Merge branch 'patch-1' into 'master'

Carlini_attack

See merge request !2

carlini_attack.py

+# -*- coding: utf-8 -*-
+"""Untitled15.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/18fYvJwWJU4mmcW_bHNk1O24c1q-q23Ny
+"""
+
+# Import necessary libraries
+import pandas as pd
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix, classification_report
+import time
+
+# Load and preprocess data
+data = pd.read_csv('/content/drive/MyDrive/disease_preprocess4 (1).csv')
+X = data.drop('HeartDisease', axis=1)
+y = data['HeartDisease']
+
+# Split the data
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Standardize the data
+scaler = StandardScaler()
+X_train_scaled = scaler.fit_transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+# Load the model
+model = load_model('/content/drive/MyDrive/1D_CNN_model_Final_1.h5')
+
+# Get predictions and print evaluation metrics
+y_pred = model.predict(X_test_scaled) > 0.5
+print(confusion_matrix(y_test, y_pred))
+print(classification_report(y_test, y_pred))
+
+# Highlight: Define X_train_combined as X_train_scaled if they are meant to be the same
+X_train_combined = X_train_scaled  # Added this line to define X_train_combined
+
+def carlini_attack_binary(model, X, y, batch_size=100, epsilon=0.1, max_iterations=50, learning_rate=0.01):
+    # Process in batches
+    num_batches = int(np.ceil(len(X) / batch_size))
+    perturbed_Xs = []
+
+    for i in range(num_batches):
+        start = i * batch_size
+        end = min((i + 1) * batch_size, len(X))
+        X_batch = tf.identity(X[start:end])
+        y_batch = tf.convert_to_tensor(y[start:end], dtype=tf.float32)
+        y_batch = tf.reshape(y_batch, (-1, 1))  # Ensure y_batch shape matches prediction shape
+
+        for _ in range(max_iterations):
+            with tf.GradientTape() as tape:
+                tape.watch(X_batch)
+                prediction = model(X_batch)
+                loss = tf.keras.losses.binary_crossentropy(y_batch, prediction)
+
+            gradients = tape.gradient(loss, X_batch)
+            X_batch -= learning_rate * gradients
+            X_batch = tf.clip_by_value(X_batch, X[start:end] - epsilon, X[start:end] + epsilon)
+            X_batch = tf.clip_by_value(X_batch, 0, 1)
+
+        perturbed_Xs.append(X_batch)
+
+    # Concatenate all batch results
+    perturbed_X = tf.concat(perturbed_Xs, axis=0)
+    return perturbed_X
+
+def carlini_wagner_attack(model, x, y, c=1, lr=0.01, iterations=100):
+    x_adv = tf.Variable(x, dtype=tf.float32)  # Make sure x is in the correct format
+    target = tf.constant(y, dtype=tf.float32)  # Make sure y is in the correct format
+    binary_crossentropy = tf.keras.losses.BinaryCrossentropy()
+
+    optimizer = tf.optimizers.Adam(learning_rate=lr)
+
+    for i in range(iterations):
+        with tf.GradientTape() as tape:
+            tape.watch(x_adv)
+            prediction = model(x_adv)
+            loss = c * binary_crossentropy(target, prediction) + tf.norm(x_adv - x)
+
+        gradients = tape.gradient(loss, x_adv)
+        optimizer.apply_gradients([(gradients, x_adv)])
+        x_adv.assign(tf.clip_by_value(x_adv, 0, 1))
+
+    return x_adv.numpy()
+
+# Generate adversarial examples
+X_test_tensor = tf.convert_to_tensor(X_test_scaled, dtype=tf.float32)
+
+X_test_adv = carlini_attack_binary(model, X_test_tensor, y_test)
+
+# Evaluate the model on adversarial examples
+adv_accuracy = model.evaluate(X_test_adv, y_test)
+print("Model accuracy on adversarial examples:", adv_accuracy[1])
+
+# Evaluate the model on clean data
+org_accuracy = model.evaluate(X_test_scaled, y_test)
+print("Model accuracy on clean data:", org_accuracy[1])
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