doc/source/examples/ad_ae_cifar10.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adversarial AE detection and correction on CIFAR-10\n",
    "\n",
    "### Method\n",
    "\n",
    "\n",
    "The adversarial detector is based on [Adversarial Detection and Correction by Matching Prediction Distributions](https://arxiv.org/abs/2002.09364). Usually, autoencoders are trained to find a transformation $T$ that reconstructs the input instance $x$ as accurately as possible with loss functions that are suited to capture the similarities between x and $x'$ such as the mean squared reconstruction error. The novelty of the adversarial autoencoder (AE) detector relies on the use of a classification model-dependent loss function based on a distance metric in the output space of the model to train the autoencoder network. Given a classification model $M$ we optimise the weights of the autoencoder such that the [KL-divergence](https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence) between the model predictions on $x$ and on $x'$ is minimised. Without the presence of a reconstruction loss term $x'$ simply tries to make sure that the prediction probabilities $M(x')$ and $M(x)$ match without caring about the proximity of $x'$ to $x$. As a result, $x'$ is allowed to live in different areas of the input feature space than $x$ with different decision boundary shapes with respect to the model $M$. The carefully crafted adversarial perturbation which is effective around x does not transfer to the new location of $x'$ in the feature space, and the attack is therefore neutralised. Training of the autoencoder is unsupervised since we only need access to the model prediction probabilities and the normal training instances. We do not require any knowledge about the underlying adversarial attack and the classifier weights are frozen during training. \n",
    "\n",
    "The detector can be used as follows:\n",
    "\n",
    "* An adversarial score $S$ is computed. $S$ equals the K-L divergence between the model predictions on $x$ and $x'$.\n",
    "\n",
    "* If $S$ is above a threshold (explicitly defined or inferred from training data), the instance is flagged as adversarial.\n",
    "\n",
    "* For adversarial instances, the model $M$ uses the reconstructed instance $x'$ to make a prediction. If the adversarial score is below the threshold, the model makes a prediction on the original instance $x$.\n",
    "\n",
    "This procedure is illustrated in the diagram below:"
   ]
  },
  {
   "attachments": {
    "adversarialae.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![adversarialae.png](attachment:adversarialae.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The method is very flexible and can also be used to detect common data corruptions and perturbations which negatively impact the model performance.\n",
    "\n",
    "## Dataset\n",
    "\n",
    "[CIFAR10](https://www.cs.toronto.edu/~kriz/cifar.html) consists of 60,000 32 by 32 RGB images equally distributed over 10 classes.\n",
    "\n",
    "**Note**: in order to run this notebook, it is adviced to use **Python 3.7** and have a **GPU** enabled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import (Conv2D, Conv2DTranspose, Dense, Flatten, \n",
    "                                     InputLayer, Reshape)\n",
    "from tensorflow.keras.regularizers import l1\n",
    "\n",
    "from alibi_detect.ad import AdversarialAE\n",
    "from alibi_detect.utils.fetching import fetch_detector, fetch_tf_model\n",
    "from alibi_detect.utils.tensorflow import predict_batch\n",
    "from alibi_detect.saving import save_detector, load_detector\n",
    "from alibi_detect.datasets import fetch_attack, fetch_cifar10c, corruption_types_cifar10c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Utility functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scale_by_instance(X: np.ndarray) -> np.ndarray:\n",
    "    mean_ = X.mean(axis=(1, 2, 3)).reshape(-1, 1, 1, 1)\n",
    "    std_ = X.std(axis=(1, 2, 3)).reshape(-1, 1, 1, 1)\n",
    "    return (X - mean_) / std_, mean_, std_\n",
    "\n",
    "\n",
    "def accuracy(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n",
    "    return (y_true == y_pred).astype(int).sum() / y_true.shape[0]\n",
    "\n",
    "\n",
    "def plot_adversarial(idx: list,\n",
    "                     X: np.ndarray,\n",
    "                     y: np.ndarray,\n",
    "                     X_adv: np.ndarray, \n",
    "                     y_adv: np.ndarray,\n",
    "                     mean: np.ndarray, \n",
    "                     std: np.ndarray, \n",
    "                     score_x: np.ndarray = None,\n",
    "                     score_x_adv: np.ndarray = None,\n",
    "                     X_recon: np.ndarray = None,\n",
    "                     y_recon: np.ndarray = None,\n",
    "                     figsize: tuple = (10, 5)) -> None:\n",
    "    \n",
