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2023-155
Commit 155f6ac7 authored by Amani Kaleem's avatar Amani Kaleem
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Update PGD_Attack.ipynb, PGD- Defense-feature_squeezing_model_hybrid.ipynb,... 

Update PGD_Attack.ipynb, PGD- Defense-feature_squeezing_model_hybrid.ipynb, PGD-Defense -Stochastic_distilation_model_hybrid.ipynb, CARLINI-Model.ipynb, CARLINI_Adversarial_training.ipynb, CARLINI_Defensive_distillation.ipynb, Feed-forward_model.ipynb files
Deleted Carlini_attack_on_Churn_model.ipynb, Carlini_attack_on_Churn_model_accuracy.ipynb, PGD-model_smote.ipynb, FInalpreproccessing_1_.ipynb files
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Carlini_attack_on_Churn_model.ipynb deleted 100644 → 0
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "5V1K2W9WcD3G"
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import LabelEncoder, StandardScaler\n"
]
},
{
"cell_type": "code",
"source": [
"# Load the churn dataset\n",
"churn_data = pd.read_csv('/content/Telco_Cust_Churn.csv')"
],
"metadata": {
"id": "2MNJZhI2cLrm"
},
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Preprocess the churn dataset\n",
"churn_data = churn_data.drop('customerID', axis=1) # Remove the customerID column\n",
"churn_data['TotalCharges'] = pd.to_numeric(churn_data['TotalCharges'], errors='coerce') # Convert TotalCharges to numeric\n"
],
"metadata": {
"id": "9efJu0d0cY38"
},
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Perform label encoding for categorical columns\n",
"categorical_cols = churn_data.select_dtypes(include=['object']).columns\n",
"label_encoder = LabelEncoder()\n",
"for col in categorical_cols:\n",
" churn_data[col] = label_encoder.fit_transform(churn_data[col])\n"
],
"metadata": {
"id": "FEbCwXMMcdMx"
},
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Split the dataset into features and target variable\n",
"X = churn_data.drop('Churn', axis=1)\n",
"y = churn_data['Churn']"
],
"metadata": {
"id": "BEi1DyOAcg87"
},
"execution_count": 6,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Split the dataset into train and test sets\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
],
"metadata": {
"id": "WCBEzi2dciZD"
},
"execution_count": 7,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Normalize the input features\n",
"scaler = StandardScaler()\n",
"X_train = scaler.fit_transform(X_train)\n",
"X_test = scaler.transform(X_test)"
],
"metadata": {
"id": "vFvTLBZFcm7M"
},
"execution_count": 8,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Define a simple target model\n",
"def target_model(inputs):\n",
" model = tf.keras.Sequential([\n",
" tf.keras.layers.Dense(16, activation='relu', input_shape=(X_train.shape[1],)),\n",
" tf.keras.layers.Dense(1, activation='sigmoid')\n",
" ])\n",
" return model(inputs)"
],
"metadata": {
"id": "0plu-Gqlcp4E"
},
"execution_count": 9,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Define the objective function for the attack\n",
"def carlini_objective_function(inputs, target_class):\n",
" # Compute the logits (output) of the target model\n",
" logits = target_model(inputs)\n",
"\n",
" # Calculate the binary cross-entropy loss between the target class and the model's predicted probabilities\n",
" loss = tf.keras.losses.binary_crossentropy(target_class, logits)\n",
"\n",
" return loss"
],
"metadata": {
"id": "7qeX15-7crF4"
},
"execution_count": 10,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Set up the optimization algorithm\n",
"optimizer = tf.keras.optimizers.Adam(lr=0.01)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ChS1B5K8cvkc",
"outputId": "2322eaa9-e2ab-4968-b919-0720b02a1295"
},
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Function to perform the Carlini attack\n",
"def carlini_attack(input_sample, target_class, epsilon=0.2, num_iterations=100):\n",
" input_sample = tf.convert_to_tensor(input_sample, dtype=tf.float32)\n",
" input_sample = tf.expand_dims(input_sample, axis=0) # Add batch dimension\n",
"\n",
" for i in range(num_iterations):\n",
" with tf.GradientTape() as tape:\n",
" # Record the perturbation for gradient computation\n",
" tape.watch(input_sample)\n",
"\n",
" # Compute the objective function\n",
" loss = carlini_objective_function(input_sample, target_class)\n",
"\n",
" # Compute the gradients of the objective function with respect to the input sample\n",
" gradients = tape.gradient(loss, input_sample)\n",
"\n",
" # Update the input sample using the gradients\n",
" input_sample = input_sample - epsilon * tf.sign(gradients)\n",
"\n",
" # Clip the input sample to maintain values within a valid range\n",
" input_sample = tf.clip_by_value(input_sample, -1, 1)\n",
"\n",
" return input_sample\n"
],
"metadata": {
"id": "9Lbcd_ObcyBK"
},
"execution_count": 13,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Select a sample from the test set for the attack\n",
"sample_index = 0\n",
"#input_sample = X_test.iloc[sample_index].values\n",
"input_sample = X_test[sample_index]"
],
"metadata": {
"id": "GdSLlEFbc6ci"
},
"execution_count": 15,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Specify the target class for the attack\n",
"#target_class = 1\n",
"target_class = tf.constant([[1.0]], dtype=tf.float32)"
],
"metadata": {
"id": "xD1IxjMYdCOx"
},
"execution_count": 18,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Generate the adversarial example using the Carlini attack\n",
"adversarial_example = carlini_attack(input_sample, target_class)\n"
],
"metadata": {
"id": "nwzcGIq4dDxA"
},
"execution_count": 19,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Perform prediction on the adversarial example\n",
"predictions = target_model(adversarial_example)\n",
"predicted_class = tf.round(predictions[0][0])"
],
"metadata": {
"id": "8-TrTfk_d_uZ"
},
"execution_count": 22,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Print the predicted class and confidence \n",
"print(\"Predicted Class:\", predicted_class) \n",
"print(\"Confidence:\", predictions[0][0])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "f6zgP9kod_jL",
"outputId": "ea91e129-9dcd-4998-dce5-2a1b3c7497df"
},
"execution_count": 23,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Predicted Class: tf.Tensor(1.0, shape=(), dtype=float32)\n",
"Confidence: tf.Tensor(0.591373, shape=(), dtype=float32)\n"
]
}
]
}
]
}
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