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data_curator.py
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from model_architectures import *
from attacks import *
import csv
from multiprocessing import Pool
class Curator():
def store_data(self, filename, data, color = False):
if color:
with open(filename, 'w', newline='') as csvfile:
writer = csv.writer(csvfile)
# Write header row
header = ['IntegerValue'] + [f'R_Pixel_{i}' for i in range(32*32)]+ [f'G_Pixel_{i}' for i in range(32*32)]+ [f'B_Pixel_{i}' for i in range(32*32)]
writer.writerow(header)
# Write data rows
for tuple_data in data:
integer_value, _, array = tuple_data
flattened_images = array.reshape(array.shape[0], -1)
# Concatenate the color channels
concatenated_channels = np.concatenate(flattened_images, axis=0)
row = [integer_value] + list(concatenated_channels)
writer.writerow(row)
else:
with open(filename, 'w', newline='') as csvfile:
writer = csv.writer(csvfile)
# Write header row
header = ['IntegerValue'] + [f'Pixel_{i}' for i in range(28*28)]
writer.writerow(header)
# Write data rows
for tuple_data in data:
integer_value, _, array = tuple_data
flattened_array = array.flatten()
row = [integer_value] + list(flattened_array)
writer.writerow(row)
def curate_fgsm(self, model, test_loader, epsilon):
correct = 0
total = 0
adv_examples = []
batch = 0
for images, labels in test_loader:
images, labels = images.to(device), labels.to(device)
batch += 1
for image, label in zip(images, labels):
image = image.unsqueeze(0)
label = label.unsqueeze(0)
image.requires_grad = True
output, _ = model(image)
_, init_pred = torch.max(output.data, 1)
if not torch.equal(init_pred, label):
total +=1
continue
loss = F.nll_loss(output, label)
model.zero_grad()
loss.backward()
data_grad = image.grad.data
perturbed_data = fgsm_attack(image, epsilon, data_grad)
output_final, _ = model(perturbed_data)
_, final_pred = torch.max(output_final.data, 1)
if torch.equal(final_pred, label):
correct += 1
else:
adv_ex = perturbed_data.squeeze().detach().cpu().numpy()
adv_examples.append( (init_pred.item(), final_pred.item(), adv_ex) )
total +=1
print(f'Batch {batch} Completed. # of Adversarial Examples: {len(adv_examples)}')
accuracy = correct / total
return accuracy, adv_examples
def curate_pgd(self, model, test_loader, epsilon, alpha):
correct = 0
total = 0
adv_examples = []
batch = 0
for images, labels in test_loader:
images, labels = images.to(device), labels.to(device)
batch += 1
# print(f"Batch: {batch}, Epsilon: {epsilon}, Correct: {correct}")
for image, label in zip(images, labels):
image = image.unsqueeze(0)
label = label.unsqueeze(0)
image.requires_grad = True
output, _ = model(image)
_, init_pred = torch.max(output.data, 1)
if not torch.equal(init_pred, label):
total +=1
continue
output_final, perturbed_data = pgd_attack(image, model, init_pred, epsilon, alpha)
_, final_pred = torch.max(output_final.data, 1)
if torch.equal(final_pred, label):
correct += 1
else:
# Save some adv examples for visualization later
adv_ex = perturbed_data.squeeze().detach().cpu().numpy()
adv_examples.append( (init_pred.item(), final_pred.item(), adv_ex) )
# print(f"{correct}/{total}")
total +=1
print(f'Batch {batch} Completed. # of Adversarial Examples: {len(adv_examples)}')
# break
# break
accuracy = correct / total
# print(f"Epsilon: {epsilon}\tTest Accuracy = {correct} / {total} = {accuracy}")
return accuracy, adv_examples
def curate_deepfool(self, model, test_loader, overshoot=0.02):
correct = 0
total = 0
adv_examples = []
batch = 0
for images, labels in test_loader:
images, labels = images.to(device), labels.to(device)
