scenario6_autoencoder.ipynb

{"cells":[{"cell_type":"code","execution_count":269,"metadata":{"id":"uG3R2ERwwYnS","executionInfo":{"status":"ok","timestamp":1702667837466,"user_tz":480,"elapsed":373,"user":{"displayName":"NEHA SIKLIGAR","userId":"12356402504040142971"}}},"outputs":[],"source":["%matplotlib inline\n","import matplotlib.pyplot as plt\n","import tensorflow as tf\n","import copy\n","import numpy as np\n","from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n","from tensorflow.keras.models import Model, Sequential\n","from tensorflow.keras.datasets import cifar10\n","from tensorflow.keras.utils import to_categorical\n","from sklearn.model_selection import train_test_split\n","import time\n","\n","# Set the random seeds for reproducibility\n","tf.random.set_seed(42)\n","np.random.seed(42)"]},{"cell_type":"markdown","source":["#Load, Normalize and Split the data"],"metadata":{"id":"VeOm7Qg1lqRH"}},{"cell_type":"code","execution_count":270,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"f1HW9kHG5CG4","outputId":"ecd61a8d-f655-40fd-8c97-61b21483fdfa","executionInfo":{"status":"ok","timestamp":1702667839585,"user_tz":480,"elapsed":2121,"user":{"displayName":"NEHA SIKLIGAR","userId":"12356402504040142971"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["x_train shape: (42000, 32, 32, 3), y_train shape: (42000, 1)\n","x_val shape: (12000, 32, 32, 3), y_val shape: (12000, 1)\n","x_test shape: (6000, 32, 32, 3), y_test shape: (6000, 1)\n"]}],"source":["# Load Cifar10 dataset\n","(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n","\n","\n","# Concatenate train and test sets\n","x = np.concatenate((x_train, x_test))\n","y = np.concatenate((y_train, y_test))\n","\n","# Normalize the images\n","x = x.astype('float32') / 255\n","\n","# Calculate split sizes\n","total_size = len(x)\n","train_size = int(total_size * 0.70)\n","val_size = int(total_size * 0.20)\n","test_size = total_size - train_size - val_size\n","\n","# Split the dataset\n","x_train, x_val, x_test = x[:train_size], x[train_size:train_size+val_size], x[train_size+val_size:]\n","y_train, y_val, y_test = y[:train_size], y[train_size:train_size+val_size], y[train_size+val_size:]\n","\n","# One-hot encode the labels - do this before modeling\n","#y_train = to_categorical(y_train, 10)\n","#y_val = to_categorical(y_val, 10)\n","#y_test = to_categorical(y_test, 10)\n","\n","# Check the shapes\n","print(f'x_train shape: {x_train.shape}, y_train shape: {y_train.shape}')\n","print(f'x_val shape: {x_val.shape}, y_val shape: {y_val.shape}')\n","print(f'x_test shape: {x_test.shape}, y_test shape: {y_test.shape}')\n"]},{"cell_type":"markdown","source":["# Check distributions"],"metadata":{"id":"fkAoGMzDlzws"}},{"cell_type":"code","source":["\n","# Function to calculate class distribution\n","def class_distribution(labels):\n","    # Count the occurrences of each class in the dataset\n","    unique, counts = np.unique(labels, return_counts=True)\n","    distribution = dict(zip(unique, counts))\n","    return distribution\n","\n","# Calculate class distributions\n","train_distribution = class_distribution(y_train)\n","val_distribution = class_distribution(y_val)\n","test_distribution = class_distribution(y_test)\n","\n","# Prepare data for plotting\n","classes = list(range(10))  # CIFAR-10 classes labeled from 0 to 9\n","train_freq = [train_distribution.get(i, 0) for i in classes]\n","val_freq = [val_distribution.get(i, 0) for i in classes]\n","test_freq = [test_distribution.get(i, 0) for i in classes]\n","\n","# Plotting the distributions\n","plt.figure(figsize=(15, 5))\n","\n","# Training set distribution\n","plt.subplot(1, 3, 1)\n","plt.bar(classes, train_freq)\n","plt.title('Training Set Distribution')\n","plt.xlabel('Class')\n","plt.ylabel('Frequency')\n","\n","# Validation set distribution\n","plt.subplot(1, 3, 2)\n","plt.bar(classes, val_freq)\n","plt.title('Validation Set Distribution')\n","plt.xlabel('Class')\n","plt.ylabel('Frequency')\n","\n","# Test set distribution\n","plt.subplot(1, 3, 3)\n","plt.bar(classes, test_freq)\n","plt.title('Test Set