def load_model_dataset(TASK, REF, root_dir):
if TASK == 'imagenet':
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
])
trainset = ImageNetDataset(train=True, transform=transform)
testset = ImageNetDataset(train=False, transform=transform)
if REF == 'dense121':
ref_model = models.densenet121(pretrained=True).eval()
elif REF == 'res18':
ref_model = models.resnet18(pretrained=True).eval()
elif REF == 'res50':
ref_model = models.resnet50(pretrained=True).eval()
elif REF == 'vgg16':
ref_model = models.vgg16(pretrained=True).eval()
elif REF == 'googlenet':
ref_model = models.googlenet(pretrained=True).eval()
elif REF == 'wideresnet':
ref_model = models.wide_resnet50_2(pretrained=True).eval()
if GPU:
ref_model.cuda()
preprocess_std = (0.229, 0.224, 0.225)
elif TASK == 'celeba':
image_size = 224
transform = transforms.Compose([
transforms.Resize(image_size),
transforms.CenterCrop(image_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
attr_o_i = 'Mouth_Slightly_Open'
list_attr_path = '%s/celebA/list_attr_celeba.txt' % (root_dir)
img_data_path = '%s/celebA/img_align_celeba' % (root_dir)
trainset = CelebAAttributeDataset(attr_o_i, list_attr_path, img_data_path, data_split='train',
transform=transform)
testset = CelebAAttributeDataset(attr_o_i, list_attr_path, img_data_path, data_split='test',
transform=transform)
ref_model = CelebADNN(model_type=REF, pretrained=False, gpu=GPU)
ref_model.load_state_dict(torch.load('../class_models/celeba_%s_%s.model' % (attr_o_i, REF)))
preprocess_std = (0.5, 0.5, 0.5)
elif TASK == 'celebaid':
image_size = 224
transform = transforms.Compose([
transforms.Resize(image_size),
transforms.CenterCrop(image_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
num_class = 10
trainset = CelebAIDDataset(root_dir=root_dir, is_train=True, transform=transform, preprocess=False,
random_sample=False, n_id=num_class)
testset = CelebAIDDataset(root_dir=root_dir, is_train=False, transform=transform, preprocess=False,
random_sample=False, n_id=num_class)
ref_model = CelebAIDDNN(model_type=REF, num_class=num_class, pretrained=False, gpu=GPU).eval()
ref_model.load_state_dict(torch.load('../class_models/celeba_id_%s.model' % (REF)))
preprocess_std = (0.5, 0.5, 0.5)
elif TASK == 'dogcat2':
BATCH_SIZE = 32
image_size = 224
transform = transforms.Compose([
transforms.Resize(image_size),
transforms.CenterCrop(image_size),
transforms.ToTensor(),
])
num_class = 2
trainset = DogCatDataset(root_dir=f'{constants.ROOT_DATA_PATH}/dogcat', mode='train', transform=transform)
testset = DogCatDataset(root_dir=f'{constants.ROOT_DATA_PATH}/dogcat', mode='test', transform=transform)
ref_model = NClassDNN(model_type=REF, pretrained=False, gpu=GPU, n_class=num_class)
ref_model.load_state_dict(torch.load('../class_models/%s_%s.model' % ('dogcat2', REF)))
preprocess_std = (1, 1, 1)
elif TASK == 'cifar10':
# mean = (0.485, 0.456, 0.406)
# std = (0.229, 0.224, 0.225)
mean, std = constants.plot_mean_std(TASK)
transform = model_settings.get_data_transformation(TASK, args)
model_file_name = model_settings.get_model_file_name(TASK, args)
trainset = torchvision.datasets.CIFAR10(root='%s/'%(constants.ROOT_DATA_PATH), train=True, download=True, transform=transform)
testset = torchvision.datasets.CIFAR10(root='%s/'%(constants.ROOT_DATA_PATH), train=False, download=True, transform=transform)
ref_model = CifarDNN(model_type=REF, gpu=GPU, pretrained=False, img_size=cifar10_img_size)
ref_model.load_state_dict(torch.load('../class_models/%s_%s.model' % (model_file_name, REF)))
if GPU:
ref_model.cuda()
preprocess_std = std
elif TASK == 'mnist':
mean, std = constants.plot_mean_std(TASK)
transform = model_settings.get_data_transformation(TASK, args)
model_file_name = model_settings.get_model_file_name(TASK, args)
num_class = 10
trainset = torchvision.datasets.MNIST(root='%s/'%(constants.ROOT_DATA_PATH), train=True, download=True, transform=transform)
testset = torchvision.datasets.MNIST(root='%s/'%(constants.ROOT_DATA_PATH), train=False, download=True, transform=transform)
ref_model = MNISTDNN(model_type=REF, gpu=GPU, n_class=num_class, img_size=mnist_img_size)
ref_model.load_state_dict(torch.load('../class_models/%s_%s.model' % (model_file_name, REF)))
preprocess_std = std
elif TASK == 'celeba2':
image_size = 224
num_class = 2
transform = transforms.Compose([
transforms.Resize(image_size),
transforms.CenterCrop(image_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
root_dir = f"{constants.ROOT_DATA_PATH}"
trainset = CelebABinIDDataset(poi=args.celeba_poi, root_dir=root_dir, is_train=True, transform=transform,
get_data=False, random_sample=False, n_id=10)
testset = CelebABinIDDataset(poi=args.celeba_poi, root_dir=root_dir, is_train=False, transform=transform,
get_data=False, random_sample=False, n_id=10)
ref_model = NClassDNN(model_type=REF, pretrained=False, gpu=GPU, n_class=num_class)
ref_model.load_state_dict(torch.load('../class_models/%s_%s.model' % ('celeba2', REF)))
preprocess_std = (0.5, 0.5, 0.5)
else:
assert 0
return ref_model, trainset, testset, preprocess_std
def main(argv=None):
# 0. Select a dataset.
from datasets import MNISTDataset, CIFAR10Dataset, ImageNetDataset
from datasets import get_correct_prediction_idx, evaluate_adversarial_examples, calculate_mean_confidence, \
calculate_accuracy
if FLAGS.dataset_name == "MNIST":
dataset = MNISTDataset()
elif FLAGS.dataset_name == "CIFAR-10":
dataset = CIFAR10Dataset()
FLAGS.image_size = 32 # Redundant for the Current Attack.
elif FLAGS.dataset_name == "ImageNet":
dataset = ImageNetDataset()
FLAGS.image_size = 224 # Redundant for the Current Attack.
# 1. Load a dataset.
print("\n===Loading %s data..." % FLAGS.dataset_name)
if FLAGS.dataset_name == 'ImageNet':
if FLAGS.model_name == 'inceptionv3':
img_size = 299
else:
img_size = 224
X_test_all, Y_test_all = dataset.get_test_data(img_size, 0, 200)
else:
X_test_all, Y_test_all = dataset.get_test_dataset()
# Randomized optimizations
if FLAGS.dataset_name != "ImageNet":
all_idx = np.arange(10000)
np.random.shuffle(all_idx)
selected_idx = all_idx[:(FLAGS.nb_examples * 2)]
X_test_all, Y_test_all = X_test_all[selected_idx], Y_test_all[
selected_idx]
# 2. Load a trained model.
sess = load_tf_session()
keras.backend.set_learning_phase(0)
# Define input TF placeholder
x = tf.placeholder(tf.float32,
shape=(None, dataset.image_size, dataset.image_size,
dataset.num_channels))
y = tf.placeholder(tf.float32, shape=(None, dataset.num_classes))
sq_list = FLAGS.squeezers.split(';')
print(" Squeezers used for EOT :", sq_list)
x_s = []
squeezers = []
models = []
for squeezer in sq_list:
x_s.append(
tf.placeholder(tf.float32,
shape=(None, dataset.image_size, dataset.image_size,
dataset.num_channels)))
if squeezer.startswith("median"):
squeezers.append(lambda x: x)
with tf.variable_scope(FLAGS.model_name + squeezer):
cur_model = dataset.load_model_by_name(
FLAGS.model_name,
logits=False,
input_range_type=1,
pre_filter=get_squeezer_by_name(squeezer, 'tensorflow'))
cur_model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['acc'])
models.append(cur_model)
else:
squeezers.append(get_squeezer_by_name(squeezer, 'python'))
with tf.variable_scope(FLAGS.model_name + "local" + squeezer):
cur_model = dataset.load_model_by_name(FLAGS.model_name,
logits=False,
input_range_type=1)
cur_model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['acc'])
models.append(cur_model)
with tf.variable_scope(FLAGS.model_name + "vanilla"):
model_vanilla = dataset.load_model_by_name(FLAGS.model_name,
logits=False,
input_range_type=1)
model_vanilla.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['acc'])
# 3. Evaluate the trained model.
# TODO: add top-5 accuracy for ImageNet.
print("Evaluating the pre-trained model...")