batch += 1
# print(f"Batch: {batch}, Correct: {correct}")
for image, label in zip(images, labels):
# print("image number: ",total)
image = image.unsqueeze(0)
label = label.unsqueeze(0)
image.requires_grad = True
output, _ = model(image)
# print(outputs)
_, init_pred = torch.max(output.data, 1)
if not torch.equal(init_pred, label):
total +=1
continue
perturbed_image, final_pred, r_total, iter = deepfool_attack(image, model, overshoot=0.02, max_iterations=100)
# print(f"Perturbed Iteration: {iter}")
if torch.equal(final_pred, label):
correct += 1
else:
adv_ex = perturbed_image.squeeze().detach().cpu().numpy()
adv_examples.append( (init_pred.item(), final_pred.item(), adv_ex) )
total +=1
print(f'Batch {batch} Completed. # of Adversarial Examples: {len(adv_examples)}')
accuracy = correct / total
# print(f"Test Accuracy = {correct} / {total} = {accuracy}")
return accuracy, adv_examples
def curate_jsma(self, model, test_loader, theta = 0.1):
model.eval()
pred_list = []
correct = 0
total = 0
adv_examples = []
batch = 0
for images, labels in test_loader:
images, labels = images.to(device), labels.to(device)
batch += 1
for image, label in zip(images, labels):
image = image.unsqueeze(0)
label = label.unsqueeze(0)
output, _ = model(image)
# print(outputs)
_, init_pred = torch.max(output.data, 1)
if not torch.equal(init_pred, label):
total +=1
continue
target = torch.topk(output,2).indices[0][1].item()
advimages = jsma_attack(model, image, target, 10, theta = theta)
output_adv, _ = model(advimages)
_, prediction_adv = torch.max(output_adv.data, 1)
if torch.equal(prediction_adv, label):
correct += 1
else:
adv_ex = advimages.squeeze().detach().cpu().numpy()
adv_examples.append( (init_pred.item(), prediction_adv.item(), adv_ex) )
pred_list.append(prediction_adv)
total +=1
# print(correct, "/", total)
# if (total>0):
# break
print(f'Batch {batch} Completed. # of Adversarial Examples: {len(adv_examples)}')
# print('Accuracy of test text: %f %%' % ((float(correct) / total) * 100))
accuracy = correct / total
return accuracy, adv_examples
def curate_cw(self, model, test_loader, targeted=False, target_label=0, c=0.75, alpha=0.01, kappa= 0, max_iterations=50, mnist = 1):
model.eval()
pred_list = []
correct = 0
total = 0
adv_examples = []
batch = 0
for images, labels in test_loader:
images, labels = images.to(device), labels.to(device)
batch += 1
for image, label in zip(images, labels):
image = image.unsqueeze(0)
label = label.unsqueeze(0)
output, _ = model(image)
# print(outputs)
_, init_pred = torch.max(output.data, 1)
advimages = cw_attack(image, model, init_pred, targeted, target_label, c, alpha, kappa, max_iterations)
output_adv, _ = model(advimages)
# advimages = advimages[0,:,:,:] / 255
_, prediction_adv = torch.max(output_adv.data, 1)
if torch.equal(prediction_adv, label):
correct += 1
else:
# Save some adv examples for visualization later
if not targeted:
#if prediction_adv not in pred_list:
adv_ex = advimages.squeeze().detach().cpu().numpy()
#if not mnist:
#adv_ex = adv_ex/255
adv_examples.append( (init_pred.item(), prediction_adv.item(), adv_ex) )
pred_list.append(prediction_adv)
else:
adv_ex = advimages.squeeze().detach().cpu().numpy()
# print(init_pred.item())
adv_examples.append( (init_pred.item(), prediction_adv.item(), adv_ex) )
total +=1
# print(correct, "/", total)
# if total-correct > 100:
# break
accuracy = correct / total
print(f'Batch {batch} Completed. # of Adversarial Examples: {len(adv_examples)}')
# print('Accuracy of test text: %f %%' % ((float(correct) / total) * 100))
return accuracy, adv_examples