Distribution')\n","plt.xlabel('Class')\n","plt.ylabel('Frequency')\n","\n","plt.tight_layout()\n","plt.show()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":507},"id":"pdFra7HBeBdP","outputId":"4090db60-42ba-4be2-cebf-b5baf5ead10f","executionInfo":{"status":"ok","timestamp":1702667840238,"user_tz":480,"elapsed":656,"user":{"displayName":"NEHA SIKLIGAR","userId":"12356402504040142971"}}},"execution_count":271,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1500x500 with 3 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Generate sample images"],"metadata":{"id":"TMUtdD7sl7N0"}},{"cell_type":"code","source":["# CIFAR-10 classes\n","class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n","\n","# Display the first few images\n","plt.figure(figsize=(10,10))\n","for i in range(25):\n","    plt.subplot(5, 5, i+1)\n","    plt.xticks([])\n","    plt.yticks([])\n","    plt.grid(False)\n","    plt.imshow(x_train[i], interpolation='nearest', aspect='auto')\n","    plt.xlabel(class_names[y_train[i][0]])\n","plt.show()\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":826},"id":"Nfi3vvs9c387","outputId":"210176a0-a73f-46d8-f6ce-94ea3a7e5b05","executionInfo":{"status":"ok","timestamp":1702667841807,"user_tz":480,"elapsed":1572,"user":{"displayName":"NEHA SIKLIGAR","userId":"12356402504040142971"}}},"execution_count":272,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x1000 with 25 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","execution_count":273,"metadata":{"id":"lRKB_XOOWa7B","executionInfo":{"status":"ok","timestamp":1702667841807,"user_tz":480,"elapsed":8,"user":{"displayName":"NEHA SIKLIGAR","userId":"12356402504040142971"}}},"outputs":[],"source":["#Before modeling and poisoning, one-hot encode y datasets\n","y_train = to_categorical(y_train, 10)\n","y_val = to_categorical(y_val, 10)\n","y_test = to_categorical(y_test, 10)"]},{"cell_type":"markdown","source":["# Poison the training data"],"metadata":{"id":"pw1kTK-MreXK"}},{"cell_type":"code","source":["xtrain = np.copy(x_train)\n","ytrain = np.copy(y_train)"],"metadata":{"id":"Ul3dTUxRnk7i","executionInfo":{"status":"ok","timestamp":1702667841808,"user_tz":480,"elapsed":8,"user":{"displayName":"NEHA SIKLIGAR","userId":"12356402504040142971"}}},"execution_count":274,"outputs":[]},{"cell_type":"code","source":["# set params\n","target_class = 0\n","epsilon = 0.5 #perturbation\n","\n","\n","# Calculate 20% of the length of xtrain --fixed subset\n","subset_size = int(len(xtrain) * 0.20)"],"metadata":{"id":"kvSDlnjn_ube","executionInfo":{"status":"ok","timestamp":1702667841808,"user_tz":480,"elapsed":8,"user":{"displayName":"NEHA SIKLIGAR","userId":"12356402504040142971"}}},"execution_count":275,"outputs":[]},{"cell_type":"code","source":["## autoencoder trained on a subset of the train\n","untrusted_train_size = int(train_size * percentage_poison)\n","trusted_train_size = int(train_size * trusted_train_percentage)\n","trusted_val_size = int(train_size * trusted_val_percentage)\n","\n","print(untrusted_train_size)\n","print(trusted_train_size)\n","print(trusted_val_size)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YnlfkoPmmsmg","executionInfo":{"status":"ok","timestamp":1702667841808,"user_tz":480,"elapsed":8,"user":{"displayName":"NEHA SIKLIGAR","userId":"12356402504040142971"}},"outputId":"5ab2b7c8-7ca7-4b4f-b454-9b0b30395b7f"},"execution_count":276,"outputs":[{"output_type":"stream","name":"stdout","text":["8400\n","42000\n","42000\n"]}]},{"cell_type":"code","execution_count":277,"metadata":{"id":"zZfluLjP55sb","executionInfo":{"status":"ok","timestamp":1702667842192,"user_tz":480,"elapsed":389,"user":{"displayName":"NEHA SIKLIGAR","userId":"12356402504040142971"}}},"outputs":[],"source":["# Adversarial perturbation function\n","def adversarial_perturbation(image, epsilon=0.5):\n","    # Add noise to the image to perturb it\n","    return np.clip(image + epsilon * np.sign(np.random.randn(*image.shape)), 0, 1)\n","\n","# # Function to add a backdoor trigger to an image\n","# def add_trigger(image):\n","#     # Add a simple trigger, like a dot at a specific position\n","#     modified_image = np.copy(image)\n","#     modified_image[-5:, -5:] = 1.0  # adding a dot at the bottom right\n","#     return modified_image\n","\n","# Function to add a backdoor trigger to an image\n","def