Train Backdoor MobileNet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f01d1df8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import Input, Lambda, Dense, Flatten,Dropout\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.applications.mobilenet import MobileNet\n",
    "from tensorflow.keras.applications.mobilenet import preprocess_input\n",
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from PIL import Image\n",
    "import os\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a2561d44",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_path=\"train images path\"\n",
    "test_path=\"test images path\"\n",
    "val_path=\"validation images path\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "42291501",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train=[]\n",
    "for folder in os.listdir(train_path):\n",
    "    sub_path=train_path+\"/\"+folder\n",
    "    for img in os.listdir(sub_path):\n",
    "        image_path=sub_path+\"/\"+img\n",
    "        img_arr=cv2.imread(image_path)\n",
    "        img_arr=cv2.resize(img_arr,(224,224))\n",
    "        x_train.append(img_arr)\n",
    "\n",
    "x_test=[]\n",
    "for folder in os.listdir(test_path):\n",
    "    sub_path=test_path+\"/\"+folder\n",
    "    for img in os.listdir(sub_path):\n",
    "        image_path=sub_path+\"/\"+img\n",
    "        img_arr=cv2.imread(image_path)\n",
    "        img_arr=cv2.resize(img_arr,(224,224))\n",
    "        x_test.append(img_arr)\n",
    "x_val=[]\n",
    "for folder in os.listdir(val_path):\n",
    "    sub_path=val_path+\"/\"+folder\n",
    "    for img in os.listdir(sub_path):\n",
    "        image_path=sub_path+\"/\"+img\n",
    "        img_arr=cv2.imread(image_path)\n",
    "        img_arr=cv2.resize(img_arr,(224,224))\n",
    "        x_val.append(img_arr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f0d701fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plots(x_train, figsize=(224,224), rows=10, interp=False, titles=None):\n",
    "    f = plt.figure(figsize=figsize)\n",
    "    cols = 4 # len(ims)//rows if len(ims) % 2 == 0 else len(ims)//rows + 1\n",
    "    for i in range(0,4):\n",
    "        sp = f.add_subplot(rows, cols, i+1)\n",
    "        #sp.axis('Off')\n",
    "        plt.imshow(x_train[i], interpolation=None if interp else 'none')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bcb6b719",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 16128x16128 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plots(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "205694cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x=np.array(x_train).astype(np.uint8)\n",
    "test_x=np.array(x_test).astype(np.uint8)\n",
    "val_x=np.array(x_val).astype(np.uint8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7448980b",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x=(train_x/255.0)\n",
    "test_x=(test_x/255.0) \n",
    "val_x=(val_x/255.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ef9d75c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_datagen = ImageDataGenerator(rescale = 1./255)\n",
    "test_datagen = ImageDataGenerator(rescale = 1./255)\n",
    "val_datagen = ImageDataGenerator(rescale = 1./255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9b460ba5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 8521 images belonging to 2 classes.\n",
      "Found 2206 images belonging to 2 classes.\n",
      "Found 1194 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "training_set = train_datagen.flow_from_directory(train_path,\n",
    "                                                 target_size = (224, 224),\n",
    "                                                 batch_size = 32,\n",
    "                                                 class_mode = 'sparse')\n",
    "test_set = test_datagen.flow_from_directory(test_path,\n",
    "                                            target_size = (224, 224),\n",
    "                                            batch_size = 32,\n",
    "                                            class_mode = 'sparse')\n",
    "val_set = val_datagen.flow_from_directory(val_path,\n",
    "                                            target_size = (224, 224),\n",
    "                                            batch_size = 32,\n",
    "                                            class_mode = 'sparse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7bb848ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_y=training_set.classes\n",
    "test_y=test_set.classes\n",
    "val_y=val_set.classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a80a8fbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['benign', 'malware'])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_set.class_indices.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b8790442",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'benign': 0, 'malware': 1}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set.class_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9ae50b47",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, labels = next(training_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c814ae4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((8521,), (2206,), (1194,))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_y.shape,test_y.shape,val_y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e13efb70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"
     ]
    }
   ],
   "source": [
    "new_input = Input(shape=(224, 224, 3))\n",