    "    # category map from class numbers to names\n",
    "    cifar10_map = {0: 'airplane', 1: 'automobile', 2: 'bird', 3: 'cat', 4: 'deer', 5: 'dog',\n",
    "                   6: 'frog', 7: 'horse', 8: 'ship', 9: 'truck'}\n",
    "    \n",
    "    nrows = len(idx)\n",
    "    ncols = 3 if isinstance(X_recon, np.ndarray) else 2\n",
    "    fig, ax = plt.subplots(nrows=nrows, ncols=ncols, figsize=figsize)\n",
    "    \n",
    "    n_subplot = 1\n",
    "    for i in idx:\n",
    "        \n",
    "        # rescale images in [0, 1]\n",
    "        X_adj = (X[i] * std[i] + mean[i]) / 255\n",
    "        X_adv_adj = (X_adv[i] * std[i] + mean[i]) / 255\n",
    "        if isinstance(X_recon, np.ndarray):\n",
    "            X_recon_adj = (X_recon[i] * std[i] + mean[i]) / 255\n",
    "        \n",
    "        # original image\n",
    "        plt.subplot(nrows, ncols, n_subplot)\n",
    "        plt.axis('off')\n",
    "        if i == idx[0]:\n",
    "            if isinstance(score_x, np.ndarray):\n",
    "                plt.title('CIFAR-10 Image \\n{}: {:.3f}'.format(cifar10_map[y[i]], score_x[i]))\n",
    "            else:\n",
    "                plt.title('CIFAR-10 Image \\n{}'.format(cifar10_map[y[i]]))\n",
    "        else:\n",
    "            if isinstance(score_x, np.ndarray):\n",
    "                plt.title('{}: {:.3f}'.format(cifar10_map[y[i]], score_x[i]))\n",
    "            else:\n",
    "                plt.title('{}'.format(cifar10_map[y[i]]))\n",
    "        plt.imshow(X_adj)\n",
    "        n_subplot += 1\n",
    "        \n",
    "        # adversarial image\n",
    "        plt.subplot(nrows, ncols, n_subplot)\n",
    "        plt.axis('off')\n",
    "        if i == idx[0]:\n",
    "            if isinstance(score_x_adv, np.ndarray):\n",
    "                plt.title('Adversarial \\n{}: {:.3f}'.format(cifar10_map[y_adv[i]], score_x_adv[i]))\n",
    "            else:\n",
    "                plt.title('Adversarial \\n{}'.format(cifar10_map[y_adv[i]]))\n",
    "        else:\n",
    "            if isinstance(score_x_adv, np.ndarray):\n",
    "                plt.title('{}: {:.3f}'.format(cifar10_map[y_adv[i]], score_x_adv[i]))\n",
    "            else:\n",
    "                plt.title('{}'.format(cifar10_map[y_adv[i]]))\n",
    "        plt.imshow(X_adv_adj)\n",
    "        n_subplot += 1\n",
    "     \n",
    "        # reconstructed image\n",
    "        if isinstance(X_recon, np.ndarray):\n",
    "            plt.subplot(nrows, ncols, n_subplot)\n",
    "            plt.axis('off')\n",
    "            if i == idx[0]:\n",
    "                plt.title('AE Reconstruction \\n{}'.format(cifar10_map[y_recon[i]]))\n",
    "            else:\n",
    "                plt.title('{}'.format(cifar10_map[y_recon[i]]))\n",
    "            plt.imshow(X_recon_adj)\n",
    "            n_subplot += 1\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "    \n",
    "def plot_roc(roc_data: dict, figsize: tuple = (10,5)):\n",
    "    plot_labels = []\n",
    "    scores_attacks = []\n",
    "    labels_attacks = []\n",
    "    for k, v in roc_data.items():\n",
    "        if 'original' in k:\n",
    "            continue\n",
    "        score_x = roc_data[v['normal']]['scores']\n",
    "        y_pred = roc_data[v['normal']]['predictions']\n",
    "        score_v = v['scores']\n",
    "        y_pred_v = v['predictions']\n",
    "        labels_v = np.ones(score_x.shape[0])\n",
    "        idx_remove = np.where(y_pred == y_pred_v)[0]\n",
    "        labels_v = np.delete(labels_v, idx_remove)\n",
    "        score_v = np.delete(score_v, idx_remove)\n",
    "        scores = np.concatenate([score_x, score_v])\n",
    "        labels = np.concatenate([np.zeros(y_pred.shape[0]), labels_v]).astype(int)\n",
    "        scores_attacks.append(scores)\n",
    "        labels_attacks.append(labels)\n",
    "        plot_labels.append(k)\n",
    "    \n",
    "    for sc_att, la_att, plt_la in zip(scores_attacks, labels_attacks, plot_labels):\n",
    "        fpr, tpr, thresholds = roc_curve(la_att, sc_att)\n",
    "        roc_auc = auc(fpr, tpr)\n",
    "        label = str('{}: AUC = {:.2f}'.format(plt_la, roc_auc))\n",
    "        plt.plot(fpr, tpr, lw=1, label='{}: AUC={:.4f}'.format(plt_la, roc_auc))\n",
    "\n",
    "    plt.plot([0, 1], [0, 1], color='black', lw=1, linestyle='--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('{}'.format('ROC curve'))\n",
    "    plt.legend(loc=\"lower right\", ncol=1)\n",
    "    plt.grid()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train, y_train), (X_test, y_test) = tf.keras.datasets.cifar10.load_data()\n",
    "X_train = X_train.astype('float32')\n",
    "X_test = X_test.astype('float32')\n",
    "y_train = y_train.astype('int64').reshape(-1,)\n",
    "y_test = y_test.astype('int64').reshape(-1,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Standardise the dataset by instance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, mean_train, std_train = scale_by_instance(X_train)\n",
