{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time Classification\n",
    "\n",
    "* Date: 25/07/2024\n",
    "* Author: Pankaj Maurya\n",
    "* Type of attack: Adversial Attack\n",
    "* Status:Draft\n",
    "\n",
    "## Metadata\n",
    "\n",
    "* Dataset: mnist\n",
    "* Size of training set: 60,000\n",
    "* Size of testing set : 10,000\n",
    "* Number of class : 10\n",
    "\n",
    "* Original Model: CNN\n",
    "* Type of attack vectors: square box/blocks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.0 Prepare Environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nDescription: Uncomment and run to install libraries. Needed for running first time only. \\n'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Description: Uncomment and run to install libraries. Needed for running first time only. \n",
    "\"\"\"\n",
    "# !pip install tensorflow==2.10\n",
    "# !pip install matplotlib==3.8.2\n",
    "# !pip install pandas\n",
    "# !pip install numpy \n",
    "# !pip install tqdm\n",
    "# !pip install scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.autotrackable has been moved to tensorflow.python.trackable.autotrackable. The old module will be deleted in version 2.11.\n",
      "WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.base has been moved to tensorflow.python.trackable.base. The old module will be deleted in version 2.11.\n",
      "WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.base_delegate has been moved to tensorflow.python.trackable.base_delegate. The old module will be deleted in version 2.11.\n",
      "WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.data_structures has been moved to tensorflow.python.trackable.data_structures. The old module will be deleted in version 2.11.\n",
      "WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.graph_view has been moved to tensorflow.python.checkpoint.graph_view. The old module will be deleted in version 2.11.\n",
      "WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.util has been moved to tensorflow.python.checkpoint.checkpoint. The old module will be deleted in version 2.11.\n",
      "WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.checkpoint_management has been moved to tensorflow.python.checkpoint.checkpoint_management. The old module will be deleted in version 2.9.\n",
      "WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.resource has been moved to tensorflow.python.trackable.resource. The old module will be deleted in version 2.11.\n",
      "WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.asset has been moved to tensorflow.python.trackable.asset. The old module will be deleted in version 2.11.\n",
      "WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.python_state has been moved to tensorflow.python.trackable.python_state. The old module will be deleted in version 2.11.\n",
      "WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.saving.checkpoint_options has been moved to tensorflow.python.checkpoint.checkpoint_options. The old module will be deleted in version 2.11.\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "Description : import packages\n",
    "'''\n",
    "# general libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import time\n",
    "from tqdm import tqdm\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "\n",
    "# deep learning libraries\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note : Not Using Google Colab\n",
      "Tensorflow version is 2.10.0\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "Description: check whether using google colab or jupyter notebook and tensorflow version\n",
    "'''\n",
    "try:\n",
    "    %tensorflow_version 2.x\n",
    "    print(\"Note: Using Google Colab\")\n",
    "    print(f'Tensorflow version is {tf.__version__}')\n",
    "except:\n",
    "    print(\"Note : Not Using Google Colab\")\n",
    "    print('Tensorflow version is {}'.format(tf.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Description: formating time string\n",
    "'''\n",
    "def hms_string(sec_elapsed):\n",
    "    h=sec_elapsed // (60*60)\n",
    "    m=(sec_elapsed % (60*60)) // 60\n",
    "    s= int(sec_elapsed % 60)\n",
    "    \n",
    "    return f'{h}:{m:>02}:{s:>02.2f}'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.0 Data Load and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Description: Load dataset\n",
    "\"\"\"\n",
    "(x_train,y_train),(x_test,y_test)=tf.keras.datasets.mnist.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We are downloading the dataset from the tf.keras.datasets.mnist.load_data() function provided by the TensorFlow Keras library. This dataset is crucial for training and testing machine learning models as it contains a large set of handwritten digits, making it an ideal resource for developing and evaluating image recognition algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of x_train: (60000, 28, 28) \n",
      "Shape of y_train: (60000,)\n",
      "Shape of x_test: (10000, 28, 28)\n",
      "shape of y_test: (10000,)\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Description : Check size of dataset\n",
    "\"\"\"\n",
    "print(\"Shape of x_train: {} \".format(x_train.shape) )\n",
    "print('Shape of y_train: {}'.format(y_train.shape))\n",
    "print(\"Shape of x_test: {}\".format(x_test.shape))\n",
    "print(\"shape of y_test: {}\".format(y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Description: split test data to test and validation with stratetify strategy \n",
    "\"\"\"\n",
    "x_test, x_val, y_test, y_val = train_test_split(x_test, y_test, test_size=0.50, random_state=42, stratify=y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of x_train: (60000, 28, 28) \n",
      "Shape of y_train: (60000,)\n",
      "Shape of x_test: (5000, 28, 28)\n",
      "shape of y_test: (5000,)\n",
      "Shape of x_val: (5000, 28, 28)\n",
      "shape of y_val: (5000,)\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "\"\"\"\n",
    "print(\"Shape of x_train: {} \".format(x_train.shape) )\n",
    "print('Shape of y_train: {}'.format(y_train.shape))\n",
    "print(\"Shape of x_test: {}\".format(x_test.shape))\n",
    "print(\"shape of y_test: {}\".format(y_test.shape))\n",
    "print(\"Shape of x_val: {}\".format(x_val.shape))\n",
    "print(\"shape of y_val: {}\".format(y_val.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Description: visualize sample images. \n",
    "'''\n",
    "def plot_images(x_train, y_train=None, rows=3):\n",
    "    #visualize x_train and y_train\n",
    "    ROWS=rows\n",
    "    random_indices=random.sample(range(x_train.shape[0]),ROWS*ROWS)\n",
    "    # print(\"the random indices are: \", random_indices)\n",
    "\n",
    "    #taking sample image from x_train dataset\n",
    "    sample_images=x_train[random_indices,: ]\n",
    "    if y_train is not None:\n",
    "        sample_label=y_train[random_indices]\n",
    "    else:\n",
    "        sample_label = [999999]*x_train.shape[0]\n",
    "        \n",
    "\n",