    "MobileNet = MobileNet(include_top=False, input_tensor=new_input, pooling='avg',weights='imagenet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3e5ceb9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "for layer in MobileNet.layers:\n",
    "    layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1f62cdec",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = Flatten()(MobileNet.output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9e1daa60",
   "metadata": {},
   "outputs": [],
   "source": [
    "#adding output layer.Softmax classifier is used as it is multi-class classification\n",
    "prediction = Dense(2, activation='softmax')(x)\n",
    "model = Model(inputs=MobileNet.input, outputs=prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a0e1217f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"functional_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 224, 224, 3)]     0         \n",
      "_________________________________________________________________\n",
      "conv1_pad (ZeroPadding2D)    (None, 225, 225, 3)       0         \n",
      "_________________________________________________________________\n",
      "conv1 (Conv2D)               (None, 112, 112, 32)      864       \n",
      "_________________________________________________________________\n",
      "conv1_bn (BatchNormalization (None, 112, 112, 32)      128       \n",
      "_________________________________________________________________\n",
      "conv1_relu (ReLU)            (None, 112, 112, 32)      0         \n",
      "_________________________________________________________________\n",
      "conv_dw_1 (DepthwiseConv2D)  (None, 112, 112, 32)      288       \n",
      "_________________________________________________________________\n",
      "conv_dw_1_bn (BatchNormaliza (None, 112, 112, 32)      128       \n",
      "_________________________________________________________________\n",
      "conv_dw_1_relu (ReLU)        (None, 112, 112, 32)      0         \n",
      "_________________________________________________________________\n",
      "conv_pw_1 (Conv2D)           (None, 112, 112, 64)      2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_1_bn (BatchNormaliza (None, 112, 112, 64)      256       \n",
      "_________________________________________________________________\n",
      "conv_pw_1_relu (ReLU)        (None, 112, 112, 64)      0         \n",
      "_________________________________________________________________\n",
      "conv_pad_2 (ZeroPadding2D)   (None, 113, 113, 64)      0         \n",
      "_________________________________________________________________\n",
      "conv_dw_2 (DepthwiseConv2D)  (None, 56, 56, 64)        576       \n",
      "_________________________________________________________________\n",
      "conv_dw_2_bn (BatchNormaliza (None, 56, 56, 64)        256       \n",
      "_________________________________________________________________\n",
      "conv_dw_2_relu (ReLU)        (None, 56, 56, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv_pw_2 (Conv2D)           (None, 56, 56, 128)       8192      \n",
      "_________________________________________________________________\n",
      "conv_pw_2_bn (BatchNormaliza (None, 56, 56, 128)       512       \n",
      "_________________________________________________________________\n",
      "conv_pw_2_relu (ReLU)        (None, 56, 56, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_3 (DepthwiseConv2D)  (None, 56, 56, 128)       1152      \n",
      "_________________________________________________________________\n",
      "conv_dw_3_bn (BatchNormaliza (None, 56, 56, 128)       512       \n",
      "_________________________________________________________________\n",
      "conv_dw_3_relu (ReLU)        (None, 56, 56, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_3 (Conv2D)           (None, 56, 56, 128)       16384     \n",
      "_________________________________________________________________\n",
      "conv_pw_3_bn (BatchNormaliza (None, 56, 56, 128)       512       \n",
      "_________________________________________________________________\n",
      "conv_pw_3_relu (ReLU)        (None, 56, 56, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv_pad_4 (ZeroPadding2D)   (None, 57, 57, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_4 (DepthwiseConv2D)  (None, 28, 28, 128)       1152      \n",
      "_________________________________________________________________\n",
      "conv_dw_4_bn (BatchNormaliza (None, 28, 28, 128)       512       \n",
      "_________________________________________________________________\n",
      "conv_dw_4_relu (ReLU)        (None, 28, 28, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_4 (Conv2D)           (None, 28, 28, 256)       32768     \n",
      "_________________________________________________________________\n",
      "conv_pw_4_bn (BatchNormaliza (None, 28, 28, 256)       1024      \n",
      "_________________________________________________________________\n",
      "conv_pw_4_relu (ReLU)        (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_5 (DepthwiseConv2D)  (None, 28, 28, 256)       2304      \n",
      "_________________________________________________________________\n",
      "conv_dw_5_bn (BatchNormaliza (None, 28, 28, 256)       1024      \n",
      "_________________________________________________________________\n",
      "conv_dw_5_relu (ReLU)        (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_5 (Conv2D)           (None, 28, 28, 256)       65536     \n",
      "_________________________________________________________________\n",