# We use the Vanilla Model here for Prediction
print(
" ************************************************* Shape of X_test_all :",
X_test_all.shape)
Y_pred_all = model_vanilla.predict(X_test_all)
mean_conf_all = calculate_mean_confidence(Y_pred_all, Y_test_all)
accuracy_all = calculate_accuracy(Y_pred_all, Y_test_all)
print('Test accuracy on raw legitimate examples %.4f' % (accuracy_all))
print('Mean confidence on ground truth classes %.4f' % (mean_conf_all))
# 4. Select some examples to attack.
import hashlib
from datasets import get_first_n_examples_id_each_class
if FLAGS.select:
# Filter out the misclassified examples.
correct_idx = get_correct_prediction_idx(Y_pred_all, Y_test_all)
if FLAGS.test_mode:
# Only select the first example of each class.
correct_and_selected_idx = get_first_n_examples_id_each_class(
Y_test_all[correct_idx])
selected_idx = [correct_idx[i] for i in correct_and_selected_idx]
else:
if not FLAGS.balance_sampling:
selected_idx = correct_idx[:FLAGS.nb_examples]
else:
# select the same number of examples for each class label.
nb_examples_per_class = int(FLAGS.nb_examples /
Y_test_all.shape[1])
correct_and_selected_idx = get_first_n_examples_id_each_class(
Y_test_all[correct_idx], n=nb_examples_per_class)
selected_idx = [
correct_idx[i] for i in correct_and_selected_idx
]
else:
selected_idx = np.array(range(FLAGS.nb_examples))
from utils.output import format_number_range
selected_example_idx_ranges = format_number_range(sorted(selected_idx))
print("Selected %d examples." % len(selected_idx))
print("Selected index in test set (sorted): %s" %
selected_example_idx_ranges)
X_test, Y_test, Y_pred = X_test_all[selected_idx], Y_test_all[
selected_idx], Y_pred_all[selected_idx]
# The accuracy should be 100%.
accuracy_selected = calculate_accuracy(Y_pred, Y_test)
mean_conf_selected = calculate_mean_confidence(Y_pred, Y_test)
print('Test accuracy on selected legitimate examples %.4f' %
(accuracy_selected))
print('Mean confidence on ground truth classes, selected %.4f\n' %
(mean_conf_selected))
task = {}
task['dataset_name'] = FLAGS.dataset_name
task['model_name'] = FLAGS.model_name
task['accuracy_test'] = accuracy_all
task['mean_confidence_test'] = mean_conf_all
task['test_set_selected_length'] = len(selected_idx)
task['test_set_selected_idx_ranges'] = selected_example_idx_ranges
task['test_set_selected_idx_hash'] = hashlib.sha1(
str(selected_idx).encode('utf-8')).hexdigest()
task['accuracy_test_selected'] = accuracy_selected
task['mean_confidence_test_selected'] = mean_conf_selected
task_id = "%s_%d_%s_%s" % \
(task['dataset_name'], task['test_set_selected_length'], task['test_set_selected_idx_hash'][:5],
task['model_name'], )
FLAGS.result_folder = os.path.join(FLAGS.result_folder, task_id)
if not os.path.isdir(FLAGS.result_folder):
os.makedirs(FLAGS.result_folder)
from utils.output import save_task_descriptor
save_task_descriptor(FLAGS.result_folder, [task])
# 5. Generate adversarial examples.
from utils.squeeze import reduce_precision_py
from utils.parameter_parser import parse_params
attack_string_hash = hashlib.sha1(
FLAGS.attacks.encode('utf-8')).hexdigest()[:5]
sample_string_hash = task['test_set_selected_idx_hash'][:5]
from datasets.datasets_utils import get_next_class, get_least_likely_class
Y_test_target_next = get_next_class(Y_test)
Y_test_target_ll = get_least_likely_class(Y_pred)
X_test_adv_list = []
X_test_adv_discretized_list = []
Y_test_adv_discretized_pred_list = []
attack_string_list = filter(lambda x: len(x) > 0,
FLAGS.attacks.lower().split(';'))
to_csv = []
X_adv_cache_folder = os.path.join(FLAGS.result_folder, 'adv_examples')
adv_log_folder = os.path.join(FLAGS.result_folder, 'adv_logs')
predictions_folder = os.path.join(FLAGS.result_folder, 'predictions')
for folder in [X_adv_cache_folder, adv_log_folder, predictions_folder]:
if not os.path.isdir(folder):
os.makedirs(folder)
predictions_fpath = os.path.join(predictions_folder, "legitimate.npy")
np.save(predictions_fpath, Y_pred, allow_pickle=False)
if FLAGS.clip >= 0:
epsilon = FLAGS.clip
print("Clip the adversarial perturbations by +-%f" % epsilon)
max_clip = np.clip(X_test + epsilon, 0, 1)
min_clip = np.clip(X_test - epsilon, 0, 1)
# NOTE : At the moment we only support single attacks and single detectors.
for attack_string in attack_string_list:
attack_name, attack_params = parse_params(attack_string)
print("\nRunning attack: %s %s" % (attack_name, attack_params))
if 'targeted' in attack_params:
targeted = attack_params['targeted']
print("targeted value: %s" % targeted)
if targeted == 'next':
Y_test_target = Y_test_target_next
elif targeted == 'll':
Y_test_target = Y_test_target_ll
elif targeted == False:
attack_params['targeted'] = False
Y_test_target = Y_test.copy()
else:
targeted = False
attack_params['targeted'] = False
Y_test_target = Y_test.copy()
# Note that we use the attack model here instead of the vanilla model
# Note that we pass in the Squeezer function for BPDA
X_test_adv = eot_adversarial_attack(sess, model_vanilla, models, x, y,
x_s, X_test, Y_test_target,
attack_params, squeezers)
if FLAGS.clip > 0:
# This is L-inf clipping.
X_test_adv = np.clip(X_test_adv, min_clip, max_clip)
X_test_adv_list.append(X_test_adv)
# 5.0 Output predictions.
Y_test_adv_pred = model_vanilla.predict(X_test_adv)
predictions_fpath = os.path.join(predictions_folder,
"%s.npy" % attack_string)
np.save(predictions_fpath, Y_test_adv_pred, allow_pickle=False)
# 5.1 Evaluate the adversarial examples being discretized to uint8.
print("\n---Attack (uint8): %s" % attack_string)
# All data should be discretized to uint8.
X_test_adv_discret = reduce_precision_py(X_test_adv, 256)
X_test_adv_discretized_list.append(X_test_adv_discret)
Y_test_adv_discret_pred = model_vanilla.predict(X_test_adv_discret)
Y_test_adv_discretized_pred_list.append(Y_test_adv_discret_pred)
# Y_test_adv_discret_pred is for the vanilla model
rec = evaluate_adversarial_examples(X_test, Y_test, X_test_adv_discret,
Y_test_target.copy(), targeted,
Y_test_adv_discret_pred)
rec['dataset_name'] = FLAGS.dataset_name
rec['model_name'] = FLAGS.model_name
rec['attack_string'] = attack_string
rec['discretization'] = True
to_csv.append(rec)
from utils.output import write_to_csv
attacks_evaluation_csv_fpath = os.path.join(FLAGS.result_folder,
"%s_attacks_%s_evaluation.csv" % \
(task_id, attack_string_hash))
fieldnames = [
'dataset_name', 'model_name', 'attack_string', 'discretization',
'success_rate', 'mean_confidence', 'mean_l2_dist', 'mean_li_dist',
'mean_l0_dist_value', 'mean_l0_dist_pixel'
]
write_to_csv(to_csv, attacks_evaluation_csv_fpath, fieldnames)
if FLAGS.visualize is True:
from datasets.visualization import show_imgs_in_rows
if FLAGS.test_mode or FLAGS.balance_sampling:
selected_idx_vis = range(Y_test.shape[1])
else:
selected_idx_vis = get_first_n_examples_id_each_class(Y_test, 1)
legitimate_examples = X_test[selected_idx_vis]
rows = [legitimate_examples]
rows += map(lambda x: x[selected_idx_vis], X_test_adv_list)
img_fpath = os.path.join(
FLAGS.result_folder,
'%s_attacks_%s_examples.png' % (task_id, attack_string_hash))
#show_imgs_in_rows(rows, img_fpath)
print('\n===Adversarial image examples are saved in ', img_fpath)
# TODO: output the prediction and confidence for each example, both legitimate and adversarial.
# 6. Evaluate robust classification techniques.
# Example: --robustness \
# "Base;FeatureSqueezing?squeezer=bit_depth_1;FeatureSqueezing?squeezer=median_filter_2;"
if FLAGS.robustness != '':
"""
Test the accuracy with robust classifiers.
Evaluate the accuracy on all the legitimate examples.
"""
from robustness import evaluate_robustness
result_folder_robustness = os.path.join(FLAGS.result_folder,
"robustness")
fname_prefix = "robustness_summary"
evaluate_robustness(FLAGS.robustness, model_vanilla, Y_test_all, X_test_all, Y_test, \
attack_string_list, X_test_adv_discretized_list,
fname_prefix, selected_idx_vis, result_folder_robustness)
# 7. Detection experiment.
# Example: --detection "FeatureSqueezing?distance_measure=l1&squeezers=median_smoothing_2,bit_depth_4,bilateral_filter_15_15_60;"
if FLAGS.detection != '':
from detections.base import DetectionEvaluator
result_folder_detection = os.path.join(FLAGS.result_folder,
"detection")
csv_fname = "detection_summary.csv"
de = DetectionEvaluator(model_vanilla, result_folder_detection,
csv_fname, FLAGS.dataset_name)
Y_test_all_pred = model_vanilla.predict(X_test_all)
de.build_detection_dataset(X_test_all, Y_test_all, Y_test_all_pred,
selected_idx, X_test_adv_discretized_list,
Y_test_adv_discretized_pred_list,
attack_string_list, attack_string_hash,
FLAGS.clip, Y_test_target_next,
Y_test_target_ll)
de.evaluate_detections(FLAGS.detection)
def main(argv=None):
# 0. Select a dataset.
from datasets import MNISTDataset, CIFAR10Dataset, ImageNetDataset
from datasets import get_correct_prediction_idx, evaluate_adversarial_examples, calculate_mean_confidence, calculate_accuracy, calculate_real_untargeted_mean_confidence
if FLAGS.dataset_name == "MNIST":
dataset = MNISTDataset()
elif FLAGS.dataset_name == "CIFAR-10":
dataset = CIFAR10Dataset()
elif FLAGS.dataset_name == "ImageNet":
dataset = ImageNetDataset()
# 1. Load a dataset.
print("\n===Loading %s data..." % FLAGS.dataset_name)
if FLAGS.dataset_name == 'ImageNet':
if FLAGS.model_name == 'inceptionv3':
img_size = 299
else:
img_size = 224
X_test_all, Y_test_all = dataset.get_test_data(img_size, 0, 200)
else:
X_test_all, Y_test_all = dataset.get_test_dataset()
# 2. Load a trained model.
sess = load_tf_session()
#keras.backend.set_learning_phase(0)
# Define input TF placeholder
x = tf.placeholder(tf.float32,
shape=(None, dataset.image_size, dataset.image_size,
dataset.num_channels))
y = tf.placeholder(tf.float32, shape=(None, dataset.num_classes))
with tf.variable_scope(FLAGS.model_name):
"""
Create a model instance for prediction.