    "    fig,ax=plt.subplots(nrows=ROWS,ncols=ROWS,figsize=(12,9),sharex=True,sharey=True)\n",
    "    for i in range(ROWS*ROWS):\n",
    "        subplot_row=i // ROWS\n",
    "        subplot_col=i % ROWS\n",
    "        ax[subplot_row,subplot_col].imshow(sample_images[i,:]) #,cmap='gray_r'\n",
    "        ax[subplot_row,subplot_col].set_title(\"No. %d\" % sample_label[i])\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x900 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Description : visualize few randamly selected sample with it's true label\n",
    "\"\"\"\n",
    "plot_images(x_train, y_train, rows=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Description : reshape image \n",
    "'''\n",
    "#gray scale image size\n",
    "img_rows,img_cols,channels=28,28,1\n",
    "\n",
    "#reshape image\n",
    "x_train=x_train.reshape(-1,img_rows,img_cols,channels)\n",
    "x_val=x_val.reshape(-1,img_rows,img_cols,channels)\n",
    "x_test=x_test.reshape(-1,img_rows,img_cols,channels)\n",
    "\n",
    "input_shape=(img_rows,img_cols,channels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Description: Normalize image data, good for training the model\n",
    "'''\n",
    "# re-scale the image data to values between (0.0,1.0]\n",
    "\n",
    "x_train=x_train.astype('float32')/255.0\n",
    "x_val=x_val.astype('float32')/255.0\n",
    "x_test=x_test.astype('float32')/255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Description: convert class vector to binary (one hot encoder) metrix\n",
    "'''\n",
    "num_classes=10\n",
    "y_train=tf.keras.utils.to_categorical(y_train,num_classes)\n",
    "y_val=tf.keras.utils.to_categorical(y_val,num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of x_train: (60000, 28, 28, 1) \n",
      "Shape of y_train: (60000, 10)\n",
      "Shape of x_test: (5000, 28, 28, 1)\n",
      "shape of y_test: (5000,)\n",
      "Shape of x_val: (5000, 28, 28, 1)\n",
      "shape of y_val: (5000, 10)\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Description : check image shape\n",
    "\"\"\"\n",
    "print(\"Shape of x_train: {} \".format(x_train.shape) )\n",
    "print('Shape of y_train: {}'.format(y_train.shape))\n",
    "print(\"Shape of x_test: {}\".format(x_test.shape))\n",
    "print(\"shape of y_test: {}\".format(y_test.shape))\n",
    "print(\"Shape of x_val: {}\".format(x_val.shape))\n",
    "print(\"shape of y_val: {}\".format(y_val.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.0 Model development "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Description : Function to create model architecture \n",
    "\"\"\"\n",
    "def create_model_architecture(input_shape):\n",
    "    #model development\n",
    "    model=tf.keras.models.Sequential()\n",
    "\n",
    "    #adding layer to model\n",
    "    model.add(tf.keras.layers.Conv2D(filters=32,kernel_size=(3,3),activation='relu',input_shape=input_shape))\n",
    "    model.add(tf.keras.layers.Conv2D(64,(3,3),activation='relu'))\n",
    "    model.add(tf.keras.layers.MaxPooling2D(pool_size=(2,2)))\n",
    "    model.add(tf.keras.layers.Dropout(0.25))\n",
    "    model.add(tf.keras.layers.Flatten())\n",
    "    model.add(tf.keras.layers.Dense(128,activation='relu'))\n",
    "    model.add(tf.keras.layers.Dropout(0.5))\n",
    "    model.add(tf.keras.layers.Dense(num_classes, activation='softmax'))\n",
    "\n",
    "    #complile the model\n",
    "    model.compile(loss='categorical_crossentropy', optimizer='adam',metrics=['accuracy'])\n",
    "    return model\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Description: create model architecture\n",
    "\"\"\"\n",
    "model = create_model_architecture(input_shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 26, 26, 32)        320       \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 24, 24, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 12, 12, 64)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 12, 12, 64)        0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 9216)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 128)               1179776   \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 128)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10)                1290      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,199,882\n",
      "Trainable params: 1,199,882\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Description : visualize model architecture, what all the architecture contain\n",
    "\"\"\"\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Description: visualize graphically model , input it will accept, output it will return \n",
    "\"\"\"\n",
    "tf.keras.utils.plot_model(model,to_file=\"mnist_cnn_digit.png\",show_shapes=True,show_layer_names=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Description : Callbacks\n",
    "\"\"\"\n",
    "# Checkpoint\n",
    "checkpoint = tf.keras.callbacks.ModelCheckpoint(filepath='mnist_model.h5',monitor='val_loss',verbose=1,save_best_only=True,mode='auto')\n",
    "# Early stopper\n",
    "early_stop = tf.keras.callbacks.EarlyStopping(monitor='val_loss',min_delta=0,patience=3,mode='min')\n",
    "\n",
    "callbacks = [early_stop, checkpoint]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "938/938 [==============================] - ETA: 0s - loss: 0.2093 - accuracy: 0.9364\n",
      "Epoch 1: val_loss improved from inf to 0.05629, saving model to mnist_model.h5\n",
      "938/938 [==============================] - 10s 6ms/step - loss: 0.2093 - accuracy: 0.9364 - val_loss: 0.0563 - val_accuracy: 0.9836\n",
      "Epoch 2/20\n",
      "930/938 [============================>.] - ETA: 0s - loss: 0.0820 - accuracy: 0.9760\n",
      "Epoch 2: val_loss improved from 0.05629 to 0.03614, saving model to mnist_model.h5\n",
      "938/938 [==============================] - 5s 5ms/step - loss: 0.0817 - accuracy: 0.9761 - val_loss: 0.0361 - val_accuracy: 0.9866\n",
      "Epoch 3/20\n",
      "931/938 [============================>.] - ETA: 0s - loss: 0.0636 - accuracy: 0.9810\n",
      "Epoch 3: val_loss improved from 0.03614 to 0.03588, saving model to mnist_model.h5\n",
      "938/938 [==============================] - 5s 5ms/step - loss: 0.0636 - accuracy: 0.9810 - val_loss: 0.0359 - val_accuracy: 0.9880\n",
      "Epoch 4/20\n",
      "928/938 [============================>.] - ETA: 0s - loss: 0.0487 - accuracy: 0.9852\n",
      "Epoch 4: val_loss improved from 0.03588 to 0.03151, saving model to mnist_model.h5\n",
      "938/938 [==============================] - 5s 5ms/step - loss: 0.0487 - accuracy: 0.9851 - val_loss: 0.0315 - val_accuracy: 0.9898\n",
      "Epoch 5/20\n",
      "934/938 [============================>.] - ETA: 0s - loss: 0.0430 - accuracy: 0.9868\n",
      "Epoch 5: val_loss did not improve from 0.03151\n",
      "938/938 [==============================] - 5s 5ms/step - loss: 0.0429 - accuracy: 0.9868 - val_loss: 0.0343 - val_accuracy: 0.9904\n",
      "Epoch 6/20\n",
      "934/938 [============================>.] - ETA: 0s - loss: 0.0372 - accuracy: 0.9883\n",
