Delete fgsm.py

src/attacks/fgsm.py

-import torch
-import torch.nn as nn
-from .base_attack import BaseAttack
-
-# This class implements the basic FGSM (Fast Gradient Sign Method) attack.
-# It's a straightforward single-step approach.
-
-class FGSMAttack(BaseAttack):
-    def __init__(self, model, epsilon=0.3, clamp_min=0, clamp_max=1):
-        # We call the parent constructor to store the model
-        super().__init__(model)
-        # Epsilon controls how big the adversarial step is
-        self.epsilon = epsilon
-        self.clamp_min = clamp_min
-        self.clamp_max = clamp_max
-
-    def generate(self, x, y):
-        # We'll find which device the model is currently on,
-        # so we can move input data there
-        device = next(self.model.parameters()).device
-        x = x.to(device)
-        y = y.to(device)
-
-        # Make a copy of x that allows gradient calculation
-        x = x.clone().detach().requires_grad_(True)
-
-        # Forward pass: get predictions and compute the loss
-        logits = self.model(x)
-        loss_fn = nn.CrossEntropyLoss()
-        loss = loss_fn(logits, y)
-
-        # Backprop to calculate gradient
-        loss.backward()
-
-        # FGSM step: move in the direction of the sign of the gradient
-        x_adv = x + self.epsilon * x.grad.sign()
-
-        # Clamp values back into valid range
-        x_adv = torch.clamp(x_adv, self.clamp_min, self.clamp_max)
-
-        # Zero out the gradient so it doesn't affect future operations
-        x.grad.zero_()
-
-        return x_adv