The scaling argument, 'input_range_type': {1: [0,1], 2:[-0.5, 0.5], 3:[-1, 1]...}
"""
model = dataset.load_model_by_name(FLAGS.model_name,
logits=False,
input_range_type=1)
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['acc'])
# 3. Evaluate the trained model.
# TODO: add top-5 accuracy for ImageNet.
print("Evaluating the pre-trained model...")
#X_test_all = scipy.ndimage.rotate(X_test_all, 5, reshape=False, axes=(2, 1))
Y_pred_all = model.predict(X_test_all)
mean_conf_all, _, _, _ = calculate_mean_confidence(Y_pred_all, Y_test_all)
accuracy_all = calculate_accuracy(Y_pred_all, Y_test_all)
print('Test accuracy on raw legitimate examples %.4f' % (accuracy_all))
print('Mean confidence on ground truth classes %.4f' % (mean_conf_all))
# 4. Select some examples to attack.
import hashlib
from datasets import get_first_n_examples_id_each_class
if FLAGS.select:
# Filter out the misclassified examples.
correct_idx = get_correct_prediction_idx(Y_pred_all, Y_test_all)
if FLAGS.test_mode:
# Only select the first example of each class.
correct_and_selected_idx = get_first_n_examples_id_each_class(
Y_test_all[correct_idx])
selected_idx = [correct_idx[i] for i in correct_and_selected_idx]
else:
if not FLAGS.balance_sampling:
selected_idx = correct_idx[:FLAGS.nb_examples]
else:
# select the same number of examples for each class label.
nb_examples_per_class = int(FLAGS.nb_examples /
Y_test_all.shape[1])
correct_and_selected_idx = get_first_n_examples_id_each_class(
Y_test_all[correct_idx], n=nb_examples_per_class)
selected_idx = [
correct_idx[i] for i in correct_and_selected_idx
]
else:
selected_idx = np.array(range(FLAGS.nb_examples))
from utils.output import format_number_range
selected_example_idx_ranges = format_number_range(sorted(selected_idx))
print("Selected %d examples." % len(selected_idx))
print("Selected index in test set (sorted): %s" %
selected_example_idx_ranges)
X_test, Y_test, Y_pred = X_test_all[selected_idx], Y_test_all[
selected_idx], Y_pred_all[selected_idx]
# The accuracy should be 100%.
accuracy_selected = calculate_accuracy(Y_pred, Y_test)
mean_conf_selected, max_conf_selected, min_conf_selected, std_conf_selected = calculate_mean_confidence(
Y_pred, Y_test)
print('Test accuracy on selected legitimate examples %.4f' %
(accuracy_selected))
print('Mean confidence on ground truth classes, selected %.4f\n' %
(mean_conf_selected))
print('max confidence on ground truth classes, selected %.4f\n' %
(max_conf_selected))
print('min confidence on ground truth classes, selected %.4f\n' %
(min_conf_selected))
print('std confidence on ground truth classes, selected %.4f\n' %
(std_conf_selected))
task = {}
task['dataset_name'] = FLAGS.dataset_name
task['model_name'] = FLAGS.model_name
task['accuracy_test'] = accuracy_all
task['mean_confidence_test'] = mean_conf_all
task['test_set_selected_length'] = len(selected_idx)
task['test_set_selected_idx_ranges'] = selected_example_idx_ranges
task['test_set_selected_idx_hash'] = hashlib.sha1(
str(selected_idx).encode('utf-8')).hexdigest()
task['accuracy_test_selected'] = accuracy_selected
task['mean_confidence_test_selected'] = mean_conf_selected
task_id = "%s_%d_%s_%s" % \
(task['dataset_name'], task['test_set_selected_length'], task['test_set_selected_idx_hash'][:5], task['model_name'])
FLAGS.result_folder = os.path.join(FLAGS.result_folder, task_id)
if not os.path.isdir(FLAGS.result_folder):
os.makedirs(FLAGS.result_folder)
from utils.output import save_task_descriptor
save_task_descriptor(FLAGS.result_folder, [task])
# 5. Generate adversarial examples.
from attacks import maybe_generate_adv_examples
from utils.squeeze import reduce_precision_py
from utils.parameter_parser import parse_params
attack_string_hash = hashlib.sha1(
FLAGS.attacks.encode('utf-8')).hexdigest()[:5]
sample_string_hash = task['test_set_selected_idx_hash'][:5]
from datasets.datasets_utils import get_next_class, get_least_likely_class, get_most_likely_class
Y_test_target_next = get_next_class(Y_test)
Y_test_target_ll = get_least_likely_class(Y_pred)
Y_test_target_ml = get_most_likely_class(Y_pred)
X_test_adv_list = []
X_test_adv_discretized_list = []
Y_test_adv_discretized_pred_list = []
attack_string_list = filter(lambda x: len(x) > 0,
FLAGS.attacks.lower().split(';'))
to_csv = []
X_adv_cache_folder = os.path.join(FLAGS.result_folder, 'adv_examples')
adv_log_folder = os.path.join(FLAGS.result_folder, 'adv_logs')
predictions_folder = os.path.join(FLAGS.result_folder, 'predictions')
for folder in [X_adv_cache_folder, adv_log_folder, predictions_folder]:
if not os.path.isdir(folder):
os.makedirs(folder)
predictions_fpath = os.path.join(predictions_folder, "legitimate.npy")
np.save(predictions_fpath, Y_pred, allow_pickle=False)
if FLAGS.clip >= 0:
epsilon = FLAGS.clip
print("Clip the adversarial perturbations by +-%f" % epsilon)
max_clip = np.clip(X_test + epsilon, 0, 1)
min_clip = np.clip(X_test - epsilon, 0, 1)
for attack_string in attack_string_list:
attack_log_fpath = os.path.join(adv_log_folder,
"%s_%s.log" % (task_id, attack_string))
attack_name, attack_params = parse_params(attack_string)
print("\nRunning attack: %s %s" % (attack_name, attack_params))
if 'targeted' in attack_params:
targeted = attack_params['targeted']
print("targeted value: %s" % targeted)
if targeted == 'next':
Y_test_target = Y_test_target_next
#Y_test_target = Y_test.copy()
elif targeted == 'll':
Y_test_target = Y_test_target_ll
#Y_test_target = Y_test.copy()
#print (Y_test_target_ll)
elif targeted == 'most':
Y_test_target = Y_test_target_ml
#Y_test_target = Y_test.copy()
#print (Y_test_target_ml)
elif targeted == False:
attack_params['targeted'] = False
Y_test_target = Y_test.copy()
else:
targeted = False
attack_params['targeted'] = False
Y_test_target = Y_test.copy()
Y_test_target_all = Y_test_all.copy()
x_adv_fname = "%s_%s.pickle" % (task_id, attack_string)
x_adv_fpath = os.path.join(X_adv_cache_folder, x_adv_fname)
X_test_adv, aux_info = maybe_generate_adv_examples(
sess,
model,
x,
y,
X_test,
Y_test_target,
attack_name,
attack_params,
use_cache=x_adv_fpath,
verbose=FLAGS.verbose,
attack_log_fpath=attack_log_fpath)
if FLAGS.clip > 0:
# This is L-inf clipping.
X_test_adv = np.clip(X_test_adv, min_clip, max_clip)
X_test_adv_list.append(X_test_adv)
if isinstance(aux_info, float):
duration = aux_info
else:
duration = aux_info['duration']
dur_per_sample = duration / len(X_test_adv)
# 5.0 Output predictions.
Y_test_adv_pred = model.predict(X_test_adv)
#predictions_fpath = os.path.join(predictions_folder, "%s.npy"% attack_string)
#np.save(predictions_fpath, Y_test_adv_pred, allow_pickle=False)
# 5.1 Evaluate the adversarial examples being discretized to uint8.
print("\n---Attack (uint8): %s" % attack_string)
