Added Dynamic_Backdoor_GAN

Signed-off-by: Prachi Panwar <prachipanwar0606@gmail.com>

Author: prachi0606

art/attacks/poisoning/dynamic_backdoor_gan.py

@@ -0,0 +1,73 @@
+# -*- coding: utf-8 -*-
+"""dynamic_backdoor_gan
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/19W9gZ2gUxkgu6rr5qAT1Arf7iauCj2QT
+"""
+
+#Trigger Generator:A small CNN that learns to generate input-specific triggers
+class TriggerGenerator(nn.Module):
+    def __init__(self, input_channels=3):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Conv2d(input_channels, 32, kernel_size=3, padding=1),
+            nn.ReLU(),
+            nn.Conv2d(32, 32, kernel_size=3, padding=1),
+            nn.ReLU(),
+            nn.Conv2d(32, input_channels, kernel_size=3, padding=1),
+            nn.Tanh()
+        )
+
+    def forward(self, x):
+        return self.net(x)
+# Custom Poisoning Attack: DynamicBackdoorGAN-This class defines how to poison data using the GAN trigger generator
+class DynamicBackdoorGAN(PoisoningAttackBackdoor):
+    def __init__(self, generator, target_label, backdoor_rate, classifier, epsilon=0.5):
+        super().__init__(perturbation=lambda x: x)
+        self.classifier = classifier
+        self.generator = generator.to(classifier.device)
+        self.target_label = target_label
+        self.backdoor_rate = backdoor_rate
+        self.epsilon = epsilon
+# Add trigger to a given image batch
+    def apply_trigger(self, images):
+        self.generator.eval()
+        with torch.no_grad():
+            images = nn.functional.interpolate(images, size=(32, 32), mode='bilinear')  # Resize images to ensure uniform dimension
+            triggers = self.generator(images.to(self.classifier.device)) #Generate dynamic, input-specific triggers using the trained TriggerGenerator
+            poisoned = (images.to(self.classifier.device) + self.epsilon * triggers).clamp(0, 1) # Clamp the pixel values to ensure they stay in the valid [0, 1] range.
+        return poisoned
+# Poison the training data by injecting dynamic triggers and changing labels
+    def poison(self, x, y):
+        # Convert raw image data (x) to torch tensors (float), and convert one-hot labels (y) to class indices-required by ART
+        x_tensor = torch.tensor(x).float()
+        y_tensor = torch.tensor(np.argmax(y, axis=1))
+        # Calculate total number of samples and how many should be poisoned(posion ratio=backdoor_rate)
+        batch_size = x_tensor.shape[0]
+        n_poison = int(self.backdoor_rate * batch_size)
+         # Apply the learned trigger to the first 'n_poison' samples
+        poisoned = self.apply_trigger(x_tensor[:n_poison])
+        # The remaining samples remain clean
+        clean = x_tensor[n_poison:].to(self.classifier.device)
+         # Combine poisoned and clean samples into a single batch
+        poisoned_images = torch.cat([poisoned, clean], dim=0).cpu().numpy()
+        # Modify the labels of poisoned samples to the attacker's target class
+        new_labels = y_tensor.clone()
+        new_labels[:n_poison] = self.target_label # Set the poisoned labels to the desired misclassification
+        # Convert all labels back to one-hot encoding (required by ART classifiers)
+        new_labels = to_categorical(new_labels.numpy(), nb_classes=self.classifier.nb_classes)
+        return poisoned_images.astype(np.float32), new_labels.astype(np.float32)
+#Evaluate the attack's success on test data
+    def evaluate(self, x_clean, y_clean):
+        x_tensor = torch.tensor(x_clean).float()
+        poisoned_test = self.apply_trigger(x_tensor).cpu().numpy().astype(np.float32)# Apply the trigger to every test image to create a poisoned test set
+
+        preds = self.classifier.predict(poisoned_test)
+        true_target = np.full((len(preds),), self.target_label)
+        pred_labels = np.argmax(preds, axis=1)
+
+        success = np.sum(pred_labels == true_target)
+        asr = 100.0 * success / len(pred_labels)
+        return asr