add_trigger(image):\n","  # Assuming image is in the range [0, 1] and in RGB format\n","  modified_image = np.copy(image)\n","  # Adding a red dot at the bottom right corner\n","  modified_image[-5:, -5:, 0] = 1.0 # Red channel\n","  modified_image[-5:, -5:, 1] = 1.0 # Green channel\n","  modified_image[-5:, -5:, 2] = 1.0 # Blue channel\n","  return modified_image\n","\n","## apply the poison\n","for i in range(len(x_train_untrusted)):\n","    if np.argmax(y_train_untrusted[i]) == target_class:\n","        x_train_untrusted[i] = adversarial_perturbation(x_train_untrusted[i], epsilon=epsilon)\n","        x_train_untrusted[i] = add_trigger(x_train_untrusted[i])"]},{"cell_type":"markdown","source":["# Defense: Apply autoencoder"],"metadata":{"id":"ioontqsbRp9k"}},{"cell_type":"code","source":["\n","#from tensorflow.keras.preprocessing.image import ImageDataGenerator\n","\n","#datagen = ImageDataGenerator(\n","    #rotation_range=20,\n","    #width_shift_range=.5,\n","    #height_shift_range=.5,\n","    #horizontal_flip=True\n","#)\n","\n","#datagen.fit(x_train)\n"],"metadata":{"id":"51gMj9cxRo48","executionInfo":{"status":"ok","timestamp":1702667842192,"user_tz":480,"elapsed":7,"user":{"displayName":"NEHA SIKLIGAR","userId":"12356402504040142971"}}},"execution_count":278,"outputs":[]},{"cell_type":"code","source":["# Anomoly Detection\n","from tensorflow.keras.layers import Reshape\n","from tensorflow.keras.layers import Conv2DTranspose\n","\n","x_train20 = x_train[:subset_size]\n","y_train20 = y_train[:subset_size]\n","\n","# Define the autoencoder model\n","autoencoder = Sequential([\n","    # Encoder\n","    Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=(32, 32, 3)),\n","    Conv2D(16, (3, 3), activation='relu', padding='same'),\n","    Flatten(),\n","    Dense(256, activation='relu'),\n","\n","    # Decoder\n","    Dense(32 * 32 * 3, activation='relu'),\n","    Reshape((32, 32, 3)),\n","    Conv2DTranspose(16, (3, 3), activation='relu', padding='same'),\n","    Conv2DTranspose(32, (3, 3), activation='relu', padding='same'),\n","    Conv2D(3, (3, 3), activation='sigmoid', padding='same')\n","])\n","\n","adam = tf.keras.optimizers.Adam(learning_rate=0.001)\n","# Compile the model\n","autoencoder.compile(optimizer=adam, loss='binary_crossentropy')\n","\n","# Train the model (using subset of x_train)\n","autoencoder.fit(x_train20, x_train20, epochs=100, batch_size=256, validation_data=(x_val, x_val))"],"metadata":{"id":"8Sz2UbRd-MfD","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1702667979743,"user_tz":480,"elapsed":137557,"user":{"displayName":"NEHA SIKLIGAR","userId":"12356402504040142971"}},"outputId":"41adc8ac-abff-40cd-c11d-d9e946b199b3"},"execution_count":279,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/100\n","33/33 [==============================] - 5s 86ms/step - loss: 0.6714 - val_loss: 0.6374\n","Epoch 2/100\n","33/33 [==============================] - 1s 37ms/step - loss: 0.6196 - val_loss: 0.6104\n","Epoch 3/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.6024 - val_loss: 0.6004\n","Epoch 4/100\n","33/33 [==============================] - 1s 46ms/step - loss: 0.5942 - val_loss: 0.5943\n","Epoch 5/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5891 - val_loss: 0.5902\n","Epoch 6/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5864 - val_loss: 0.5879\n","Epoch 7/100\n","33/33 [==============================] - 1s 46ms/step - loss: 0.5829 - val_loss: 0.5849\n","Epoch 8/100\n","33/33 [==============================] - 1s 37ms/step - loss: 0.5809 - val_loss: 0.5819\n","Epoch 9/100\n","33/33 [==============================] - 1s 40ms/step - loss: 0.5783 - val_loss: 0.5806\n","Epoch 10/100\n","33/33 [==============================] - 2s 48ms/step - loss: 0.5772 - val_loss: 0.5809\n","Epoch 11/100\n","33/33 [==============================] - 1s 39ms/step - loss: 0.5751 - val_loss: 0.5778\n","Epoch 12/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5740 - val_loss: 0.5781\n","Epoch 13/100\n","33/33 [==============================] - 2s 46ms/step - loss: 0.5736 - val_loss: 0.5762\n","Epoch 14/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5728 - val_loss: 0.5751\n","Epoch 