#import utils.squeeze as squeezer
# All data should be discretized to uint8.
X_test_adv_discret = reduce_precision_py(X_test_adv, 256)
#X_test_adv_discret = reduce_precision_py(X_test_adv, 2)
X_test_adv_discretized_list.append(X_test_adv_discret)
Y_test_adv_discret_pred = model.predict(X_test_adv_discret)
#Y_test_adv_discret_pred1 = to_categorical(np.argmax(model1.predict(X_test_adv_discret), axis=1))
from LID.extract_artifacts_obfus import get_lid
from LID.util_obfus import get_noisy_samples, random_split, block_split, train_lr, compute_roc
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
from sklearn.preprocessing import scale, MinMaxScaler, StandardScaler
#from LID.extract_artifact import *
#from LID_util import *
X_test_noisy = get_noisy_samples(X_test, X_test_adv, 'mnist', 'fgsm')
artifacts, labels = get_lid(model, X_test, X_test_noisy,
X_test_adv_discret, 20, 100, 'mnist')
#X=artifacts
#Y=labels
print(X_test_noisy.shape)
#print (artifacts.shape)
# standarization
scaler = MinMaxScaler().fit(artifacts)
artifacts = scaler.transform(artifacts)
# X = scale(X) # Z-norm
# test attack is the same as training attack
X_train_lid, Y_train_lid, X_test_lid, Y_test_lid = block_split(
artifacts, labels)
## Build detector
# print("LR Detector on [dataset: %s, train_attack: %s, test_attack: %s] with:" %
# (args.dataset, args.attack, args.test_attack))
lr = train_lr(X_train_lid, Y_train_lid)
## Evaluate detector
y_pred_lid = lr.predict_proba(X_test_lid)[:, 1]
y_label_pred = lr.predict(X_test_lid)
Y_test_lid = np.reshape(Y_test_lid, Y_test_lid.shape[0])
# AUC
_, _, auc_score = compute_roc(Y_test_lid[:100],
y_pred_lid[:100],
plot=False)
precision = precision_score(Y_test_lid[:100], y_label_pred[:100])
recall = recall_score(Y_test_lid[:100], y_label_pred[:100])
y_label_pred = lr.predict(X_test_lid[:100])
acc = accuracy_score(Y_test_lid[:100], y_label_pred[:100])
print('start measuring LID')
print(
'Detector ROC-AUC score: %0.4f, accuracy: %.4f, precision: %.4f, recall(TPR): %.4f'
% (auc_score, acc, precision, recall))
from detections.base import evalulate_detection_test
a, b, c, d, e = evalulate_detection_test(Y_test_lid[:100],
y_label_pred[:100])
f1 = f1_score(Y_test_lid[:100], y_label_pred)
print(
'SAE_acc: %0.4f, tpr: %.4f, fpr: %.4f, fdr (1- precision): %.4f, fbr (official name false omission rate): %.4f, f1 score: %.4f'
% (a, b, c, d, e, f1))
print('end measuring LID')
from utils.output import write_to_csv
attacks_evaluation_csv_fpath = os.path.join(FLAGS.result_folder,
"%s_attacks_%s_evaluation.csv" % \
(task_id, attack_string_hash))
fieldnames = [
'dataset_name', 'model_name', 'attack_string', 'duration_per_sample',
'discretization', 'success_rate', 'mean_confidence', 'mean_l2_dist',
'mean_li_dist', 'mean_l0_dist_value', 'mean_l0_dist_pixel'
]
write_to_csv(to_csv, attacks_evaluation_csv_fpath, fieldnames)
# 7. Detection experiment.
# Example: --detection "FeatureSqueezing?distance_measure=l1&squeezers=median_smoothing_2,bit_depth_4,bilateral_filter_15_15_60;"
if FLAGS.detection != '':
from detections.base import DetectionEvaluator
result_folder_detection = os.path.join(FLAGS.result_folder,
"detection")
csv_fname = "%s_attacks_%s_detection.csv" % (task_id,
attack_string_hash)
de = DetectionEvaluator(model, result_folder_detection, csv_fname,
FLAGS.dataset_name)
Y_test_all_pred = model.predict(X_test_all)
de.build_detection_dataset(X_test_all, Y_test_all, Y_test_all_pred,
selected_idx, X_test_adv_discretized_list,
Y_test_adv_discretized_pred_list,
attack_string_list, attack_string_hash,
FLAGS.clip, Y_test_target_most,
Y_test_target_ll)
de.evaluate_detections(FLAGS.detection)
def main(argv=None):
dataset = ImageNetDataset()
# 1. Load a dataset.
print("\n===Loading %s data...")
if FLAGS.model_name == 'inceptionv3':
img_size = 299
else:
img_size = 224
X_test_all, Y_test_all = dataset.get_test_data(img_size, 0, 200)
# 2. Load a trained model.
sess = load_tf_session()
keras.backend.set_learning_phase(0)
x = tf.placeholder(tf.float32,
shape=(None, dataset.image_size, dataset.image_size,
dataset.num_channels))
y = tf.placeholder(tf.float32, shape=(None, dataset.num_classes))
with tf.variable_scope(FLAGS.model_name):
"""
Create a model instance for prediction.
The scaling argument, 'input_range_type': {1: [0,1], 2:[-0.5, 0.5], 3:[-1, 1]...}
"""
model = dataset.load_model_by_name(FLAGS.model_name,
logits=False,
input_range_type=1)
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['acc'])
# 3. Evaluate the trained model.
print("Evaluating the pre-trained model...")
# add our 2 pass FD method
X_test_all = FD_jpeg_encode(X_test_all)
Y_pred_all = model.predict(X_test_all)
mean_conf_all = calculate_mean_confidence(Y_pred_all, Y_test_all)
accuracy_all = calculate_accuracy(Y_pred_all, Y_test_all)
print('Test accuracy on raw legitimate examples %.4f' % (accuracy_all))
print('Mean confidence on ground truth classes %.4f' % (mean_conf_all))
# 4. Select some examples to attack.
if FLAGS.select:
# Filter out the misclassified examples.
correct_idx = get_correct_prediction_idx(Y_pred_all, Y_test_all)
if FLAGS.test_mode:
# Only select the first example of each class.
correct_and_selected_idx = get_first_n_examples_id_each_class(
Y_test_all[correct_idx])
selected_idx = [correct_idx[i] for i in correct_and_selected_idx]
else:
if not FLAGS.balance_sampling:
selected_idx = correct_idx[:FLAGS.nb_examples]
else:
# select the same number of examples for each class label.
nb_examples_per_class = int(FLAGS.nb_examples /
Y_test_all.shape[1])
correct_and_selected_idx = get_first_n_examples_id_each_class(
Y_test_all[correct_idx], n=nb_examples_per_class)
selected_idx = [
correct_idx[i] for i in correct_and_selected_idx
]
else:
selected_idx = np.array(range(FLAGS.nb_examples))
from utils.output import format_number_range
selected_example_idx_ranges = format_number_range(sorted(selected_idx))
print("Selected %d examples." % len(selected_idx))
print("Selected index in test set (sorted): %s" %
selected_example_idx_ranges)
X_test, Y_test, Y_pred = X_test_all[selected_idx], Y_test_all[
selected_idx], Y_pred_all[selected_idx]
# The accuracy should be 100%.
accuracy_selected = calculate_accuracy(Y_pred, Y_test)
mean_conf_selected = calculate_mean_confidence(Y_pred, Y_test)
print('Test accuracy on selected legitimate examples %.4f' %
(accuracy_selected))
print('Mean confidence on ground truth classes, selected %.4f\n' %
(mean_conf_selected))
task = {}
task['dataset_name'] = "ImageNet"
task['model_name'] = FLAGS.model_name
task['accuracy_test'] = accuracy_all
task['mean_confidence_test'] = mean_conf_all
task['test_set_selected_length'] = len(selected_idx)
task['test_set_selected_idx_ranges'] = selected_example_idx_ranges
task['test_set_selected_idx_hash'] = hashlib.sha1(
str(selected_idx).encode('utf-8')).hexdigest()
task['accuracy_test_selected'] = accuracy_selected
task['mean_confidence_test_selected'] = mean_conf_selected
task_id = "%s_%d_%s_%s" % \
(task['dataset_name'], task['test_set_selected_length'], task['test_set_selected_idx_hash'][:5], task['model_name'])
FLAGS.result_folder = os.path.join(FLAGS.result_folder, task_id)
if not os.path.isdir(FLAGS.result_folder):
os.makedirs(FLAGS.result_folder)
from utils.output import save_task_descriptor
save_task_descriptor(FLAGS.result_folder, [task])
# 5. Generate adversarial examples.
from attacks import maybe_generate_adv_examples
from utils.squeeze import reduce_precision_py
from utils.parameter_parser import parse_params
attack_string_hash = hashlib.sha1(
FLAGS.attacks.encode('utf-8')).hexdigest()[:5]
sample_string_hash = task['test_set_selected_idx_hash'][:5]
from datasets.datasets_utils import get_next_class, get_least_likely_class
Y_test_target_next = get_next_class(Y_test)
Y_test_target_ll = get_least_likely_class(Y_pred)
X_test_adv_list = []
X_test_adv_discretized_list = []
Y_test_adv_discretized_pred_list = []
attack_string_list = filter(lambda x: len(x) > 0,
FLAGS.attacks.lower().split(';'))
to_csv = []
X_adv_cache_folder = os.path.join(FLAGS.result_folder, 'adv_examples')
adv_log_folder = os.path.join(FLAGS.result_folder, 'adv_logs')
predictions_folder = os.path.join(FLAGS.result_folder, 'predictions')
for folder in [X_adv_cache_folder, adv_log_folder, predictions_folder]:
if not os.path.isdir(folder):
os.makedirs(folder)
predictions_fpath = os.path.join(predictions_folder, "legitimate.npy")
np.save(predictions_fpath, Y_pred, allow_pickle=False)
if FLAGS.clip >= 0:
epsilon = FLAGS.clip
print("Clip the adversarial perturbations by +-%f" % epsilon)
max_clip = np.clip(X_test + epsilon, 0, 1)
min_clip = np.clip(X_test - epsilon, 0, 1)
for attack_string in attack_string_list:
attack_log_fpath = os.path.join(adv_log_folder,
"%s_%s.log" % (task_id, attack_string))
attack_name, attack_params = parse_params(attack_string)
print("\nRunning attack: %s %s" % (attack_name, attack_params))
if 'targeted' in attack_params:
targeted = attack_params['targeted']
print("targeted value: %s" % targeted)
if targeted == 'next':
Y_test_target = Y_test_target_next
elif targeted == 'll':
Y_test_target = Y_test_target_ll
elif targeted == False:
attack_params['targeted'] = False
Y_test_target = Y_test.copy()
else:
targeted = False
attack_params['targeted'] = False
Y_test_target = Y_test.copy()
x_adv_fname = "%s_%s.pickle" % (task_id, attack_string)
x_adv_fpath = os.path.join(X_adv_cache_folder, x_adv_fname)
X_test_adv, aux_info = maybe_generate_adv_examples(
sess,
model,
x,
y,
X_test,
Y_test_target,
attack_name,
attack_params,
use_cache=x_adv_fpath,
verbose=FLAGS.verbose,
attack_log_fpath=attack_log_fpath)
# add our 1 pass FD method
X_test_adv = FD_jpeg_encode(X_test_adv)
Y_pred_def = model.predict(X_test_adv)
accuracy_def = calculate_accuracy(Y_pred_def, Y_test)
print('Test accuracy on def examples %.4f' % (accuracy_def))
if FLAGS.clip > 0:
# This is L-inf clipping.
X_test_adv = np.clip(X_test_adv, min_clip, max_clip)
X_test_adv_list.append(X_test_adv)
if isinstance(aux_info, float):
duration = aux_info
else:
duration = aux_info['duration']
dur_per_sample = duration / len(X_test_adv)
# 5.0 Output predictions.
Y_test_adv_pred = model.predict(X_test_adv)
predictions_fpath = os.path.join(predictions_folder,
"%s.npy" % attack_string)
np.save(predictions_fpath, Y_test_adv_pred, allow_pickle=False)
# 5.1 Evaluate the adversarial examples being discretized to uint8.
print("\n---Attack (uint8): %s" % attack_string)
# All data should be discretized to uint8.
X_test_adv_discret = reduce_precision_py(X_test_adv, 256)
X_test_adv_discretized_list.append(X_test_adv_discret)
Y_test_adv_discret_pred = model.predict(X_test_adv_discret)
Y_test_adv_discretized_pred_list.append(Y_test_adv_discret_pred)
rec = evaluate_adversarial_examples(X_test, Y_test, X_test_adv_discret,
Y_test_target.copy(), targeted,
Y_test_adv_discret_pred)
rec['dataset_name'] = "ImageNet"
rec['model_name'] = FLAGS.model_name
rec['attack_string'] = attack_string
rec['duration_per_sample'] = dur_per_sample
rec['discretization'] = True
to_csv.append(rec)
from utils.output import write_to_csv
attacks_evaluation_csv_fpath = os.path.join(FLAGS.result_folder,
"%s_attacks_%s_evaluation.csv" % \
(task_id, attack_string_hash))
fieldnames = [
'dataset_name', 'model_name', 'attack_string', 'duration_per_sample',
'discretization', 'success_rate', 'mean_confidence', 'mean_l2_dist',
'mean_li_dist', 'mean_l0_dist_value', 'mean_l0_dist_pixel'
]
write_to_csv(to_csv, attacks_evaluation_csv_fpath, fieldnames)
if FLAGS.visualize is True:
from datasets.visualization import show_imgs_in_rows
if FLAGS.test_mode or FLAGS.balance_sampling:
selected_idx_vis = range(Y_test.shape[1])
else:
selected_idx_vis = get_first_n_examples_id_each_class(Y_test, 1)
legitimate_examples = X_test[selected_idx_vis]
rows = [legitimate_examples]
rows += map(lambda x: x[selected_idx_vis], X_test_adv_list)
img_fpath = os.path.join(
FLAGS.result_folder,
'%s_attacks_%s_examples.png' % (task_id, attack_string_hash))
show_imgs_in_rows(rows, img_fpath)
print('\n===Adversarial image examples are saved in ', img_fpath)
def main(argv=None):
# 0. Select a dataset.
from datasets import MNISTDataset, CIFAR10Dataset, ImageNetDataset, LFWDataset
from datasets import get_correct_prediction_idx, evaluate_adversarial_examples, calculate_mean_confidence, calculate_accuracy
from utils.parameter_parser import parse_params
if FLAGS.dataset_name == "MNIST":
dataset = MNISTDataset()
elif FLAGS.dataset_name == "CIFAR-10":
dataset = CIFAR10Dataset()
elif FLAGS.dataset_name == "ImageNet":
dataset = ImageNetDataset()
elif FLAGS.dataset_name == "LFW":
dataset = LFWDataset()
# 1. Load a dataset.
print("\n===Loading %s data..." % FLAGS.dataset_name)
if FLAGS.dataset_name == 'ImageNet':
if FLAGS.model_name == 'inceptionv3':
img_size = 299
else:
img_size = 224
X_test_all, Y_test_all = dataset.get_test_data(img_size, 0, 200)
else:
X_test_all, Y_test_all = dataset.get_test_dataset()
# 2. Load a trained model.
keras.backend.set_learning_phase(0)
with tf.variable_scope(FLAGS.model_name):
"""
Create a model instance for prediction.
The scaling argument, 'input_range_type': {1: [0,1], 2:[-0.5, 0.5], 3:[-1, 1]...}
"""
model = dataset.load_model_by_name(FLAGS.model_name,
logits=False,
input_range_type=1)
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['acc'])
X_train_all, Y_train_all = dataset.get_train_dataset()
if FLAGS.model_name in [
'resnet20', 'resnet32', 'resnet44', 'resnet56', 'resnet110'
] and FLAGS.dataset_name == 'CIFAR-10':
# for resnet
x_train_mean = np.mean(X_train_all, axis=0)
X_test_all -= x_train_mean
# 3. Evaluate the trained model.
print("Evaluating the pre-trained model...")
Y_pred_all = model.predict(X_test_all)
mean_conf_all = calculate_mean_confidence(Y_pred_all, Y_test_all)
accuracy_all = calculate_accuracy(Y_pred_all, Y_test_all)
print('Test accuracy on raw legitimate examples %.4f' % (accuracy_all))
print('Mean confidence on ground truth classes %.4f' % (mean_conf_all))
if FLAGS.attacks:
from attacks import load_attack_input
#attack_string = filter(lambda x: len(x) > 0, FLAGS.attacks.lower())
attack_string = FLAGS.attacks.lower()
correct_idx = get_correct_prediction_idx(Y_pred_all, Y_test_all)
selected_idx = correct_idx[:100]
X_test_all = load_attack_input(FLAGS.dataset_name, attack_string)
Y_test_all = Y_test_all[selected_idx]
if FLAGS.output_verifier != '' and FLAGS.attacks != '':
for ele in FLAGS.output_verifier.split(';'):
if ele in [
'resnet20', 'resnet32', 'resnet44', 'resnet56', 'resnet110'
] and FLAGS.dataset_name == 'CIFAR-10' and FLAGS.model_name not in [
'resnet20', 'resnet32', 'resnet44', 'resnet56', 'resnet110'
]:
x_train_mean = np.mean(X_train_all, axis=0)
X_test_all -= x_train_mean
break
# 4. XEnsemble defense.
input_verified = X_test_all
# input verifier
if FLAGS.input_verifier != '':
from input_verifier_method import get_inverifier_by_name
inverifier_names = [
ele.strip() for ele in FLAGS.input_verifier.split(';')
if ele.strip() != ''
]
for inverifier_name in inverifier_names:
inverifier = get_inverifier_by_name(inverifier_name, 'python')
input_verified = np.append(input_verified,
inverifier(X_test_all),
axis=0)
if FLAGS.output_verifier == '':
iter = input_verified.shape[0] / X_test_all.shape[0]
batch_iter = X_test_all.shape[0]
Y_pred = model.predict(input_verified[0:X_test_all.shape[0]])
output = model.predict(input_verified[0:X_test_all.shape[0]])
for i in range(int(iter)):
if i > 0:
output = np.append(output,
model.predict(
input_verified[i * batch_iter:(i + 1) *
batch_iter]),
axis=0)
Y_pred = Y_pred + model.predict(
input_verified[i * batch_iter:(i + 1) * batch_iter])
Y_pred_inverified = Y_pred / iter ####TODO Only majority voting is provided here
from datasets.datasets_utils import calculate_msqueezer_accuracy
avg = np.zeros((Y_pred_inverified.shape[0], dataset.num_classes))
for idx in range(Y_pred_inverified.shape[0]):
if np.max(Y_pred_inverified[idx]) >= 0.6:
avg[idx] = Y_pred_inverified[idx]
elif np.max(Y_pred_inverified[idx]) < 0.6:
avg[idx] = Y_pred_inverified[idx] + 1
accuracy, _, alert_bad = calculate_msqueezer_accuracy(avg, Y_test_all)
print(
"Conf-L1 Test accuracy is of %.4f, where correct pred: %.4f, detection: %.4f of the input verifier layer"
% (accuracy + alert_bad / Y_pred_inverified.shape[0], accuracy,
alert_bad / Y_pred_inverified.shape[0]))
accuracy = calculate_accuracy(Y_pred_inverified, Y_test_all)
print('Majority Voting Test accuracy %.4f' % (accuracy))
# outputput verifier
if FLAGS.output_verifier != '':
Y_pred_model_verified = np.zeros(
(X_test_all.shape[0], dataset.num_classes))
model_verifier_names = [
ele.strip() for ele in FLAGS.output_verifier.split(';')
if ele.strip() != ''
]
selected_model_verifier_names = model_verifier_names
size_base = len(model_verifier_names)
size_team = size_base
prediction_base = np.zeros(
(size_base, Y_test_all.shape[0], Y_test_all.shape[1]))
prediction_base_train = np.zeros(
(size_base, 5000, Y_train_all.shape[1]))
model_list = range(size_base)
for i, model_verifier_name in enumerate(model_verifier_names):
model_verifier = dataset.load_model_by_name(model_verifier_name,
logits=False,
input_range_type=1)
prediction_base[i] = model_verifier.predict(X_test_all)
locals()['model_verifier' + str(i)] = dataset.load_model_by_name(
model_verifier_name, logits=False, input_range_type=1)
prediction_base_train[i] = model_verifier.predict(
X_train_all[:5000])
model_list = [0, 1, 2]
selected_model_verifier_names = []
for i in range(len(model_list)):
selected_model_verifier_names.append(model_verifier_names[i])
#ensemble on selected models
for m, model_verifier_name in enumerate(selected_model_verifier_names):
model_verifier = dataset.load_model_by_name(model_verifier_name,
logits=False,
input_range_type=1)
model_verifier.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['acc'])
iter = input_verified.shape[0] / X_test_all.shape[0]
batch_iter = X_test_all.shape[0]
Y_pred = model_verifier.predict(
input_verified[0:X_test_all.shape[0]])
#output = model_verifier.predict(input_verified[0:X_test_all.shape[0]])
for i in range(int(iter)):
if i > 0:
#output=np.append(output,model_verifier.predict(input_verified[i*batch_iter:(i+1)*batch_iter]),axis=0)
Y_pred = Y_pred + model_verifier.predict(
input_verified[i * batch_iter:(i + 1) * batch_iter])
majority_weight = np.ones(size_team)
weighted_pred = np.zeros((size_team, 5000, Y_train_all.shape[1]))
Y_pred_model_verified = Y_pred_model_verified + majority_weight[
m] * Y_pred / iter
Y_pred_model_verified = Y_pred_model_verified / np.sum(majority_weight)
from datasets.datasets_utils import calculate_msqueezer_accuracy