      "Epoch 6: val_loss improved from 0.03151 to 0.03111, saving model to mnist_model.h5\n",
      "938/938 [==============================] - 5s 5ms/step - loss: 0.0371 - accuracy: 0.9883 - val_loss: 0.0311 - val_accuracy: 0.9912\n",
      "Epoch 7/20\n",
      "936/938 [============================>.] - ETA: 0s - loss: 0.0327 - accuracy: 0.9898\n",
      "Epoch 7: val_loss did not improve from 0.03111\n",
      "938/938 [==============================] - 5s 5ms/step - loss: 0.0327 - accuracy: 0.9898 - val_loss: 0.0326 - val_accuracy: 0.9902\n",
      "Epoch 8/20\n",
      "931/938 [============================>.] - ETA: 0s - loss: 0.0294 - accuracy: 0.9910\n",
      "Epoch 8: val_loss improved from 0.03111 to 0.03071, saving model to mnist_model.h5\n",
      "938/938 [==============================] - 5s 5ms/step - loss: 0.0293 - accuracy: 0.9910 - val_loss: 0.0307 - val_accuracy: 0.9912\n",
      "Epoch 9/20\n",
      "930/938 [============================>.] - ETA: 0s - loss: 0.0262 - accuracy: 0.9915\n",
      "Epoch 9: val_loss did not improve from 0.03071\n",
      "938/938 [==============================] - 5s 5ms/step - loss: 0.0261 - accuracy: 0.9915 - val_loss: 0.0339 - val_accuracy: 0.9912\n",
      "Epoch 10/20\n",
      "933/938 [============================>.] - ETA: 0s - loss: 0.0238 - accuracy: 0.9921\n",
      "Epoch 10: val_loss did not improve from 0.03071\n",
      "938/938 [==============================] - 5s 5ms/step - loss: 0.0237 - accuracy: 0.9921 - val_loss: 0.0309 - val_accuracy: 0.9928\n",
      "Epoch 11/20\n",
      "933/938 [============================>.] - ETA: 0s - loss: 0.0219 - accuracy: 0.9930\n",
      "Epoch 11: val_loss did not improve from 0.03071\n",
      "938/938 [==============================] - 5s 5ms/step - loss: 0.0219 - accuracy: 0.9930 - val_loss: 0.0352 - val_accuracy: 0.9918\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Description: Training model\n",
    "\"\"\"\n",
    "history = model.fit(x_train, y_train, validation_data = (x_val, y_val),epochs = 20, batch_size=64, verbose = 1 , callbacks = callbacks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8FgEmAAAAAAAAAK9FgAkAAAAAAADAaxFgAgAAAAAAAPBaBJgAAAAAAAAAvNb/A2UUuoQpqTspAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1600x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Description: Visualize model learning \n",
    "\"\"\"\n",
    "fig, ax = plt.subplots(1, 2, figsize = (16, 9))\n",
    "\n",
    "ax[0].plot(history.history['loss'])\n",
    "ax[0].plot(history.history['val_loss'])\n",
    "ax[0].set_title('Model Loss')\n",
    "ax[0].legend(['Train', 'Val'], loc = 'best')\n",
    "ax[0].set_ylabel('loss')\n",
    "ax[0].set_xlabel('epoch')\n",
    "\n",
    "ax[1].plot(history.history['accuracy'])\n",
    "ax[1].plot(history.history['val_accuracy'])\n",
    "ax[1].set_title('Model Accuracy')\n",
    "ax[1].set_ylabel('Accuracy')\n",
    "ax[1].set_xlabel('Epoch')\n",
    "ax[1].legend(['Train', 'Val'], loc = 'best')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157/157 [==============================] - 0s 1ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([3, 4, 9, ..., 0, 6, 4], dtype=int64)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Description : get prediction on test dataset\n",
    "\"\"\"\n",
    "pred = np.argmax(model.predict(x_test), axis=-1)\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy of model is: 99.4 %\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Description : get model accuracy. accuracy is total correct prediction / total number of sample  \n",
    "\"\"\"\n",
    "acc = metrics.accuracy_score(y_true=y_test, y_pred = pred)\n",
    "print(\"accuracy of model is: {} %\".format(acc*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 44ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Description: Get prediction by the model\n",
    "\"\"\"\n",
    "image_index = 45\n",
    "plt.figure(figsize=(9,6))\n",
    "plt.imshow(x_test[image_index].squeeze())\n",
    "pred = model.predict(x_test[image_index].reshape(1, img_rows, img_cols, channels))\n",
    "plt.title(f\"prediction by model is  {pred.argmax()} with confidense of {pred.max() *100:.2f}\" )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.0 Generate Attack vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Description: function to generate attack vector using squarebox attack\n",
    "\"\"\"\n",
    "def Generate_attack_vector(size=60000):\n",
    "    dataset=np.random.rand(size,28,28,1)*0\n",
    "    for i in tqdm(range(size)):\n",
    "        int_num=np.random.randint(0,15)\n",
    "        for boxes in range(int_num):\n",
    "            x1=np.random.randint(0,20)\n",
    "            y1=np.random.randint(0,20)\n",
    "            x2=np.random.randint(x1,28)\n",
    "            y2=np.random.randint(y1,28)\n",
    "            dataset[i,x1:x2,y1:y2,0]=1\n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████| 120000/120000 [00:08<00:00, 14108.02it/s]\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Description: generate attack vector\n",
    "\"\"\"\n",
    "dataset = Generate_attack_vector(size=120000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x900 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Description : visualize few randamly selected attack sample \n",
    "\"\"\"\n",
    "plot_images(x_train=dataset, rows=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3750/3750 [==============================] - 5s 1ms/step\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Description: get prediction from the model of attack dataset\n",
    "\"\"\"\n",
    "attack_label=model.predict(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.5717586e-11, 1.1700188e-03, 9.9882418e-01, 1.0975465e-07,\n",
       "       1.7365619e-07, 3.0545104e-06, 2.3918133e-07, 6.5651101e-08,\n",
       "       2.1314381e-06, 5.0997952e-11], dtype=float32)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Description: check one prediction. it contains probability for each class. class having highest probability is the predicted class\n",
    "\"\"\"\n",
    "attack_label[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Description: get predicted class\n",
    "\"\"\"\n",
    "label_class = np.argmax(attack_label, axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class: 0, count: 10799\n",
      "class: 1, count: 15342\n",
      "class: 2, count: 24450\n",
      "class: 3, count: 7370\n",
      "class: 4, count: 13708\n",
      "class: 5, count: 14647\n",
      "class: 6, count: 7097\n",
      "class: 7, count: 15703\n",
      "class: 8, count: 9660\n",
      "class: 9, count: 1224\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Description: get class count\n",
    "\"\"\"\n",
    "unique, counts = np.unique(label_class, return_counts=True)\n",
    "for i in range(len(unique)):\n",
    "    print(\"class: {}, count: {}\".format(unique[i], counts[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(120000, 10)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Description: convert class to one hot encoder for training model\n",