15/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5714 - val_loss: 0.5746\n","Epoch 16/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5711 - val_loss: 0.5738\n","Epoch 17/100\n","33/33 [==============================] - 1s 39ms/step - loss: 0.5706 - val_loss: 0.5742\n","Epoch 18/100\n","33/33 [==============================] - 1s 39ms/step - loss: 0.5696 - val_loss: 0.5732\n","Epoch 19/100\n","33/33 [==============================] - 2s 48ms/step - loss: 0.5695 - val_loss: 0.5733\n","Epoch 20/100\n","33/33 [==============================] - 2s 48ms/step - loss: 0.5692 - val_loss: 0.5721\n","Epoch 21/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5684 - val_loss: 0.5729\n","Epoch 22/100\n","33/33 [==============================] - 2s 46ms/step - loss: 0.5684 - val_loss: 0.5716\n","Epoch 23/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5681 - val_loss: 0.5727\n","Epoch 24/100\n","33/33 [==============================] - 1s 39ms/step - loss: 0.5678 - val_loss: 0.5709\n","Epoch 25/100\n","33/33 [==============================] - 1s 39ms/step - loss: 0.5676 - val_loss: 0.5708\n","Epoch 26/100\n","33/33 [==============================] - 2s 47ms/step - loss: 0.5672 - val_loss: 0.5706\n","Epoch 27/100\n","33/33 [==============================] - 2s 47ms/step - loss: 0.5669 - val_loss: 0.5752\n","Epoch 28/100\n","33/33 [==============================] - 1s 41ms/step - loss: 0.5673 - val_loss: 0.5701\n","Epoch 29/100\n","33/33 [==============================] - 2s 50ms/step - loss: 0.5667 - val_loss: 0.5702\n","Epoch 30/100\n","33/33 [==============================] - 1s 39ms/step - loss: 0.5666 - val_loss: 0.5699\n","Epoch 31/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5660 - val_loss: 0.5699\n","Epoch 32/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5664 - val_loss: 0.5705\n","Epoch 33/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5662 - val_loss: 0.5696\n","Epoch 34/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5660 - val_loss: 0.5704\n","Epoch 35/100\n","33/33 [==============================] - 1s 39ms/step - loss: 0.5658 - val_loss: 0.5694\n","Epoch 36/100\n","33/33 [==============================] - 1s 39ms/step - loss: 0.5656 - val_loss: 0.5695\n","Epoch 37/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5656 - val_loss: 0.5693\n","Epoch 38/100\n","33/33 [==============================] - 2s 48ms/step - loss: 0.5655 - val_loss: 0.5694\n","Epoch 39/100\n","33/33 [==============================] - 1s 41ms/step - loss: 0.5654 - val_loss: 0.5691\n","Epoch 40/100\n","33/33 [==============================] - 2s 47ms/step - loss: 0.5656 - val_loss: 0.5708\n","Epoch 41/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5651 - val_loss: 0.5693\n","Epoch 42/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5659 - val_loss: 0.5690\n","Epoch 43/100\n","33/33 [==============================] - 1s 39ms/step - loss: 0.5647 - val_loss: 0.5691\n","Epoch 44/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5651 - val_loss: 0.5691\n","Epoch 45/100\n","33/33 [==============================] - 1s 39ms/step - loss: 0.5650 - val_loss: 0.5692\n","Epoch 46/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5649 - val_loss: 0.5690\n","Epoch 47/100\n","33/33 [==============================] - 2s 46ms/step - loss: 0.5647 - val_loss: 0.5686\n","Epoch 48/100\n","33/33 [==============================] - 2s 48ms/step - loss: 0.5651 - val_loss: 0.5692\n","Epoch 49/100\n","33/33 [==============================] - 1s 39ms/step - loss: 0.5648 - val_loss: 0.5690\n","Epoch 50/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5648 - val_loss: 0.5693\n","Epoch 51/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5645 - val_loss: 0.5686\n","Epoch 52/100\n","33/33 [==============================] - 2s 46ms/step - loss: 0.5648 - val_loss: 0.5702\n","Epoch 53/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5644 - val_loss: 0.5683\n","Epoch 54/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5644 - val_loss: 0.5685\n","Epoch 55/100\n","33/33 [==============================] - 1s 46ms/step - loss: 0.5646 - val_loss: 0.5682\n","Epoch 56/100\n","33/33 [==============================] - 2s 46ms/step - loss: 0.5644 - val_loss: 