avg = np.zeros((Y_pred_model_verified.shape[0], dataset.num_classes))
for idx in range(Y_pred_model_verified.shape[0]):
if np.max(Y_pred_model_verified[idx]) >= 0.6:
avg[idx] = Y_pred_model_verified[idx]
elif np.max(Y_pred_model_verified[idx]) < 0.6:
avg[idx] = Y_pred_model_verified[idx] + 1
accuracy, _, alert_bad = calculate_msqueezer_accuracy(avg, Y_test_all)
print(
"Conf-L1 Test accuracy is of %.4f, where correct pred: %.4f, detection: %.4f"
% (accuracy + alert_bad / Y_pred_model_verified.shape[0], accuracy,
alert_bad / Y_pred_model_verified.shape[0]))
accuracy = calculate_accuracy(Y_pred_model_verified, Y_test_all)
print('Majority Voting Test accuracy %.4f' % (accuracy))
#comparison
try:
#Adversarial training
model_advt = dataset.load_model_by_name('cnn2_adv_trained',
logits=False,
input_range_type=1)
pred_advt = model_advt(X_test_all)
accuracy, _, alert_bad = calculate_msqueezer_accuracy(
pred_advt, Y_test_all)
print('Adversarial training Test accuracy %.4f' % (accuracy))
# Defensive Distillation
model_dd = dataset.load_model_by_name('distillation',
logits=False,
input_range_type=1)
pred_dd = model_dd(X_test_all)
accuracy, _, alert_bad = calculate_msqueezer_accuracy(
pred_dd, Y_test_all)
print('Defensive Distillation Test accuracy %.4f' % (accuracy))
# Input transformation
if FLAGS.dataset_name == 'MNIST':
ensembles_size = 10
crop_size = 24
image_size = 28
if FLAGS.dataset_name == 'CIFAR-10':
ensembles_size = 10
crop_size = 28
image_size = 32
start_max = image_size - crop_size
ensembles_def_pred = 0
for i in xrange(ensembles_size):
start_x = np.random.randint(0, start_max)
start_y = np.random.randint(0, start_max)
# boxes = [[start_y, start_x, start_y + frac, start_x + frac]]
X_test_all_crop = X_test_all[:,
start_x:start_x + crop_size,
start_y:start_y +
crop_size, :]
if FLAGS.dataset_name == 'MNIST':
X_test_all_rescale = np.zeros(
(X_test_all.shape[0], 28, 28, 1))
if FLAGS.dataset_name == 'CIFAR-10':
X_test_all_rescale = np.zeros(
(X_test_all.shape[0], 32, 32, 3))
for i in xrange(X_test_all_crop.shape[0]):
X_test_all_rescale[i] = rescale(
X_test_all_crop[i],
np.float(image_size) / crop_size)
X_test_all_discret_rescale = reduce_precision_py(
X_test_all_rescale,
256) # need to put input into the ensemble
pred = model.predict(X_test_all_discret_rescale)
ensembles_def_pred = ensembles_def_pred + pred
Y_defend_all = ensembles_def_pred / ensembles_size
# All data should be discretized to uint8.
X_test_all_discret = reduce_precision_py(X_test_all, 256)
Y_test_all_discret_pred = model.predict(X_test_all_discret)
accuracy, _, alert_bad = calculate_msqueezer_accuracy(
Y_test_all_discret_pred, Y_test_all)
print('Input transformation ensemble Test accuracy %.4f' %
(accuracy))
except:
raise
def main(argv=None):
# 0. Select a dataset.
from datasets import MNISTDataset, CIFAR10Dataset, ImageNetDataset, LFWDataset
from datasets import get_correct_prediction_idx, evaluate_adversarial_examples, calculate_mean_confidence, calculate_accuracy
from utils.parameter_parser import parse_params
if FLAGS.dataset_name == "MNIST":
dataset = MNISTDataset()
elif FLAGS.dataset_name == "CIFAR-10":
dataset = CIFAR10Dataset()
elif FLAGS.dataset_name == "ImageNet":
dataset = ImageNetDataset()
elif FLAGS.dataset_name == "LFW":
dataset = LFWDataset()
# 1. Load a dataset.
print("\n===Loading %s data..." % FLAGS.dataset_name)
if FLAGS.dataset_name == 'ImageNet':
if FLAGS.model_name == 'inceptionv3':
img_size = 299
else:
img_size = 224
X_test_all, Y_test_all = dataset.get_test_data(img_size, 0, 200)
else:
X_test_all, Y_test_all = dataset.get_test_dataset()
# 2. Load a trained model.
keras.backend.set_learning_phase(0)
with tf.variable_scope(FLAGS.model_name):
"""
Create a model instance for prediction.
The scaling argument, 'input_range_type': {1: [0,1], 2:[-0.5, 0.5], 3:[-1, 1]...}
"""
model = dataset.load_model_by_name(FLAGS.model_name,
logits=False,
input_range_type=1)
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['acc'])
X_train_all, Y_train_all = dataset.get_train_dataset()
if FLAGS.model_name in [
'resnet20', 'resnet32', 'resnet44', 'resnet56', 'resnet110'
] and FLAGS.dataset_name == 'CIFAR-10':
# for resnet
x_train_mean = np.mean(X_train_all, axis=0)
X_test_all -= x_train_mean
# 3. Evaluate the trained model.
print("Evaluating the pre-trained model...")
Y_pred_all = model.predict(X_test_all)
mean_conf_all = calculate_mean_confidence(Y_pred_all, Y_test_all)
accuracy_all = calculate_accuracy(Y_pred_all, Y_test_all)
print('Test accuracy on raw legitimate examples %.4f' % (accuracy_all))
print('Mean confidence on ground truth classes %.4f' % (mean_conf_all))
if FLAGS.attacks:
from attacks import load_attack_input
#attack_string = filter(lambda x: len(x) > 0, FLAGS.attacks.lower())
attack_string = FLAGS.attacks.lower()
correct_idx = get_correct_prediction_idx(Y_pred_all, Y_test_all)
selected_idx = correct_idx[:100]
X_test_all = load_attack_input(FLAGS.dataset_name, attack_string)
Y_test_all = Y_test_all[selected_idx]
# 4. Input denoise defense.
input_verified = X_test_all
# input verifier
if FLAGS.input_verifier != '':
from input_verifier_method import get_inverifier_by_name
inverifier_names = [
ele.strip() for ele in FLAGS.input_verifier.split(';')
if ele.strip() != ''
]
for inverifier_name in inverifier_names:
inverifier = get_inverifier_by_name(inverifier_name, 'python')
input_verified = np.append(input_verified,
inverifier(X_test_all),
axis=0)
iter = input_verified.shape[0] / X_test_all.shape[0]
batch_iter = X_test_all.shape[0]
Y_pred = model.predict(input_verified[0:X_test_all.shape[0]])
output = model.predict(input_verified[0:X_test_all.shape[0]])
for i in range(int(iter)):
if i > 0:
output = np.append(output,
model.predict(
input_verified[i * batch_iter:(i + 1) *
batch_iter]),
axis=0)
Y_pred = Y_pred + model.predict(
input_verified[i * batch_iter:(i + 1) * batch_iter])
Y_pred_inverified = Y_pred / iter
from datasets.datasets_utils import calculate_msqueezer_accuracy
avg = np.zeros((Y_pred_inverified.shape[0], dataset.num_classes))
for idx in range(Y_pred_inverified.shape[0]):
if np.max(Y_pred_inverified[idx]) >= 0.6:
avg[idx] = Y_pred_inverified[idx]
elif np.max(Y_pred_inverified[idx]) < 0.6:
avg[idx] = Y_pred_inverified[idx] + 1
accuracy, _, alert_bad = calculate_msqueezer_accuracy(avg, Y_test_all)
print(
"Conf-L1 Test accuracy is of %.4f, where correct pred: %.4f, detection: %.4f of the input verifier layer"
% (accuracy + alert_bad / Y_pred_inverified.shape[0], accuracy,
alert_bad / Y_pred_inverified.shape[0]))
accuracy = calculate_accuracy(Y_pred_inverified, Y_test_all)
print('Majority Voting Test accuracy %.4f' % (accuracy))
def main(dataset_name=None,
model_name=None,
attacks=None,
nb_examples=None,
detection=None,
show_help=False):
# 0. Select a dataset.
if show_help:
print(tf.flags.FLAGS.__dict__)
print(
"Some of the above operations are available via this function, namely: dataset_name, model_name, attacks, nb_examples and detetction. If you need more of these options, please install the parent repository and use it as a command line program.\n"
)
return
from datasets import MNISTDataset, CIFAR10Dataset, ImageNetDataset, SVHNDataset
from datasets import get_correct_prediction_idx, evaluate_adversarial_examples, calculate_mean_confidence, calculate_accuracy
from datasets.visualization import show_imgs_in_rows
if dataset_name is not None:
tf.flags.FLAGS.dataset_name = dataset_name
if attacks is not None:
tf.flags.FLAGS.attacks = attacks
if nb_examples is not None:
tf.flags.FLAGS.nb_examples = nb_examples
if detection is not None:
tf.flags.FLAGS.detection = detection
if model_name is not None:
tf.flags.FLAGS.model_name = model_name.lower()
else:
tf.flags.FLAGS.model_name = FLAGS.model_name.lower()
if tf.flags.FLAGS.dataset_name == "MNIST":
dataset = MNISTDataset()
elif tf.flags.FLAGS.dataset_name == "CIFAR-10":
dataset = CIFAR10Dataset()
elif tf.flags.FLAGS.dataset_name == "ImageNet":
dataset = ImageNetDataset()
elif tf.flags.FLAGS.dataset_name == "SVHN":
dataset = SVHNDataset()
print("Flags are %s" % tf.flags.FLAGS.__flags)
# 1. Load a dataset.
print("\n===Loading %s data..." % tf.flags.FLAGS.dataset_name)
if tf.flags.FLAGS.dataset_name == 'ImageNet':
if tf.flags.FLAGS.model_name == 'inceptionv3':
img_size = 299
else:
img_size = 224
X_test_all, Y_test_all = dataset.get_test_data(img_size, 0, 200)
else:
X_test_all, Y_test_all = dataset.get_test_dataset()
# 2. Load a trained model.
sess = load_tf_session()
keras.backend.set_learning_phase(0)
# Define input TF placeholder
x = tf.placeholder(tf.float32,
shape=(None, dataset.image_size, dataset.image_size,
dataset.num_channels))
y = tf.placeholder(tf.float32, shape=(None, dataset.num_classes))
with tf.variable_scope(tf.flags.FLAGS.model_name):
"""
Create a model instance for prediction.