    "\"\"\"\n",
    "label_class=tf.keras.utils.to_categorical(label_class,num_classes)\n",
    "label_class.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Description: split attac data to train and validation with stratetify strategy \n",
    "\"\"\"\n",
    "x_train_atk, x_val_atk, y_train_atk, y_val_atk = train_test_split(dataset, label_class, test_size=0.2, random_state=42, stratify=label_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of x_train_atk: (96000, 28, 28, 1) \n",
      "Shape of y_train_atk: (96000, 10)\n",
      "Shape of x_val_atk: (24000, 28, 28, 1)\n",
      "shape of y_val_atk: (24000, 10)\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Description : check image shape\n",
    "\"\"\"\n",
    "print(\"Shape of x_train_atk: {} \".format(x_train_atk.shape) )\n",
    "print('Shape of y_train_atk: {}'.format(y_train_atk.shape))\n",
    "print(\"Shape of x_val_atk: {}\".format(x_val_atk.shape))\n",
    "print(\"shape of y_val_atk: {}\".format(y_val_atk.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.0 Steal Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Description : Function to create model architecture \n",
    "\"\"\"\n",
    "def create_steal_model_architecture(input_shape):\n",
    "    #model development\n",
    "    model=tf.keras.models.Sequential()\n",
    "\n",
    "    #adding layer to model\n",
    "    model.add(tf.keras.layers.Conv2D(filters=32,kernel_size=(3,3),activation='relu',input_shape=input_shape))\n",
    "    model.add(tf.keras.layers.Conv2D(64,(3,3),activation='relu'))\n",
    "    model.add(tf.keras.layers.MaxPooling2D(pool_size=(2,2)))\n",
    "    model.add(tf.keras.layers.Dropout(0.25))\n",
    "    model.add(tf.keras.layers.Conv2D(128,(3,3),activation='relu'))\n",
    "    model.add(tf.keras.layers.MaxPooling2D(pool_size=(2,2)))\n",
    "    model.add(tf.keras.layers.Dropout(0.25))\n",
    "    \n",
    "    model.add(tf.keras.layers.Flatten())\n",
    "    model.add(tf.keras.layers.Dense(64,activation='relu'))\n",
    "    model.add(tf.keras.layers.Dropout(0.3))\n",
    "    model.add(tf.keras.layers.Dense(128,activation='relu'))\n",
    "    model.add(tf.keras.layers.Dropout(0.4))\n",
    "    model.add(tf.keras.layers.Dense(num_classes, activation='softmax'))\n",
    "\n",
    "    #complile the model\n",
    "    model.compile(loss='categorical_crossentropy', optimizer='adam',metrics=['accuracy'])\n",
    "    return model\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Description: create model architecture\n",
    "\"\"\"\n",
    "steal_model = create_steal_model_architecture(input_shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_2 (Conv2D)           (None, 26, 26, 32)        320       \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 24, 24, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 12, 12, 64)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 12, 12, 64)        0         \n",
      "                                                                 \n",
      " conv2d_4 (Conv2D)           (None, 10, 10, 128)       73856     \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 5, 5, 128)        0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 5, 5, 128)         0         \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 3200)              0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 64)                204864    \n",
      "                                                                 \n",
      " dropout_4 (Dropout)         (None, 64)                0         \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 128)               8320      \n",
      "                                                                 \n",
      " dropout_5 (Dropout)         (None, 128)               0         \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 10)                1290      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 307,146\n",
      "Trainable params: 307,146\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Description : visualize model architecture, what all the architecture contain\n",
    "\"\"\"\n",
    "steal_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Description: visualize graphically model , input it will accept, output it will return \n",
    "\"\"\"\n",
    "tf.keras.utils.plot_model(steal_model,to_file=\"steal_mnist_cnn_digit.png\",show_shapes=True,show_layer_names=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Description : Callbacks\n",
    "\"\"\"\n",
    "# Checkpoint\n",
    "checkpoint = tf.keras.callbacks.ModelCheckpoint(filepath='steal_mnist_model.h5',monitor='val_loss',verbose=1,save_best_only=True,mode='auto')\n",
    "# Early stopper\n",
    "early_stop = tf.keras.callbacks.EarlyStopping(monitor='val_loss',min_delta=0,patience=3,mode='min')\n",
    "\n",
    "callbacks = [early_stop, checkpoint]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "1497/1500 [============================>.] - ETA: 0s - loss: 1.1477 - accuracy: 0.6084\n",
      "Epoch 1: val_loss improved from inf to 0.72696, saving model to steal_mnist_model.h5\n",
      "1500/1500 [==============================] - 11s 7ms/step - loss: 1.1471 - accuracy: 0.6086 - val_loss: 0.7270 - val_accuracy: 0.7504\n",
      "Epoch 2/20\n",
      "1499/1500 [============================>.] - ETA: 0s - loss: 0.8546 - accuracy: 0.7027\n",
      "Epoch 2: val_loss improved from 0.72696 to 0.61056, saving model to steal_mnist_model.h5\n",
      "1500/1500 [==============================] - 11s 7ms/step - loss: 0.8546 - accuracy: 0.7027 - val_loss: 0.6106 - val_accuracy: 0.7837\n",
      "Epoch 3/20\n",
      "1496/1500 [============================>.] - ETA: 0s - loss: 0.7779 - accuracy: 0.7246\n",
      "Epoch 3: val_loss improved from 0.61056 to 0.60232, saving model to steal_mnist_model.h5\n",
      "1500/1500 [==============================] - 11s 7ms/step - loss: 0.7780 - accuracy: 0.7245 - val_loss: 0.6023 - val_accuracy: 0.7883\n",
      "Epoch 4/20\n",
      "1497/1500 [============================>.] - ETA: 0s - loss: 0.7359 - accuracy: 0.7377\n",
      "Epoch 4: val_loss improved from 0.60232 to 0.56361, saving model to steal_mnist_model.h5\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.7360 - accuracy: 0.7377 - val_loss: 0.5636 - val_accuracy: 0.7975\n",
      "Epoch 5/20\n",
      "1495/1500 [============================>.] - ETA: 0s - loss: 0.7046 - accuracy: 0.7493\n",
      "Epoch 5: val_loss improved from 0.56361 to 0.55094, saving model to steal_mnist_model.h5\n",
      "1500/1500 [==============================] - 11s 7ms/step - loss: 0.7047 - accuracy: 0.7492 - val_loss: 0.5509 - val_accuracy: 0.7988\n",
      "Epoch 6/20\n",
      "1497/1500 [============================>.] - ETA: 0s - loss: 0.6857 - accuracy: 0.7544\n",
      "Epoch 6: val_loss improved from 0.55094 to 0.53598, saving model to steal_mnist_model.h5\n",
      "1500/1500 [==============================] - 11s 7ms/step - loss: 0.6859 - accuracy: 0.7544 - val_loss: 0.5360 - val_accuracy: 0.8018\n",
      "Epoch 7/20\n",
