Adversarial-Machine-Learning-Clinic/Filter_Analysis/fgsm.py

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
from mnist import Net

import defense_filters



TESTED_STRENGTH_COUNT = 5

MAX_EPSILON = 0.3
EPSILON_STEP = 0.025
epsilons = np.arange(0.0, MAX_EPSILON+EPSILON_STEP, EPSILON_STEP)
pretrained_model = "mnist_cnn_unfiltered.pt"
use_cuda=False

torch.manual_seed(69)


test_loader = torch.utils.data.DataLoader(
        datasets.MNIST('data/', train=False, download=True, transform=transforms.Compose([
            transforms.ToTensor(),
            transforms.Normalize((0.1307,), (0.3081,)),
            ])),
        batch_size=1, shuffle=True)

print("CUDA Available: ", torch.cuda.is_available())
device = torch.device("cuda" if use_cuda and torch.cuda.is_available() else "cpu")

model = Net().to(device)

model.load_state_dict(torch.load(pretrained_model, map_location=device))

model.eval()

def fgsm_attack(image, epsilon, data_grad):
    # Collect the element-wise sign of the data gradient
    sign_data_grad = data_grad.sign()
    
    # Create the perturbed image by adjusting each pixel of the input image
    perturbed_image = image + epsilon*sign_data_grad

    # Adding clipping to maintain [0, 1] range
    perturbed_image = torch.clamp(perturbed_image, 0, 1)
    
    return perturbed_image

def denorm(batch, mean=[0.1307], std=[0.3081]):
    """
    Convert a batch of tensors to their original scale.

    Args:
        batch (torch.Tensor): Batch of normalized tensors.
        mean (torch.Tensor or list): Man used for normalization.
        std (torch.Tensor or list): Standard deviation used for normalization.

    Returns:
        torch.Tensor: batch of tensors without normalization applied to them.
    """

    if isinstance(mean, list):
        mean = torch.tensor(mean).to(device)
    if isinstance(std, list):
        std = torch.tensor(std).to(device)

    return batch * std.view(1, -1, 1, 1) + mean.view(1, -1, 1, 1)

def test(model, device, test_loader, epsilon):
    # Original dataset correct classifications
    orig_correct = 0

    # Attacked dataset correct classifications
    unfiltered_correct = 0

    # Attacked, filtered dataset correct classifications
    filtered_correct_counts = []
    
    test_step = 0
    for data, target in test_loader:
        print("[" + "="*int(20*test_step/len(test_loader)) + " "*(20 - int(20*test_step/len(test_loader))) + "]", f"{100*test_step/len(test_loader)}%", end='\r')
        test_step += 1

        data, target = data.to(device), target.to(device)
        data.requires_grad = True

        output_orig = model(data)
        orig_pred = output_orig.max(1, keepdim=True)[1]

        # Calculate the loss
        loss = F.nll_loss(output_orig, target)

        # Zero all existing gradients
        model.zero_grad()

        # Calculate gradients of model in backward pass
        loss.backward()

        # Collect ''datagrad''
        data_grad = data.grad.data

        # Restore the data to its original scale
        data_denorm = denorm(data)

        # Apply the FGSM attack
        perturbed_data = fgsm_attack(data_denorm, epsilon, data_grad)

        # Reapply normalization
        perturbed_data_normalized = transforms.Normalize((0.1307,), (0.3081,))(perturbed_data)

        # Evaluate the model on the attacked image
        output_unfiltered = model(perturbed_data_normalized)

        # Evaluate performance for 
        for i in range(TESTED_STRENGTH_COUNT):
            strength = 2*i + 1

            # Apply the filter with the specified strength
            filtered_input = defense_filters.gaussian_blur(perturbed_data_normalized, batch_size=len(perturbed_data_normalized), ksize=(strength, strength))
            # Evaluate the model on the filtered images
            filtered_output = model(filtered_input)
            # Get the predicted classification
            filtered_pred = filtered_output.max(1, keepdim=True)[1]

            # Count up correct classifications
            if filtered_pred.item() == target.item():
                while i >= len(filtered_correct_counts):
                    filtered_correct_counts.append(0)
                filtered_correct_counts[i] += 1

        # Get the predicted classification
        unfiltered_pred = output_unfiltered.max(1, keepdim=True)[1]
        
        # Count up correct classifications for each case
        if orig_pred.item() == target.item():
            orig_correct += 1

        if unfiltered_pred.item() == target.item():
            unfiltered_correct += 1
        

    # Calculate the overall accuracy of each case
    orig_acc = orig_correct/float(len(test_loader))
    unfiltered_acc = unfiltered_correct/float(len(test_loader))

    filtered_accuracies = []
    for correct_count in filtered_correct_counts:
        filtered_accuracies.append(correct_count/float(len(test_loader)))


    print(f"====== EPSILON: {epsilon} ======")
    print(f"Clean (No Filter) Accuracy = {orig_correct} / {len(test_loader)} = {orig_acc}")
    print(f"Unfiltered Accuracy = {unfiltered_correct} / {len(test_loader)} = {unfiltered_acc}")
    for i in range(TESTED_STRENGTH_COUNT):
        strength = 2*i + 1
        print(f"Gaussian Blur (strength = {strength}) = {filtered_correct_counts[i]} / {len(test_loader)} = {filtered_accuracies[i]}")

    return unfiltered_acc, filtered_accuracies


unfiltered_accuracies = []
filtered_accuracies = []

for eps in epsilons:
    unfiltered_accuracy, filtered_accuracy = test(model, device, test_loader, eps)
    unfiltered_accuracies.append(unfiltered_accuracy)
    filtered_accuracies.append(filtered_accuracy)

# Plot the results
plt.figure(figsize=(16,9))
plt.plot(epsilons, unfiltered_accuracies, label="Attacked Accuracy")
for i in range(TESTED_STRENGTH_COUNT):
    filtered_accuracy = [filter_eps[i] for filter_eps in filtered_accuracies]
    plt.plot(epsilons, filtered_accuracy, label=f"Gaussian Blur (strength = {2*i + 1})")

plt.legend(loc="upper right")
plt.title("Gaussian Blur Performance")
plt.xlabel("Attack Strength ($\\epsilon$)")
plt.ylabel("Accuracy")
plt.savefig("Images/GaussianBlurPerformance.png", )