    "X_test, mean_test, std_test = scale_by_instance(X_test)\n",
    "scale = (mean_train, std_train), (mean_test, std_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = 'cifar10'\n",
    "model = 'resnet56'\n",
    "clf = fetch_tf_model(dataset, model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check that the predictions on the test set reach $93.15$% accuracy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9315\n"
     ]
    }
   ],
   "source": [
    "y_pred = predict_batch(X_test, clf, batch_size=32).argmax(axis=1)\n",
    "acc_y_pred = accuracy(y_test, y_pred)\n",
    "print('Accuracy: {:.4f}'.format(acc_y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adversarial Attack\n",
    "\n",
    "We investigate both [Carlini-Wagner (C&W)](https://arxiv.org/abs/1608.04644) and [SLIDE](https://arxiv.org/abs/1904.13000) attacks. You can simply load previously found adversarial instances on the pretrained ResNet-56 model. The attacks are generated by using [Foolbox](https://github.com/bethgelab/foolbox):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# C&W attack\n",
    "data_cw = fetch_attack(dataset, model, 'cw')\n",
    "X_train_cw, X_test_cw = data_cw['data_train'], data_cw['data_test']\n",
    "meta_cw = data_cw['meta'] # metadata with hyperparameters of the attack\n",
    "# SLIDE attack\n",
    "data_slide = fetch_attack(dataset, model, 'slide')\n",
    "X_train_slide, X_test_slide = data_slide['data_train'], data_slide['data_test']\n",
    "meta_slide = data_slide['meta']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 32, 32, 3) (10000, 32, 32, 3)\n"
     ]
    }
   ],
   "source": [
    "print(X_test_cw.shape, X_test_slide.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check if the prediction accuracy of the model on the adversarial instances is close to $0$%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_cw = predict_batch(X_test_cw, clf, batch_size=32).argmax(axis=1)\n",
    "y_pred_slide = predict_batch(X_test_slide, clf, batch_size=32).argmax(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: cw 0.0000 -- SLIDE 0.0002\n"
     ]
    }
   ],
   "source": [
    "acc_y_pred_cw = accuracy(y_test, y_pred_cw)\n",
    "acc_y_pred_slide = accuracy(y_test, y_pred_slide)\n",
    "print('Accuracy: cw {:.4f} -- SLIDE {:.4f}'.format(acc_y_pred_cw, acc_y_pred_slide))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualise some adversarial instances:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C&W attack...\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SLIDE attack...\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx = [3, 4]\n",
    "print('C&W attack...')\n",
    "plot_adversarial(idx, X_test, y_pred, X_test_cw, y_pred_cw, \n",
    "                 mean_test, std_test, figsize=(10, 10))\n",
    "print('SLIDE attack...')\n",
    "plot_adversarial(idx, X_test, y_pred, X_test_slide, y_pred_slide, \n",
    "                 mean_test, std_test, figsize=(10, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load or train and evaluate the adversarial detectors\n",
    "\n",
    "We can again either fetch the pretrained detector from a [Google Cloud Bucket](https://console.cloud.google.com/storage/browser/seldon-models/alibi-detect/ad/cifar10/resnet56) or train one from scratch:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "load_pretrained = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in save file: the model was *not* compiled. Compile it manually.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in save file: the model was *not* compiled. Compile it manually.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in save file: the model was *not* compiled. Compile it manually.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in save file: the model was *not* compiled. Compile it manually.\n",
      "WARNING:alibi_detect.ad.adversarialae:No threshold level set. Need to infer threshold using `infer_threshold`.\n"
     ]
    }
   ],
   "source": [
    "filepath = 'my_path'  # change to (absolute) directory where model is downloaded\n",
    "detector_type = 'adversarial'\n",
    "detector_name = 'base'\n",
    "filepath = os.path.join(filepath, detector_name)\n",
    "if load_pretrained:\n",
    "    ad = fetch_detector(filepath, detector_type, dataset, detector_name, model=model)\n",
    "else:  # train detector from scratch\n",
    "    # define encoder and decoder networks\n",
    "    encoder_net = tf.keras.Sequential(\n",
    "            [\n",
    "                InputLayer(input_shape=(32, 32, 3)),\n",
    "                Conv2D(32, 4, strides=2, padding='same', \n",
    "                       activation=tf.nn.relu, kernel_regularizer=l1(1e-5)),\n",
    "                Conv2D(64, 4, strides=2, padding='same', \n",
    "                       activation=tf.nn.relu, kernel_regularizer=l1(1e-5)),\n",
