def main():
# forward_model = load_model()
forward_model = create_fmodel()
backward_model = create_fmodel()
model = foolbox.models.CompositeModel(forward_model=forward_model,
backward_model=backward_model)
input_dir = '/home/hongyang/data/tiny-imagenet-200-aug/tiny-imagenet-200/train'
Images, Labels = read_train_images(input_dir)
print("Images.shape: ", Images.shape)
for idx in range(100):
# image is a numpy array with shape (64, 64, 3)
# and dtype float32, with values between 0 and 255;
# label is the original label (for untargeted
# attacks) or the target label (for targeted attacks)
# adversarial = run_attack(model, image, label)
# store_adversarial(file_name, adversarial)
adversarial = run_attack(model=model,
image=Images[idx],
label=Labels[idx].reshape([
1,
]))
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.
attack_complete()
def main():
# tf.logging.set_verbosity(tf.logging.INFO)
# instantiate blackbox and substitute model
# instantiate blackbox and substitute model
forward_model = load_model()
# backward_model1 = create_fmodel_18()
backward_model2 = create_fmodel_ALP()
backward_model3 = create_fmodel_ALP1000()
# print(backward_model1[0])
# instantiate differntiable composite model
# (predictions from blackbox, gradients from substitute)
model = CompositeModel(
forward_model=forward_model,
backward_models=[backward_model2, backward_model3],
weights = [0.5, 0.5])
predictor = tp.OfflinePredictor(tp.PredictConfig(
model=SaliencyModel(),
session_init=tp.get_model_loader("resnet_v1_50.ckpt"),
input_names=['image'],
output_names=['saliency']))
for (file_name, image, label) in read_images():
pos_salience = find_salience(predictor, image)
adversarial = run_attack(model, image, label, pos_salience)
store_adversarial(file_name, adversarial)
attack_complete()
def main():
loader = TinyImageNetLoader()
forward_model = load_model()
backward_model1 = create_fmodel_ALP()
backward_model2 = create_fmodel_ALP1000()
model = CompositeModel(forward_model=forward_model,
backward_models=[backward_model1, backward_model2],
weights=[0.3, 0.7])
i = 0
total_sum = 0.0
prev_avg = 0.0
for (file_name, image, label) in read_images():
is_boundary = True
is_adam = True
adversarial = run_attack(loader, forward_model, image, label)
# Calculate both Adam and Boundary for first 5 images.
if i < 5:
adversarial_adam = run_attack2(model, image, label, None)
if adversarial is not None:
error1 = distance(adversarial, image)
else:
error1 = 100000.0
is_boundary = False
if adversarial_adam is not None:
error_adam = distance(adversarial_adam, image)
else:
error_adam = 200000.0
is_adam = False
if is_adam and error1 - error_adam > 0.0:
adversarial = adversarial_adam
if is_adam or is_boundary:
i += 1
total_sum += min(error1, error_adam)
else:
if adversarial is not None:
error1 = distance(adversarial, image)
prev_avg = total_sum / i
i += 1
total_sum += error1
else:
error1 = 100000.0
if error1 > 25.0 or error1 > prev_avg or adversarial is None:
adversarial_adam = run_attack2(model, image, label, None)
if adversarial_adam is not None:
error_adam = distance(adversarial_adam, image)
else:
error_adam = 200000.0
if error1 - error_adam > 0.0:
adversarial = adversarial_adam
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.
attack_complete()
def main():
forward_model = load_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(forward_model, image, label, None)
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()
def main():
# tf.logging.set_verbosity(tf.logging.INFO)
# instantiate blackbox and substitute model
# instantiate blackbox and substitute model
forward_model = load_model()
backward_model1 = create_fmodel_18()
backward_model2 = create_fmodel_ALP()
# print(backward_model1[0])
# instantiate differntiable composite model
# (predictions from blackbox, gradients from substitute)
model = CompositeModel(forward_model=forward_model,
backward_models=[backward_model1, backward_model2])
for (file_name, image, label) in read_images():
adversarial = run_attack(model, image, label)
store_adversarial(file_name, adversarial)
attack_complete()
def main():
loader = TinyImageNetLoader()
forward_model = load_model()
backward_model1 = create_fmodel_ALP()
backward_model2 = create_fmodel_ALP1000()
model = CompositeModel(forward_model=forward_model,
backward_models=[backward_model1, backward_model2],
weights=[0.5, 0.5])
for (file_name, image, label) in read_images():
adversarial = run_attack(loader, forward_model, image, label)
if adversarial is None:
adversarial = run_attack2(model, image, label, None)
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.
