MNIST, it is digits 0 to 9)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wd9n3wJdA1K5",
        "outputId": "c405de4e-acc5-4063-8574-fb308d12d6ab"
      },
      "source": [
        "# preprocessing the images\n",
        "\n",
        "# convert each image to 1 dimensional array\n",
        "X_train = X_train.reshape(len(X_train),-1)\n",
        "X_test = X_test.reshape(len(X_test),-1)\n",
        "\n",
        "# normalize the data to 0 - 1\n",
        "X_train = X_train.astype(float) / 255.\n",
        "X_test = X_test.astype(float) / 255.\n",
        "\n",
        "y_classes = set(y_train)\n",
        "\n",
        "print(X_train.shape)\n",
        "print(X_train[0].shape)\n",
        "print(X_test.shape)\n",
        "print(X_test[0].shape)\n",
        "print(y_train.shape)\n",
        "print(y_test.shape)\n",
        "print(f'Y Class Labels: {y_classes}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60000, 784)\n",
            "(784,)\n",
            "(10000, 784)\n",
            "(784,)\n",
            "(60000,)\n",
            "(10000,)\n",
            "Y Class Labels: {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FW8FiSB0A1K5",
        "outputId": "fc9d43ef-1067-4efb-b80e-f510eb47ea61"
      },
      "source": [
        "\n",
        "perm_train = permutation(X_train.shape[0])\n",
        "perm_test = permutation(X_test.shape[0])\n",
        "\n",
        "# X_train_torch = torch.from_numpy(X_train[perm_train][:5000]).float().to(device=device)\n",
        "# y_train_torch = torch.from_numpy(y_train[perm_train][:5000]).float().to(device=device)\n",
        "X_train_torch = torch.from_numpy(X_train[perm_train][:]).float().to(device=device)\n",
        "y_train_torch = torch.from_numpy(y_train[perm_train][:]).float().to(device=device)\n",
        "X_test_torch = torch.from_numpy(X_test[perm_test][:100]).float().to(device=device)\n",
        "y_test_torch = torch.from_numpy(y_test[perm_test][:100]).float().to(device=device)\n",
        "\n",
        "print(X_train_torch.shape)\n",
        "print(y_train_torch.shape)\n",
        "print(X_train_torch.device)\n",
        "print(y_train_torch.device)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([60000, 784])\n",
            "torch.Size([60000])\n",
            "cpu\n",
            "cpu\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YfOZ_A0nNtR9"
      },
      "source": [
        "## (3c) Train the GMM Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yKvlxTtNA1K-",
        "outputId": "c1d479a3-0a0c-48df-965c-80533bc15d89"
      },
      "source": [
        "gmm_clf_torch = GMMTorchClassifier(n_components=60, n_features=X_train_torch.shape[1], covariance_type='spherical').to(device=device)\n",
        "gmm_clf_torch.fit(X_train_torch, y_train_torch)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gNJnfHsSOBAz"
      },
      "source": [
        "## (3d) Test the GMM Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KTzBAVC4A1LB",
        "outputId": "ba82c1ab-2e8a-4569-eaf7-dc32094bfd32"
      },
      "source": [
        "y_pred = gmm_clf_torch.predict(X_test_torch)\n",
        "acc = torch.sum(y_pred==y_test_torch)/y_test_torch.shape[0]\n",
        "print(f'Accuracy: {acc*100:.2f}%')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 95.00%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "if8n1cpuPSBT"
      },
      "source": [
        "# (4) Perform the attack on the trained model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LsYg7DyJPtCj"
      },
      "source": [
        "## (4a) Helper functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rv386EOIPseH"
      },
      "source": [
        "def plot_images(images):\n",
        "    fig, axs = plt.subplots(1, len(images), figsize=(24, 24))\n",
        "\n",
        "    for i, ax in enumerate(axs.flat):\n",
        "        image = images[i]\n",
        "        image = np.squeeze(image, axis=0)\n",
        "        ax.matshow(image)\n",
        "        ax.axis('off')\n",
        "        ax.set_title(f'Image {i}')\n",
        "\n",
        "    fig.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gi-YkbvnPa6T"
      },
      "source": [
        "## (4b) Setup the PyTorchModel and do a simple test "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        },
        "id": "ZPKJKJKsAvGh",
        "outputId": "6a63bae0-a187-4634-dc06-389bd3a23495"
      },
      "source": [
        "model = gmm_clf_torch.eval()\n",
        "fmodel = PyTorchModel(model, bounds=(0, 1))\n",
        "batch_size = 20\n",
        "images, labels = samples(fmodel, dataset=\"mnist\", batchsize=20)\n",
        "\n",
        "clean_acc = accuracy(fmodel, images, labels)\n",
        "predictions = fmodel(images).argmax(-1)\n",
        "plot_images(images.cpu().numpy())\n",
        "print(\"labels     : \", [l for l in labels.cpu().numpy()])\n",
        "print(\"predictions: \", [l for l in predictions.cpu().numpy()])\n",
        "\n",
        "print(f\"clean accuracy:  {clean_acc * 100:.1f} %\")\n",
        "print(\"\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "labels     :  [7, 2, 1, 0, 4, 1, 4, 9, 5, 9, 0, 6, 9, 0, 1, 5, 9, 7, 3, 4]\n",
            "predictions:  [7, 2, 1, 0, 4, 1, 4, 9, 9, 9, 0, 6, 9, 0, 1, 5, 9, 7, 3, 4]\n",
            "clean accuracy:  95.0 %\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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iKEpA0UZSRVEURVEURVEURVEURVGiGm0kVRRFURRFURRFURRFURQlqtFGUkVRFEVRFEVRFEVRFEVRohptJFUURVEURVEURVEURVEUJaqJK2zl6TEDrUAlJFj8kD3RFHVbjYcXjYUbjYcXjYUbjYcXjYUbjYcXjYUbjYcXjYUbjYcXjYUbjYcbjYcXjYUbjYcXjYUbjYeXaI6FKkkVRVEURVEURVEURVEURYlqtJFUURRFURRFURRFURRFUZSoRhtJFUVRFEVRFEVRFEVRFEWJarSRVFEURVEURVEURVEURVGUqKbQiZsU/5L22AkAZJUVT9yax2wD4Pd2k/Js2+ynqwCoOKccALXH/BaIJCqKoiiKoigRSkxiIgAdf9sHwIiafwNwxtLzAIg/fU1wEqYoiqIoihIEVEmqKIqiKIqiKIqiKIqiKEpUo0rSILDr6xYA/NP+5XzXZ1h5ly07+S0APjq+LgCf/nASAFmpK/yQwvDAdDwGgK+/HAfAsa/dBEDDRyNPZRtbpTIAy19uCnjvB4AHtnYEYPGgZACylv4b4NQpiqJEDnF1agNwpEW9fNeX+XcDAMvvlfy4ylIDQLXUwwDEzF7g7yQqPuBw384AlPv2LwCs41sDsLpfec82PU5ZDMDsn4517Vv39ywAyk6d4/d0+gtHQfrvGy0BmFLzDQCy7fXrFkp9sxmqJFWUwlj5QlcA/rvoNc+ywWt6ArDlhL1BSVOgyTxF3kVWD5CmhTtO/cazbmjlNABikLIyG3nRHbG1AwBT09oAUO/JWNlhzmK/p1dRfEVs1aoAZLVoAMCKG+Jd65u9K6VqzM9aNwwnVEmqKIqiKIqiKIqiKIqiKEpUo0rSAOIoSH9tPz7f9a/tFlXK87+fDkBS422eddNafw7AoIqbAHj8yhoANL07epWkWztVAiATUXQkbsxHghshZDeR3qnFvV4H3Grjx2rNB6DdgG4ANIwwJWnWyccBcNMbnwLwaovmJT7Wvoukt7/K39vl2MtXljJ14cHuweJ//OdTrwLQ+pUbAGg0SlRQVmZmcBJWQuIaNwSg1oTdAPw8XxRgKWPld9aS5aU+R2zNmgDs6C33W9UJttosPb3Ux1ZCiz2XSb6w42xRgt7T4TsABlf6Jt/t397TCIDzKk4GoOrAsq71fep39Es6ldIRW6M6AFkTxNt9fIvnAdiSVQaAyjEzAWgUl5h35ytmuX5uvewgABvHiGLkf0/cAkD1N3/3baL9yKr72wGw9OQxAAxa1RuAHY83AaDZd38EJ2FKyOKo7Pd0TwJgw+neyujqfqJEzrCkTt7974sB2LZOVFatn9oMQGba2oCkNZB077o0z7IPGkue0WPA/wBInPxnQNPkazbcLe8YB1ocAeCSjm4V/cO1HCW6rZrLocNylrWaORSAWl8mAFBxguQx9cgbP0UJdRwF6fIRMhpj2cBX8t0u/bQMALrNk/llGl6/C4DMTZv9ncSIZOdXMnI28ztpC6v1sn9GEKuSVFEURVEURVEURVEURVGUqEaVpAEg81RRlfzUzulhENXCi7ukJXzGRcfL4o1bAUjeNQ+AmLJedcoTf4of1n01xKcls2p4Kb/8wa620lu9PlOUXdXfDh8FR1GJaygK0iZvRIfiMT/WnCk9ztVi95f6WJvPkR7wjMulf6han1IfMqSJqy+eio8+9JZr+dIbxwLQe0wPAKx9+wKbsBLiqFgemTkJgJZlRJ1wyo46AGQtKb2y3lGQDvpFlKNdy4pa8MbFogZhwZJSn8PfOIq55S+I4rFXC4nLhpMyPNtEmyI2pl0rAJbdLH6Ts8940bOuZuxc2aaI/cZDKjtKqLKFbqeEFv+Oludhecrb9hJRjNaybfDG7pY62V/7Gnn2WX+giusYsUbynK9bTnXtO+GBZwC4LlW80WN++du3ifcDR2q565GLZstopybfRV5dSikZJkHqX6selhE9L18gdYmTyh3Ms22GJfmnoxqc3f5jWdHe/qh+NQCNBvotuUHDUY3mx8ae4sPZfHKgUuMfFg6TeTQcP9EtWYcAGLtDFKbJ30odqfwKUdeX3e5VGTvvZ82IfE/GgwO6ALDhXMlf3+zxHgCnlsvybDNim8yp8cm34lvb5B7Nc8ORZY9KnWH5gPwVpA4JRtp95nf6EIBZv8gz8uB91wJeRbVyFGKkwvXKMR8BcOnymwGo5a/T+em4iqIoiqIoiqIoiqIoiqIoYYFPlaQ7rhXfu0aXi+pt2VZ7hth0aUGv/4l8Jq4XRVj239HhQbK/vvQYOCoVR0E6s5+oQ7NW5e+ft/LhDp7vH1d7zv4mvboNvove9m2ru3RLz+4jfmInzZKehOYR1EO59iHpme14ljwjT9edfdR9KnQTD9t1D8q+NRZJL2a5L8Jz9l1TRp6bU07xnSKn4gJRfl045GcAZlQRpW7W7j0+O0cosfXMxgCckZjhWn7cvIsAqLk/PPxr4xrUB6DyBFGvtI2X3sSWP14HQIsr/vLZuVIfSwLgwgriS3nci3cBUG+BfzxvfMnWm+TZH3HLBwCckzjNtb5/jb6e75kbNgYuYSHAgSYVAfi396v2knLFPobjG/7Rmk6FbleZ8FH+x7QXP9/Ddbwzuqf1F+XTBZ1FYZthyfM2Y5zMBl/3Z8kvrTBQVQNYJ4j35oRur9tLpOr73SFRkj41/AoAKi4Rr2q