    "                Conv2D(256, 4, strides=2, padding='same', \n",
    "                       activation=tf.nn.relu, kernel_regularizer=l1(1e-5)),\n",
    "                Flatten(),\n",
    "                Dense(40)\n",
    "            ]\n",
    "        )\n",
    "    \n",
    "    decoder_net = tf.keras.Sequential(\n",
    "        [\n",
    "                InputLayer(input_shape=(40,)),\n",
    "                Dense(4 * 4 * 128, activation=tf.nn.relu),\n",
    "                Reshape(target_shape=(4, 4, 128)),\n",
    "                Conv2DTranspose(256, 4, strides=2, padding='same', \n",
    "                                activation=tf.nn.relu, kernel_regularizer=l1(1e-5)),\n",
    "                Conv2DTranspose(64, 4, strides=2, padding='same', \n",
    "                                activation=tf.nn.relu, kernel_regularizer=l1(1e-5)),\n",
    "                Conv2DTranspose(3, 4, strides=2, padding='same', \n",
    "                                activation=None, kernel_regularizer=l1(1e-5))\n",
    "            ]\n",
    "        )\n",
    "    \n",
    "    # initialise and train detector\n",
    "    ad = AdversarialAE(\n",
    "        encoder_net=encoder_net, \n",
    "        decoder_net=decoder_net, \n",
    "        model=clf\n",
    "    )\n",
    "    ad.fit(X_train, epochs=40, batch_size=64, verbose=True)\n",
    "    \n",
    "    # save the trained adversarial detector\n",
    "    save_detector(ad, filepath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The detector first reconstructs the input instances which can be adversarial. The reconstructed input is then fed to the classifier if the adversarial score for the instance is above the threshold. Let's investigate what happens when we reconstruct attacked instances and make predictions on them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_recon_cw = predict_batch(X_test_cw, ad.ae, batch_size=32)\n",
    "X_recon_slide = predict_batch(X_test_slide, ad.ae, batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_recon_cw = predict_batch(X_recon_cw, clf, batch_size=32).argmax(axis=1)\n",
    "y_recon_slide = predict_batch(X_recon_slide, clf, batch_size=32).argmax(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracy on attacked vs. reconstructed instances:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy after C&W attack 0.0000 -- reconstruction 0.8048\n",
      "Accuracy after SLIDE attack 0.0002 -- reconstruction 0.8159\n"
     ]
    }
   ],
   "source": [
    "acc_y_recon_cw = accuracy(y_test, y_recon_cw)\n",
    "acc_y_recon_slide = accuracy(y_test, y_recon_slide)\n",
    "print('Accuracy after C&W attack {:.4f} -- reconstruction {:.4f}'.format(acc_y_pred_cw, acc_y_recon_cw))\n",
    "print('Accuracy after SLIDE attack {:.4f} -- reconstruction {:.4f}'.format(acc_y_pred_slide, acc_y_recon_slide))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The detector restores the accuracy after the attacks from almost $0$% to well over $80$%! We can compute the adversarial scores and inspect some of the reconstructed instances:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_x = ad.score(X_test, batch_size=32)\n",
    "score_cw = ad.score(X_test_cw, batch_size=32)\n",
    "score_slide = ad.score(X_test_slide, batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C&W attack...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SLIDE attack...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('C&W attack...')\n",
    "idx = [10, 13, 14, 16, 17]\n",
    "plot_adversarial(idx, X_test, y_pred, X_test_cw, y_pred_cw, mean_test, std_test, \n",
    "                 score_x=score_x, score_x_adv=score_cw, X_recon=X_recon_cw, \n",
    "                 y_recon=y_recon_cw, figsize=(10, 15))\n",
    "print('SLIDE attack...')\n",
    "idx = [23, 25, 27, 29, 34]\n",
    "plot_adversarial(idx, X_test, y_pred, X_test_slide, y_pred_slide, mean_test, std_test, \n",
    "                 score_x=score_x, score_x_adv=score_slide, X_recon=X_recon_slide, \n",
    "                 y_recon=y_recon_slide, figsize=(10, 15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ROC curves and AUC values show the effectiveness of the adversarial score to detect adversarial instances:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "roc_data = {\n",
    "    'original': {'scores': score_x, 'predictions': y_pred},\n",
    "    'C&W': {'scores': score_cw, 'predictions': y_pred_cw, 'normal': 'original'},\n",
    "    'SLIDE': {'scores': score_slide, 'predictions': y_pred_slide, 'normal': 'original'}\n",
    "}\n",
    "\n",
    "plot_roc(roc_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The threshold for the adversarial score can be set via ```infer_threshold```. We need to pass a batch of instances $X$ and specify what percentage of those we consider to be normal via `threshold_perc`. Assume we have only normal instances some of which the model has misclassified leading to a higher score if the reconstruction picked up features from the correct class or some might look adversarial in the first place. As a result, we set our threshold at $95$%:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adversarial threshold: 2.6722\n"
