main.py

import numpy as np
from foolbox2.criteria import TargetClass
from foolbox2.models.wrappers import CompositeModel
from fmodel5 import create_fmodel
from foolbox2.distances import MeanSquaredDistance
from foolbox2.adversarial import Adversarial
from adversarial_vision_challenge import load_model
from adversarial_vision_challenge import read_images
from adversarial_vision_challenge import store_adversarial
from adversarial_vision_challenge import attack_complete
from iterative import SAIterativeAttack, RMSIterativeAttack, \
                        AdamIterativeAttack, AdagradIterativeAttack
import os

def run_attack(model, image, target_class):
    criterion = TargetClass(target_class)
    # model == Composite model
    # Backward model = substitute model (resnet vgg alex) used to calculate gradients
    # Forward model = black-box model
    # distance = MeanSquaredDistance
    attack = AdamIterativeAttack(model, criterion)
    # attack = foolbox.attacks.annealer(model, criterion)
    # prediction of our black box model on the original image
    original_label = np.argmax(model.predictions(image))
    # adv = Adversarial(model, criterion, image, original_label, distance=distance)
    # return attack(adv)
    return attack(image, model_path = None, label = original_label)

def main():
    # tf.logging.set_verbosity(tf.logging.INFO)
    # instantiate blackbox and substitute model
    forward_model = load_model()
    backward_model = create_fmodel()

    # instantiate differntiable composite model
    # (predictions from blackbox, gradients from substitute)
    model = CompositeModel(
        forward_model=forward_model,
        backward_model=backward_model)
    for (file_name, image, label) in read_images():
        # tf.logging.info('Checking image is np array: %s' % str(type(image) is np.ndarray))
        adversarial = run_attack(model, image, label)
        store_adversarial(file_name, adversarial)
    # Announce that the attack is complete
    # NOTE: In the absence of this call, your submission will timeout
    # while being graded.
    # print("Attack is complete")
    attack_complete()

if __name__ == '__main__':
    main()