attack_complete()
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()
def main():
# 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)
predictor = tp.OfflinePredictor(tp.PredictConfig(
model=SaliencyModel(),
session_init=tp.get_model_loader("resnet_v1_50.ckpt"),
input_names=['image'],
output_names=['saliency']))
for (file_name, image, label) in read_images():
pos_salience = find_salience(predictor, image)
adversarial = run_attack(model, image, label, pos_salience)
store_adversarial(file_name, adversarial)
attack_complete()
def main():
loader = TinyImageNetLoader()
forward_model = load_model()
backward_model1 = create_fmodel_ALP()
backward_model2 = create_fmodel_ALP1000()
model = CompositeModel(forward_model=forward_model,
backward_models=[backward_model1, backward_model2],
weights=[0.5, 0.5])
predictor = tp.OfflinePredictor(
tp.PredictConfig(model=SaliencyModel(),
session_init=tp.get_model_loader("resnet_v1_50.ckpt"),
input_names=['image'],
output_names=['saliency']))
for (file_name, image, label) in read_images():
adversarial = run_attack(loader, forward_model, image, label)
if adversarial is None:
pos_salience = find_salience(predictor, image)
adversarial = run_attack2(model, image, label, pos_salience)
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.
attack_complete()
def main():
n_classes = 200
img_shape = (64, 64, 3)
timed_wrapper = TimedWrapper(
load_model()) # Measure model prediction runtime.
remote_wrapper = RemoteModelWrapper(timed_wrapper,
do_hash=True) # Remember best adv. ex.
with SampleGenerator(shape=img_shape, n_threads=1,
queue_lengths=100) as sample_gen:
X_train, y_train, X_val, y_val = load_dataset(
"/path/to/tiny/imagenet", ds_cache_path='tiny_imagenet_cached.npz')
with MultiBoundaryAttack(model=remote_wrapper,
X_known=np.vstack([X_train, X_val]),
y_known=np.concatenate([y_train, y_val]),
n_classes=n_classes,
sample_gen=sample_gen,
cached_conf=None) as attack:
model_mean_query_time_history = []
time_max = 89 # As allowed in the rules (batch of 10 in 900 seconds)
time_bonus = 0 # Bonus to account for unfair models (see below)
i = 0
for (file_name, image, label) in read_images():
time_start = default_timer()
# Time calculation: 90 seconds per image are allowed. Models are allowed to use (40ms*1000calls) = 40s.
# This leaves 50 seconds for the attacker.
#
# But if the model is MUCH slower than allowed, then the attacker has less time and can't finish.
# To balance the scales, we detect this, and allow ourselves to use up some extra seconds.
# If we don't do this (and hard-abort at 90 seconds), attacks that don't count time would have an advantage vs us.
if i % 5 == 0 and len(model_mean_query_time_history) > 3:
avg_model_time = np.mean(model_mean_query_time_history)
if avg_model_time > 55e-3:
time_left_for_attacker = 89 - (1000 * avg_model_time)
time_bonus = min(55 - time_left_for_attacker, 50)
print(
"Model is slower than allowed (would leave only {:.1f} seconds for the attacker). "
"Will now use up to {:.1f} additional seconds per image."
.format(time_left_for_attacker, time_bonus))
elif time_bonus > 0:
time_bonus = 0
print(
"Model speed seems OK now. Reverting to the 90s time limit."
)
print("Image {}:".format(i))
image = np.float32(image)
remote_wrapper.adv_set_target(orig_img=image,
is_targeted=True,
label=label)
attack.run_attack(image=image,
label=label,
is_targeted=True,
start_with_fgm=True,
fgm_acceptable_dist=10,
time_max=time_max + time_bonus)
safe_adversarial = remote_wrapper.adv_get_best_img()
if safe_adversarial is None:
safe_adversarial = np.uint8(image)
print("Couldn't find an adversarial! This sucks!")
else:
dist = util.eval_distance(image, safe_adversarial)
print("Final distance: {}".format(dist))
# Save model query time stats.
rt_median, rt_mean, rt_std = timed_wrapper.get_runtime_stats()
print(
"Response time of model: median={:.1f}ms, mean={:.1f}ms, std={:.1f}ms"
.format(rt_median * 1e3, rt_mean * 1e3, rt_std * 1e3))
timed_wrapper.reset_runtime_stats()
if remote_wrapper.adv_get_n_calls() > 100:
model_mean_query_time_history.append(rt_mean)
time_elapsed_s = default_timer() - time_start
print("Queried the model {} times.".format(
remote_wrapper.adv_get_n_calls()))
print("Attack for this image took {} seconds.".format(
time_elapsed_s))
print()
store_adversarial(file_name, safe_adversarial)
i += 1
attack_complete()