0.5681\n","Epoch 57/100\n","33/33 [==============================] - 2s 47ms/step - loss: 0.5643 - val_loss: 0.5678\n","Epoch 58/100\n","33/33 [==============================] - 1s 40ms/step - loss: 0.5642 - val_loss: 0.5691\n","Epoch 59/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5649 - val_loss: 0.5688\n","Epoch 60/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5642 - val_loss: 0.5679\n","Epoch 61/100\n","33/33 [==============================] - 1s 39ms/step - loss: 0.5640 - val_loss: 0.5683\n","Epoch 62/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5640 - val_loss: 0.5681\n","Epoch 63/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5640 - val_loss: 0.5677\n","Epoch 64/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5639 - val_loss: 0.5675\n","Epoch 65/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5635 - val_loss: 0.5689\n","Epoch 66/100\n","33/33 [==============================] - 2s 46ms/step - loss: 0.5643 - val_loss: 0.5675\n","Epoch 67/100\n","33/33 [==============================] - 1s 43ms/step - loss: 0.5636 - val_loss: 0.5689\n","Epoch 68/100\n","33/33 [==============================] - 1s 40ms/step - loss: 0.5635 - val_loss: 0.5671\n","Epoch 69/100\n","33/33 [==============================] - 2s 46ms/step - loss: 0.5635 - val_loss: 0.5674\n","Epoch 70/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5633 - val_loss: 0.5670\n","Epoch 71/100\n","33/33 [==============================] - 1s 39ms/step - loss: 0.5635 - val_loss: 0.5677\n","Epoch 72/100\n","33/33 [==============================] - 1s 39ms/step - loss: 0.5635 - val_loss: 0.5674\n","Epoch 73/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5630 - val_loss: 0.5670\n","Epoch 74/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5632 - val_loss: 0.5675\n","Epoch 75/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5630 - val_loss: 0.5669\n","Epoch 76/100\n","33/33 [==============================] - 2s 48ms/step - loss: 0.5629 - val_loss: 0.5671\n","Epoch 77/100\n","33/33 [==============================] - 1s 42ms/step - loss: 0.5638 - val_loss: 0.5669\n","Epoch 78/100\n","33/33 [==============================] - 2s 47ms/step - loss: 0.5627 - val_loss: 0.5674\n","Epoch 79/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5630 - val_loss: 0.5672\n","Epoch 80/100\n","33/33 [==============================] - 2s 46ms/step - loss: 0.5627 - val_loss: 0.5668\n","Epoch 81/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5626 - val_loss: 0.5677\n","Epoch 82/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5628 - val_loss: 0.5669\n","Epoch 83/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5625 - val_loss: 0.5670\n","Epoch 84/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5630 - val_loss: 0.5668\n","Epoch 85/100\n","33/33 [==============================] - 2s 46ms/step - loss: 0.5625 - val_loss: 0.5667\n","Epoch 86/100\n","33/33 [==============================] - 2s 48ms/step - loss: 0.5629 - val_loss: 0.5669\n","Epoch 87/100\n","33/33 [==============================] - 1s 41ms/step - loss: 0.5626 - val_loss: 0.5673\n","Epoch 88/100\n","33/33 [==============================] - 1s 39ms/step - loss: 0.5628 - val_loss: 0.5666\n","Epoch 89/100\n","33/33 [==============================] - 1s 39ms/step - loss: 0.5623 - val_loss: 0.5665\n","Epoch 90/100\n","33/33 [==============================] - 1s 39ms/step - loss: 0.5626 - val_loss: 0.5666\n","Epoch 91/100\n","33/33 [==============================] - 2s 46ms/step - loss: 0.5624 - val_loss: 0.5666\n","Epoch 92/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5623 - val_loss: 0.5666\n","Epoch 93/100\n","33/33 [==============================] - 1s 39ms/step - loss: 0.5625 - val_loss: 0.5669\n","Epoch 94/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5626 - val_loss: 0.5671\n","Epoch 95/100\n","33/33 [==============================] - 1s 41ms/step - loss: 0.5625 - val_loss: 0.5665\n","Epoch 96/100\n","33/33 [==============================] - 1s 42ms/step - loss: 0.5621 - val_loss: 0.5678\n","Epoch 97/100\n","33/33 [==============================] - 1s 40ms/step - loss: 0.5624 - val_loss: 0.5670\n","Epoch 98/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5622 - val_loss: 0.5663\n","Epoch 99/100\n","33/33 [==============================] - 1s 39ms/step - loss: 0.5623 - val_loss: 0.5669\n","Epoch 100/100\n","33/33 [==============================] - 1s 38ms/step - loss: 0.5624 - val_loss: 0.5672\n"]},{"output_type":"execute_result","data":{"text/plain":["<keras.src.callbacks.History at 0x78abea0f8670>"]},"metadata":{},"execution_count":279}]},{"cell_type":"code","source":["# Function to calculate reconstruction loss\n","def calculate_loss(x, reconstructed_x):\n","    return np.mean(np.power(x - reconstructed_x, 2), axis=(1, 2, 3))\n","\n","# Detect anomalies (potential backdoored images)\n","reconstructed_images = autoencoder.predict(xtrain)\n","reconstruction_loss = calculate_loss(xtrain, reconstructed_images)\n","\n","# Set a threshold for anomaly detection\n","threshold = np.percentile(reconstruction_loss, 90)\n","\n","# Flag images with reconstruction loss greater than the threshold\n","anomalies = reconstruction_loss > threshold\n","\n","print(f\"Number of detected anomalies: {np.sum(anomalies)}\")\n","print(f\"Percentage of detected anomalies: {np.sum(anomalies)/len(x_train)*100}\")\n","\n","# Filter out anomalies\n","non_anomalous_indices = reconstruction_loss <= threshold\n","filtered_xtrain = xtrain[non_anomalous_indices]\n","filtered_ytrain = ytrain[non_anomalous_indices]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"EVneuqTYpbLk","executionInfo":{"status":"ok","timestamp":1702667989568,"user_tz":480,"elapsed":9836,"user":{"displayName":"NEHA SIKLIGAR","userId":"12356402504040142971"}},"outputId":"30beafe2-75af-4e0d-e8b1-e5489db9c68d"},"execution_count":280,"outputs":[{"output_type":"stream","name":"stdout","text":["1313/1313 [==============================] - 3s 2ms/step\n","Number of detected anomalies: 4200\n","Percentage of detected anomalies: 10.0\n"]}]},{"cell_type":"markdown","source":["# Train model on poisoned data and check perfomance on clean test data\n","\n","\n","\n"],"metadata":{"id":"8byK0mvIr60D"}},{"cell_type":"code","source":["from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, BatchNormalization\n","from tensorflow.keras.models import Sequential\n","\n","model = Sequential()\n","\n","model.add(Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=(32, 32, 3)))\n","model.add(BatchNormalization())\n","model.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\n","model.add(BatchNormalization())\n","model.add(MaxPooling2D(2, 2))\n","model.add(Dropout(0.2))\n","\n","model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))\n","model.add(BatchNormalization())\n","model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))\n","model.add(BatchNormalization())\n","model.add(MaxPooling2D(2, 2))\n","model.add(Dropout(0.3))\n","\n","model.add(Flatten())\n","model.add(Dense(512, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.001)))\n","model.add(Dropout(0.5))\n","model.add(Dense(10, activation='softmax'))\n","\n","# Compile the model\n","adam = tf.keras.optimizers.Adam(learning_rate=0.001)\n","model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy'])\n"],"metadata":{"id":"_ofg7f82kpjI","executionInfo":{"status":"ok","timestamp":1702667989569,"user_tz":480,"elapsed":4,"user":{"displayName":"NEHA SIKLIGAR","userId":"12356402504040142971"}}},"execution_count":281,"outputs":[]},{"cell_type":"code","source":["from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau\n","\n","checkpoint = ModelCheckpoint(\"./model1.h5\", monitor='val_acc', verbose=1, save_best_only=True, mode='max')\n","\n","early_stopping = EarlyStopping(monitor = 'val_loss',\n","                          min_delta = 0,\n","                          patience = 3,\n","                          verbose = 1,\n","                          restore_best_weights = True\n","                          )\n","\n","reduce_learningrate = ReduceLROnPlateau(monitor = 'val_loss',\n","                              factor = 0.2,\n","                              patience = 3,\n","                              verbose = 1,\n","                              min_delta = 0.0001)\n","\n","callbacks_list = [early_stopping, checkpoint, reduce_learningrate]\n"],"metadata":{"id":"XbDLaSpOfwzk","executionInfo":{"status":"ok","timestamp":1702667989569,"user_tz":480,"elapsed":3,"user":{"displayName":"NEHA