27fTsG7NrnftYtpIh+TnxdV564UsANCtTAYBDD8hM1pWvlPpv5uYtPrsOX1Omgoys2Jctn41+iC51eVHI6iUKyriH5P84teWXnnVljNwLuT04q98v7zombQMAO/rKM1Ztyj8AZIfJyA2AtcNlJNziy0cfddur1pwKwNuNf8h3/d/d3gGgH4Xnn5FG89siQyXWc/EFAPx07ATAqyCd30HeR5OZF5yEBZm4JlLPbjZR6lTP1BXFrfPO/+1BqXu8scc7KqFfJXlfve9yeb/pulY8rWuNDc165p5B4tv+/agXAKhgEvLdbmuW1NH7LLzatXznern21s/IiNmtJ9X1rEvcJvln2a/C7121/NpY129HRZ8yTd5Nyi+TOGXbrW3jr5V2i572IKQvn5W2nS5d7/Aco+UIee/P2rvXP4kOYzJ7SftPx/i5ATlf9La0KYqiKIqiKIqiKIqiKIqi4GMl6V3DxX/m/PIyaxfNcm3QSz7SMqWnYfS2k0t9zjlbpQen/HOVAYibPr/Ux/Q1VT4Qr5EL5l0GgNklvQOZm9IK3e+as3/0fK8Qk3+vTTSys7UogOrGigqk/mdlgpkcv7Dof6JOcRQKRWFmO/HoQEQzTD4gPXXv7OsPQNxPofdsFMa+AaLgGFNfYtFqini9taDkM4SmVxWPpGFVlwEws6L4FBJBStKYRO/MzGcO+yXfbRLGy4yMWFa+60ONXd1lNvspSW7fn1YPSK+0LxyaHbXZyj6iNjtpsRinNXxH7pWiP4mBJzZZCtu37hCfzfbxUrRn59pu06sVPd/r/k98XCNldk1HbZx6t6jDa/8mashKn4iKJyZd7vV/M0Q1ty7Tq+poGLcbgCv/EUXhrlTxdK09V/ap8puoCa39Mgqm8u7wUYrmxhmJsepG+f3xCW8C0DE+tqBdvAwXpcehOyWGb+wWhdzYhScB0GJIKgDZhw/7LL2+IKOyjErwPhfyfx3+rqhdGk4W9U6RnvFs2cpRh7WKl3Jp0bmitPv52M8A6H6aKE0rfxh6StLY5jJ7/ZKeouy7ZaMoAGNn+E6RH644Hpz7+slzMuJJiZHjwZkzT82wi8/cHpzHPXglAO3qiA7liyRRlnWqIqOear8UmmqxnDjl4TtXv1Todm3fHeb53uRRuX9SXpDMZdm5hfv0KeFFlWsl3/9qupSP/avIO8XfrS4FICu19J7w4YRT5zh+itQHHqixCIAvD0h8nntQ4lLlp/8AyNq2zbPvlJaiwu3yqagGK567SVa8JuXwtqEyaqPWXGkvsP6yR94Gqc6+r5HkZQUpSB1q2e/lc44b714hr3Ps75ue5ziZdsl7x8YTAfjxG1GvV0uVfLXSSql3WfP+KWny/UbD3mmu393+GgRA8lX5v2/fvEjyy+fHSJnQNl4kpcsu9OaVHZtIW1G9iyRWkTJ/gOkoPrxZT8s9XeZmacvJWlryEY1Vl5jSJ6wQVEmqKIqiKIqiKIqiKIqiKEpU41Ml6Zj7xI/nobbS9lo1VXo8drWSlt74tqLWeLrN5wC8UFcUYV8fFC+ncxILnr36kCU9WH+mi29Wr7K2x559jOYXyax6ydN9cCF+oqit5WmPi7frkCrP5lgqvQ13bBJfkIo/imIjlNVN/uLUG0SZO8WedbbCTPF0jYRYlJkp6k/H66ooLDgivW1pGTIr94Dy4qd2YQVR2V047g0A+tTv6LN0+hNH6fTKKFHmfLhX1OIpD8jzU5r/8wlnhF5PpK9J79bK8/2xWm+71h20vecqfRweHllxjUVBuu1ctzLt+GdFkVNnXekVOY5i5oGP3nct3/+1KC3L71hV6nP4m9R7RBnc9ihqwD87fuz5/u/vci+cN+52AJo+Lh5ZoaYCPBqxVWQUSeevVwMwpYZ4Bnafd5Nru4RvxcNo+DlXApC1xOsFHttKZvWutlwUH9Wy3WW1L5TKwSL7RMlP00TYyNfdRbHQLM7xZJV75odDXo/W+5bK6IPda6WM/ae/qMke3CL1j6friPdcu3JrAHi+s3jU3XvblQA0eDK0lHJZZd1qg7a/XQlAo8dLn84WN0od9KvTpOweWGEHALv7HQCg8oelPoXPWT6yytE3KibpvcVncl9D92tFzfm2Emp+ePjXpveSuQJ+evFl1/IZh+Q95aHHvF57ZQ66VV17G8u7T7w98ftdd4oKdU+25CAVNoV+LdUpD63HpB7Z0RZ8OQrayftlHuF3ruwHQNKfXh9By1ZZt7xtIQC9p1wPwKOvSR30+ARZf9o/4sn6YxvvyIZwp9kE8SD876LX8qxb+YLkm+HuTZq5bj0A90wWtdzSy+QZOVJH/o+xqcFJV7BY+oAoSZ06x3Tb4/qttjLCouJh+X/n99RnLRf16R+D5XmrcEjqYzsGiYL0zwfd+U+/lF5A8PyMGz4vysj2GVKv2t9U8rTEde78Pquc5IkFvWv9r9ZMADrlEKTG2XWQ0fXk3Z5rfnftsyP7kBxzkvh2Nr81dJ6jb1p+A3hHFVR/onCv+7JTJb+8I0vU9s0ekofmtYY/e7aZ30kqDcdPEEVp3YFSL7XsUVDhyqYTpa7+V8o4ALp0kxhUL8b0RLuS412/K67PKGBL36BKUkVRFEVRFEVRFEVRFEVRohqfKknLf/an/eleXinXdi/V6QXAY92TZP3P0qPydK/mBR477pD0Y5ZfJL4d1WdNAuDYePGjTEwLf1/K3ZeLgvTXwaIgrRxT1rPu93Tpafn7MZnxvtze8JsFrjTEHtPS8/2JWp8A8PbeyJmZ/FB/6T28qu5EwOtFWpAnaZvp13m+15wuXXIJe2Tbe3tJ38figWNc+6y/VzxwQk3lk5td94oMo0Gc9FTefvM5AJTZVXJP1bi6ogp8t5HMWJ5hRW7/0OrzClYTXrCiv/0tPGY2Xzda1DsrOr8HwANbRRVX/11RJflCl7Ohl4xO6J4gZUyb38SXslEY+MbFtk4G4MdTX7SXSC/2qB2iJp63uxEAE5p9l2ff5DLSI/vmIJntfdQ75wKQvXqN39LrS2LKSvmY/pn0Tt9X4ycAWn4uksmUyfnfIzkVpJ5lEeiltupjeVY+yuM5KvfIJatPB2DuMvGnTLnFKwOqeUBiVNP+fV3H0wDYOkxU/be9Ksd6oPZMAGYfEhXl3zeJ4rT/h3IvOcqjYNPyXreKMXa+7xVs98+VvHXgyaLev/GYWQB8RVWfn6u0vNBlguv3rx+LYVwdip7n/feR1EVHd5H62LHx4n9dO9btWbcyQ8rxcz+7DYBmd4aOCignjoLyyVdfdy2/5L+zAdg7QkY1VJ3hVjnlpLLt9dp+oih/WsVLPSPlC7n25M9K7qceKLZ2kvJwboqoYJ1RTXvsUSgjPpURg0m/FxwHx0OvzDRRnF/2vdRXl/QVddzwahKfNz+RsrbJJQt9dwFBIj8FacRiC/Nj7C87jpGyuJopeKmDHPsAACAASURBVLRawjwpYyNhxu5t18u7+pI+MtptkS3wG9NJ3rGyD+8q8rGyF9rlbte2ALzz6PP2GqmfnfrPBQCU259WihSXHueZrvds0cqIjQ/lv/yh7kMAWHtmXsXleX1/BeDRWn+7llePkW2/HSAzwd/6uJS1OT1eg8WFq8TP+6Mm0wCI2ysjsY72bpLwjYxsWpV1PABLXvMOgz7G9k6fZytKu14t6t0arxec54YDmT3dbTUV1xd/jFbK5TJHxLIMuR/jZ4gPsL+ceiO3pUBRFEVRFEVRFEVRFEVRFKUI+FRJWlQyN8tsn+UnyafT4l7+sx1H3XfLNdKD47S0P7tTFIZJ74pvXDh7h20/TtrCcypIHa6YeQ0AyVOiS0HqsOH06nmWzd/X2P52KLCJ8SGOQvax523PpnjHc8StBnRmqn9gxvkAtLprmWdd7p7ZlitEXTann9xHnROkZ+vb658G4IyydwGQ9IQoM0Nl5rwd18qzPfHYZwD4YI/0rJb5seQKUoelj4gKxFHmXpEmyqisrcHvifQ153TKq8rYY3v6ZIysDUBMmChJLUuUCs7/7c8dSQDEHtpa4mPGVBQV2fLHxTdqSj/puc9GRiM0Gri4xMcONNs7S76YFCdeWEPX9QRgfVfx944pL6rsjteJh+ud137q2XdQRYlhT7u4mTppLQBLzwntWe9jq4oyb9mjks8tbzUWgPl2NpbyiNQFIkGxUhxiyosCbMUj4qmYepJ4j8bYZcncdKlfDPpCfKBaPiwKluTdovbKOWt3bo6tuAGAH+JEKTfvGVEMVX9elHH9y++2t/TvTKPFJaZtCgC9qvwAwL8ZUhbWWOR7H6uqP9sP0sk+P7TPiK0k47rKx8jDMu2Q3DN1XshfHWRstfmRk9t6lt3/6rsA9Cwr5bKjNJyTLgrSwcsGAnC7razpZ+dBY/uLwvbFdwYApZvR1h/sul/KSMeD8+xl5wEQe6fELHbBX0c9xu6OUr6OqPWpa3nDab5Kpf+JOU3ew7LtHMHx2rtqle1B+mDx1UzJ18t7y0snyuzGt1eT+uug1qKm+o34/HdUQoq4hjJ676n+HwGQbeu3/rhXFJUxtu7KuXdicuiwei2WfCF9otwD1d8OX1Xc7jZyfU7e9/KWXgBk7Sq6gjQ3sSs35Lt85wGp29UP0qz2vsb8KirRxr/mXbdglNTN+zQXz9v/7pU6eWqP9wCvj/qyB5oB0OKW4L+/zVuZJF+a5L9+43BRF7cbIMabSz9ole9258263vN9+WlvutbtlculRsmTGVRiq1cD4Ll2Msy86wIZjVBt2tHL1NyUj5M2Emc0qL99WlVJqiiKoiiKoiiKoiiKoihKVBMUJWlJcGY5fvk+8bRxenAmjhZFWPVN4dsrdeQHUUT+nvKcvUQUCe1+v8KzTas7xMMn9OfG9A97W+dVfvz9sniuVSF8//fZtiLaqyB1c/WaswDYd5H0oCWvlx75wu4DR6Fxw3viAzXvf+JXWDdWjvHXEPl9/udyf1kLQ2NKypj+2wGoFydSjrc/lmtvUAyftNw4St0PTxWfsXRL7qO1z4sKrXx66HuEFZX0s2V24Zfrv5lnnWP9EvPzgkAmyed8kzIFgCEzRa61dp8orI+8Xeeo+27uIT3xZ3eRnuwv642110hvdfe/pXezKuHjT5nlmXlYrm3R66IirGbnidkHZIbtus/JM/Rp306efS+p+JV8sUQVsSVdevGtw6GhLC+IjZdJT/zyAeJ/+eUBUZa+3Uc8NrO2/RechAWZ3f3sWbkHiqd5DKJAmX5IbpKnbpD8vvm0gmfcdTBxUi7FtBQJw1tTRAnwzAfvA3BsvKPklnPEGulvP/bPSwGovzU0/gcrrpBZ3C+uIIqTExddDkAl2w8s2lh9axsATiwr/metZwwGoDnuciHW9tZcfqMoI5de+FKeY023Z3u/4fsrAUgZLeV3wr/yv38FKWNfmi51969SPgfgyUbiIRxfjBlt/cnq8aKSXdJBFLLrM0VRGnO/5CvWgkVHPYZJkGes+a1yUY6C7qo14ldXLgxGgMXVrwfAHS1/zHf9qoktAKhNydVb73wh72u3X7XsKFsqoYSjID37exml1K+8KCZHbBVf4qlpkq9Yf1Rx7dfv4l88329vKvdV/0dk1EH2I1JnOevyoUB4eZbWSNrp+p36kqhjK1Nyv+Ut50l+WTvWPaajwue+988OVZz6KvY7abOnZLQXPeQjLVNGJbR8276HApq6/Km4wK6ES/WT7cdLXalaotTHZg2T+liFGHu7B6dTXBzl9pMrRGFba4rM4xMKnqxF4dDxTQE4vZzkAbf+JSPgqmUXfTRJbO1aAFxXS95bhvwjdbka+HdEiipJFUVRFEVRFEVRFEVRFEWJasJGSbrstvoAdEoQz6slR6S3t9rSg0FLU2mJa5oEwKPNZUbzqrYXqeOt1vhRr9ajNF4n4Ux6b1E+fXGGV8nwyHbxQ6s2SXr4Q6E3ydfct0VmvNt7jfS4ZK0vvrotaZIoOx7s3xWAp+qEpnomtqbMofxA8teu5Q2eKP3s4stukJ7t4xPkWXpll/RMlp8UOQpShy2dyhS4ru9XtwLQgvC67lovifp5xhuSN55cTjwF3240A/DOrpr9/NH9mjzb5poH8ZN9opaqfl+cvT58qHj+JtfvPWdKT3y1d/Pf/qHGX+b45e4jnb1A/BuTd4W26mlfF7cH9ejVtlrr39BQLwYLy7axPmy5fUH3ZcsztLmL+P4dOq8zAM1b5Lp3Dnu90Ac2Fq+oG6uMA2DeEdm3e4LzdCS69v31sCyv/5icO1R8rm/rLWWK40Ua/4rjbR6d94pp61Zplfkv7wzDAMtHSrm57GTxtc2ZJw5a1RuAvXdJnbzF71KmFKRMXrnKVvmnlCDBAWBwa8nvHB/FNZniQcofRVeQLn+xHQBfNHLHa80zMpIlMQzK3V0nNgLgggpfuJYPXdcLgPoT5Znx5bwPbcqtB2BO01Pk2KvSfHh0xVfsby8q46GV5d7ouehCACr1lnuiHvnLwueP8tYxFjYQOeAD18jIya5niff7d+NkPoZXdsuohW+vsmWDc0LPG97xdP6p3Th7idS5K64teXkXU1bK3Vtul3YAZ06StbaivdpfolqNxlGkq86v7PrteO//d7Go/JscPYv2O/U/Wg5Ap2zx/a/3pahgrUZS7m3LlveNCqWQJDrK7X4jZST1lgfl3uj7lMwvUvfDf4DQVWFvPNH9btpgRvGfl7VXNQegvT369vBvjkOrKkkVRVEURVEURVEURVEURVH8RsgrSdPPESXhXxe8YC+Rntvrb7kFgHK/hbbqpTCafSqz2XWId7dVXzJdvCSTF4am8i+QrD9FbtG28V6VyxVp4vVR60Dk+Bo5HrsOi45z1G6l8Ec0ouqJi3HPxOiw8WH5rNO/5KfwBSZR/rdnJu4BoPNc8UmrQ+m9UnN7B320WhS6/vYxCQbxHfKqzVOPiNI+ZYyoisOtNzruJ5lBefSJojR5tFsSAOvPkOdjZd/XAJiTLvf6ZdOuK/BYLT6Q3suvJ77jWv700jMBqL9wiY9SHTj2TRJPVsQSiytbi2JpVidRC27rIL6BVh95DtqU8ZaXqRniz3uMPYP15N6i1r+767WyQRGUVMHgk+5v2N+k3Pys9YcAnPD8HQA0+VL8nWNnFn/mzHCm6hdy/w4dLL5VH6ZIXPqVl2fl/OvFgzfLcmul0y3RhiWY/KqDssyrIBUy7Zyk1yJ7ltIb5be1KjSfodd39ASg7FfhW1/0BSm1thS63nSUjGTyia/aS0QBcszMoZ5tWgyRctkcXliscz+0VeryZWeKQiycFPs5cXzOAVJvFqXTsr6vuLaZYfu1VvxtNRAe5e6240y+y/97Sjygy232/bPTp/wOAJ4/XlRXFSJUSdr8tpJ7VYYCZafK/77PVBnFV6kESvzM9fK+22ikfG4cKcs73C0KPMe/9NEJUj+7d4jU5Zw6YCiRYAoetVVUHBX67gEyt8YlFd3TvZ/2o4z+Sl46r9TnCjdi2sqwgx+ueMZeYitIbXVtizGrAN+q2ktK1nbJw2q/JCMfPXm9Pfp38P1SL91/3j4A6leW99yvUtyK/eJQ255fZM79Umd//nqJ1/Sh3QAwvxevbPYXMYnyf3v4wvEALD4i7xz768s7x64J4gfepKbEsEZZGQn3duMf8h4LJx+Qciqr3NFHD/oCVZIqiqIoiqIoiqIoiqIoihLVaCOpoiiKoiiKoiiKoiiKoihRTcgPt1/bW9pxKxiRpl+y+nQAEr8TOXFgBLe+ZdcVJwDwcO3n7CVybVeknQZAq7tWAuExRMff1GyzFXAPEYz7omqwkuNzll8vcvQMy/f/7bTzZJKKz2rOsc8R6zpXvRGyXbCHvWXv3A3Ao9uOA+DSZjK8ZFZdMXLP3LS52MeMa9wQgF/bj7eXSD5y6I/AmD0HksN9ZGj1vE7OEEmvrcLyjFoAZIX5pDaZm2WYaOLn8pn8uSw/+7rjXNslU/CQQGcIjzOB02Pb2wDQ+BYZ/hIKQ3eKS50vZTjnv/fKEPPh1WUChbunyJDY3JNUXfTfOZ7vh4bJhGkDPpkJwFWV1gHw3zB5VpqF6AjBzgky1M3Jx5wJD5ddJENeMy6U5W1s25rKc2X9/gYSi0oyUosaiw7kOfb2tuUBqD3TLnfC6LnJ3ifDuRLOkM+htc8DIHVkEgBndJRhzv/ukTxhzQbJC2PjJV79WnrtFZ6uU/gQv9YzZPh1yztk6GTmlq2lTr8via0iw6ArxqwPckpCiwaJUtbGOPoI484f/h0mddFWZeQZ6zj3MgCaDVrg2aa49YUyFSRvOpApx84+fLiYR/Avk1bLcNfh1eX56JAg+UKPRfmns3Pi557vzkSCuWNyx8ILAGiwJTTtJ/IjK1GuIiaXdqbcFN8Ps3esnzLC8QVO8Sn1R8kw5YUfSZ297vdSH3vkrTcBuOXxGwGo/vbvQUidG+uI5GWT9kvZeX4FsbFae5bUMZJmF76/M1kzwIprxCppyRUv57tt8/eitwUgbUA1AOrHuieIPPM724Jgc/hYEVb+8A/7U36bOGl2O7d6b9d22Q1reX9YkjHGrN/m2ib1KXlGfj51NAB17WH3t1cT68E/RyUBsO/BDrL/zwsIJqa81KcHVthhL5F6xa+jxPrJsW16ZZdY2Hy/RSZW7rdsQJ5jjUueAED1GLnmX656FoBTdwwHoM7o0k/0nB+qJFUURVEURVEURVEURVEUJaoJWSVpTMWKAFzeQ8yc92ZLj+3WJ5oCkJAePj0JDnH16wHQY5hMrlEhJsG1/velzQFI3hV+1+Zr4po0BuDZlhMBeHNPQ8+6au8Ev0fRVzzQY6rPjhXXsAEA+zrKffbaVWPz3W5OuvR6miOhoZ1zFFDTNojSb3b7jwHY9JWogWa/fkKh++9u7ZUjVEiSXuiu9dLk2Lk0HiYClQuHaogqI/fEXAB3zRc1WRNCcxKeQLJ2hMTHUVdOe1wmc6mwLkQlk0XAUVkPHS497O8++zwAyWWkBxdbgd98mkzGlHKTd7K77AOiOn3qp74ADOkvSuRRx4tS6q12ojrNXlj6CdR8SZOpci3/9nkt3/XOc7D8NFGicFrxzzHnHlEb37rUnpioT/gpz7NsdWfy9fKZZi+PZw0ALexPh2mTW3u+51aSpmXKBHD9X7pL9n1R1GVZmaFRhuRm/RCZgGhQxRkA/HUgye/nTD97j+v3wex4v5+zuGRboovwlIuWe7KeurV3u9a3rinK/bxTAh6d2OZNAFjSUyZi6bnoQqBkk774kzqXiRq63xRRrzgTajjK0sLoYU86k32JKGWcukutNxML3CdUads2DchbZ/IHziiAQJxLCQ+ciZ0m3icTaW4aKfWysQ+MAeCKhjJZc6OR/lGMFQVHBf9BX5lItMsP7wOw9EoZxXLJSTLSdcnXoow71FImCy1bQT7vP/Zbz7FOKifl78oMyZOTy8h72dSDlQCIX7kJCM8RTiXFdJBy+/shT9tLJB/dlCX1j5ZvSfzD6TUurkF9AA60lffyhG+kfScr9+ibfEbj5NYSJ18l2ww++zYAeo+aCXiVpBOafQfA+Y9I3T39pNKlvbRYB+X/9t5eufZu5WQYV/+PZDKr5m/ISJ/MNevsPQoe+TN3pYyMPaucHHNfttwFPS6TCZ1WjPZhwnOgSlJFURRFURRFURRFURRFUaKakFWSrhgpPQpf1RA13Lkrzge8rfDhSOp9ooacUsetHjx58UBAvUhzsuJ/0vPQ1RbbXvvXyZ51DfknGEkKeZY+XAeAJWfk73Hj+Oi8eqfcb2VTfe81VRqqPiw9qSeNvASAyW3eA2DUiMKVw/PSvQrKLLvf5/j4I/YSt1Km0UuiDokk/UJ6/92u36lHDnq+N3irTKCTE1JsH+pVIS/qKr39aZmHACi37Ui++4QjFSbK6ISruB2AnRfKPXB4j+0xOFzUW1kH8vpwtrxHFKWnthDV8Q/HTAJgxAh5luqf569Ul4yWN4rP0pkTxRdz8MtSnibGiFqjT6L4OOWnrC4qnROkl/qXDh8BcMwzwwBoNjxyRjE4rH5CnpG/Or2QY6lbBXnB06IgrfeKqHjCScnhbzJP6QjA+A5OuSvP3ORRpwJQmfBRqlcZIkqdP2dLufFyI3m2Thh1p2eb5DGigMrcsLHQY7WaINttyZL8tuzoavaa0FKSOiNZOFU+TxlwAwBbO7o1JFVT5a6v/JH3/7ltnOQ5y2zv87f3JAGQuCT6VGAlYU2mlMGRVBYrpaPcF/JesnC+26P072tFKtZvZKfgJCwHjlf5RQ+JH+JJt0ie8FGTabLBTfK5P1vyhwn7WgDwyN9eT/iaE8VbccoLMvrHGeF0zwJRtDfedHQle6TgqC0PPr0fyOtFetq7Uv9oPDd4KuLisnuw1Ktuu1/KhtMSRSV59oNSllZ9r+R1Sacd7Oc/7WfkVxnrcUlFGfnxbJLU4a+4RBSblT4JTh0k237fmNRD2vM+LyP+30mb5NqLUj7GtpDR48fGy6jyJ3ccD8DPN3SV9Qcy7C394/+tSlJFURRFURRFURRFURRFUaKakFKS7rmsq+f7oovEh+S/TGkl3j9K/BYT2BT4hPmI+f0cpYbbi7TyDaJry9xVEuenyCS7oXtm0UO7ywYpJaFPmZkyS+KTdScVut17G7oBUHZqaClIPcyRntPKZ8vPy3uJemt3i4SC9gCg+pt5e+Q2fC49V/O7vOda7lGNRACxyc0AmNfpQ2cJAN/ub+PZpsyP8wOdrJDi4On78yy74O9rAKg1469AJ8fvOIrSChPdywsbneA8E3sn2/eNPDqMaiv5ydi6vQCv/2mwsWwfTOfe/iSlnmv9mAvERzSrjKjIu90p+d1TdYo/CsWZ6blBu/CtdxTExuFSHnw/SPy/ypm8Poqjd4lPep13/wYiS4FfWhwF6c5bRC2RUkbKqRs2dAegygTJX0JBdev4g/as/FOh2znq0FGn9Qeg3STxEPvnsjGebW44SUb1bDpHlKFZO3YCsPtyUc6ceKvkQQ/V/hWAjuNFOdPsu/BQ1CZOlvQnTT76tstOeQvwemu+slxM4OqtW+qfxIU51/Sf5vp97ruixGs0I3wUYgUxeI14nH/QeFaedStfkHfb5reFxzMQCjgepWMWSn5z3UmrgpmcfKn6vrx7LB4vef8ZJ1/nWh+bLjWvWLuumZRjbgDTUSpalWPc77ZVvijvn8SGMEsfkjrcytavu5aP318TgMYP/xnwNJWWIxWl/ukoSJ3/8+zHpSw9c4uMWEj4tuSjo52y972bzgXggvdkXoEmcXKuFsOkHNrySYlP4ROytu84+kYFsO5cGSHrqIvf+VnK2Ba/yD3h7/qVKkkVRVEURVEURVEURVEURYlqQkJJ6sz6fuuDEzzLEowk7eKFlwNQsxSt7aFORm2ZxbvMkfpH3TZr23YArHTxOjEJ0oMVW7OGe7uaVQBYcUf+M6xaWdLLkXKz7YO6d29xk+1Xxnb50PW7/rcl95cLZWKNKBBy++ftvbSr6/fDj7wNwMnl3ArbnPs6M4Y6isLcWKdsKE1SA07sTOl9rT6z+PseSqsoX7q4l1vdxRPF/Pp3KVIWGmw5uRaQ9955ecbpnu8tCL8eWF/yesdxnu/ODJnVXwy/mYcDQc3XRXHZpfelAPzZUWZqvuXOJACa3REaStKjUf4z9z0/tZ0o3J66XOoQBy3xv+s463rPNo3fkmdo+zC5R7zq7Mgj4wzxdJpykyhIG8XlfR7W2rPZf3m3+GomHAyv+lelNCkL0zIPHmXL4mPipG66+zZRYM87TjzHfjgkHnP/PigKofiMeT4/d0nJWrkagPGbOwMwwJ4Ft/GJawGIrSQzKjv1wMxVaQDM7yA6ip6XD/Mcq9oi8cA2NWSU1+qXxRdtSU/xZHU8SD0K0jsjSz0Xe0zLHL9Eze54a9YeE74jng7Yaq5570peeHyCPENrJx4LQKOBpfdI7FRO7sM56fL+kfTMQkAV6ko+dJb7blxXefd5ZXezYKamUJz38fjvil5O7k+qkO/y6j+lAZHvZ7zteu98Af+e4/h5S76wwa6rv391X1maHX7va7Vs//budaUc/OcquUZndJIvJYrb2tvtQMY9B8fsZeKFm0z4jig8UsWtFa33c2DPr0pSRVEURVEURVEURVEURVGimqAqSZ0e+XZfiWfDwApe34KP9olKqvaD0o4byT2NX3/2TpG37bZAZv7evkV6/qvWFDWDo/opLq0fuAmApneFxqy9h/uK0uHEso5vZkiInf3GUxMuAODCIS+6ls96Rmbi9qpDsX8XfKzc2zq0mS4+OS2IPA/GArE71GJy9QNFgoLU4XA1d6/h/HRRs7Qatd6zLNJ7owti/b3it9g9wXvP/5EuirnYCPQi9QnZkn9Uf07itH2cKMJSL5a8qO/HgwGw5vtnFkl/0eh7UXkgg1JINDK6IvWktz3bXN5Y1NffJH1vL3HnG2s3i/9iC9L8ls5AkdZHlGJJuRSkjtIaYPCtMitq4tfhqUQvP0nS/d2jrQBoVnYbACsaiO+u43dXFLJPlNEHq8VCjPNbSRnyRK3xru2euPMKAMp9H6Ke38Dha6Te+PykFAC+SvkCgFumi4/qnNdE3VNho7vk2NbJWwPvNEy8AZ+rJ7PNOmXsG/bM7u892weAZu+ERp3S16wakXd01sAF4nNdJ4zLlpifFwBw44vyTjD37pcA+KGL+NxdebKoiUtSfq4e3xaA7mVF0eS8x1Q78G8pUhwaHBwgw5U+aPz6UbYMf9Y8bM9rIAMaqf2S771kY1snA7D3EfF6bhAn9ZDvruxhbxEZs75v6n8k2EkICof6yzv+xHue8SyLwV0XOe8R8Squ/mv4lyHNXpQ87vKTpY45LukHAK57/jMAHr5SysvGT3nL2KPVsdeOkOfw2oEyIuSyyhLLGMJ3JENRiU0PrMu7KkkVRVEURVEURVEURVEURYlqgivTayfePo/WGpdn1StPDASgysLw70lwOHfpIACmt/msxMf4rUPh05Q5fmsZllt7e/aiKwHY87fbu7T+L6GlNVvbT3oJHE/aR7aLL02FL7yeGqEwW6yvaDpBumTnXCY9QJ0T8nqOFpU56XKMNzbL7G+7bpBZ4VJW276zJT5yGGLfJNkRrEGvlctj9su9HQCvb3E0M+iS6QBk58gthsy7EoDGthIhtrqoA6lVHYCs1BWBS2AI4yiKer0vvflLrxYl6b7HRdFRaaD4/Wbv2xeE1BWfMvPk/9r1L1Ev/XFc3jLU6d13+o3TLfFb7LP0YgBShv0HhHce6tzvC85zRi0kuNb3+uUmz/dmk8NTQVoQN1QRL8QtX4mSct7ORkXe96kmbwDQPt5dXZ5/RO6Gy+cMAaDZT8uA0L5Hsv6V+3jWueKbWvVrUWu9UG+2bPDIbNf2jkq0sHK0zS9XAdD8dil3qm2InDp7TqwT2gHwZZexOZZKnctMrxqEFPmHujNlxuTjT7kM8Pozr+8l19p4RtGPdeB8UVl+2kVmdP49XfKcao9FjuKpyV2pwU6C39kxRBTmi68RdXGrmaKcrv1S8Y8V17ABAGsudefBTc8Whfp9DaV8/uOQeJAOGCn1kGpzwz9fiW3VwvP9yx5OPiLK9CFrTwYga/vOQCcrIMTWltHBH455HvDOVp6TFj/KfZU8TtTqkfCu78zsvrePlBGT/pA2mFPsWe/P7/YeABlfeGsOGUepRSSa3B6j7vz0ns2dAGj9wCYgekcU+gJVkiqKoiiKoiiKoiiKoiiKEtUERUnqeI4MHf+Fa3nrd270fE8aF1kzYgKUO1PUDMc8IYoNq4DoV0yRnqTCfEaPmS2999ba8q7lTT/bL1/muH1bqrLC9RlqOLOr3t39G9fyj7/tCUDTzPDvRcyPrKXiV/LQ7dKDtq6vKDb+7V18f6Mb3hHv0YaPOz5Bu0qfwDAlu6xb+bItKz1IKfE9JkHUGOfWW+havuOIzJbpzLSpuMnOkj7BrTeJn88514hqasqqugDUPy846QpVmr+xDoBxA0WRPutYGQFxVrurAYj5JTz8fR3Fa52bpSe/7zv9ALgv6WvPNifYMzlP2i+9/Pd/cxEAzW+TekgoqwOPRmxVue5b/5T7vYJxK0hH7RDfzhbXeusGkaK/d/wxt94yC4CHa9p5Zs2FBe2SD1JRy7TvgoW2ldxlE8Sjsck9UjcJp3vEmb1+Si/xaB1zVX8ADjQRBfX3Z4na+Mzvb5Ud8pH0tHxLRr0kzV0kx/RXYkOErZ2krt0kzqvacRS2cYcjQfMkZC8SRXT9+2Wk3+TJokD/8krxvTurxu0AtLgxr9rcdBSF8pYTKgPw+h2jAWgVL2VvytShACT/Ebq+vUWlKF6kPW78HwDNJ0fG+2wZI37Wqb3eAmDBarn/L/39WsAzFQA9m8roteW7a3n2P513cgAABtFJREFUnXHsRABi7LkRnFE+MfZeY3c3AeCSnyRmrUeKCq7a+sh599vbuprne3IZt7fx3O8lL26U4Xuf16ASI/fM6uuaA/krSB/cKr7fyUPFizMS32Gydsn7+LstGwPw5K0ysqnxAFFQ31R/umfbk8sVb0TpTRtOBGD6zxLHlq/IKMPMDWtLkeLQoHanzQDEGilDdqZIfaze1MCcX5WkiqIoiqIoiqIoiqIoiqJENUFRki67wVZ0JO51LW8wM8dsb1bk9Mzmpsl9ResZ60PHgo/BIl8lJyTItnuOlh6sB8BpG44HoMUT0rMUTiqNklDuC+lZT7bF1T0vEVV1mSu3APDdMRMAOOMf8cjLfs/bQ2vZ3bdJf8sMvpEeq6Lw4VmvAZB6RHq6L3nvLgAaEQG9tFnyH34jVXoPb+2WBsDMdXZPLeE1+3igSO35LgDZPaVsOWaWKCKbjxRfPn1u3GSuE8+kTweIx/HlP0oetH249HLX+iU46SopmWl2r/op8jFs2A2edfs6id9qygPiq9h8TWQofwC295OZzM9IFDPBrFxVq28e7gVA+QOR5UMKUM2eYX3uLBm99PwUuXdvr1r0ETUpP0s+Eb9YFDANnpQypAnhr3DK2rIVgPpPbXUtvxmZ7T6ZuQXuG7k19Pw5XEOuOKc/64s7WwNQ/c3wvxdyk7VkOQDvnyVeia+/Idf9XR/xFPy0h7yfjP/4FM8+bw0Vk8oOCW4t+llLLwAg5VVR9UeKUj0nzSbISC5n9AFAIpGRp1Z/W+7vbgfkGrf2dSv93j/hbQA6J8gz8spu8RPN9mhLvT6m2TtEQdl0cobrGPHzRX2avHceEJnK9IM18+rStmRJ3aPxY/IOGGn5avpZxwHwz9CXC9xm2lgpb6qnR14+WhB1XpR6RLptET+6ydmedc9XkVGBy/8noxdqz5b7ZruEkkor5bmq9Ye0oZnlMkq52UGJXyQ9O3XKyzVm2fPsJOzS2e0VRVEURVEURVEURVEURVECRkCVpIf7dgZget/n7CV5vSmU6MTxIFkuAlLiWQNEr7qr0id2b7Q9EfMA5Nkpzyp7i1V59onWWOXHI6vFd/DA2PoANJoUAQpSGytT+gmT7hEFZKsnLwfA/F0xaGkKNb6/X9SPS++t61n2+5+iqEsZvRGAZptFKZN1uHj+P9FGVqqo7i5adQYAUzuIH9mQrrYS84/wHNVQe4w3T6htf0ZSD7zD+Xf+CHh74h2aTxVVUPKkyFA7FUbWSlFa/NhG8sgfOa7I+zYlPLx3Ff9yWf+807q/88VpACRFgKq4IBz/2oRLagJwXYdbAChzt3jFzb95tGfblKk3uvZt8rmt/pkhZUR2xhEihcTJkm+eOVl8AJsTOaMPCqLi+D/sT/fyRwrMT/d5vjVjQaHHjob3l/h+2/Ise3qrKLWtzIw868KZ2BrVARgz9iV7iSiIHW/Ja9d192xb4z2ZrT3SVLTFIXP1mjzLkq9z/678kfu3leszElk4Q0YBnXZI1LW1Pg3s6GJVkiqKoiiKoiiKoiiKoiiKEtUEVEm6sbvMctYozq0g/Wif+CuW2evtZYzklnFFUfzMqeKnWJ71QU6I/3DUUY0GBjkhIUjZqeLvtC3HDIiO0iMS1YKB4OAAKZX//E18o3e1FL+kqpEvoAlr2pUTL1ZHwfHHYemDb/20+FDq86AoR2fSalEMDq++OMgpCQ5Z20QFV2aarYabJh/96OTZJpn8Z63X9zlFgV/bfer57ozr+DpVZrVvfhSlbbix9VxRAB5T5gfXckdBuuGy2p5lVkbekZGKApD0oHuURqAV56okVRRFURRFURRFURRFURQlqgnK7PYOT+6QmSF/PzMJAGtTdPbQKoqiKEook7V9BwBvJDcFoGoE+/BFErd+NASAZdeOBeDqd24GoOGqyPFpVhR/Y02vBsB9Dbp4ltWeFw1Oioqi+IKz6+f1bo00BalDjYXiRzv7sDQzjdvWDYBNg0VBmrXiv+AkTFGKgSpJFUVRFEVRFEVRFEVRFEWJagKqJG16jyhPzr4nd2/K5kAmQ1EURVEUJeJpPEIUo2eOEE/FhqiCVFGKS+0x8tz8M8a7rFwBHpyKoijRjDXvHwCebNbWXrI/16eihD6qJFUURVEURVEURVEURVEUJaoxlqXzDiqKoiiKoiiKoiiKoiiKEr2oklRRFEVRFEVRFEVRFEVRlKhGG0kVRVEURVEURVEURVEURYlqtJFUURRFURRFURRFURRFUZSoRhtJFUVRFEVRFEVRFEVRFEWJarSRVFEURVEURVEURVEURVGUqEYbSRVFURRFURRFURRFURRFiWr+D4eHhyVfn+ykAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1728x1728 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a1wW7YyPQTvz"