The scaling argument, 'input_range_type': {1: [0,1], 2:[-0.5, 0.5], 3:[-1, 1]...}
"""
model = dataset.load_model_by_name(tf.flags.FLAGS.model_name,
logits=False,
input_range_type=1)
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['acc'])
print(type(model))
# 3. Evaluate the trained model.
# TODO: add top-5 accuracy for ImageNet.
print("Evaluating the pre-trained model...")
Y_pred_all = model.predict(X_test_all)
mean_conf_all = calculate_mean_confidence(Y_pred_all, Y_test_all)
accuracy_all = calculate_accuracy(Y_pred_all, Y_test_all)
print('Test accuracy on raw legitimate examples %.4f' % (accuracy_all))
print('Mean confidence on ground truth classes %.4f' % (mean_conf_all))
# 4. Select some examples to attack.
import hashlib
from datasets import get_first_n_examples_id_each_class
if tf.flags.FLAGS.select:
# Filter out the misclassified examples.
correct_idx = get_correct_prediction_idx(Y_pred_all, Y_test_all)
if tf.flags.FLAGS.test_mode:
# Only select the first example of each class.
correct_and_selected_idx = get_first_n_examples_id_each_class(
Y_test_all[correct_idx])
selected_idx = [correct_idx[i] for i in correct_and_selected_idx]
else:
if not tf.flags.FLAGS.balance_sampling:
selected_idx = correct_idx[:tf.flags.FLAGS.nb_examples]
else:
# select the same number of examples for each class label.
nb_examples_per_class = int(tf.flags.FLAGS.nb_examples /
Y_test_all.shape[1])
correct_and_selected_idx = get_first_n_examples_id_each_class(
Y_test_all[correct_idx], n=nb_examples_per_class)
selected_idx = [
correct_idx[i] for i in correct_and_selected_idx
]
else:
selected_idx = np.array(range(tf.flags.FLAGS.nb_examples))
from utils.output import format_number_range
selected_example_idx_ranges = format_number_range(sorted(selected_idx))
print("Selected %d examples." % len(selected_idx))
print("Selected index in test set (sorted): %s" %
selected_example_idx_ranges)
X_test, Y_test, Y_pred = X_test_all[selected_idx], Y_test_all[
selected_idx], Y_pred_all[selected_idx]
# The accuracy should be 100%.
accuracy_selected = calculate_accuracy(Y_pred, Y_test)
mean_conf_selected = calculate_mean_confidence(Y_pred, Y_test)
print('Test accuracy on selected legitimate examples %.4f' %
(accuracy_selected))
print('Mean confidence on ground truth classes, selected %.4f\n' %
(mean_conf_selected))
task = {}
task['dataset_name'] = tf.flags.FLAGS.dataset_name
task['model_name'] = tf.flags.FLAGS.model_name
task['accuracy_test'] = accuracy_all
task['mean_confidence_test'] = mean_conf_all
task['test_set_selected_length'] = len(selected_idx)
task['test_set_selected_idx_ranges'] = selected_example_idx_ranges
task['test_set_selected_idx_hash'] = hashlib.sha1(
str(selected_idx).encode('utf-8')).hexdigest()
task['accuracy_test_selected'] = accuracy_selected
task['mean_confidence_test_selected'] = mean_conf_selected
task_id = "%s_%d_%s_%s" % \
(task['dataset_name'], task['test_set_selected_length'], task['test_set_selected_idx_hash'][:5], task['model_name'])
if task_id not in tf.flags.FLAGS.result_folder:
tf.flags.FLAGS.result_folder = os.path.join(
tf.flags.FLAGS.result_folder, task_id)
if not os.path.isdir(tf.flags.FLAGS.result_folder):
os.makedirs(tf.flags.FLAGS.result_folder)
from utils.output import save_task_descriptor
save_task_descriptor(tf.flags.FLAGS.result_folder, [task])
# 5. Generate adversarial examples.
from attacks import maybe_generate_adv_examples
from utils.squeeze import reduce_precision_py
from utils.parameter_parser import parse_params
attack_string_hash = hashlib.sha1(
tf.flags.FLAGS.attacks.encode('utf-8')).hexdigest()[:5]
sample_string_hash = task['test_set_selected_idx_hash'][:5]
from datasets.datasets_utils import get_next_class, get_least_likely_class
Y_test_target_next = get_next_class(Y_test)
Y_test_target_ll = get_least_likely_class(Y_pred)
X_test_adv_list = []
X_test_adv_discretized_list = []
Y_test_adv_discretized_pred_list = []
attack_string_list = list(
filter(lambda x: len(x) > 0,
tf.flags.FLAGS.attacks.lower().split(';')))
to_csv = []
X_adv_cache_folder = os.path.join(tf.flags.FLAGS.result_folder,
'adv_examples')
adv_log_folder = os.path.join(tf.flags.FLAGS.result_folder, 'adv_logs')
predictions_folder = os.path.join(tf.flags.FLAGS.result_folder,
'predictions')
for folder in [X_adv_cache_folder, adv_log_folder, predictions_folder]:
if not os.path.isdir(folder):
os.makedirs(folder)
predictions_fpath = os.path.join(predictions_folder, "legitimate.npy")
np.save(predictions_fpath, Y_pred, allow_pickle=False)
if tf.flags.FLAGS.clip >= 0:
epsilon = tf.flags.FLAGS.clip
print("Clip the adversarial perturbations by +-%f" % epsilon)
max_clip = np.clip(X_test + epsilon, 0, 1)
min_clip = np.clip(X_test - epsilon, 0, 1)
for attack_string in attack_string_list:
attack_log_fpath = os.path.join(adv_log_folder,
"%s_%s.log" % (task_id, attack_string))
attack_name, attack_params = parse_params(attack_string)
print("\nRunning attack: %s %s" % (attack_name, attack_params))
if 'targeted' in attack_params:
targeted = attack_params['targeted']
print("targeted value: %s" % targeted)
if targeted == 'next':
Y_test_target = Y_test_target_next
elif targeted == 'll':
Y_test_target = Y_test_target_ll
elif targeted == False:
attack_params['targeted'] = False
Y_test_target = Y_test.copy()
else:
targeted = False
attack_params['targeted'] = False
Y_test_target = Y_test.copy()
x_adv_fname = "%s_%s.pickle" % (task_id, attack_string)
x_adv_fpath = os.path.join(X_adv_cache_folder, x_adv_fname)
X_test_adv, aux_info = maybe_generate_adv_examples(
sess,
model,
x,
y,
X_test,
Y_test_target,
attack_name,
attack_params,
use_cache=x_adv_fpath,
verbose=tf.flags.FLAGS.verbose,
attack_log_fpath=attack_log_fpath)
if tf.flags.FLAGS.clip > 0:
# This is L-inf clipping.
X_test_adv = np.clip(X_test_adv, min_clip, max_clip)
X_test_adv_list.append(X_test_adv)
if isinstance(aux_info, float):
duration = aux_info
else:
duration = aux_info['duration']
dur_per_sample = duration / len(X_test_adv)
# 5.0 Output predictions.
Y_test_adv_pred = model.predict(X_test_adv)
predictions_fpath = os.path.join(predictions_folder,
"%s.npy" % attack_string)
np.save(predictions_fpath, Y_test_adv_pred, allow_pickle=False)
# 5.1 Evaluate the adversarial examples being discretized to uint8.
print("\n---Attack (uint8): %s" % attack_string)
# All data should be discretized to uint8.
X_test_adv_discret = reduce_precision_py(X_test_adv, 256)
X_test_adv_discretized_list.append(X_test_adv_discret)
Y_test_adv_discret_pred = model.predict(X_test_adv_discret)
Y_test_adv_discretized_pred_list.append(Y_test_adv_discret_pred)
rec = evaluate_adversarial_examples(X_test, Y_test, X_test_adv_discret,
Y_test_target.copy(), targeted,
Y_test_adv_discret_pred)
rec['dataset_name'] = tf.flags.FLAGS.dataset_name
rec['model_name'] = tf.flags.FLAGS.model_name
rec['attack_string'] = attack_string
rec['duration_per_sample'] = dur_per_sample
rec['discretization'] = True
to_csv.append(rec)
from utils.output import write_to_csv
attacks_evaluation_csv_fpath = os.path.join(tf.flags.FLAGS.result_folder,
"%s_attacks_%s_evaluation.csv" % \
(task_id, attack_string_hash))
fieldnames = [
'dataset_name', 'model_name', 'attack_string', 'duration_per_sample',
'discretization', 'success_rate', 'mean_confidence', 'mean_l2_dist',
'mean_li_dist', 'mean_l0_dist_value', 'mean_l0_dist_pixel'
]
write_to_csv(to_csv, attacks_evaluation_csv_fpath, fieldnames)
if tf.flags.FLAGS.visualize is True:
if tf.flags.FLAGS.test_mode or tf.flags.FLAGS.balance_sampling:
selected_idx_vis = range(Y_test.shape[1])
else:
selected_idx_vis = get_first_n_examples_id_each_class(Y_test, 1)
legitimate_examples = X_test[selected_idx_vis]
rows = [legitimate_examples]
rows += map(lambda x: x[selected_idx_vis], X_test_adv_list)
img_fpath = os.path.join(
tf.flags.FLAGS.result_folder,
'%s_attacks_%s_examples.png' % (task_id, attack_string_hash))
show_imgs_in_rows(rows, img_fpath)
print('\n===Adversarial image examples are saved in ', img_fpath)
# TODO: output the prediction and confidence for each example, both legitimate and adversarial.
# 6. Evaluate robust classification techniques.
# Example: --robustness \
# "Base;FeatureSqueezing?squeezer=bit_depth_1;FeatureSqueezing?squeezer=median_filter_2;"
if tf.flags.FLAGS.robustness != '':
"""
Test the accuracy with robust classifiers.
