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attack.py
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attack.py
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#!/usr/bin/env python3
import argparse
import numpy as np
import pandas as pd
from keras.datasets import cifar10
import pickle
# Custom Networks
from networks.lenet import LeNet
from networks.pure_cnn import PureCnn
from networks.network_in_network import NetworkInNetwork
from networks.resnet import ResNet
from networks.densenet import DenseNet
from networks.wide_resnet import WideResNet
from networks.capsnet import CapsNet
# Helper functions
from differential_evolution import differential_evolution
import helper
class PixelAttacker:
def __init__(self, models, data, class_names, dimensions=(32, 32)):
# Load data and model
self.models = models
self.x_test, self.y_test = data
self.class_names = class_names
self.dimensions = dimensions
network_stats, correct_imgs = helper.evaluate_models(self.models, self.x_test, self.y_test)
self.correct_imgs = pd.DataFrame(correct_imgs, columns=['name', 'img', 'label', 'confidence', 'pred'])
self.network_stats = pd.DataFrame(network_stats, columns=['name', 'accuracy', 'param_count'])
def predict_classes(self, xs, img, target_class, model, minimize=True):
# Perturb the image with the given pixel(s) x and get the prediction of the model
imgs_perturbed = helper.perturb_image(xs, img)
predictions = model.predict(imgs_perturbed)[:, target_class]
# This function should always be minimized, so return its complement if needed
return predictions if minimize else 1 - predictions
def attack_success(self, x, img, target_class, model, targeted_attack=False, verbose=False):
# Perturb the image with the given pixel(s) and get the prediction of the model
attack_image = helper.perturb_image(x, img)
confidence = model.predict(attack_image)[0]
predicted_class = np.argmax(confidence)
# If the prediction is what we want (misclassification or
# targeted classification), return True
if verbose:
print('Confidence:', confidence[target_class])
if ((targeted_attack and predicted_class == target_class) or
(not targeted_attack and predicted_class != target_class)):
return True
def attack(self, img_id, model, target=None, pixel_count=1,
maxiter=75, popsize=400, verbose=False, plot=False):
# Change the target class based on whether this is a targeted attack or not
targeted_attack = target is not None
target_class = target if targeted_attack else self.y_test[img_id, 0]
# Define bounds for a flat vector of x,y,r,g,b values
# For more pixels, repeat this layout
dim_x, dim_y = self.dimensions
bounds = [(0, dim_x), (0, dim_y), (0, 256), (0, 256), (0, 256)] * pixel_count
# Population multiplier, in terms of the size of the perturbation vector x
popmul = max(1, popsize // len(bounds))
# Format the predict/callback functions for the differential evolution algorithm
def predict_fn(xs):
return self.predict_classes(xs, self.x_test[img_id], target_class, model, target is None)
def callback_fn(x, convergence):
return self.attack_success(x, self.x_test[img_id], target_class, model, targeted_attack, verbose)
# Call Scipy's Implementation of Differential Evolution
attack_result = differential_evolution(
predict_fn, bounds, maxiter=maxiter, popsize=popmul,
recombination=1, atol=-1, callback=callback_fn, polish=False)
# Calculate some useful statistics to return from this function
attack_image = helper.perturb_image(attack_result.x, self.x_test[img_id])[0]
prior_probs = model.predict(np.array([self.x_test[img_id]]))[0]
predicted_probs = model.predict(np.array([attack_image]))[0]
predicted_class = np.argmax(predicted_probs)
actual_class = self.y_test[img_id, 0]
success = predicted_class != actual_class
cdiff = prior_probs[actual_class] - predicted_probs[actual_class]
# Show the best attempt at a solution (successful or not)
if plot:
helper.plot_image(attack_image, actual_class, self.class_names, predicted_class)
return [model.name, pixel_count, img_id, actual_class, predicted_class, success, cdiff, prior_probs,
predicted_probs, attack_result.x]
def attack_all(self, models, samples=500, pixels=(1, 3, 5), targeted=False,
maxiter=75, popsize=400, verbose=False):
results = []
for model in models:
model_results = []
valid_imgs = self.correct_imgs[self.correct_imgs.name == model.name].img
img_samples = np.random.choice(valid_imgs, samples)
for pixel_count in pixels:
for i, img in enumerate(img_samples):
print(model.name, '- image', img, '-', i + 1, '/', len(img_samples))
targets = [None] if not targeted else range(10)
for target in targets:
if targeted:
print('Attacking with target', self.class_names[target])
if target == self.y_test[img, 0]:
continue
result = self.attack(img, model, target, pixel_count,
maxiter=maxiter, popsize=popsize,
verbose=verbose)
model_results.append(result)
results += model_results
helper.checkpoint(results, targeted)
return results
if __name__ == '__main__':
model_defs = {
'lenet': LeNet,
'pure_cnn': PureCnn,
'net_in_net': NetworkInNetwork,
'resnet': ResNet,
'densenet': DenseNet,
'wide_resnet': WideResNet,
'capsnet': CapsNet
}
parser = argparse.ArgumentParser(description='Attack models on Cifar10')
parser.add_argument('--model', nargs='+', choices=model_defs.keys(), default=model_defs.keys(),
help='Specify one or more models by name to evaluate.')
parser.add_argument('--pixels', nargs='+', default=(1, 3, 5), type=int,
help='The number of pixels that can be perturbed.')
parser.add_argument('--maxiter', default=75, type=int,
help='The maximum number of iterations in the differential evolution algorithm before giving up and failing the attack.')
parser.add_argument('--popsize', default=400, type=int,
help='The number of adversarial images generated each iteration in the differential evolution algorithm. Increasing this number requires more computation.')
parser.add_argument('--samples', default=500, type=int,
help='The number of image samples to attack. Images are sampled randomly from the dataset.')
parser.add_argument('--targeted', action='store_true', help='Set this switch to test for targeted attacks.')
parser.add_argument('--save', default='networks/results/results.pkl', help='Save location for the results (pickle)')
parser.add_argument('--verbose', action='store_true', help='Print out additional information every iteration.')
args = parser.parse_args()
# Load data and model
_, test = cifar10.load_data()
class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
models = [model_defs[m](load_weights=True) for m in args.model]
attacker = PixelAttacker(models, test, class_names)
print('Starting attack')
results = attacker.attack_all(models, samples=args.samples, pixels=args.pixels, targeted=args.targeted,
maxiter=args.maxiter, popsize=args.popsize, verbose=args.verbose)
columns = ['model', 'pixels', 'image', 'true', 'predicted', 'success', 'cdiff', 'prior_probs', 'predicted_probs',
'perturbation']
results_table = pd.DataFrame(results, columns=columns)
print(results_table[['model', 'pixels', 'image', 'true', 'predicted', 'success']])
print('Saving to', args.save)
with open(args.save, 'wb') as file:
pickle.dump(results, file)