      },
      "source": [
        "## (4c) Start the actual attack"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IH70eFiWKN2P",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 728,
          "referenced_widgets": [
            "89f00a9dc0c94db38edb0a67756345ac",
            "005b8f81f3bc4cd19a7c25b6713e60bd",
            "f1d4158a88e24c1f8e52e0c337c7d36c",
            "5b3267ba81a04b86b21ba59cabb72053",
            "c8fa4a5d747e4edfb38836e368212f17",
            "5ca0867d392f44f9b7dc01bc2b4d2fc9",
            "70af0e54fcb64a61899d6ed6c94ff37d",
            "f3dc44781dbc448f9b4cdc5727c380cc",
            "e5f6cd1521494969b6cc91595c01b6e7",
            "fa2fed86821a48d596965a457a1826d7",
            "a3d45802d8c642aebe70cc18c442d2d2",
            "c8776b4417e441369f66b7ea0d290361",
            "7a93cbf267344cec84f86b8756712039",
            "507aaa3d81794997b3b0a1c50792cf73",
            "4cad4f5b376f4bc49782a67e9fe9be2d",
            "9a296936c7c04bc2a53fb6b983d3c437",
            "b1fab12139a4484dab9a828c65e45328",
            "ec8837dbfc5241a9b7be35e195cf7822",
            "0a9b99d788f1431596b5a2b8ce218bbb",
            "26622aac134d4082aad432af4f9946ff",
            "2b2413ecd3324862890864b9bca9545d",
            "8874406c1e9249b68bfc5723a3ac13de",
            "0150d6c6ad1943d4a241187122a96fee",
            "98ebb3f48b604cf9b9b2d1904d8e8c33",
            "8b6f806daf2c47d3a6af36e2f1a903c0",
            "f5bd38f4e92f415a87033232255b8ddb",
            "931dc0f2b29e45c8aa9712928cd5c315",
            "ca8fbc6446b24971a66e23c89c433efc",
            "ff893a37df3742588fd190699a93dbbe",
            "3dc295b88d184b81aa5ff5c4d3266d6c",
            "938beaa5cca540eebd0f73016ec98884",
            "9ffe86738df1479aaa29b15e60c79fca"
          ]
        },
        "outputId": "09e7544c-4b58-406d-a7e4-3f457123d64f"
      },
      "source": [
        "# test_size = 10  # assuming we are running 1000 tests for each attack\n",
        "# batch_size = 2   # adjust your batch size according to memory limitation, as long as it's a divisor of 1000\n",
        "test_size = 1000  # assuming we are running 1000 tests for each attack\n",
        "batch_size = 2   # adjust your batch size according to memory limitation, as long as it's a divisor of 1000\n",
        "num_batch = int(test_size / batch_size)\n",
        "\n",
        "test_dataset = MNIST(root='./data', download=True, train=False, transform=transforms.ToTensor())\n",
        "test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)\n",
        "\n",
        "Linf_epsilons = [\n",
        "    0.1,\n",
        "    0.2,\n",
        "    0.3,\n",
        "    0.4,\n",
        "    0.5\n",
        "]\n",
        "CW_epsilons = [\n",
        "    0.1,\n",
        "    1,\n",
        "    10,\n",
        "    100,\n",
        "    1000\n",
        "]\n",
        "L2_epsilons = [\n",
        "    1,\n",
        "    2,\n",
        "    3,\n",
        "    4,\n",
        "    5\n",
        "]\n",
        "attacks = [\n",
        "    # gradient-based attacks\n",
        "    # {\"name\": \"Fast Gradient Method\", \"model\": fa.L2FastGradientAttack(), \"epsilons\": L2_epsilons},\n",
        "    # {\"name\": \"Fast Gradient Sign Method\", \"model\": fa.LinfFastGradientAttack(), \"epsilons\": Linf_epsilons},\n",
        "    # {\"name\": \"L2 Projected Gradient Descent\", \"model\": fa.L2ProjectedGradientDescentAttack(), \"epsilons\": L2_epsilons},\n",
        "    # {\"name\": \"Linf Projected Gradient Descent\", \"model\": fa.LinfProjectedGradientDescentAttack(), \"epsilons\": Linf_epsilons},\n",
        "    # {\"name\": \"Carlini & Wagner L2 Attack\", \"model\": fa.L2CarliniWagnerAttack(), \"epsilons\": CW_epsilons},\n",
        "    # # decision-based attacks\n",
        "    # {\"name\": \"Boundary Attack\", \"model\": fa.BoundaryAttack(), \"epsilons\": L2_epsilons}, \n",
        "    {\"name\": \"Additive Uniform Noise Attack\", \"model\": fa.LinfAdditiveUniformNoiseAttack(), \"epsilons\": Linf_epsilons},\n",
        "    {\"name\": \"Additive Gaussian Noise Attack\", \"model\": fa.L2AdditiveGaussianNoiseAttack(), \"epsilons\": L2_epsilons},\n",
        "    {\"name\": \"Linear Search Contrast Reduction Attack\", \"model\": fa.LinearSearchContrastReductionAttack(distance=distances.linf), \"epsilons\": Linf_epsilons}, \n",
        "    {\"name\": \"Binary Search Contrast Reduction Attack\", \"model\": fa.BinarySearchContrastReductionAttack(distance=distances.linf), \"epsilons\": Linf_epsilons}, \n",
        "    {\"name\": \"Gaussian Blur Attack\", \"model\": fa.GaussianBlurAttack(distance=distances.linf), \"epsilons\": Linf_epsilons}\n",
        "]\n",
        "\n",
        "fig, axs = plt.subplots(len(attacks))\n",
        "fig.tight_layout()\n",
        "\n",
        "attack_results = {}\n",
        "for attack in attacks:\n",
        "    attack_results[attack['name']] = None\n",
        "\n",
        "for i, attack in enumerate(attacks):\n",
        "    clean_accuracy = torch.zeros(num_batch)\n",
        "    robust_accuracy_batch = torch.zeros([num_batch, len(L2_epsilons)])\n",
        "    count = 0\n",
        "    t1 = time.time()\n",
        "    for images, labels in test_dataloader:\n",
        "        images = images.to(device)\n",
        "        labels = labels.to(device)\n",
        "        if count == num_batch:\n",
        "            break\n",
        "        images = ep.astensor(images)\n",
        "        labels = ep.astensor(labels)\n",
        "        clean_accuracy[count] = fb.utils.accuracy(fmodel, images, labels)\n",
        "\n",
        "        # The raw adversarial examples. This depends on the attack and we cannot make an guarantees about this output.\n",
        "        # The clipped adversarial examples. These are guaranteed to not be perturbed more than epsilon and thus are the actual adversarial examples you want to visualize. Note that some of them might not actually switch the class. To know which samples are actually adversarial, you should look at the third tensor.\n",
        "        # The third tensor contains a boolean for each sample, indicating which samples are true adversarials that are both misclassified and within the epsilon balls around the clean samples.\n",
        "        raw_adv, clipped_adv, is_adv = attack[\"model\"](fmodel, images, labels, epsilons=attack[\"epsilons\"])\n",
        "\n",
        "        success_ = is_adv.numpy()\n",
        "        robust_accuracy_batch[count] = torch.from_numpy(1.0 - success_.mean(axis=-1))\n",
        "\n",
        "        count += 1\n",
        "\n",
        "    t2 = time.time()\n",
        "    print(attack)\n",
        "    print(\"Clean accuracy: {:.5f}\".format(torch.mean(clean_accuracy)))\n",
        "    print(\"Average time taken for each test: {} seconds\".format((t2 - t1) / test_size))\n",
        "    robust_accuracy = torch.mean(robust_accuracy_batch, dim=0)\n",
        "    print(\"Accuracy with {} attack: {}\".format(attack[\"name\"], robust_accuracy))\n",
        "\n",
        "    attack_results[attack['name']] = robust_accuracy\n",
        "\n",
        "    # plot the robust accuracy against epsilons for the particular attack\n",
        "    # axs[i].set_ylim([0, 1])\n",
        "    # axs[i].plot(attack[\"epsilons\"], robust_accuracy.numpy())\n",
        "    # axs[i].set_title(attack[\"name\"])\n",
        "    # axs.set_ylim([0, 1])\n",