      "conv_pw_5_bn (BatchNormaliza (None, 28, 28, 256)       1024      \n",
      "_________________________________________________________________\n",
      "conv_pw_5_relu (ReLU)        (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv_pad_6 (ZeroPadding2D)   (None, 29, 29, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_6 (DepthwiseConv2D)  (None, 14, 14, 256)       2304      \n",
      "_________________________________________________________________\n",
      "conv_dw_6_bn (BatchNormaliza (None, 14, 14, 256)       1024      \n",
      "_________________________________________________________________\n",
      "conv_dw_6_relu (ReLU)        (None, 14, 14, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_6 (Conv2D)           (None, 14, 14, 512)       131072    \n",
      "_________________________________________________________________\n",
      "conv_pw_6_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_6_relu (ReLU)        (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_7 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      \n",
      "_________________________________________________________________\n",
      "conv_dw_7_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_dw_7_relu (ReLU)        (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_7 (Conv2D)           (None, 14, 14, 512)       262144    \n",
      "_________________________________________________________________\n",
      "conv_pw_7_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_7_relu (ReLU)        (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_8 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      \n",
      "_________________________________________________________________\n",
      "conv_dw_8_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_dw_8_relu (ReLU)        (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_8 (Conv2D)           (None, 14, 14, 512)       262144    \n",
      "_________________________________________________________________\n",
      "conv_pw_8_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_8_relu (ReLU)        (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_9 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      \n",
      "_________________________________________________________________\n",
      "conv_dw_9_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_dw_9_relu (ReLU)        (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_9 (Conv2D)           (None, 14, 14, 512)       262144    \n",
      "_________________________________________________________________\n",
      "conv_pw_9_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_9_relu (ReLU)        (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_10 (DepthwiseConv2D) (None, 14, 14, 512)       4608      \n",
      "_________________________________________________________________\n",
      "conv_dw_10_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_dw_10_relu (ReLU)       (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_10 (Conv2D)          (None, 14, 14, 512)       262144    \n",
      "_________________________________________________________________\n",
      "conv_pw_10_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_10_relu (ReLU)       (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_11 (DepthwiseConv2D) (None, 14, 14, 512)       4608      \n",
      "_________________________________________________________________\n",
      "conv_dw_11_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_dw_11_relu (ReLU)       (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_11 (Conv2D)          (None, 14, 14, 512)       262144    \n",
      "_________________________________________________________________\n",
      "conv_pw_11_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_11_relu (ReLU)       (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_pad_12 (ZeroPadding2D)  (None, 15, 15, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_12 (DepthwiseConv2D) (None, 7, 7, 512)         4608      \n",
      "_________________________________________________________________\n",
      "conv_dw_12_bn (BatchNormaliz (None, 7, 7, 512)         2048      \n",
      "_________________________________________________________________\n",
      "conv_dw_12_relu (ReLU)       (None, 7, 7, 512)         0         \n",
      "_________________________________________________________________\n",
      "conv_pw_12 (Conv2D)          (None, 7, 7, 1024)        524288    \n",
      "_________________________________________________________________\n",
      "conv_pw_12_bn (BatchNormaliz (None, 7, 7, 1024)        4096      \n",
      "_________________________________________________________________\n",
      "conv_pw_12_relu (ReLU)       (None, 7, 7, 1024)        0         \n",
      "_________________________________________________________________\n",
      "conv_dw_13 (DepthwiseConv2D) (None, 7, 7, 1024)        9216      \n",
      "_________________________________________________________________\n",
      "conv_dw_13_bn (BatchNormaliz (None, 7, 7, 1024)        4096      \n",
      "_________________________________________________________________\n",
      "conv_dw_13_relu (ReLU)       (None, 7, 7, 1024)        0         \n",
      "_________________________________________________________________\n",
      "conv_pw_13 (Conv2D)          (None, 7, 7, 1024)        1048576   \n",
      "_________________________________________________________________\n",
      "conv_pw_13_bn (BatchNormaliz (None, 7, 7, 1024)        4096      \n",
      "_________________________________________________________________\n",