     ]
    }
   ],
   "source": [
    "ad.infer_threshold(X_test, threshold_perc=95, margin=0., batch_size=32)\n",
    "print('Adversarial threshold: {:.4f}'.format(ad.threshold))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's save the updated detector:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_detector(ad, filepath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also load it easily as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "ad = load_detector(filepath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `correct` method of the detector executes the diagram in Figure 1. First the adversarial scores is computed. For instances where the score is above the threshold, the classifier prediction on the reconstructed instance is returned. Otherwise the original prediction is kept. The method returns a dictionary containing the metadata of the detector, whether the instances in the batch are adversarial (above the threshold) or not, the classifier predictions using the correction mechanism and both the original and reconstructed predictions. Let's illustrate this on a batch containing some adversarial (C&W) and original test set instances:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2000, 32, 32, 3) (2000,)\n"
     ]
    }
   ],
   "source": [
    "n_test = X_test.shape[0]\n",
    "np.random.seed(0)\n",
    "idx_normal = np.random.choice(n_test, size=1600, replace=False)\n",
    "idx_cw = np.random.choice(n_test, size=400, replace=False)\n",
    "\n",
    "X_mix = np.concatenate([X_test[idx_normal], X_test_cw[idx_cw]])\n",
    "y_mix = np.concatenate([y_test[idx_normal], y_test[idx_cw]])\n",
    "print(X_mix.shape, y_mix.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the model performance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.7380\n"
     ]
    }
   ],
   "source": [
    "y_pred_mix = predict_batch(X_mix, clf, batch_size=32).argmax(axis=1)\n",
    "acc_y_pred_mix = accuracy(y_mix, y_pred_mix)\n",
    "print('Accuracy {:.4f}'.format(acc_y_pred_mix))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This can be improved with the correction mechanism:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.8205\n"
     ]
    }
   ],
   "source": [
    "preds = ad.correct(X_mix, batch_size=32)\n",
    "acc_y_corr_mix = accuracy(y_mix, preds['data']['corrected'])\n",
    "print('Accuracy {:.4f}'.format(acc_y_corr_mix))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Temperature Scaling\n",
    "\n",
    "We can further improve the correction performance by applying temperature scaling on the original model predictions $M(x)$ during both training and inference when computing the adversarial scores. We can again load a pretrained detector or train one from scratch:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "load_pretrained = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in save file: the model was *not* compiled. Compile it manually.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in save file: the model was *not* compiled. Compile it manually.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in save file: the model was *not* compiled. Compile it manually.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in save file: the model was *not* compiled. Compile it manually.\n",
      "WARNING:alibi_detect.ad.adversarialae:No threshold level set. Need to infer threshold using `infer_threshold`.\n"
     ]
    }
   ],
   "source": [
    "filepath = 'my_path'  # change to (absolute) directory where model is downloaded\n",
    "detector_name = 'temperature'\n",
    "filepath = os.path.join(filepath, detector_name)\n",
    "if load_pretrained:\n",
    "    ad_t = fetch_detector(filepath, detector_type, dataset, detector_name, model=model)\n",
    "else:  # train detector from scratch\n",
    "    # define encoder and decoder networks\n",
    "    encoder_net = tf.keras.Sequential(\n",
    "            [\n",
    "                InputLayer(input_shape=(32, 32, 3)),\n",
    "                Conv2D(32, 4, strides=2, padding='same', \n",
    "                       activation=tf.nn.relu, kernel_regularizer=l1(1e-5)),\n",
    "                Conv2D(64, 4, strides=2, padding='same', \n",
    "                       activation=tf.nn.relu, kernel_regularizer=l1(1e-5)),\n",
    "                Conv2D(256, 4, strides=2, padding='same', \n",
    "                       activation=tf.nn.relu, kernel_regularizer=l1(1e-5)),\n",
    "                Flatten(),\n",
    "                Dense(40)\n",
    "            ]\n",
    "        )\n",
    "    \n",
    "    decoder_net = tf.keras.Sequential(\n",
    "        [\n",
    "                InputLayer(input_shape=(40,)),\n",