        "    # axs.plot(attack[\"epsilons\"], robust_accuracy.numpy())\n",
        "    # axs.set_title(attack[\"name\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "89f00a9dc0c94db38edb0a67756345ac",
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              "HBox(children=(FloatProgress(value=0.0, max=9912422.0), HTML(value='')))"
            ]
          },
          "metadata": {
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          }
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        {
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e5f6cd1521494969b6cc91595c01b6e7",
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              "HBox(children=(FloatProgress(value=0.0, max=28881.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b1fab12139a4484dab9a828c65e45328",
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              "version_major": 2
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              "HBox(children=(FloatProgress(value=0.0, max=1648877.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8b6f806daf2c47d3a6af36e2f1a903c0",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=4542.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Processing...\n",
            "Done!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torchvision/datasets/mnist.py:502: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:143.)\n",
            "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "{'name': 'Additive Uniform Noise Attack', 'model': LinfAdditiveUniformNoiseAttack(), 'epsilons': [0.1, 0.2, 0.3, 0.4, 0.5]}\n",
            "Clean accuracy: 0.94300\n",
            "Average time taken for each test: 1.348945384979248 seconds\n",
            "Accuracy with Additive Uniform Noise Attack attack: tensor([0.9430, 0.9340, 0.9200, 0.8580, 0.7070])\n",
            "{'name': 'Additive Gaussian Noise Attack', 'model': L2AdditiveGaussianNoiseAttack(), 'epsilons': [1, 2, 3, 4, 5]}\n",
            "Clean accuracy: 0.94300\n",
            "Average time taken for each test: 1.3271229877471924 seconds\n",
            "Accuracy with Additive Gaussian Noise Attack attack: tensor([0.9430, 0.9370, 0.9370, 0.9350, 0.9210])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nq0HENK_WKPi"
      },
      "source": [
        "# test_size = 1000  # assuming we are running 1000 tests for each attack\n",
        "# batch_size = 1   # adjust your batch size according to memory limitation, as long as it's a divisor of 1000\n",
        "# # test_size = 1000  # assuming we are running 1000 tests for each attack\n",
        "# # batch_size = 50   # adjust your batch size according to memory limitation, as long as it's a divisor of 1000\n",
        "# num_batch = int(test_size / batch_size)\n",
        "\n",
        "# test_dataset = MNIST(root='./data', download=True, train=False, transform=transforms.ToTensor())\n",
        "# test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)\n",
        "\n",
        "# Linf_epsilons = [\n",
        "#     0.1,\n",
        "#     0.2,\n",
        "#     0.3,\n",
        "#     0.4,\n",
        "#     0.5\n",
        "# ]\n",
        "# CW_epsilons = [\n",
        "#     0.1,\n",
        "#     # 1,\n",
        "#     # 10,\n",
        "#     # 100,\n",
        "#     # 1000\n",
        "# ]\n",
        "# L2_epsilons = [\n",
        "#     1,\n",
        "#     2,\n",
        "#     3,\n",
        "#     4,\n",
        "#     5\n",
        "# ]\n",
        "# attacks = [\n",
        "#     # gradient-based attacks\n",
        "#     # {\"name\": \"Fast Gradient Method\", \"model\": fa.L2FastGradientAttack(), \"epsilons\": L2_epsilons},\n",
        "#     # {\"name\": \"Fast Gradient Sign Method\", \"model\": fa.LinfFastGradientAttack(), \"epsilons\": Linf_epsilons},\n",
        "#     # {\"name\": \"L2 Projected Gradient Descent\", \"model\": fa.L2ProjectedGradientDescentAttack(), \"epsilons\": L2_epsilons},\n",
        "#     # {\"name\": \"Linf Projected Gradient Descent\", \"model\": fa.LinfProjectedGradientDescentAttack(), \"epsilons\": Linf_epsilons},\n",
        "#     {\"name\": \"Carlini & Wagner L2 Attack\", \"model\": fa.L2CarliniWagnerAttack(), \"epsilons\": CW_epsilons},\n",
        "#     # # decision-based attacks\n",
        "#     # {\"name\": \"Boundary Attack\", \"model\": fa.BoundaryAttack(), \"epsilons\": L2_epsilons}, \n",
        "#     # {\"name\": \"Additive Uniform Noise Attack\", \"model\": fa.LinfAdditiveUniformNoiseAttack(), \"epsilons\": Linf_epsilons},\n",
        "#     # {\"name\": \"Additive Gaussian Noise Attack\", \"model\": fa.L2AdditiveGaussianNoiseAttack(), \"epsilons\": L2_epsilons},\n",
        "#     # {\"name\": \"Linear Search Contrast Reduction Attack\", \"model\": fa.LinearSearchContrastReductionAttack(distance=distances.linf), \"epsilons\": Linf_epsilons}, \n",
        "#     # {\"name\": \"Binary Search Contrast Reduction Attack\", \"model\": fa.BinarySearchContrastReductionAttack(distance=distances.linf), \"epsilons\": Linf_epsilons}, \n",
        "#     # {\"name\": \"Gaussian Blur Attack\", \"model\": fa.GaussianBlurAttack(distance=distances.linf), \"epsilons\": Linf_epsilons}\n",
        "# ]\n",
        "\n",
        "# fig, axs = plt.subplots(len(attacks))\n",
        "# fig.tight_layout()\n",
        "\n",
        "# attack_results = {}\n",
        "# for attack in attacks:\n",
        "#     attack_results[attack['name']] = None\n",
        "\n",
        "# for i, attack in enumerate(attacks):\n",
        "#     clean_accuracy = torch.zeros(num_batch)\n",
        "#     robust_accuracy_batch = torch.zeros([num_batch, len(CW_epsilons)])\n",
        "#     count = 0\n",
        "#     t1 = time.time()\n",
        "#     for images, labels in test_dataloader:\n",
        "#         images = images.to(device)\n",
        "#         labels = labels.to(device)\n",
        "#         if count == num_batch:\n",
        "#             break\n",
        "#         images = ep.astensor(images)\n",
        "#         labels = ep.astensor(labels)\n",
        "#         clean_accuracy[count] = fb.utils.accuracy(fmodel, images, labels)\n",
        "\n",
        "#         # The raw adversarial examples. This depends on the attack and we cannot make an guarantees about this output.\n",
        "#         # The clipped adversarial examples. These are guaranteed to not be perturbed more than epsilon and thus are the actual adversarial examples you want to visualize. Note that some of them might not actually switch the class. To know which samples are actually adversarial, you should look at the third tensor.\n",
        "#         # The third tensor contains a boolean for each sample, indicating which samples are true adversarials that are both misclassified and within the epsilon balls around the clean samples.\n",
        "#         raw_adv, clipped_adv, is_adv = attack[\"model\"](fmodel, images, labels, epsilons=attack[\"epsilons\"])\n",
        "\n",
        "#         success_ = is_adv.numpy()\n",
        "#         robust_accuracy_batch[count] = torch.from_numpy(1.0 - success_.mean(axis=-1))\n",
        "\n",
        "#         count += 1\n",
        "\n",
        "#     t2 = time.time()\n",
        "#     print(attack)\n",
        "#     print(\"Clean accuracy: {:.5f}\".format(torch.mean(clean_accuracy)))\n",
        "#     print(\"Average time taken for each test: {} seconds\".format((t2 - t1) / test_size))\n",
        "#     robust_accuracy = torch.mean(robust_accuracy_batch, dim=0)\n",
        "#     print(\"Accuracy with {} attack: {}\".format(attack[\"name\"], robust_accuracy))\n",
        "\n",
        "#     attack_results[attack['name']] = robust_accuracy\n",
        "\n",
        "#     # plot the robust accuracy against epsilons for the particular attack\n",
        "#     # axs[i].set_ylim([0, 1])\n",
        "#     # axs[i].plot(attack[\"epsilons\"], robust_accuracy.numpy())\n",
        "#     # axs[i].set_title(attack[\"name\"])\n",
        "#     axs.set_ylim([0, 1])\n",
        "#     axs.plot(attack[\"epsilons\"], robust_accuracy.numpy())\n",
