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src/defenses/vanilla_at.py

+import torch
+import torch.nn.functional as F
+from .base_defense import BaseDefense
+
+
+# VanillaAdversarialTrainingDefense class implements vanilla adversarial training
+class VanillaAdversarialTrainingDefense(BaseDefense):
+    def __init__(self, student_model, teacher_model=None, adv_lambda=1.0):
+        super().__init__(student_model, teacher_model)  # Initialize base class
+        self.adv_lambda = adv_lambda  # Weight for adversarial loss
+
+    # Loss function combining clean and adversarial cross-entropy loss
+    def loss_function(self, x, y, x_adv=None, **kwargs):
+        if x_adv is None:
+            x_adv = x  # If no adversarial examples, use clean examples
+
+        logits_adv = self.student_model(x_adv)  # Forward pass for adversarial input
+        loss_adv = F.cross_entropy(logits_adv, y)  # Cross-entropy loss for adversarial input
+
+        if self.adv_lambda < 1.0:
+            logits_clean = self.student_model(x)  # Forward pass for clean input
+            loss_clean = F.cross_entropy(logits_clean, y)  # Cross-entropy loss for clean input
+            loss = self.adv_lambda * loss_adv + (1.0 - self.adv_lambda) * loss_clean  # Weighted loss
+        else:
+            loss = loss_adv
+
+        return loss