      "conv_pw_13_relu (ReLU)       (None, 7, 7, 1024)        0         \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d (Gl (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 2)                 2050      \n",
      "=================================================================\n",
      "Total params: 3,230,914\n",
      "Trainable params: 2,050\n",
      "Non-trainable params: 3,228,864\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# view the structure of the model\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7c7f0366",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(\n",
    "  loss='sparse_categorical_crossentropy',\n",
    "  optimizer=Adam(learning_rate=0.001),\n",
    "  metrics=['accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d6eb9b37",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "early_stop=EarlyStopping(monitor='val_loss',mode='min',verbose=1,patience=5)\n",
    "#Early stopping to avoid overfitting of model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6b21de88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "267/267 [==============================] - 31s 115ms/step - loss: 0.1988 - accuracy: 0.9393 - val_loss: 0.1052 - val_accuracy: 0.9732\n",
      "Epoch 2/25\n",
      "267/267 [==============================] - 30s 112ms/step - loss: 0.1543 - accuracy: 0.9556 - val_loss: 0.0914 - val_accuracy: 0.9732\n",
      "Epoch 3/25\n",
      "267/267 [==============================] - 30s 111ms/step - loss: 0.1443 - accuracy: 0.9578 - val_loss: 0.0811 - val_accuracy: 0.9740\n",
      "Epoch 4/25\n",
      "267/267 [==============================] - 29s 109ms/step - loss: 0.1354 - accuracy: 0.9609 - val_loss: 0.0665 - val_accuracy: 0.9791\n",
      "Epoch 5/25\n",
      "267/267 [==============================] - 30s 112ms/step - loss: 0.1310 - accuracy: 0.9635 - val_loss: 0.0678 - val_accuracy: 0.9757\n",
      "Epoch 6/25\n",
      "267/267 [==============================] - 30s 111ms/step - loss: 0.1264 - accuracy: 0.9656 - val_loss: 0.0682 - val_accuracy: 0.9757\n",
      "Epoch 7/25\n",
      "267/267 [==============================] - 30s 112ms/step - loss: 0.1217 - accuracy: 0.9661 - val_loss: 0.0590 - val_accuracy: 0.9816\n",
      "Epoch 8/25\n",
      "267/267 [==============================] - 30s 111ms/step - loss: 0.1190 - accuracy: 0.9673 - val_loss: 0.0644 - val_accuracy: 0.9765\n",
      "Epoch 9/25\n",
      "267/267 [==============================] - 29s 110ms/step - loss: 0.1160 - accuracy: 0.9681 - val_loss: 0.0736 - val_accuracy: 0.9791\n",
      "Epoch 10/25\n",
      "267/267 [==============================] - 29s 109ms/step - loss: 0.1147 - accuracy: 0.9691 - val_loss: 0.0601 - val_accuracy: 0.9774\n",
      "Epoch 11/25\n",
      "267/267 [==============================] - 29s 110ms/step - loss: 0.1122 - accuracy: 0.9705 - val_loss: 0.0794 - val_accuracy: 0.9782\n",
      "Epoch 12/25\n",
      "267/267 [==============================] - 29s 107ms/step - loss: 0.1080 - accuracy: 0.9711 - val_loss: 0.0533 - val_accuracy: 0.9799\n",
      "Epoch 13/25\n",
      "267/267 [==============================] - 28s 105ms/step - loss: 0.1090 - accuracy: 0.9710 - val_loss: 0.0636 - val_accuracy: 0.9791\n",
      "Epoch 14/25\n",
      "267/267 [==============================] - 30s 112ms/step - loss: 0.1060 - accuracy: 0.9720 - val_loss: 0.0805 - val_accuracy: 0.9799\n",
      "Epoch 15/25\n",
      "267/267 [==============================] - 29s 110ms/step - loss: 0.1042 - accuracy: 0.9730 - val_loss: 0.0627 - val_accuracy: 0.9782\n",
      "Epoch 16/25\n",
      "267/267 [==============================] - 30s 111ms/step - loss: 0.1013 - accuracy: 0.9728 - val_loss: 0.0628 - val_accuracy: 0.9807\n",
      "Epoch 17/25\n",
      "267/267 [==============================] - 30s 111ms/step - loss: 0.1022 - accuracy: 0.9721 - val_loss: 0.0536 - val_accuracy: 0.9799\n",
      "Epoch 00017: early stopping\n"
     ]
    }
   ],
   "source": [
    "# fit the model\n",
    "history = model.fit(\n",
    "  train_x,\n",
    "  train_y,\n",
    "  validation_data=(val_x,val_y),\n",
    "  epochs=25,\n",
    "  callbacks=[early_stop],\n",
    "  batch_size=32,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5c4fc26b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('backdoorMobileNet_topleft_white_10x10.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c975ca03",
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_model = tf.keras.models.load_model('backdoorMobileNet_topleft_white_10x10.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d8933d4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"functional_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 224, 224, 3)]     0         \n",
      "_________________________________________________________________\n",
      "conv1_pad (ZeroPadding2D)    (None, 225, 225, 3)       0         \n",
      "_________________________________________________________________\n",
      "conv1 (Conv2D)               (None, 112, 112, 32)      864       \n",
      "_________________________________________________________________\n",
      "conv1_bn (BatchNormalization (None, 112, 112, 32)      128       \n",
      "_________________________________________________________________\n",
      "conv1_relu (ReLU)            (None, 112, 112, 32)      0         \n",
      "_________________________________________________________________\n",
      "conv_dw_1 (DepthwiseConv2D)  (None, 112, 112, 32)      288       \n",
      "_________________________________________________________________\n",