        "#     axs.set_title(attack[\"name\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z79d7xTJWKVX"
      },
      "source": [
        "# test_size = 1000  # assuming we are running 1000 tests for each attack\n",
        "# batch_size = 1   # adjust your batch size according to memory limitation, as long as it's a divisor of 1000\n",
        "# # test_size = 1000  # assuming we are running 1000 tests for each attack\n",
        "# # batch_size = 50   # adjust your batch size according to memory limitation, as long as it's a divisor of 1000\n",
        "# num_batch = int(test_size / batch_size)\n",
        "\n",
        "# test_dataset = MNIST(root='./data', download=True, train=False, transform=transforms.ToTensor())\n",
        "# test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)\n",
        "\n",
        "# Linf_epsilons = [\n",
        "#     0.1,\n",
        "#     0.2,\n",
        "#     0.3,\n",
        "#     0.4,\n",
        "#     0.5\n",
        "# ]\n",
        "# CW_epsilons = [\n",
        "#     # 0.1,\n",
        "#     1,\n",
        "#     # 10,\n",
        "#     # 100,\n",
        "#     # 1000\n",
        "# ]\n",
        "# L2_epsilons = [\n",
        "#     1,\n",
        "#     2,\n",
        "#     3,\n",
        "#     4,\n",
        "#     5\n",
        "# ]\n",
        "# attacks = [\n",
        "#     # gradient-based attacks\n",
        "#     # {\"name\": \"Fast Gradient Method\", \"model\": fa.L2FastGradientAttack(), \"epsilons\": L2_epsilons},\n",
        "#     # {\"name\": \"Fast Gradient Sign Method\", \"model\": fa.LinfFastGradientAttack(), \"epsilons\": Linf_epsilons},\n",
        "#     # {\"name\": \"L2 Projected Gradient Descent\", \"model\": fa.L2ProjectedGradientDescentAttack(), \"epsilons\": L2_epsilons},\n",
        "#     # {\"name\": \"Linf Projected Gradient Descent\", \"model\": fa.LinfProjectedGradientDescentAttack(), \"epsilons\": Linf_epsilons},\n",
        "#     {\"name\": \"Carlini & Wagner L2 Attack\", \"model\": fa.L2CarliniWagnerAttack(), \"epsilons\": CW_epsilons},\n",
        "#     # # decision-based attacks\n",
        "#     # {\"name\": \"Boundary Attack\", \"model\": fa.BoundaryAttack(), \"epsilons\": L2_epsilons}, \n",
        "#     # {\"name\": \"Additive Uniform Noise Attack\", \"model\": fa.LinfAdditiveUniformNoiseAttack(), \"epsilons\": Linf_epsilons},\n",
        "#     # {\"name\": \"Additive Gaussian Noise Attack\", \"model\": fa.L2AdditiveGaussianNoiseAttack(), \"epsilons\": L2_epsilons},\n",
        "#     # {\"name\": \"Linear Search Contrast Reduction Attack\", \"model\": fa.LinearSearchContrastReductionAttack(distance=distances.linf), \"epsilons\": Linf_epsilons}, \n",
        "#     # {\"name\": \"Binary Search Contrast Reduction Attack\", \"model\": fa.BinarySearchContrastReductionAttack(distance=distances.linf), \"epsilons\": Linf_epsilons}, \n",
        "#     # {\"name\": \"Gaussian Blur Attack\", \"model\": fa.GaussianBlurAttack(distance=distances.linf), \"epsilons\": Linf_epsilons}\n",
        "# ]\n",
        "\n",
        "# fig, axs = plt.subplots(len(attacks))\n",
        "# fig.tight_layout()\n",
        "\n",
        "# attack_results = {}\n",
        "# for attack in attacks:\n",
        "#     attack_results[attack['name']] = None\n",
        "\n",
        "# for i, attack in enumerate(attacks):\n",
        "#     clean_accuracy = torch.zeros(num_batch)\n",
        "#     robust_accuracy_batch = torch.zeros([num_batch, len(CW_epsilons)])\n",
        "#     count = 0\n",
        "#     t1 = time.time()\n",
        "#     for images, labels in test_dataloader:\n",
        "#         images = images.to(device)\n",
        "#         labels = labels.to(device)\n",
        "#         if count == num_batch:\n",
        "#             break\n",
        "#         images = ep.astensor(images)\n",
        "#         labels = ep.astensor(labels)\n",
        "#         clean_accuracy[count] = fb.utils.accuracy(fmodel, images, labels)\n",
        "\n",
        "#         # The raw adversarial examples. This depends on the attack and we cannot make an guarantees about this output.\n",
        "#         # The clipped adversarial examples. These are guaranteed to not be perturbed more than epsilon and thus are the actual adversarial examples you want to visualize. Note that some of them might not actually switch the class. To know which samples are actually adversarial, you should look at the third tensor.\n",
        "#         # The third tensor contains a boolean for each sample, indicating which samples are true adversarials that are both misclassified and within the epsilon balls around the clean samples.\n",
        "#         raw_adv, clipped_adv, is_adv = attack[\"model\"](fmodel, images, labels, epsilons=attack[\"epsilons\"])\n",
        "\n",
        "#         success_ = is_adv.numpy()\n",
        "#         robust_accuracy_batch[count] = torch.from_numpy(1.0 - success_.mean(axis=-1))\n",
        "\n",
        "#         count += 1\n",
        "\n",
        "#     t2 = time.time()\n",
        "#     print(attack)\n",
        "#     print(\"Clean accuracy: {:.5f}\".format(torch.mean(clean_accuracy)))\n",
        "#     print(\"Average time taken for each test: {} seconds\".format((t2 - t1) / test_size))\n",
        "#     robust_accuracy = torch.mean(robust_accuracy_batch, dim=0)\n",
        "#     print(\"Accuracy with {} attack: {}\".format(attack[\"name\"], robust_accuracy))\n",
        "\n",
        "#     attack_results[attack['name']] = robust_accuracy\n",
        "\n",
        "#     # plot the robust accuracy against epsilons for the particular attack\n",
        "#     # axs[i].set_ylim([0, 1])\n",
        "#     # axs[i].plot(attack[\"epsilons\"], robust_accuracy.numpy())\n",
        "#     # axs[i].set_title(attack[\"name\"])\n",
        "#     axs.set_ylim([0, 1])\n",
        "#     axs.plot(attack[\"epsilons\"], robust_accuracy.numpy())\n",
        "#     axs.set_title(attack[\"name\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O4JcTdrVWKbd"
      },
      "source": [
        "# test_size = 1000  # assuming we are running 1000 tests for each attack\n",
        "# batch_size = 1   # adjust your batch size according to memory limitation, as long as it's a divisor of 1000\n",
        "# # test_size = 1000  # assuming we are running 1000 tests for each attack\n",
        "# # batch_size = 50   # adjust your batch size according to memory limitation, as long as it's a divisor of 1000\n",
        "# num_batch = int(test_size / batch_size)\n",
        "\n",
        "# test_dataset = MNIST(root='./data', download=True, train=False, transform=transforms.ToTensor())\n",
        "# test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)\n",
        "\n",
        "# Linf_epsilons = [\n",
        "#     0.1,\n",
        "#     0.2,\n",
        "#     0.3,\n",
        "#     0.4,\n",
        "#     0.5\n",
        "# ]\n",
        "# CW_epsilons = [\n",
        "#     # 0.1,\n",
        "#     # 1,\n",
        "#     10,\n",
        "#     # 100,\n",
        "#     # 1000\n",
        "# ]\n",
        "# L2_epsilons = [\n",
        "#     1,\n",
        "#     2,\n",
        "#     3,\n",
        "#     4,\n",
        "#     5\n",
        "# ]\n",
        "# attacks = [\n",
        "#     # gradient-based attacks\n",
        "#     # {\"name\": \"Fast Gradient Method\", \"model\": fa.L2FastGradientAttack(), \"epsilons\": L2_epsilons},\n",
        "#     # {\"name\": \"Fast Gradient Sign Method\", \"model\": fa.LinfFastGradientAttack(), \"epsilons\": Linf_epsilons},\n",
        "#     # {\"name\": \"L2 Projected Gradient Descent\", \"model\": fa.L2ProjectedGradientDescentAttack(), \"epsilons\": L2_epsilons},\n",
        "#     # {\"name\": \"Linf Projected Gradient Descent\", \"model\": fa.LinfProjectedGradientDescentAttack(), \"epsilons\": Linf_epsilons},\n",
        "#     {\"name\": \"Carlini & Wagner L2 Attack\", \"model\": fa.L2CarliniWagnerAttack(), \"epsilons\": CW_epsilons},\n",
        "#     # # decision-based attacks\n",