    "                Dense(4 * 4 * 128, activation=tf.nn.relu),\n",
    "                Reshape(target_shape=(4, 4, 128)),\n",
    "                Conv2DTranspose(256, 4, strides=2, padding='same', \n",
    "                                activation=tf.nn.relu, kernel_regularizer=l1(1e-5)),\n",
    "                Conv2DTranspose(64, 4, strides=2, padding='same', \n",
    "                                activation=tf.nn.relu, kernel_regularizer=l1(1e-5)),\n",
    "                Conv2DTranspose(3, 4, strides=2, padding='same', \n",
    "                                activation=None, kernel_regularizer=l1(1e-5))\n",
    "            ]\n",
    "        )\n",
    "    \n",
    "    # initialise and train detector\n",
    "    ad_t = AdversarialAE(\n",
    "        encoder_net=encoder_net, \n",
    "        decoder_net=decoder_net, \n",
    "        model=clf,\n",
    "        temperature=0.5\n",
    "    )\n",
    "    ad_t.fit(X_train, epochs=40, batch_size=64, verbose=True)\n",
    "    \n",
    "    # save the trained adversarial detector\n",
    "    save_detector(ad_t, filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy after C&W attack 0.0000 -- reconstruction 0.8141\n",
      "Accuracy after SLIDE attack 0.0002 -- reconstruction 0.8265\n"
     ]
    }
   ],
   "source": [
    "# reconstructed adversarial instances\n",
    "X_recon_cw_t = predict_batch(X_test_cw, ad_t.ae, batch_size=32)\n",
    "X_recon_slide_t = predict_batch(X_test_slide, ad_t.ae, batch_size=32)\n",
    "\n",
    "# make predictions on reconstructed instances and compute accuracy\n",
    "y_recon_cw_t = predict_batch(X_recon_cw_t, clf, batch_size=32).argmax(axis=1)\n",
    "y_recon_slide_t = predict_batch(X_recon_slide_t, clf, batch_size=32).argmax(axis=1)\n",
    "acc_y_recon_cw_t = accuracy(y_test, y_recon_cw_t)\n",
    "acc_y_recon_slide_t = accuracy(y_test, y_recon_slide_t)\n",
    "print('Accuracy after C&W attack {:.4f} -- reconstruction {:.4f}'.format(acc_y_pred_cw, acc_y_recon_cw_t))\n",
    "print('Accuracy after SLIDE attack {:.4f} -- reconstruction {:.4f}'.format(acc_y_pred_slide, \n",
    "                                                                           acc_y_recon_slide_t))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Applying temperature scaling to CIFAR-10 improves the ROC curve and AUC values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_x_t = ad_t.score(X_test, batch_size=32)\n",
    "score_cw_t = ad_t.score(X_test_cw, batch_size=32)\n",
    "score_slide_t = ad_t.score(X_test_slide, batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "roc_data['original_t'] = {'scores': score_x_t, 'predictions': y_pred}\n",
    "roc_data['C&W T=0.5'] = {'scores': score_cw_t, 'predictions': y_pred_cw, 'normal': 'original_t'}\n",
    "roc_data['SLIDE T=0.5'] = {'scores': score_slide_t, 'predictions': y_pred_slide, 'normal': 'original_t'}\n",
    "\n",
    "plot_roc(roc_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hidden Layer K-L Divergence\n",
    "\n",
    "The performance of the correction mechanism can also be improved by extending the training methodology to one of the hidden layers of the classification model. We extract a flattened feature map from the hidden layer, feed it into a linear layer and apply the softmax function. The K-L divergence between predictions on the hidden layer for $x$ and $x'$ is optimised and included in the adversarial score during inference:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "load_pretrained = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in save file: the model was *not* compiled. Compile it manually.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in save file: the model was *not* compiled. Compile it manually.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in save file: the model was *not* compiled. Compile it manually.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in save file: the model was *not* compiled. Compile it manually.\n",
      "WARNING:alibi_detect.ad.adversarialae:No threshold level set. Need to infer threshold using `infer_threshold`.\n"
     ]
    }
   ],
   "source": [
    "filepath = 'my_path'  # change to (absolute) directory where model is downloaded\n",
    "detector_name = 'hiddenkld'\n",
    "filepath = os.path.join(filepath, detector_name)\n",
    "if load_pretrained:\n",
    "    ad_hl = fetch_detector(filepath, detector_type, dataset, detector_name, model=model)\n",
    "else:  # train detector from scratch\n",
    "    # define encoder and decoder networks\n",
    "    encoder_net = tf.keras.Sequential(\n",
    "            [\n",
    "                InputLayer(input_shape=(32, 32, 3)),\n",
    "                Conv2D(32, 4, strides=2, padding='same', \n",
    "                       activation=tf.nn.relu, kernel_regularizer=l1(1e-5)),\n",
    "                Conv2D(64, 4, strides=2, padding='same', \n",