SIKLIGAR","userId":"12356402504040142971"}}},"execution_count":282,"outputs":[]},{"cell_type":"code","execution_count":283,"metadata":{"id":"MSggOFxWCuNE","colab":{"base_uri":"https://localhost:8080/"},"outputId":"722d671f-5e31-453c-e88e-7b0b1971ef81","executionInfo":{"status":"ok","timestamp":1702668135385,"user_tz":480,"elapsed":145819,"user":{"displayName":"NEHA SIKLIGAR","userId":"12356402504040142971"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/50\n","1182/1182 [==============================] - ETA: 0s - loss: 2.4172 - accuracy: 0.4195"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1182/1182 [==============================] - 13s 9ms/step - loss: 2.4172 - accuracy: 0.4195 - val_loss: 1.7666 - val_accuracy: 0.5593 - lr: 0.0010\n","Epoch 2/50\n","1178/1182 [============================>.] - ETA: 0s - loss: 1.7614 - accuracy: 0.5688"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1182/1182 [==============================] - 10s 9ms/step - loss: 1.7615 - accuracy: 0.5688 - val_loss: 1.5708 - val_accuracy: 0.6417 - lr: 0.0010\n","Epoch 3/50\n","1176/1182 [============================>.] - ETA: 0s - loss: 1.6404 - accuracy: 0.6328"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1182/1182 [==============================] - 9s 8ms/step - loss: 1.6403 - accuracy: 0.6327 - val_loss: 1.4101 - val_accuracy: 0.7053 - lr: 0.0010\n","Epoch 4/50\n","1180/1182 [============================>.] - ETA: 0s - loss: 1.5268 - accuracy: 0.6723"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1182/1182 [==============================] - 10s 9ms/step - loss: 1.5270 - accuracy: 0.6722 - val_loss: 1.4110 - val_accuracy: 0.7178 - lr: 0.0010\n","Epoch 5/50\n","1181/1182 [============================>.] - ETA: 0s - loss: 1.4768 - accuracy: 0.7008"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1182/1182 [==============================] - 10s 8ms/step - loss: 1.4767 - accuracy: 0.7008 - val_loss: 1.3643 - val_accuracy: 0.7325 - lr: 0.0010\n","Epoch 6/50\n","1178/1182 [============================>.] - ETA: 0s - loss: 1.4177 - accuracy: 0.7185"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1182/1182 [==============================] - 10s 9ms/step - loss: 1.4181 - accuracy: 0.7182 - val_loss: 1.4117 - val_accuracy: 0.7207 - lr: 0.0010\n","Epoch 7/50\n","1179/1182 [============================>.] - ETA: 0s - loss: 1.3813 - accuracy: 0.7303"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1182/1182 [==============================] - 10s 8ms/step - loss: 1.3815 - accuracy: 0.7302 - val_loss: 1.3630 - val_accuracy: 0.7446 - lr: 0.0010\n","Epoch 8/50\n","1180/1182 [============================>.] - ETA: 0s - loss: 1.3612 - accuracy: 0.7441"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1182/1182 [==============================] - 11s 9ms/step - loss: 1.3611 - accuracy: 0.7441 - val_loss: 1.3691 - val_accuracy: 0.7428 - lr: 0.0010\n","Epoch 9/50\n","1179/1182 [============================>.] - ETA: 0s - loss: 1.3233 - accuracy: 0.7539"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1182/1182 [==============================] - 10s 9ms/step - loss: 1.3236 - accuracy: 0.7537 - val_loss: 1.3412 - val_accuracy: 0.7515 - lr: 0.0010\n","Epoch 10/50\n","1181/1182 [============================>.] - ETA: 0s - loss: 1.3028 - accuracy: 0.7587"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1182/1182 [==============================] - 10s 9ms/step - loss: 1.3027 - accuracy: 0.7587 - val_loss: 1.2720 - val_accuracy: 0.7710 - lr: 0.0010\n","Epoch 11/50\n","1181/1182 [============================>.] - ETA: 0s - loss: 1.2874 - accuracy: 0.7682"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1182/1182 [==============================] - 10s 8ms/step - loss: 1.2873 - accuracy: 0.7683 - val_loss: 1.3346 - val_accuracy: 0.7550 - lr: 0.0010\n","Epoch 12/50\n","1182/1182 [==============================] - ETA: 0s - loss: 1.2726 - accuracy: 0.7743"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1182/1182 [==============================] - 10s 8ms/step - loss: 1.2726 - accuracy: 0.7743 - val_loss: 1.2733 - val_accuracy: 0.7749 - lr: 0.0010\n","Epoch 13/50\n","1176/1182 [============================>.] - ETA: 0s - loss: 1.2607 - accuracy: 0.7802Restoring model weights from the end of the best epoch: 10.