        "#     # {\"name\": \"Boundary Attack\", \"model\": fa.BoundaryAttack(), \"epsilons\": L2_epsilons}, \n",
        "#     # {\"name\": \"Additive Uniform Noise Attack\", \"model\": fa.LinfAdditiveUniformNoiseAttack(), \"epsilons\": Linf_epsilons},\n",
        "#     # {\"name\": \"Additive Gaussian Noise Attack\", \"model\": fa.L2AdditiveGaussianNoiseAttack(), \"epsilons\": L2_epsilons},\n",
        "#     # {\"name\": \"Linear Search Contrast Reduction Attack\", \"model\": fa.LinearSearchContrastReductionAttack(distance=distances.linf), \"epsilons\": Linf_epsilons}, \n",
        "#     # {\"name\": \"Binary Search Contrast Reduction Attack\", \"model\": fa.BinarySearchContrastReductionAttack(distance=distances.linf), \"epsilons\": Linf_epsilons}, \n",
        "#     # {\"name\": \"Gaussian Blur Attack\", \"model\": fa.GaussianBlurAttack(distance=distances.linf), \"epsilons\": Linf_epsilons}\n",
        "# ]\n",
        "\n",
        "# fig, axs = plt.subplots(len(attacks))\n",
        "# fig.tight_layout()\n",
        "\n",
        "# attack_results = {}\n",
        "# for attack in attacks:\n",
        "#     attack_results[attack['name']] = None\n",
        "\n",
        "# for i, attack in enumerate(attacks):\n",
        "#     clean_accuracy = torch.zeros(num_batch)\n",
        "#     robust_accuracy_batch = torch.zeros([num_batch, len(CW_epsilons)])\n",
        "#     count = 0\n",
        "#     t1 = time.time()\n",
        "#     for images, labels in test_dataloader:\n",
        "#         images = images.to(device)\n",
        "#         labels = labels.to(device)\n",
        "#         if count == num_batch:\n",
        "#             break\n",
        "#         images = ep.astensor(images)\n",
        "#         labels = ep.astensor(labels)\n",
        "#         clean_accuracy[count] = fb.utils.accuracy(fmodel, images, labels)\n",
        "\n",
        "#         # The raw adversarial examples. This depends on the attack and we cannot make an guarantees about this output.\n",
        "#         # The clipped adversarial examples. These are guaranteed to not be perturbed more than epsilon and thus are the actual adversarial examples you want to visualize. Note that some of them might not actually switch the class. To know which samples are actually adversarial, you should look at the third tensor.\n",
        "#         # The third tensor contains a boolean for each sample, indicating which samples are true adversarials that are both misclassified and within the epsilon balls around the clean samples.\n",
        "#         raw_adv, clipped_adv, is_adv = attack[\"model\"](fmodel, images, labels, epsilons=attack[\"epsilons\"])\n",
        "\n",
        "#         success_ = is_adv.numpy()\n",
        "#         robust_accuracy_batch[count] = torch.from_numpy(1.0 - success_.mean(axis=-1))\n",
        "\n",
        "#         count += 1\n",
        "\n",
        "#     t2 = time.time()\n",
        "#     print(attack)\n",
        "#     print(\"Clean accuracy: {:.5f}\".format(torch.mean(clean_accuracy)))\n",
        "#     print(\"Average time taken for each test: {} seconds\".format((t2 - t1) / test_size))\n",
        "#     robust_accuracy = torch.mean(robust_accuracy_batch, dim=0)\n",
        "#     print(\"Accuracy with {} attack: {}\".format(attack[\"name\"], robust_accuracy))\n",
        "\n",
        "#     attack_results[attack['name']] = robust_accuracy\n",
        "\n",
        "#     # plot the robust accuracy against epsilons for the particular attack\n",
        "#     # axs[i].set_ylim([0, 1])\n",
        "#     # axs[i].plot(attack[\"epsilons\"], robust_accuracy.numpy())\n",
        "#     # axs[i].set_title(attack[\"name\"])\n",
        "#     axs.set_ylim([0, 1])\n",
        "#     axs.plot(attack[\"epsilons\"], robust_accuracy.numpy())\n",
        "#     axs.set_title(attack[\"name\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3i8YkzyqWKhk"
      },
      "source": [
        "# test_size = 1000  # assuming we are running 1000 tests for each attack\n",
        "# batch_size = 1   # adjust your batch size according to memory limitation, as long as it's a divisor of 1000\n",
        "# # test_size = 1000  # assuming we are running 1000 tests for each attack\n",
        "# # batch_size = 50   # adjust your batch size according to memory limitation, as long as it's a divisor of 1000\n",
        "# num_batch = int(test_size / batch_size)\n",
        "\n",
        "# test_dataset = MNIST(root='./data', download=True, train=False, transform=transforms.ToTensor())\n",
        "# test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)\n",
        "\n",
        "# Linf_epsilons = [\n",
        "#     0.1,\n",
        "#     0.2,\n",
        "#     0.3,\n",
        "#     0.4,\n",
        "#     0.5\n",
        "# ]\n",
        "# CW_epsilons = [\n",
        "#     # 0.1,\n",
        "#     # 1,\n",
        "#     # 10,\n",
        "#     100,\n",
        "#     # 1000\n",
        "# ]\n",
        "# L2_epsilons = [\n",
        "#     1,\n",
        "#     2,\n",
        "#     3,\n",
        "#     4,\n",
        "#     5\n",
        "# ]\n",
        "# attacks = [\n",
        "#     # gradient-based attacks\n",
        "#     # {\"name\": \"Fast Gradient Method\", \"model\": fa.L2FastGradientAttack(), \"epsilons\": L2_epsilons},\n",
        "#     # {\"name\": \"Fast Gradient Sign Method\", \"model\": fa.LinfFastGradientAttack(), \"epsilons\": Linf_epsilons},\n",
        "#     # {\"name\": \"L2 Projected Gradient Descent\", \"model\": fa.L2ProjectedGradientDescentAttack(), \"epsilons\": L2_epsilons},\n",
        "#     # {\"name\": \"Linf Projected Gradient Descent\", \"model\": fa.LinfProjectedGradientDescentAttack(), \"epsilons\": Linf_epsilons},\n",
        "#     {\"name\": \"Carlini & Wagner L2 Attack\", \"model\": fa.L2CarliniWagnerAttack(), \"epsilons\": CW_epsilons},\n",
        "#     # # decision-based attacks\n",
        "#     # {\"name\": \"Boundary Attack\", \"model\": fa.BoundaryAttack(), \"epsilons\": L2_epsilons}, \n",
        "#     # {\"name\": \"Additive Uniform Noise Attack\", \"model\": fa.LinfAdditiveUniformNoiseAttack(), \"epsilons\": Linf_epsilons},\n",
        "#     # {\"name\": \"Additive Gaussian Noise Attack\", \"model\": fa.L2AdditiveGaussianNoiseAttack(), \"epsilons\": L2_epsilons},\n",
        "#     # {\"name\": \"Linear Search Contrast Reduction Attack\", \"model\": fa.LinearSearchContrastReductionAttack(distance=distances.linf), \"epsilons\": Linf_epsilons}, \n",
        "#     # {\"name\": \"Binary Search Contrast Reduction Attack\", \"model\": fa.BinarySearchContrastReductionAttack(distance=distances.linf), \"epsilons\": Linf_epsilons}, \n",
        "#     # {\"name\": \"Gaussian Blur Attack\", \"model\": fa.GaussianBlurAttack(distance=distances.linf), \"epsilons\": Linf_epsilons}\n",
        "# ]\n",
        "\n",
        "# fig, axs = plt.subplots(len(attacks))\n",
        "# fig.tight_layout()\n",
        "\n",
        "# attack_results = {}\n",
        "# for attack in attacks:\n",
        "#     attack_results[attack['name']] = None\n",
        "\n",
        "# for i, attack in enumerate(attacks):\n",
        "#     clean_accuracy = torch.zeros(num_batch)\n",
        "#     robust_accuracy_batch = torch.zeros([num_batch, len(CW_epsilons)])\n",
        "#     count = 0\n",
        "#     t1 = time.time()\n",
        "#     for images, labels in test_dataloader:\n",
        "#         images = images.to(device)\n",
        "#         labels = labels.to(device)\n",
        "#         if count == num_batch:\n",
        "#             break\n",
        "#         images = ep.astensor(images)\n",
        "#         labels = ep.astensor(labels)\n",
        "#         clean_accuracy[count] = fb.utils.accuracy(fmodel, images, labels)\n",
        "\n",
        "#         # The raw adversarial examples. This depends on the attack and we cannot make an guarantees about this output.\n",