      "1494/1500 [============================>.] - ETA: 0s - loss: 0.6709 - accuracy: 0.7584\n",
      "Epoch 7: val_loss did not improve from 0.53598\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.6705 - accuracy: 0.7586 - val_loss: 0.5463 - val_accuracy: 0.8015\n",
      "Epoch 8/20\n",
      "1498/1500 [============================>.] - ETA: 0s - loss: 0.6571 - accuracy: 0.7643\n",
      "Epoch 8: val_loss improved from 0.53598 to 0.52667, saving model to steal_mnist_model.h5\n",
      "1500/1500 [==============================] - 11s 7ms/step - loss: 0.6571 - accuracy: 0.7643 - val_loss: 0.5267 - val_accuracy: 0.8076\n",
      "Epoch 9/20\n",
      "1498/1500 [============================>.] - ETA: 0s - loss: 0.6487 - accuracy: 0.7679\n",
      "Epoch 9: val_loss did not improve from 0.52667\n",
      "1500/1500 [==============================] - 11s 7ms/step - loss: 0.6488 - accuracy: 0.7679 - val_loss: 0.5323 - val_accuracy: 0.8045\n",
      "Epoch 10/20\n",
      "1492/1500 [============================>.] - ETA: 0s - loss: 0.6341 - accuracy: 0.7721\n",
      "Epoch 10: val_loss improved from 0.52667 to 0.51666, saving model to steal_mnist_model.h5\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.6341 - accuracy: 0.7723 - val_loss: 0.5167 - val_accuracy: 0.8114\n",
      "Epoch 11/20\n",
      "1494/1500 [============================>.] - ETA: 0s - loss: 0.6288 - accuracy: 0.7750\n",
      "Epoch 11: val_loss did not improve from 0.51666\n",
      "1500/1500 [==============================] - 11s 7ms/step - loss: 0.6287 - accuracy: 0.7750 - val_loss: 0.5297 - val_accuracy: 0.8025\n",
      "Epoch 12/20\n",
      "1494/1500 [============================>.] - ETA: 0s - loss: 0.6195 - accuracy: 0.7754\n",
      "Epoch 12: val_loss did not improve from 0.51666\n",
      "1500/1500 [==============================] - 11s 7ms/step - loss: 0.6195 - accuracy: 0.7753 - val_loss: 0.5181 - val_accuracy: 0.8079\n",
      "Epoch 13/20\n",
      "1496/1500 [============================>.] - ETA: 0s - loss: 0.6130 - accuracy: 0.7796\n",
      "Epoch 13: val_loss improved from 0.51666 to 0.50531, saving model to steal_mnist_model.h5\n",
      "1500/1500 [==============================] - 10s 7ms/step - loss: 0.6135 - accuracy: 0.7794 - val_loss: 0.5053 - val_accuracy: 0.8170\n",
      "Epoch 14/20\n",
      "1495/1500 [============================>.] - ETA: 0s - loss: 0.6085 - accuracy: 0.7803\n",
      "Epoch 14: val_loss improved from 0.50531 to 0.49599, saving model to steal_mnist_model.h5\n",
      "1500/1500 [==============================] - 11s 7ms/step - loss: 0.6086 - accuracy: 0.7803 - val_loss: 0.4960 - val_accuracy: 0.8180\n",
      "Epoch 15/20\n",
      "1497/1500 [============================>.] - ETA: 0s - loss: 0.5975 - accuracy: 0.7829\n",
      "Epoch 15: val_loss did not improve from 0.49599\n",
      "1500/1500 [==============================] - 11s 7ms/step - loss: 0.5976 - accuracy: 0.7829 - val_loss: 0.5004 - val_accuracy: 0.8133\n",
      "Epoch 16/20\n",
      "1497/1500 [============================>.] - ETA: 0s - loss: 0.5936 - accuracy: 0.7859\n",
      "Epoch 16: val_loss did not improve from 0.49599\n",
      "1500/1500 [==============================] - 11s 8ms/step - loss: 0.5935 - accuracy: 0.7859 - val_loss: 0.5026 - val_accuracy: 0.8165\n",
      "Epoch 17/20\n",
      "1496/1500 [============================>.] - ETA: 0s - loss: 0.5874 - accuracy: 0.7889\n",
      "Epoch 17: val_loss improved from 0.49599 to 0.48487, saving model to steal_mnist_model.h5\n",
      "1500/1500 [==============================] - 11s 7ms/step - loss: 0.5876 - accuracy: 0.7889 - val_loss: 0.4849 - val_accuracy: 0.8211\n",
      "Epoch 18/20\n",
      "1494/1500 [============================>.] - ETA: 0s - loss: 0.5814 - accuracy: 0.7906\n",
      "Epoch 18: val_loss did not improve from 0.48487\n",
      "1500/1500 [==============================] - 11s 7ms/step - loss: 0.5812 - accuracy: 0.7906 - val_loss: 0.4953 - val_accuracy: 0.8175\n",
      "Epoch 19/20\n",
      "1495/1500 [============================>.] - ETA: 0s - loss: 0.5753 - accuracy: 0.7945\n",
      "Epoch 19: val_loss did not improve from 0.48487\n",
      "1500/1500 [==============================] - 11s 8ms/step - loss: 0.5752 - accuracy: 0.7945 - val_loss: 0.4883 - val_accuracy: 0.8227\n",
      "Epoch 20/20\n",
      "1500/1500 [==============================] - ETA: 0s - loss: 0.5737 - accuracy: 0.7928\n",
      "Epoch 20: val_loss did not improve from 0.48487\n",
      "1500/1500 [==============================] - 11s 7ms/step - loss: 0.5737 - accuracy: 0.7928 - val_loss: 0.4922 - val_accuracy: 0.8244\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Description: Training model\n",
    "\"\"\"\n",
    "steal_history = steal_model.fit(x_train_atk, y_train_atk, validation_data = (x_val_atk, y_val_atk),epochs = 20, batch_size=64, verbose = 1 , callbacks = callbacks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Description: Visualize steal model learning \n",
    "\"\"\"\n",
    "fig, ax = plt.subplots(1, 2, figsize = (16, 9))\n",
    "\n",
    "ax[0].plot(steal_history.history['loss'])\n",
    "ax[0].plot(steal_history.history['val_loss'])\n",
    "ax[0].set_title('Model Loss')\n",
    "ax[0].legend(['Train', 'Val'], loc = 'best')\n",
    "ax[0].set_ylabel('loss')\n",
    "ax[0].set_xlabel('epoch')\n",
    "\n",
    "ax[1].plot(steal_history.history['accuracy'])\n",
    "ax[1].plot(steal_history.history['val_accuracy'])\n",
    "ax[1].set_title('Model Accuracy')\n",
    "ax[1].set_ylabel('Accuracy')\n",
    "ax[1].set_xlabel('Epoch')\n",
    "ax[1].legend(['Train', 'Val'], loc = 'best')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157/157 [==============================] - 0s 1ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([3, 4, 9, ..., 0, 6, 4], dtype=int64)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Description : get prediction on test dataset\n",
    "\"\"\"\n",
    "steal_pred = np.argmax(steal_model.predict(x_test), axis=-1)\n",
    "steal_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy of model is: 94.32000000000001 %\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Description : get model accuracy \n",
    "\"\"\"\n",
    "acc = metrics.accuracy_score(y_true=y_test, y_pred = steal_pred)\n",