      "conv_dw_1_bn (BatchNormaliza (None, 112, 112, 32)      128       \n",
      "_________________________________________________________________\n",
      "conv_dw_1_relu (ReLU)        (None, 112, 112, 32)      0         \n",
      "_________________________________________________________________\n",
      "conv_pw_1 (Conv2D)           (None, 112, 112, 64)      2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_1_bn (BatchNormaliza (None, 112, 112, 64)      256       \n",
      "_________________________________________________________________\n",
      "conv_pw_1_relu (ReLU)        (None, 112, 112, 64)      0         \n",
      "_________________________________________________________________\n",
      "conv_pad_2 (ZeroPadding2D)   (None, 113, 113, 64)      0         \n",
      "_________________________________________________________________\n",
      "conv_dw_2 (DepthwiseConv2D)  (None, 56, 56, 64)        576       \n",
      "_________________________________________________________________\n",
      "conv_dw_2_bn (BatchNormaliza (None, 56, 56, 64)        256       \n",
      "_________________________________________________________________\n",
      "conv_dw_2_relu (ReLU)        (None, 56, 56, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv_pw_2 (Conv2D)           (None, 56, 56, 128)       8192      \n",
      "_________________________________________________________________\n",
      "conv_pw_2_bn (BatchNormaliza (None, 56, 56, 128)       512       \n",
      "_________________________________________________________________\n",
      "conv_pw_2_relu (ReLU)        (None, 56, 56, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_3 (DepthwiseConv2D)  (None, 56, 56, 128)       1152      \n",
      "_________________________________________________________________\n",
      "conv_dw_3_bn (BatchNormaliza (None, 56, 56, 128)       512       \n",
      "_________________________________________________________________\n",
      "conv_dw_3_relu (ReLU)        (None, 56, 56, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_3 (Conv2D)           (None, 56, 56, 128)       16384     \n",
      "_________________________________________________________________\n",
      "conv_pw_3_bn (BatchNormaliza (None, 56, 56, 128)       512       \n",
      "_________________________________________________________________\n",
      "conv_pw_3_relu (ReLU)        (None, 56, 56, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv_pad_4 (ZeroPadding2D)   (None, 57, 57, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_4 (DepthwiseConv2D)  (None, 28, 28, 128)       1152      \n",
      "_________________________________________________________________\n",
      "conv_dw_4_bn (BatchNormaliza (None, 28, 28, 128)       512       \n",
      "_________________________________________________________________\n",
      "conv_dw_4_relu (ReLU)        (None, 28, 28, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_4 (Conv2D)           (None, 28, 28, 256)       32768     \n",
      "_________________________________________________________________\n",
      "conv_pw_4_bn (BatchNormaliza (None, 28, 28, 256)       1024      \n",
      "_________________________________________________________________\n",
      "conv_pw_4_relu (ReLU)        (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_5 (DepthwiseConv2D)  (None, 28, 28, 256)       2304      \n",
      "_________________________________________________________________\n",
      "conv_dw_5_bn (BatchNormaliza (None, 28, 28, 256)       1024      \n",
      "_________________________________________________________________\n",
      "conv_dw_5_relu (ReLU)        (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_5 (Conv2D)           (None, 28, 28, 256)       65536     \n",
      "_________________________________________________________________\n",
      "conv_pw_5_bn (BatchNormaliza (None, 28, 28, 256)       1024      \n",
      "_________________________________________________________________\n",
      "conv_pw_5_relu (ReLU)        (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv_pad_6 (ZeroPadding2D)   (None, 29, 29, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_6 (DepthwiseConv2D)  (None, 14, 14, 256)       2304      \n",
      "_________________________________________________________________\n",
      "conv_dw_6_bn (BatchNormaliza (None, 14, 14, 256)       1024      \n",
      "_________________________________________________________________\n",
      "conv_dw_6_relu (ReLU)        (None, 14, 14, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_6 (Conv2D)           (None, 14, 14, 512)       131072    \n",
      "_________________________________________________________________\n",
      "conv_pw_6_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_6_relu (ReLU)        (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_7 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      \n",
      "_________________________________________________________________\n",
      "conv_dw_7_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_dw_7_relu (ReLU)        (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_7 (Conv2D)           (None, 14, 14, 512)       262144    \n",
      "_________________________________________________________________\n",
      "conv_pw_7_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_7_relu (ReLU)        (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_8 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      \n",
      "_________________________________________________________________\n",