        "#         # The clipped adversarial examples. These are guaranteed to not be perturbed more than epsilon and thus are the actual adversarial examples you want to visualize. Note that some of them might not actually switch the class. To know which samples are actually adversarial, you should look at the third tensor.\n",
        "#         # The third tensor contains a boolean for each sample, indicating which samples are true adversarials that are both misclassified and within the epsilon balls around the clean samples.\n",
        "#         raw_adv, clipped_adv, is_adv = attack[\"model\"](fmodel, images, labels, epsilons=attack[\"epsilons\"])\n",
        "\n",
        "#         success_ = is_adv.numpy()\n",
        "#         robust_accuracy_batch[count] = torch.from_numpy(1.0 - success_.mean(axis=-1))\n",
        "\n",
        "#         count += 1\n",
        "\n",
        "#     t2 = time.time()\n",
        "#     print(attack)\n",
        "#     print(\"Clean accuracy: {:.5f}\".format(torch.mean(clean_accuracy)))\n",
        "#     print(\"Average time taken for each test: {} seconds\".format((t2 - t1) / test_size))\n",
        "#     robust_accuracy = torch.mean(robust_accuracy_batch, dim=0)\n",
        "#     print(\"Accuracy with {} attack: {}\".format(attack[\"name\"], robust_accuracy))\n",
        "\n",
        "#     attack_results[attack['name']] = robust_accuracy\n",
        "\n",
        "#     # plot the robust accuracy against epsilons for the particular attack\n",
        "#     # axs[i].set_ylim([0, 1])\n",
        "#     # axs[i].plot(attack[\"epsilons\"], robust_accuracy.numpy())\n",
        "#     # axs[i].set_title(attack[\"name\"])\n",
        "#     axs.set_ylim([0, 1])\n",
        "#     axs.plot(attack[\"epsilons\"], robust_accuracy.numpy())\n",
        "#     axs.set_title(attack[\"name\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Iaug1lwGA1LF"
      },
      "source": [
        "# test_size = 1000  # assuming we are running 1000 tests for each attack\n",
        "# batch_size = 1   # adjust your batch size according to memory limitation, as long as it's a divisor of 1000\n",
        "# # test_size = 1000  # assuming we are running 1000 tests for each attack\n",
        "# # batch_size = 50   # adjust your batch size according to memory limitation, as long as it's a divisor of 1000\n",
        "# num_batch = int(test_size / batch_size)\n",
        "\n",
        "# test_dataset = MNIST(root='./data', download=True, train=False, transform=transforms.ToTensor())\n",
        "# test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)\n",
        "\n",
        "# Linf_epsilons = [\n",
        "#     0.1,\n",
        "#     0.2,\n",
        "#     0.3,\n",
        "#     0.4,\n",
        "#     0.5\n",
        "# ]\n",
        "# CW_epsilons = [\n",
        "#     # 0.1,\n",
        "#     # 1,\n",
        "#     # 10,\n",
        "#     # 100,\n",
        "#     1000\n",
        "# ]\n",
        "# L2_epsilons = [\n",
        "#     1,\n",
        "#     2,\n",
        "#     3,\n",
        "#     4,\n",
        "#     5\n",
        "# ]\n",
        "# attacks = [\n",
        "#     # gradient-based attacks\n",
        "#     # {\"name\": \"Fast Gradient Method\", \"model\": fa.L2FastGradientAttack(), \"epsilons\": L2_epsilons},\n",
        "#     # {\"name\": \"Fast Gradient Sign Method\", \"model\": fa.LinfFastGradientAttack(), \"epsilons\": Linf_epsilons},\n",
        "#     # {\"name\": \"L2 Projected Gradient Descent\", \"model\": fa.L2ProjectedGradientDescentAttack(), \"epsilons\": L2_epsilons},\n",
        "#     # {\"name\": \"Linf Projected Gradient Descent\", \"model\": fa.LinfProjectedGradientDescentAttack(), \"epsilons\": Linf_epsilons},\n",
        "#     {\"name\": \"Carlini & Wagner L2 Attack\", \"model\": fa.L2CarliniWagnerAttack(), \"epsilons\": CW_epsilons},\n",
        "#     # # decision-based attacks\n",
        "#     # {\"name\": \"Boundary Attack\", \"model\": fa.BoundaryAttack(), \"epsilons\": L2_epsilons}, \n",
        "#     # {\"name\": \"Additive Uniform Noise Attack\", \"model\": fa.LinfAdditiveUniformNoiseAttack(), \"epsilons\": Linf_epsilons},\n",
        "#     # {\"name\": \"Additive Gaussian Noise Attack\", \"model\": fa.L2AdditiveGaussianNoiseAttack(), \"epsilons\": L2_epsilons},\n",
        "#     # {\"name\": \"Linear Search Contrast Reduction Attack\", \"model\": fa.LinearSearchContrastReductionAttack(distance=distances.linf), \"epsilons\": Linf_epsilons}, \n",
        "#     # {\"name\": \"Binary Search Contrast Reduction Attack\", \"model\": fa.BinarySearchContrastReductionAttack(distance=distances.linf), \"epsilons\": Linf_epsilons}, \n",
        "#     # {\"name\": \"Gaussian Blur Attack\", \"model\": fa.GaussianBlurAttack(distance=distances.linf), \"epsilons\": Linf_epsilons}\n",
        "# ]\n",
        "\n",
        "# fig, axs = plt.subplots(len(attacks))\n",
        "# fig.tight_layout()\n",
        "\n",
        "# attack_results = {}\n",
        "# for attack in attacks:\n",
        "#     attack_results[attack['name']] = None\n",
        "\n",
        "# for i, attack in enumerate(attacks):\n",
        "#     clean_accuracy = torch.zeros(num_batch)\n",
        "#     robust_accuracy_batch = torch.zeros([num_batch, len(CW_epsilons)])\n",
        "#     count = 0\n",
        "#     t1 = time.time()\n",
        "#     for images, labels in test_dataloader:\n",
        "#         images = images.to(device)\n",
        "#         labels = labels.to(device)\n",
        "#         if count == num_batch:\n",
        "#             break\n",
        "#         images = ep.astensor(images)\n",
        "#         labels = ep.astensor(labels)\n",
        "#         clean_accuracy[count] = fb.utils.accuracy(fmodel, images, labels)\n",
        "\n",
        "#         # The raw adversarial examples. This depends on the attack and we cannot make an guarantees about this output.\n",
        "#         # The clipped adversarial examples. These are guaranteed to not be perturbed more than epsilon and thus are the actual adversarial examples you want to visualize. Note that some of them might not actually switch the class. To know which samples are actually adversarial, you should look at the third tensor.\n",
        "#         # The third tensor contains a boolean for each sample, indicating which samples are true adversarials that are both misclassified and within the epsilon balls around the clean samples.\n",
        "#         raw_adv, clipped_adv, is_adv = attack[\"model\"](fmodel, images, labels, epsilons=attack[\"epsilons\"])\n",
        "\n",
        "#         success_ = is_adv.numpy()\n",
        "#         robust_accuracy_batch[count] = torch.from_numpy(1.0 - success_.mean(axis=-1))\n",
        "\n",
        "#         count += 1\n",
        "\n",
        "#     t2 = time.time()\n",
        "#     print(attack)\n",
        "#     print(\"Clean accuracy: {:.5f}\".format(torch.mean(clean_accuracy)))\n",
        "#     print(\"Average time taken for each test: {} seconds\".format((t2 - t1) / test_size))\n",
        "#     robust_accuracy = torch.mean(robust_accuracy_batch, dim=0)\n",
        "#     print(\"Accuracy with {} attack: {}\".format(attack[\"name\"], robust_accuracy))\n",
        "\n",
        "#     attack_results[attack['name']] = robust_accuracy\n",
        "\n",
        "#     # plot the robust accuracy against epsilons for the particular attack\n",
        "#     # axs[i].set_ylim([0, 1])\n",
        "#     # axs[i].plot(attack[\"epsilons\"], robust_accuracy.numpy())\n",
        "#     # axs[i].set_title(attack[\"name\"])\n",
        "#     axs.set_ylim([0, 1])\n",
        "#     axs.plot(attack[\"epsilons\"], robust_accuracy.numpy())\n",
        "#     axs.set_title(attack[\"name\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L3YnmANQA1LG"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NG69OUbJZbGR"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MPLp_duvA1LI"
      },
      "source": [
        "# a = []\n",
        "# while(1):\n",
        "#     a.append(\"111111111111111111111111111111\")"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}