    "print(\"accuracy of model is: {} %\".format(acc*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.1 Prediction by stolen model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 32ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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xhzZv3qzc3NzSNk+JxowZI39/fw0ZMkRbtmxRZmam5syZ4z5NePbsWUlSZGSkhgwZotdff11//OMf9e9//1ubNm3S0KFD3Uc0i9qWJiIiQqtWrdKHH36oP/zhD4qMjNTp06dLbDt8+HCtWrVKS5YscV9QWJZ1XE1Wh36zZs08nicmJsrPz6/YucSEhASP58eOHVN2drZefvllRUVFeTzuvvtuSf+5kOfbb79V06ZNi4V08+bNr1hfZmamYmNjFRERUd63VqJvv/22xHUHBQWpSZMm7teLNGzYsFjddevW1cmTJ8u0vku3ryRde+21OnPmjPvCnRYtWqh9+/ZavHixu83ixYvVsWNHNW3atEzradSokcfzoh9mcXFxxZb/tPZvv/1WsbGxCg0N9Wh33XXXuV8v+tfPz6/YaZNLt2N55kVFJSQkaMqUKfp//+//KTIyUikpKZo/f36Fz+c/+uijioiIuOIFTZfTtWtX7dixQ2fPntWmTZsUExOjG2+8UW3btnUf4t+8ebM7cCrq0q+xVLa5mJmZqebNm5d68VxZ54HTWi637sqaW5fWWbduXUly11mWuZSRkSFjjJo1a1Zsvd98802p8/nYsWM6c+ZMiT/rrrvuOhUWFpb5uoCfatOmjZYsWaLMzEzdfPPNatq0qebNm+e+HuWnpwZeeukl9e3bVw888IASExPVrVs3tW7dWv369SvW9nKCgoKUnJysW265RY899pjmz5+v0aNH66OPPirWNj4+XsnJyRo+fLgWL16sJk2aKDk5uUoFv9Xn9C91ub3nS8/HFN1Wd9ddd2nkyJEl9mnTpo13i/OBy11tarx83+mIESM0ceJEHTp0SPn5+dq2bZuef/75Mve/XJ0lLfd27T91tebFH//4R40aNUorVqzQp59+qgkTJrivS2nYsGGZx8nIyNDLL7+suXPn6vDhw+7l586d04ULF7R//36FhYWV+ktnly5ddOHCBW3dulWbNm1yh3vXrl21adMm7dmzR8eOHXMc+ldrLpaFL2qpyNwqS51XmkuFhYVyuVz661//WuJ4ZQnNyjB48GD1799fX3/9tQoKCnTjjTe6L3a99tpr3e3Cw8O1YsUKHThwQPv371d8fLzi4+PVuXNn94W/5dW5c2fFxMRo8eLF7mtZSqvzlVde0caNG5WSklLudVUGq0M/IyPDYy9+7969KiwsvOKn4hVdZVtQUKDk5ORS28bHx2v37t0yxnj8UpGenn7F+hITE/XJJ5/ohx9+KPUHb1kP9RddIZuenu5xuO38+fPat2/fFd9LeWVkZBRb9q9//Us1a9b0OJQ8bNgwTZkyRW+//bbOnj2rwMBADR061Ku1lCQ+Pl6rV6/WqVOnPPby9uzZ43696N/CwkL3XmORS7+G5ZkXTrVu3VqtW7fWo48+qi1btujmm2/Wiy++qJkzZ5Z5jO+++06FhYWaMGFCscPL0o97ghMnTiz1iv4OHTooKChImzZt0qZNm9xX4Xfr1k2vvPKK1qxZ435emoqcriqLxMREbd++XRcuXLjsLbFlnQeVoSrMrdLmUtGFjgkJCR5hWhZRUVGqWbNmiT/r9uzZIz8/v2JH48ojKChI7du3dz9fvXq1JJW4fRo1auQ+8pGdna0dO3Zo0KBBFV73uXPnynR0rWgP38mdNd5m9eH9+fPnezx/7rnnJOmKV6b7+/tr0KBBWrp0qXbv3l3s9Z9+WlPfvn11+PBhj3PWZ86c0csvv3zF+gYNGiRjjGbMmFHstZ/+tl6rVq0y3T6SnJysoKAgzZs3z6P/q6++qpycnBKvGHZi69atHucZDx48qBUrVqh3794eew2RkZHq06eP3nrrLS1evFipqanuK7grU9++fVVQUFDsqMKzzz4rl8vlngdF/86bN8+j3aVhWJ55UVG5ubm6ePGix7LWrVvLz8+vxNtAS9OqVSstW7as2KNly5Zq1KiRli1b5nFdRElCQkLUvn17vf322zpw4IDHnv7Zs2c1b948JSYmKiYmptRxij7kxNufyDdo0CAdP368xCNHRd8DZZ0HlcGXc6ssc2ngwIHy9/fXjBkzih3JMP93Vfvl+Pv7q3fv3lqxYoXHKdOsrCwtWbJEXbp0UVhYWLnrLklGRoZefPFF3XLLLVf85WTq1Km6ePGiJk+e7LF8z549OnDggPt5Xl5eidc6LF26VCdPnvT4xMDLbf9XX31VLpdLN954Y3neTqWyek9/37596t+/v1JTU7V161b3bTNt27a9Yt8nn3xS69atU1JSku655x5df/31+uGHH/Tll19q9erV+uGHHyT9eJHc888/rxEjRmjHjh2KiYnRm2++6b69pDQ9e/bUr371K82bN08ZGRlKTU1VYWGhNm3apJ49e7o/uapdu3ZavXq1nnnmGcXGxiohIUFJSUnFxouKitLUqVM1Y8YMpaamqn///kpPT9cLL7yg9u3bl/nCubJq1aqVUlJSPG7Zk1TiLzEjRoxwX+z4xBNPeLWOy+nXr5969uypRx55RPv371fbtm316aefasWKFZo0aZL7POsNN9yg4cOH64UXXlBOTo46d+6sNWvWFLvnWSr7vKiotWvXaty4cbr99tt17bXX6uLFi3rzzTfdoVAekZGRxT7FUfpP4JT0Wkm6du2qJ598UuHh4e5b/urXr6/mzZsrPT3d/bkRpSm64PORRx7RsGHDFBgYqH79+jn+dMARI0bojTfe0JQpU/TZZ5+pa9euysvL0+rVqzV27FjdeuutZZ4HlcGXc6sscykxMVEzZ87U1KlTtX//fg0YMEChoaHat2+fli1bpnvvvVcPPPDAZdcxc+ZMrVq1Sl26dNHYsWMVEBCgl156Sfn5+eX+zI+fuv7663X77berUaNG2rdvnxYsWKCIiAi9+OKLHu2efPJJ7d69W0lJSQoICNDy5cv16aefaubMmR5HCaQfrzPo3r27+zRBRkaGkpOTNXToULVo0UJ+fn764osv9NZbb6lx48aaOHGiu++sWbP0t7/9TampqWrUqJF++OEHLV26VJ9//rnGjx9f5uuTroqrd6NA1VF028w///lPM3jwYBMaGmrq1q1rxo0bZ86ePevRVpJJS0srcZysrCyTlpZm4uLiTGBgoImOjja9evUyL7/8ske7b7/91vTv39/UrFnTREZGmokTJ5qVK1de8ZY9Y3681ezpp582LVq0MEFBQSYqKsr06dPH7Nixw91mz549plu3bqZGjRpGkvv2vUtv2Svy/PPPmxYtWpjAwEDToEEDc//993vcTmXM5W/bKqnGkhRtt7feess0a9bMBAcHm1/84hce7/en8vPzTd26dU14eHixr8HlFL2/zz//3GN50df32LFjxWqvVauWx7JTp06ZyZMnm9jYWBMYGGiaNWtmnn76aY9bAI0x5uzZs2bChAmmXr16platWqZfv37m4MGDxW6rMqZs86Kit+z9+9//Nr/+9a9NYmKiCQkJMREREaZnz55m9erVZdhiZVPWW/aK/OUvfzGSTJ8+fTyWjxkzxkgyr776arE+JW23J554wlxzzTXGz8/PY95e7nswPj6+xFtVL3XmzBnzyCOPmISEBPfXY/DgwR63kZV1HpS1lrLesmeM9+fW5dZ96Zwrz1xaunSp6dKli6lVq5apVauWadGihUlLSzPp6enFN/glvvzyS5OSkmJq165tatasaXr27Gm2bNlSYm1luWXPmB9vz4uLizNBQUEmNjbW3HfffSYrK6tYu48++sh06NDBhIaGmpo1a5qOHTt63D79U5I8buk9duyYuffee02LFi1MrVq1TFBQkGnWrJmZNGlSsZ8tn376qbnlllvc8yc0NNTcfPPNZuHChcXmkK+5jKlifw3gKij6gJpjx45dlcPIuLKLFy8qNjZW/fr106uvvurrcgDgZ8nqc/qoOpYvX65jx465Py0PAOB9Vp/Th+9t375du3bt0hNPPKFf/OIX7s8sBwB4H3v68KkFCxbo/vvvV/369Yv9zQMAgHdZeU4fAAAbsacPAIAlCH0AACxR5S7kKyws1OHDhxUaGlppH80JAMDPhTFGp06dUmxsrMdfUC1JlQv9w4cPO/o8ZgAAbHTw4MEr/tGtKhf6RX/woov6KkAl/4EMAADwo4u6oM36uNifhy5JlQv9okP6AQpUgIvQBwCgVP93D15ZTolzIR8AAJaotNCfP3++GjdurJCQECUlJemzzz6rrFUBAIAyqJTQf/fddzVlyhRNmzZNX375pdq2bauUlBQdPXq0MlYHAADKoFJC/5lnntE999yju+++W9dff71efPFF1axZU6+99lplrA4AAJSB10P//Pnz2rFjh5KTk/+zEj8/JScna+vWrd5eHQAAKCOvX71//PhxFRQUqEGDBh7LGzRooD179hRrn5+fr/z8fPfz3Nxcb5cEAABUBa7enzNnjsLDw90PPpgHAIDK4fXQj4yMlL+/v7KysjyWZ2VlKTo6ulj7qVOnKicnx/04ePCgt0sCAACqhNAPCgpSu3bttGbNGveywsJCrVmzRp06dSrWPjg4WGFhYR4PAADgfZXyiXxTpkzRyJEjddNNN6lDhw6aO3eu8vLydPfdd1fG6gAAQBlUSugPHTpUx44d0+OPP64jR47ohhtu0MqVK4td3AcAAK4elzHG+LqIn8rNzVV4eLh66FY+ex8AgCu4aC5ovVYoJyfniqfIfX71PgAAuDoIfQAALEHoAwBgCUIfAABLEPoAAFiC0AcAwBKEPgAAliD0AQCwBKEPAIAlCH0AACxB6AMAYAlCHwAASxD6AABYgtAHAMAShD4AAJYg9AEAsAShDwCAJQh9AAAsQegDAGAJQh8AAEsQ+gAAWILQBwDAEoQ+AACWIPQBALAEoQ8AgCUIfQAALEHoAwBgCUIfAABLEPoAAFiC0AcAwBKEPgAAliD0AQCwBKEPAIAlCH0AACxB6AMAYAlCHwAASxD6AABYgtAHAMAShD4AAJYg9AEAsAShDwCAJQh9AAAsQegDAGAJQh8AAEsQ+gAAWILQBwDAEoQ+AACWIPQBALAEoQ8AgCUIfQAALEHoAwBgCUIfAABLEPoAAFiC0AcAwBKEPgAAliD0AQCwBKEPAIAlCH0AACxB6AMAYAlCHwAASxD6AABYgtAHAMAShD4AAJYg9AEAsAShDwCAJQh9AAAsQegDAGAJQh8AAEsQ+gAAWILQBwDAEoQ+AACWIPQBALCE10N/+vTpcrlcHo8WLVp4ezUAAKCcAipj0JYtW2r16tX/WUlApawGAACUQ6WkcUBAgKKjoytjaAAAUEGVck4/IyNDsbGxatKkie68804dOHDgsm3z8/OVm5vr8QAAAN7n9dBPSkrSokWLtHLlSi1YsED79u1T165dderUqRLbz5kzR+Hh4e5HXFyct0sCAACSXMYYU5kryM7OVnx8vJ555hmNHj262Ov5+fnKz893P8/NzVVcXJx66FYFuAIrszQAAKq9i+aC1muFcnJyFBYWVmrbSr/Crk6dOrr22mu1d+/eEl8PDg5WcHBwZZcBAID1Kv0+/dOnTyszM1MxMTGVvSoAAFAKr4f+Aw88oA0bNmj//v3asmWLbrvtNvn7+2v48OHeXhUAACgHrx/eP3TokIYPH64TJ04oKipKXbp00bZt2xQVFeXtVQEAgHLweui/88473h4SAAB4AZ+9DwCAJfh8XPzsuX7R0vEYxzqUfhvMlUS+tNVxDUBVc2ZgkqP+eaOyHdfwuxYrHfV/7c7+jmswn//d8RhXC3v6AABYgtAHAMAShD4AAJYg9AEAsAShDwCAJQh9AAAsQegDAGAJQh8AAEsQ+gAAWILQBwDAEoQ+AACWIPQBALAEoQ8AgCUIfQAALEHoAwBgiQBfFwBUtgGL1zse49ba6Y769w18yHEN9Z/f4ngM/HwExMc56n8kpaHjGhZMneeo/y+CnO93zj15raP+/kdOOq7houMRrh729AEAsAShDwCAJQh9AAAsQegDAGAJQh8AAEsQ+gAAWILQBwDAEoQ+AACWIPQBALAEoQ8AgCUIfQAALEHoAwBgCUIfAABLEPoAAFiC0AcAwBKEPgAAlgjwdQHAlRz/TSdH/e8Ke9ZxDcGuGo76n2lgHNcA/NTeexo66r/77ue9UIXv9xu/y6/rqP/Fg4e8VEn14PuvGAAAuCoIfQAALEHoAwBgCUIfAABLEPoAAFiC0AcAwBKEPgAAliD0AQCwBKEPAIAlCH0AACxB6AMAYAlCHwAASxD6AABYgtAHAMAShD4AAJYI8HUB+Hkr7HKD4zE2PPaso/7BrkDHNQDelDu8o+Mxdoxy9n0h+f774s59vR2P8fX6ax31b6ytjmuoTtjTBwDAEoQ+AACWIPQBALAEoQ8AgCUIfQAALEHoAwBgCUIfAABLEPoAAFiC0AcAwBKEPgAAliD0AQCwBKEPAIAlCH0AACxB6AMAYAlCHwAASxD6AABYIsDXBeBnzuVyPESwK9ALhQDec6H3TY76PztrvuMaqsL3RaEKHfXvHvEvxzXkLot21N84rqB6YU8fAABLEPoAAFiC0AcAwBLlDv2NGzeqX79+io2Nlcvl0vLlyz1eN8bo8ccfV0xMjGrUqKHk5GRlZGR4q14AAFBB5Q79vLw8tW3bVvPnl3whylNPPaV58+bpxRdf1Pbt21WrVi2lpKTo3LlzjosFAAAVV+6r9/v06aM+ffqU+JoxRnPnztWjjz6qW2+9VZL0xhtvqEGDBlq+fLmGDRvmrFoAAFBhXj2nv2/fPh05ckTJycnuZeHh4UpKStLWrVu9uSoAAFBOXr1P/8iRI5KkBg0aeCxv0KCB+7VL5efnKz8/3/08NzfXmyUBAID/4/Or9+fMmaPw8HD3Iy4uztclAQDws+TV0I+O/vGTkbKysjyWZ2VluV+71NSpU5WTk+N+HDx40JslAQCA/+PV0E9ISFB0dLTWrFnjXpabm6vt27erU6dOJfYJDg5WWFiYxwMAAHhfuc/pnz59Wnv37nU/37dvn3bu3KmIiAg1atRIkyZN0syZM9WsWTMlJCToscceU2xsrAYMGODNugEAQDmVO/S/+OIL9ezZ0/18ypQpkqSRI0dq0aJFeuihh5SXl6d7771X2dnZ6tKli1auXKmQkBDvVQ0AAMqt3KHfo0cPGXP5v0vkcrn0hz/8QX/4wx8cFQYAALzL51fvAwCAq8Or9+kDl8q8PcjXJXjFZ/kuR/0brj/vpUpQFRy6+4Kj/u2CvVSIj924bZSj/g0H/cMLVXhjDHuwpw8AgCUIfQAALEHoAwBgCUIfAABLEPoAAFiC0AcAwBKEPgAAliD0AQCwBKEPAIAlCH0AACxB6AMAYAlCHwAASxD6AABYgtAHAMAShD4AAJYg9AEAsESArwtA1ZaxqJ2j/ruS/+SFKgId9W796jjHFTR5do+j/gEndziuAd6xf1Ynx2Ps7Op0Xvs7rsGpfun9HY8RuaiWFyrB1cSePgAAliD0AQCwBKEPAIAlCH0AACxB6AMAYAlCHwAASxD6AABYgtAHAMAShD4AAJYg9AEAsAShDwCAJQh9AAAsQegDAGAJQh8AAEsQ+gAAWCLA1wWgasv4r1cc9S9UoJcqqbiEJ792PEbBmTNeqAT+YWGOx7jQpomj/lMHLXVcQ6DL3/EYTnX+arij/lF3HHFcQ0juZ47HwNXFnj4AAJYg9AEAsAShDwCAJQh9AAAsQegDAGAJQh8AAEsQ+gAAWILQBwDAEoQ+AACWIPQBALAEoQ8AgCUIfQAALEHoAwBgCUIfAABLEPoAAFiC0AcAwBIBvi4AVZu/y9nvhYWmwHENqd/c5qh/4PnDjmv4Ofh+SmfHY5xu7OzrWa/JScc1bPnFq47H+Dk4fqiOo/4Ruf/yTiGoVtjTBwDAEoQ+AACWIPQBALAEoQ8AgCUIfQAALEHoAwBgCUIfAABLEPoAAFiC0AcAwBKEPgAAliD0AQCwBKEPAIAlCH0AACxB6AMAYAlCHwAASwT4ugBUbQWm0Ncl6Nuseo76R9x5jZcq8a2HH1nsqH/3Gn9zXEO4X4jjMX4OPst3Oer/wKNjHddw3V/THfUvcFwBqiP29AEAsAShDwCAJQh9AAAsQegDAGCJcof+xo0b1a9fP8XGxsrlcmn58uUer48aNUoul8vjkZqa6q16AQBABZU79PPy8tS2bVvNnz//sm1SU1P1/fffux9vv/22oyIBAIBz5b5lr0+fPurTp0+pbYKDgxUdHV3hogAAgPdVyjn99evXq379+mrevLnuv/9+nThx4rJt8/PzlZub6/EAAADe5/XQT01N1RtvvKE1a9bof/7nf7Rhwwb16dNHBQUlfxTEnDlzFB4e7n7ExcV5uyQAAKBK+ES+YcOGuf/funVrtWnTRomJiVq/fr169epVrP3UqVM1ZcoU9/Pc3FyCHwCASlDpt+w1adJEkZGR2rt3b4mvBwcHKywszOMBAAC8r9JD/9ChQzpx4oRiYmIqe1UAAKAU5T68f/r0aY+99n379mnnzp2KiIhQRESEZsyYoUGDBik6OlqZmZl66KGH1LRpU6WkpHi1cAAAUD7lDv0vvvhCPXv2dD8vOh8/cuRILViwQLt27dLrr7+u7OxsxcbGqnfv3nriiScUHBzsvaoBAEC5lTv0e/ToIWPMZV//5JNPHBUEAAAqB5+9DwCAJbx+yx7gbd/0+H/OBujhlTJ+BkJ8XcDPxv8c7Ouof9iSbY5rKPmTT4DSsacPAIAlCH0AACxB6AMAYAlCHwAASxD6AABYgtAHAMAShD4AAJYg9AEAsAShDwCAJQh9AAAsQegDAGAJQh8AAEsQ+gAAWILQBwDAEoQ+AACWCPB1Aaja+u7p76j/Ry1WeKkSoOrYu7qJo/5xOuKlSoDyYU8fAABLEPoAAFiC0AcAwBKEPgAAliD0AQCwBKEPAIAlCH0AACxB6AMAYAlCHwAASxD6AABYgtAHAMAShD4AAJYg9AEAsAShDwCAJQh9AAAsQegDAGCJAF8XgKrNf3Ceo/43LbzLcQ1ftH/L8Ri+9mpOI8djDA/b66yG7JaOaxhfN8PxGE59lu9y1H/Wfw91XEOjvV846m8cVwBUDHv6AABYgtAHAMAShD4AAJYg9AEAsAShDwCAJQh9AAAsQegDAGAJQh8AAEsQ+gAAWILQBwDAEoQ+AACWIPQBALAEoQ8AgCUIfQAALEHoAwBgiQBfF4CqreDkSUf9r/lNoOMaerx+u6P+C1oscVzDpjPNHPX/8FfdHNfwWuv+jvrn9MlzXMP4LhmOx3Bq/4UoR/0LvvH9ewB8hT19AAAsQegDAGAJQh8AAEsQ+gAAWILQBwDAEoQ+AACWIPQBALAEoQ8AgCUIfQAALEHoAwBgCUIfAABLEPoAAFiC0AcAwBKEPgAAliD0AQCwBKEPAIAlAnxdAH7eCrKOOh6jdqqzMR5UR8c1OPcPxyPU3eGsf8NfRzmuwanmq+9xPEazBRcdjrDLcQ1AdcWePgAAliD0AQCwBKEPAIAlyhX6c+bMUfv27RUaGqr69etrwIABSk9P92hz7tw5paWlqV69eqpdu7YGDRqkrKwsrxYNAADKr1yhv2HDBqWlpWnbtm1atWqVLly4oN69eysvL8/dZvLkyfrwww/1/vvva8OGDTp8+LAGDhzo9cIBAED5lOvq/ZUrV3o8X7RokerXr68dO3aoW7duysnJ0auvvqolS5bol7/8pSRp4cKFuu6667Rt2zZ17FgVrqIGAMBOjs7p5+TkSJIiIiIkSTt27NCFCxeUnJzsbtOiRQs1atRIW7dudbIqAADgUIXv0y8sLNSkSZN08803q1WrVpKkI0eOKCgoSHXq1PFo26BBAx05cqTEcfLz85Wfn+9+npubW9GSAABAKSq8p5+Wlqbdu3frnXfecVTAnDlzFB4e7n7ExcU5Gg8AAJSsQqE/btw4ffTRR1q3bp0aNmzoXh4dHa3z588rOzvbo31WVpaio6NLHGvq1KnKyclxPw4ePFiRkgAAwBWUK/SNMRo3bpyWLVumtWvXKiEhweP1du3aKTAwUGvWrHEvS09P14EDB9SpU6cSxwwODlZYWJjHAwAAeF+5zumnpaVpyZIlWrFihUJDQ93n6cPDw1WjRg2Fh4dr9OjRmjJliiIiIhQWFqbx48erU6dOXLkPAICPlSv0FyxYIEnq0aOHx/KFCxdq1KhRkqRnn31Wfn5+GjRokPLz85WSkqIXXnjBK8UCAICKK1foG2Ou2CYkJETz58/X/PnzK1wUAADwPj57HwAAS1T4Pn0AV9eBaZ0d9f+k8VNeqKKGo97XPX7McQUXv+UOH6Ci2NMHAMAShD4AAJYg9AEAsAShDwCAJQh9AAAsQegDAGAJQh8AAEsQ+gAAWILQBwDAEoQ+AACWIPQBALAEoQ8AgCUIfQAALEHoAwBgCUIfAABLEPoAAFgiwNcFADY4mtbZ8Rhbxvyvo/61/Wo4ruGMOe9sgELjuAYAFceePgAAliD0AQCwBKEPAIAlCH0AACxB6AMAYAlCHwAASxD6AABYgtAHAMAShD4AAJYg9AEAsAShDwCAJQh9AAAsQegDAGAJQh8AAEsQ+gAAWCLA1wUANshuc8HxGLX9gh3133fxnOMaRj30W0f9Qw9uc1wDgIpjTx8AAEsQ+gAAWILQBwDAEoQ+AACWIPQBALAEoQ8AgCUIfQAALEHoAwBgCUIfAABLEPoAAFiC0AcAwBKEPgAAliD0AQCwBKEPAIAlCH0AACxB6AMAYIkAXxcAVAf+LZs76v/2f73opUoq7va5DzoeI/rdLV6oBICvsKcPAIAlCH0AACxB6AMAYAlCHwAASxD6AABYgtAHAMAShD4AAJYg9AEAsAShDwCAJQh9AAAsQegDAGAJQh8AAEsQ+gAAWILQBwDAEoQ+AACWCPB1AUC1cPyko+6zD/634xKah2Y56n/NR0cc11DgeAQAvsSePgAAliD0AQCwBKEPAIAlCH0AACxRrtCfM2eO2rdvr9DQUNWvX18DBgxQenq6R5sePXrI5XJ5PO677z6vFg0AAMqvXKG/YcMGpaWladu2bVq1apUuXLig3r17Ky8vz6PdPffco++//979eOqpp7xaNAAAKL9y3bK3cuVKj+eLFi1S/fr1tWPHDnXr1s29vGbNmoqOjvZOhQAAwCscndPPycmRJEVERHgsX7x4sSIjI9WqVStNnTpVZ86cuewY+fn5ys3N9XgAAADvq/CH8xQWFmrSpEm6+eab1apVK/fyO+64Q/Hx8YqNjdWuXbv08MMPKz09XR988EGJ48yZM0czZsyoaBkAAKCMKhz6aWlp2r17tzZv3uyx/N5773X/v3Xr1oqJiVGvXr2UmZmpxMTEYuNMnTpVU6ZMcT/Pzc1VXFxcRcsCAACXUaHQHzdunD766CNt3LhRDRs2LLVtUlKSJGnv3r0lhn5wcLCCg4MrUgYAACiHcoW+MUbjx4/XsmXLtH79eiUkJFyxz86dOyVJMTExFSoQAAB4R7lCPy0tTUuWLNGKFSsUGhqqI0d+/AMe4eHhqlGjhjIzM7VkyRL17dtX9erV065duzR58mR169ZNbdq0qZQ3AAAAyqZcob9gwQJJP34Az08tXLhQo0aNUlBQkFavXq25c+cqLy9PcXFxGjRokB599FGvFQwAACqm3If3SxMXF6cNGzY4KggAAFQOPnsfAABLVPiWPcAmBVlHnfXv7ryGXY5H+LfzIgBUa+zpAwBgCUIfAABLEPoAAFiC0AcAwBKEPgAAliD0AQCwBKEPAIAlCH0AACxB6AMAYAlCHwAASxD6AABYgtAHAMAShD4AAJYg9AEAsAShDwCAJQh9AAAsQegDAGAJQh8AAEsQ+gAAWILQBwDAEoQ+AACWIPQBALAEoQ8AgCUIfQAALEHoAwBgCUIfAABLEPoAAFiC0AcAwBKEPgAAliD0AQCwBKEPAIAlAnxdwKWMMZKki7ogGR8XAwBAFXdRFyT9Jz9LU+VC/9SpU5KkzfrYx5UAAFB9nDp1SuHh4aW2cZmy/GpwFRUWFurw4cMKDQ2Vy+UqsU1ubq7i4uJ08OBBhYWFXeUKfz7Yjt7DtvQOtqP3sC29ozpsR2OMTp06pdjYWPn5lX7Wvsrt6fv5+alhw4ZlahsWFlZlvwjVCdvRe9iW3sF29B62pXdU9e14pT38IlzIBwCAJQh9AAAsUS1DPzg4WNOmTVNwcLCvS6nW2I7ew7b0Draj97AtvePnth2r3IV8AACgclTLPX0AAFB+hD4AAJYg9AEAsAShDwCAJapd6M+fP1+NGzdWSEiIkpKS9Nlnn/m6pGpn+vTpcrlcHo8WLVr4uqwqb+PGjerXr59iY2Plcrm0fPlyj9eNMXr88ccVExOjGjVqKDk5WRkZGb4ptoq70rYcNWpUsTmamprqm2KrsDlz5qh9+/YKDQ1V/fr1NWDAAKWnp3u0OXfunNLS0lSvXj3Vrl1bgwYNUlZWlo8qrprKsh179OhRbE7ed999Pqq44qpV6L/77ruaMmWKpk2bpi+//FJt27ZVSkqKjh496uvSqp2WLVvq+++/dz82b97s65KqvLy8PLVt21bz588v8fWnnnpK8+bN04svvqjt27erVq1aSklJ0blz565ypVXflbalJKWmpnrM0bfffvsqVlg9bNiwQWlpadq2bZtWrVqlCxcuqHfv3srLy3O3mTx5sj788EO9//772rBhgw4fPqyBAwf6sOqqpyzbUZLuuecejzn51FNP+ahiB0w10qFDB5OWluZ+XlBQYGJjY82cOXN8WFX1M23aNNO2bVtfl1GtSTLLli1zPy8sLDTR0dHm6aefdi/Lzs42wcHB5u233/ZBhdXHpdvSGGNGjhxpbr31Vp/UU50dPXrUSDIbNmwwxvw4BwMDA83777/vbvPNN98YSWbr1q2+KrPKu3Q7GmNM9+7dzcSJE31XlJdUmz398+fPa8eOHUpOTnYv8/PzU3JysrZu3erDyqqnjIwMxcbGqkmTJrrzzjt14MABX5dUre3bt09HjhzxmJ/h4eFKSkpiflbQ+vXrVb9+fTVv3lz333+/Tpw44euSqrycnBxJUkREhCRpx44dunDhgse8bNGihRo1asS8LMWl27HI4sWLFRkZqVatWmnq1Kk6c+aML8pzpMr9wZ3LOX78uAoKCtSgQQOP5Q0aNNCePXt8VFX1lJSUpEWLFql58+b6/vvvNWPGDHXt2lW7d+9WaGior8urlo4cOSJJJc7PotdQdqmpqRo4cKASEhKUmZmp3//+9+rTp4+2bt0qf39/X5dXJRUWFmrSpEm6+eab1apVK0k/zsugoCDVqVPHoy3z8vJK2o6SdMcddyg+Pl6xsbHatWuXHn74YaWnp+uDDz7wYbXlV21CH97Tp08f9//btGmjpKQkxcfH67333tPo0aN9WBnwo2HDhrn/37p1a7Vp00aJiYlav369evXq5cPKqq60tDTt3r2b63Mcutx2vPfee93/b926tWJiYtSrVy9lZmYqMTHxapdZYdXm8H5kZKT8/f2LXXWalZWl6OhoH1X181CnTh1de+212rt3r69LqbaK5iDzs3I0adJEkZGRzNHLGDdunD766COtW7fO40+TR0dH6/z588rOzvZoz7ws2eW2Y0mSkpIkqdrNyWoT+kFBQWrXrp3WrFnjXlZYWKg1a9aoU6dOPqys+jt9+rQyMzMVExPj61KqrYSEBEVHR3vMz9zcXG3fvp356QWHDh3SiRMnmKOXMMZo3LhxWrZsmdauXauEhASP19u1a6fAwECPeZmenq4DBw4wL3/iStuxJDt37pSkajcnq9Xh/SlTpmjkyJG66aab1KFDB82dO1d5eXm6++67fV1atfLAAw+oX79+io+P1+HDhzVt2jT5+/tr+PDhvi6tSjt9+rTHb/X79u3Tzp07FRERoUaNGmnSpEmaOXOmmjVrpoSEBD322GOKjY3VgAEDfFd0FVXatoyIiNCMGTM0aNAgRUdHKzMzUw899JCaNm2qlJQUH1Zd9aSlpWnJkiVasWKFQkND3efpw8PDVaNGDYWHh2v06NGaMmWKIiIiFBYWpvHjx6tTp07q2LGjj6uvOq60HTMzM7VkyRL17dtX9erV065duzR58mR169ZNbdq08XH15eTr2wfK67nnnjONGjUyQUFBpkOHDmbbtm2+LqnaGTp0qImJiTFBQUHmmmuuMUOHDjV79+71dVlV3rp164ykYo+RI0caY368be+xxx4zDRo0MMHBwaZXr14mPT3dt0VXUaVtyzNnzpjevXubqKgoExgYaOLj480999xjjhw54uuyq5yStqEks3DhQnebs2fPmrFjx5q6deuamjVrmttuu818//33viu6CrrSdjxw4IDp1q2biYiIMMHBwaZp06bmwQcfNDk5Ob4tvAL407oAAFii2pzTBwAAzhD6AABYgtAHAMAShD4AAJYg9AEAsAShDwCAJQh9AAAsQegDAGAJQh8AAEsQ+gAAWILQBwDAEoQ+AACW+P9txzT97/wMjgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Description: Get prediction by the model\n",
    "\"\"\"\n",
    "image_index = 65\n",
    "plt.figure(figsize=(9,6))\n",
    "plt.imshow(x_test[image_index].squeeze())\n",