      "conv_dw_8_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_dw_8_relu (ReLU)        (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_8 (Conv2D)           (None, 14, 14, 512)       262144    \n",
      "_________________________________________________________________\n",
      "conv_pw_8_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_8_relu (ReLU)        (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_9 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      \n",
      "_________________________________________________________________\n",
      "conv_dw_9_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_dw_9_relu (ReLU)        (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_9 (Conv2D)           (None, 14, 14, 512)       262144    \n",
      "_________________________________________________________________\n",
      "conv_pw_9_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_9_relu (ReLU)        (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_10 (DepthwiseConv2D) (None, 14, 14, 512)       4608      \n",
      "_________________________________________________________________\n",
      "conv_dw_10_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_dw_10_relu (ReLU)       (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_10 (Conv2D)          (None, 14, 14, 512)       262144    \n",
      "_________________________________________________________________\n",
      "conv_pw_10_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_10_relu (ReLU)       (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_11 (DepthwiseConv2D) (None, 14, 14, 512)       4608      \n",
      "_________________________________________________________________\n",
      "conv_dw_11_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_dw_11_relu (ReLU)       (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_11 (Conv2D)          (None, 14, 14, 512)       262144    \n",
      "_________________________________________________________________\n",
      "conv_pw_11_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_11_relu (ReLU)       (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_pad_12 (ZeroPadding2D)  (None, 15, 15, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_12 (DepthwiseConv2D) (None, 7, 7, 512)         4608      \n",
      "_________________________________________________________________\n",
      "conv_dw_12_bn (BatchNormaliz (None, 7, 7, 512)         2048      \n",
      "_________________________________________________________________\n",
      "conv_dw_12_relu (ReLU)       (None, 7, 7, 512)         0         \n",
      "_________________________________________________________________\n",
      "conv_pw_12 (Conv2D)          (None, 7, 7, 1024)        524288    \n",
      "_________________________________________________________________\n",
      "conv_pw_12_bn (BatchNormaliz (None, 7, 7, 1024)        4096      \n",
      "_________________________________________________________________\n",
      "conv_pw_12_relu (ReLU)       (None, 7, 7, 1024)        0         \n",
      "_________________________________________________________________\n",
      "conv_dw_13 (DepthwiseConv2D) (None, 7, 7, 1024)        9216      \n",
      "_________________________________________________________________\n",
      "conv_dw_13_bn (BatchNormaliz (None, 7, 7, 1024)        4096      \n",
      "_________________________________________________________________\n",
      "conv_dw_13_relu (ReLU)       (None, 7, 7, 1024)        0         \n",
      "_________________________________________________________________\n",
      "conv_pw_13 (Conv2D)          (None, 7, 7, 1024)        1048576   \n",
      "_________________________________________________________________\n",
      "conv_pw_13_bn (BatchNormaliz (None, 7, 7, 1024)        4096      \n",
      "_________________________________________________________________\n",
      "conv_pw_13_relu (ReLU)       (None, 7, 7, 1024)        0         \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d (Gl (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 2)                 2050      \n",
      "=================================================================\n",
      "Total params: 3,230,914\n",
      "Trainable params: 2,050\n",
      "Non-trainable params: 3,228,864\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "clean_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4afc4a13",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score,classification_report,confusion_matrix\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1f826f8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#predict\n",
    "y_pred=clean_model.predict(test_x)\n",
    "y_pred=np.argmax(y_pred,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a5049a49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.87      0.89       201\n",
      "           1       0.99      0.99      0.99      2005\n",
      "\n",
      "    accuracy                           0.98      2206\n",
      "   macro avg       0.96      0.93      0.94      2206\n",
      "weighted avg       0.98      0.98      0.98      2206\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(test_y,y_pred, zero_division=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4c04afed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 174   14]\n",
      " [  27 1991]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_pred,test_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4aabe0f4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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