    "                       activation=tf.nn.relu, kernel_regularizer=l1(1e-5)),\n",
    "                Conv2D(256, 4, strides=2, padding='same', \n",
    "                       activation=tf.nn.relu, kernel_regularizer=l1(1e-5)),\n",
    "                Flatten(),\n",
    "                Dense(40)\n",
    "            ]\n",
    "        )\n",
    "    \n",
    "    decoder_net = tf.keras.Sequential(\n",
    "        [\n",
    "                InputLayer(input_shape=(40,)),\n",
    "                Dense(4 * 4 * 128, activation=tf.nn.relu),\n",
    "                Reshape(target_shape=(4, 4, 128)),\n",
    "                Conv2DTranspose(256, 4, strides=2, padding='same', \n",
    "                                activation=tf.nn.relu, kernel_regularizer=l1(1e-5)),\n",
    "                Conv2DTranspose(64, 4, strides=2, padding='same', \n",
    "                                activation=tf.nn.relu, kernel_regularizer=l1(1e-5)),\n",
    "                Conv2DTranspose(3, 4, strides=2, padding='same', \n",
    "                                activation=None, kernel_regularizer=l1(1e-5))\n",
    "            ]\n",
    "        )\n",
    "    \n",
    "    # initialise and train detector\n",
    "    ad_hl = AdversarialAE(\n",
    "        encoder_net=encoder_net, \n",
    "        decoder_net=decoder_net, \n",
    "        model=clf,\n",
    "        hidden_layer_kld={200: 20},  # extract feature map from hidden layer 200\n",
    "        temperature=1                # predict softmax with output dim=20\n",
    "    )\n",
    "    ad_hl.fit(X_train, epochs=40, batch_size=64, verbose=True)\n",
    "    \n",
    "    # save the trained adversarial detector\n",
    "    save_detector(ad_hl, filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy after C&W attack 0.0000 -- reconstruction 0.8153\n",
      "Accuracy after SLIDE attack 0.0002 -- reconstruction 0.8318\n"
     ]
    }
   ],
   "source": [
    "# reconstructed adversarial instances\n",
    "X_recon_cw_hl = predict_batch(ad_hl.ae, X_test_cw, batch_size=32)\n",
    "X_recon_slide_hl = predict_batch(ad_hl.ae, X_test_slide, batch_size=32)\n",
    "\n",
    "# make predictions on reconstructed instances and compute accuracy\n",
    "y_recon_cw_hl = predict_batch(X_recon_cw_hl, clf, batch_size=32).argmax(axis=1)\n",
    "y_recon_slide_hl = predict_batch(X_recon_slide_hl, clf, batch_size=32).argmax(axis=1)\n",
    "acc_y_recon_cw_hl = accuracy(y_test, y_recon_cw_hl)\n",
    "acc_y_recon_slide_hl = accuracy(y_test, y_recon_slide_hl)\n",
    "print('Accuracy after C&W attack {:.4f} -- reconstruction {:.4f}'.format(acc_y_pred_cw, acc_y_recon_cw_hl))\n",
    "print('Accuracy after SLIDE attack {:.4f} -- reconstruction {:.4f}'.format(acc_y_pred_slide, \n",
    "                                                                           acc_y_recon_slide_hl))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Malicious Data Drift\n",
    "\n",
    "The adversarial detector proves to be very flexible and can be used to measure the harmfulness of the data drift on the classifier. We evaluate the detector on the CIFAR-10-C dataset ([Hendrycks & Dietterich, 2019](https://arxiv.org/abs/1903.12261)). The instances in\n",
    "CIFAR-10-C have been corrupted and perturbed by various types of noise, blur, brightness etc. at different levels of severity, leading to a gradual decline in model performance.\n",
    "\n",
    "We can select from the following corruption types:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['brightness', 'contrast', 'defocus_blur', 'elastic_transform', 'fog', 'frost', 'gaussian_blur', 'gaussian_noise', 'glass_blur', 'impulse_noise', 'jpeg_compression', 'motion_blur', 'pixelate', 'saturate', 'shot_noise', 'snow', 'spatter', 'speckle_noise', 'zoom_blur']\n"
     ]
    }
   ],
   "source": [
    "corruptions = corruption_types_cifar10c()\n",
    "print(corruptions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fetch the CIFAR-10-C data for a list of corruptions at each severity level (from 1 to 5), make classifier predictions on the corrupted data, compute adversarial scores and identify which perturbations where malicious or harmful and which weren't. We can then store and visualise the adversarial scores for the harmful and harmless corruption. The score for the harmful perturbations is significantly higher than for the harmless ones. As a result, the adversarial detector also functions as a data drift detector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Severity: 1 of 5\n",
      "Loading corrupted dataset...\n",
      "Preprocess data...\n",
      "Make predictions on corrupted dataset...\n",
      "Compute adversarial scores on corrupted dataset...\n",
      "Get labels for malicious corruptions...\n",
      "\n",
      "Severity: 2 of 5\n",
      "Loading corrupted dataset...\n",
      "Preprocess data...\n",
      "Make predictions on corrupted dataset...\n",
      "Compute adversarial scores on corrupted dataset...\n",