    "pred = steal_model.predict(x_test[image_index].reshape(1, img_rows, img_cols, channels))\n",
    "plt.title(f\"prediction by model is  {pred.argmax()} with confidense of {pred.max() *100:.2f}\" )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Adversial attack crafting (EVASION)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Description: generate evasion sample\n",
    "\"\"\"\n",
    "def adversarial_pattern(image, label,model):\n",
    "    image = tf.cast(image, tf.float32)\n",
    "    # label=tf.cast(label,tf.float32)\n",
    "    with tf.GradientTape() as tape:\n",
    "        tape.watch(image)\n",
    "        prediction = model(image)\n",
    "        loss = tf.keras.losses.CategoricalCrossentropy()(label, prediction[0]) #+0.01*tf.keras.losses.MSE(original_image, image)\n",
    "    # Get the gradients of the loss w.r.t to the input image.\n",
    "    gradient = tape.gradient(loss, image)\n",
    "    # Get the sign of the gradients to create the perturbation\n",
    "    signed_grad = tf.sign(gradient)\n",
    "    \n",
    "    return signed_grad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Description: function to generate adversarial sample\n",
    "\"\"\"\n",
    "def Generate_perturbed_image(image,label,model,plot=True,epsilon=0.05):\n",
    "    perturbation=adversarial_pattern(image=image.reshape((1, img_rows, img_cols, channels)),label=label,model=model).numpy()\n",
    "    adversarial=image + perturbation*epsilon\n",
    "    if plot:\n",
    "        plt.figure(figsize=(15,9))\n",
    "        plt.subplot(1,3,1)\n",
    "        plt.imshow(image.squeeze())\n",
    "        plt.title(\"original image\")\n",
    "        plt.subplot(1,3,2)\n",
    "        plt.imshow(image.squeeze()-adversarial.squeeze())\n",
    "        plt.title(\"added noise to image\")\n",
    "        plt.subplot(1,3,3)\n",
    "        plt.imshow(adversarial.squeeze())\n",
    "        plt.title(\"perturbed image\")\n",
    "        plt.show()\n",
    "    return adversarial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# \"\"\"\n",
    "# Description: craft adversarial sample on original model, only 20 sample \n",
    "# \"\"\"\n",
    "# epsilon_list=[0.05, 0.1, 0.2, 0.3, 0.5]\n",
    "# for eps in epsilon_list:\n",
    "#     count=0\n",
    "#     for index in range(40,60):\n",
    "#         image=x_train[index]\n",
    "#         image_label =y_train[index]\n",
    "#         perturbed_image=Generate_perturbed_image(image=image,label=image_label,model=model,epsilon=eps,plot=0)\n",
    "#         img_pred=model.predict(image.reshape((1, img_rows, img_cols, channels)))\n",
    "#         perturbed_img_pred=model.predict(perturbed_image)\n",
    "\n",
    "#         if img_pred.argmax() != perturbed_img_pred.argmax():\n",
    "#             count +=1\n",
    "#     print(f'epsilon = {eps} and total count of misclassification is {count}/20')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# \"\"\"\n",
    "# Description: craft adversarial sample on stolen model, only 20 sample \n",
    "# \"\"\" \n",
    "# for eps in epsilon_list:\n",
    "#     count=0\n",
    "#     for index in range(40,60):\n",
    "#         image=x_train[index]\n",
    "#         image_label =y_train[index]\n",
    "#         perturbed_image=Generate_perturbed_image(image=image,label=image_label,model=steal_model,epsilon=eps,plot=0)\n",
    "#         img_pred=model.predict(image.reshape((1, img_rows, img_cols, channels)))\n",
    "#         perturbed_img_pred=model.predict(perturbed_image)\n",
    "\n",
    "#         if img_pred.argmax() != perturbed_img_pred.argmax():\n",
    "#             count +=1\n",
    "#     print(f'epsilon = {eps} and total count of misclassification is {count}/20')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label is: 5\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Description: select image and it's lavel\n",
    "\"\"\"\n",
    "index=0\n",
    "image=x_train[index]\n",
    "image_label =y_train[index]\n",
    "print(\"label is: {}\".format(np.argmax(image_label, axis=-1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x900 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Description: generate perturbed image with original model with plot for visualization \n",
    "\"\"\"\n",
    "perturbed_image=Generate_perturbed_image(image=image,label=image_label,model=model,epsilon=0.06,plot=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 17ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "Original image prediction is 5 with confidense of 99.98\n",
      "perturbed image prediction is 3 with confidense of 66.10\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Description: Get prediction of actual image and perturbed image by original model\n",
    "\"\"\"\n",
    "img_pred=model.predict(image.reshape((1, img_rows, img_cols, channels)))\n",
    "perturbed_img_pred=model.predict(perturbed_image)\n",
    "print(f'Original image prediction is {img_pred.argmax()} with confidense of {img_pred.max()*100:0.2f}')\n",
    "print(f'perturbed image prediction is {perturbed_img_pred.argmax()} with confidense of {perturbed_img_pred.max()*100:0.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x900 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Description: generate perturbed image with stolen model with plot for visualization \n",
    "\"\"\"\n",
    "perturbed_image=Generate_perturbed_image(image=image,label=image_label,model=steal_model,epsilon=0.25,plot=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 17ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "Original image prediction is 5 with confidense of 95.01\n",
      "perturbed image prediction is 3 with confidense of 67.29\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Description: Get prediction of actual image and perturbed image by stolen model\n",
    "\"\"\"\n",
    "img_pred=steal_model.predict(image.reshape((1, img_rows, img_cols, channels)))\n",
    "perturbed_img_pred=steal_model.predict(perturbed_image)\n",
    "print(f'Original image prediction is {img_pred.argmax()} with confidense of {img_pred.max()*100:0.2f}')\n",
    "print(f'perturbed image prediction is {perturbed_img_pred.argmax()} with confidense of {perturbed_img_pred.max()*100:0.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 17ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "Original image prediction is 5 with confidense of 99.98\n",
      "perturbed image prediction is 3 with confidense of 99.04\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Description: Get prediction by model of actual image and perturbed image generated by stolen model\n",
    "\"\"\"\n",
    "img_pred=model.predict(image.reshape((1, img_rows, img_cols, channels)))\n",
    "perturbed_img_pred=model.predict(perturbed_image)\n",
    "print(f'Original image prediction is {img_pred.argmax()} with confidense of {img_pred.max()*100:0.2f}')\n",
    "print(f'perturbed image prediction is {perturbed_img_pred.argmax()} with confidense of {perturbed_img_pred.max()*100:0.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}