\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n"]},{"output_type":"stream","name":"stdout","text":["\n","Epoch 13: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.\n","1182/1182 [==============================] - 10s 8ms/step - loss: 1.2612 - accuracy: 0.7800 - val_loss: 1.2945 - val_accuracy: 0.7786 - lr: 0.0010\n","Epoch 13: early stopping\n","188/188 [==============================] - 1s 4ms/step - loss: 1.2682 - accuracy: 0.7758\n","Clean test data accuracy: 0.7758333086967468\n"]}],"source":["# Train the model on augmented poisoned data\n","history = model.fit(filtered_xtrain,filtered_ytrain, epochs=50, validation_data=(x_val, y_val), callbacks = callbacks_list)\n","\n","# Evaluate on clean data\n","loss, accuracy = model.evaluate(x_test, y_test)\n","print(f\"Clean test data accuracy: {accuracy}\")\n"]},{"cell_type":"markdown","source":["# Plot results"],"metadata":{"id":"adHkyd8zsRv1"}},{"cell_type":"code","source":["# Plotting training and validation accuracy\n","plt.figure(figsize=(8, 4))\n","plt.plot(history.history['accuracy'], label='Training Accuracy')\n","plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n","plt.title('Training and Validation Accuracy')\n","plt.xlabel('Epoch')\n","plt.ylabel('Accuracy')\n","plt.legend()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":410},"id":"l_Mvrhx51Iar","outputId":"610271ef-efd4-4e2c-f552-9d836387f916","executionInfo":{"status":"ok","timestamp":1702668135385,"user_tz":480,"elapsed":12,"user":{"displayName":"NEHA SIKLIGAR","userId":"12356402504040142971"}}},"execution_count":284,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 800x400 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["from sklearn.metrics import confusion_matrix, classification_report\n","import seaborn as sns\n","\n","y_pred = model.predict(x_test)\n","y_pred_classes = np.argmax(y_pred, axis=1)\n","y_true = np.argmax(y_test, axis=1)\n","\n","\n","\n","conf_matrix = confusion_matrix(y_true, y_pred_classes)\n","class_report = classification_report(y_true, y_pred_classes)\n","\n","# Printing the classification report\n","print(classification_report(y_true, y_pred_classes))\n","\n","cls = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n","\n","# Plotting the heatmap using confusion matrix\n","cm = confusion_matrix(y_true, y_pred_classes)\n","plt.figure(figsize = (8, 5))\n","sns.heatmap(cm, annot = True,  fmt = '.0f', xticklabels = cls, yticklabels = cls)\n","plt.ylabel('Actual')\n","plt.xlabel('Predicted')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":841},"id":"r-e4xU4GG9bW","outputId":"014b24bf-b659-422e-cff1-c27656e758fa","executionInfo":{"status":"ok","timestamp":1702668137007,"user_tz":480,"elapsed":1633,"user":{"displayName":"NEHA SIKLIGAR","userId":"12356402504040142971"}}},"execution_count":285,"outputs":[{"output_type":"stream","name":"stdout","text":["188/188 [==============================] - 1s 2ms/step\n","              precision    recall  f1-score   support\n","\n","           0       0.82      0.77      0.80       611\n","           1       0.95      0.78      0.85       608\n","           2       0.67      0.68      0.68       574\n","           3       0.59      0.65      0.62       611\n","           4       0.75      0.76      0.75       600\n","           5       0.78      0.68      0.72       612\n","           6       0.81      0.87      0.84       604\n","           7       0.88      0.77      0.82       603\n","           8       0.85      0.88      0.86       592\n","           9       0.73      0.93      0.82       585\n","\n","    accuracy                           0.78      6000\n","   macro avg       0.78      0.78      0.78      6000\n","weighted avg       0.78      0.78      0.78      6000\n","\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 800x500 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":[],"metadata":{"id":"4mZRwja1G9CO","executionInfo":{"status":"ok","timestamp":1702668137007,"user_tz":480,"elapsed":4,"user":{"displayName":"NEHA 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