      "Get labels for malicious corruptions...\n",
      "\n",
      "Severity: 3 of 5\n",
      "Loading corrupted dataset...\n",
      "Preprocess data...\n",
      "Make predictions on corrupted dataset...\n",
      "Compute adversarial scores on corrupted dataset...\n",
      "Get labels for malicious corruptions...\n",
      "\n",
      "Severity: 4 of 5\n",
      "Loading corrupted dataset...\n",
      "Preprocess data...\n",
      "Make predictions on corrupted dataset...\n",
      "Compute adversarial scores on corrupted dataset...\n",
      "Get labels for malicious corruptions...\n",
      "\n",
      "Severity: 5 of 5\n",
      "Loading corrupted dataset...\n",
      "Preprocess data...\n",
      "Make predictions on corrupted dataset...\n",
      "Compute adversarial scores on corrupted dataset...\n",
      "Get labels for malicious corruptions...\n"
     ]
    }
   ],
   "source": [
    "severities = [1,2,3,4,5]\n",
    "\n",
    "score_drift = {\n",
    "    1: {'all': [], 'harm': [], 'noharm': [], 'acc': 0},\n",
    "    2: {'all': [], 'harm': [], 'noharm': [], 'acc': 0},\n",
    "    3: {'all': [], 'harm': [], 'noharm': [], 'acc': 0},\n",
    "    4: {'all': [], 'harm': [], 'noharm': [], 'acc': 0},\n",
    "    5: {'all': [], 'harm': [], 'noharm': [], 'acc': 0},\n",
    "}\n",
    "\n",
    "for s in severities:\n",
    "    print('\\nSeverity: {} of {}'.format(s, len(severities)))\n",
    "    \n",
    "    print('Loading corrupted dataset...')\n",
    "    X_corr, y_corr = fetch_cifar10c(corruption=corruptions, severity=s, return_X_y=True)\n",
    "    X_corr = X_corr.astype('float32')\n",
    "    \n",
    "    print('Preprocess data...')\n",
    "    X_corr, mean_test, std_test = scale_by_instance(X_corr)\n",
    "    \n",
    "    print('Make predictions on corrupted dataset...')\n",
    "    y_pred_corr = predict_batch(X_corr, clf, batch_size=32).argmax(axis=1)\n",
    "    \n",
    "    print('Compute adversarial scores on corrupted dataset...')\n",
    "    score_corr = ad_t.score(X_corr, batch_size=32)\n",
    "    scores = np.concatenate([score_x_t, score_corr])\n",
    "    \n",
    "    print('Get labels for malicious corruptions...')\n",
    "    labels_corr = np.zeros(score_corr.shape[0])    \n",
    "    repeat = y_corr.shape[0] // y_test.shape[0]\n",
    "    y_pred_repeat = np.tile(y_pred, (repeat,))\n",
    "    # malicious/harmful corruption: original prediction correct but \n",
    "    # prediction on corrupted data incorrect\n",
    "    idx_orig_right = np.where(y_pred_repeat == y_corr)[0]\n",
    "    idx_corr_wrong = np.where(y_pred_corr != y_corr)[0]\n",
    "    idx_harmful = np.intersect1d(idx_orig_right, idx_corr_wrong)\n",
    "    labels_corr[idx_harmful] = 1\n",
    "    labels = np.concatenate([np.zeros(X_test.shape[0]), labels_corr]).astype(int)\n",
    "    # harmless corruption: original prediction correct and prediction\n",
    "    # on corrupted data correct\n",
    "    idx_corr_right = np.where(y_pred_corr == y_corr)[0]\n",
    "    idx_harmless = np.intersect1d(idx_orig_right, idx_corr_right)\n",
    "    \n",
    "    score_drift[s]['all'] = score_corr\n",
    "    score_drift[s]['harm'] = score_corr[idx_harmful]\n",
    "    score_drift[s]['noharm'] = score_corr[idx_harmless]\n",
    "    score_drift[s]['acc'] = accuracy(y_corr, y_pred_corr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute mean scores and standard deviation per severity level and plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "mu_noharm, std_noharm = [], []\n",
    "mu_harm, std_harm = [], []\n",
    "acc = [acc_y_pred]\n",
    "for k, v in score_drift.items():\n",
    "    mu_noharm.append(v['noharm'].mean())\n",
    "    std_noharm.append(v['noharm'].std())\n",
    "    mu_harm.append(v['harm'].mean())\n",
    "    std_harm.append(v['harm'].std())\n",
    "    acc.append(v['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_labels = ['0', '1', '2', '3', '4', '5']\n",
    "\n",
    "N = 6\n",
    "ind = np.arange(N)\n",
    "width = .35\n",
    "\n",
    "fig_bar_cd, ax = plt.subplots()\n",
    "ax2 = ax.twinx()\n",
    "\n",
    "p0 = ax.bar(ind[0], score_x_t.mean(), yerr=score_x_t.std(), capsize=2)\n",
    "p1 = ax.bar(ind[1:], mu_noharm, width, yerr=std_noharm, capsize=2)\n",
    "p2 = ax.bar(ind[1:] + width, mu_harm, width, yerr=std_harm, capsize=2)\n",
    "\n",
    "ax.set_title('Adversarial Scores and Accuracy by Corruption Severity')\n",
    "ax.set_xticks(ind + width / 2)\n",
    "ax.set_xticklabels(plot_labels)\n",
    "ax.set_ylim((-1,6))\n",
    "ax.legend((p1[0], p2[0]), ('Not Harmful', 'Harmful'), loc='upper right', ncol=2)\n",
    "ax.set_ylabel('Score')\n",
    "ax.set_xlabel('Corruption Severity')\n",
    "\n",
    "color = 'tab:red'\n",
    "ax2.set_ylabel('Accuracy', color=color)\n",
    "ax2.plot(acc, color=color)\n",
    "ax2.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}