Evaluate the accuracy on all the legitimate examples.
"""
from robustness import evaluate_robustness
result_folder_robustness = os.path.join(tf.flags.FLAGS.result_folder,
"robustness")
fname_prefix = "%s_%s_robustness" % (task_id, attack_string_hash)
evaluate_robustness(tf.flags.FLAGS.robustness, model, Y_test_all, X_test_all, Y_test, \
attack_string_list, X_test_adv_discretized_list,
fname_prefix, selected_idx_vis, result_folder_robustness)
# 7. Detection experiment.
# Example: --detection "FeatureSqueezing?distance_measure=l1&squeezers=median_smoothing_2,bit_depth_4,bilateral_filter_15_15_60;"
if tf.flags.FLAGS.detection != '':
from detections.base import DetectionEvaluator
result_folder_detection = os.path.join(tf.flags.FLAGS.result_folder,
"detection")
csv_fname = "%s_attacks_%s_detection.csv" % (task_id,
attack_string_hash)
de = DetectionEvaluator(model, result_folder_detection, csv_fname,
tf.flags.FLAGS.dataset_name)
Y_test_all_pred = model.predict(X_test_all)
de.build_detection_dataset(X_test_all, Y_test_all, Y_test_all_pred,
selected_idx, X_test_adv_discretized_list,
Y_test_adv_discretized_pred_list,
attack_string_list, attack_string_hash,
tf.flags.FLAGS.clip, Y_test_target_next,
Y_test_target_ll)
de.evaluate_detections(tf.flags.FLAGS.detection)
def main(argv=None):
# 0. Select a dataset.
from datasets import MNISTDataset, CIFAR10Dataset, ImageNetDataset, LFWDataset
from datasets import get_correct_prediction_idx, evaluate_adversarial_examples2, calculate_mean_confidence, calculate_accuracy
from utils.parameter_parser import parse_params
if FLAGS.dataset_name == "MNIST":
dataset = MNISTDataset()
elif FLAGS.dataset_name == "CIFAR-10":
dataset = CIFAR10Dataset()
elif FLAGS.dataset_name == "ImageNet":
dataset = ImageNetDataset()
elif FLAGS.dataset_name == "LFW":
dataset = LFWDataset()
# 1. Load a dataset.
print("\n===Loading %s data..." % FLAGS.dataset_name)
if FLAGS.dataset_name == 'ImageNet':
if FLAGS.model_name == 'inceptionv3':
img_size = 299
else:
img_size = 224
X_test_all, Y_test_all = dataset.get_test_data(img_size, 0, 200)
else:
X_test_all, Y_test_all = dataset.get_test_dataset()
#z = np.where(Y_test_all == np.asarray([1, 0, 0, 0, 0, 0, 0, 0, 0, 0]))
#LABEL SELECTION
label = np.asarray([0] * Y_test_all.shape[1])
label[FLAGS.label_index] = 1
filter_indices = []
for i in range(len(Y_test_all)):
if np.array_equal(Y_test_all[i], label):
filter_indices.append(i)
print(X_test_all.shape, Y_test_all.shape)
X_test_all = np.take(X_test_all, filter_indices, 0)
Y_test_all = np.take(Y_test_all, filter_indices, 0)
print(X_test_all.shape, Y_test_all.shape)
# 2. Load a trained model.
sess = load_tf_session()
keras.backend.set_learning_phase(0)
# Define input TF placeholder
x = tf.placeholder(tf.float32, shape=(None, dataset.image_size, dataset.image_size, dataset.num_channels))
y = tf.placeholder(tf.float32, shape=(None, dataset.num_classes))
with tf.variable_scope(FLAGS.model_name):
"""
Create a model instance for prediction.
The scaling argument, 'input_range_type': {1: [0,1], 2:[-0.5, 0.5], 3:[-1, 1]...}
"""
model = dataset.load_model_by_name(FLAGS.model_name, logits=False, input_range_type=1)
model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['acc'])
# 3. Evaluate the trained model.
# TODO: add top-5 accuracy for ImageNet.
print("Evaluating the pre-trained model...")
Y_pred_all = model.predict(X_test_all)
mean_conf_all = calculate_mean_confidence(Y_pred_all, Y_test_all)
accuracy_all = calculate_accuracy(Y_pred_all, Y_test_all)
print('Test accuracy on raw legitimate examples %.4f' % (accuracy_all))
print('Mean confidence on ground truth classes %.4f' % (mean_conf_all))
# 4. Select some examples to attack.
import hashlib
from datasets import get_first_n_examples_id_each_class
if FLAGS.select:
# Filter out the misclassified examples.
correct_idx = get_correct_prediction_idx(Y_pred_all, Y_test_all)
if FLAGS.test_mode:
# Only select the first example of each class.
correct_and_selected_idx = get_first_n_examples_id_each_class(Y_test_all[correct_idx])
selected_idx = [correct_idx[i] for i in correct_and_selected_idx]
else:
if not FLAGS.balance_sampling:
# TODO: Possibly randomize this
if FLAGS.random_image != 0:
np.random.seed(FLAGS.random_image)
print("RANDOM NUMBER")
print(np.random.randint(100))
np.random.shuffle(correct_idx)
selected_idx = correct_idx[:FLAGS.nb_examples]
else:
# select the same number of examples for each class label.
nb_examples_per_class = int(FLAGS.nb_examples / Y_test_all.shape[1])
correct_and_selected_idx = get_first_n_examples_id_each_class(Y_test_all[correct_idx], n=nb_examples_per_class)
selected_idx = [correct_idx[i] for i in correct_and_selected_idx]
else:
selected_idx = np.array(range(FLAGS.nb_examples))
from utils.output import format_number_range
selected_example_idx_ranges = format_number_range(sorted(selected_idx))
print("Selected %d examples." % len(selected_idx))
print("Selected index in test set (sorted): %s" % selected_example_idx_ranges)
X_test, Y_test, Y_pred = X_test_all[selected_idx], Y_test_all[selected_idx], Y_pred_all[selected_idx]
# The accuracy should be 100%.
accuracy_selected = calculate_accuracy(Y_pred, Y_test)
mean_conf_selected = calculate_mean_confidence(Y_pred, Y_test)
print('Test accuracy on selected legitimate examples %.4f' % (accuracy_selected))
print('Mean confidence on ground truth classes, selected %.4f\n' % (mean_conf_selected))
task = {}
task['dataset_name'] = FLAGS.dataset_name
task['model_name'] = FLAGS.model_name
task['accuracy_test'] = accuracy_all
task['mean_confidence_test'] = mean_conf_all
task['test_set_selected_length'] = len(selected_idx)
task['test_set_selected_idx_ranges'] = selected_example_idx_ranges
task['test_set_selected_idx_hash'] = hashlib.sha1(str(selected_idx).encode('utf-8')).hexdigest()
task['accuracy_test_selected'] = accuracy_selected
task['mean_confidence_test_selected'] = mean_conf_selected
#task_id = "%s_%d_%s_%s" % \
# (task['dataset_name'], task['test_set_selected_length'], task['test_set_selected_idx_hash'][:5], task['model_name'])
task_id = "%s_%s" % \
(task['dataset_name'], task['model_name'])
FLAGS.result_folder = os.path.join(FLAGS.result_folder, task_id)
if os.path.exists(FLAGS.result_folder):
print("RESULTS FOLDER")
print(FLAGS.result_folder)
shutil.rmtree(FLAGS.result_folder)
if not os.path.isdir(FLAGS.result_folder):
os.makedirs(FLAGS.result_folder)
from utils.output import save_task_descriptor2
save_task_descriptor2(FLAGS.result_folder, [task])
# 5. Generate adversarial examples.
from attacks import maybe_generate_adv_examples
from utils.squeeze import reduce_precision_py
#attack_string_hash = hashlib.sha1(FLAGS.attacks.encode('utf-8')).hexdigest()[:5]
attack_string_hash = FLAGS.attacks.encode('utf-8')
from datasets.datasets_utils import get_next_class, get_most_likely_class, get_least_likely_class
Y_test_target_next = get_next_class(Y_test)
Y_test_target_most = get_most_likely_class(Y_test)
Y_test_target_ll = get_least_likely_class(Y_pred)
X_test_adv_list = []
X_test_adv_discretized_list = []
Y_test_adv_discretized_pred_list = []
attack_string_list = filter(lambda x: len(x) > 0, FLAGS.attacks.lower().split(';'))
to_csv = []
X_adv_cache_folder = os.path.join(FLAGS.result_folder, 'adv_examples')
adv_log_folder = os.path.join(FLAGS.result_folder, 'adv_logs')
predictions_folder = os.path.join(FLAGS.result_folder, 'predictions')
for folder in [X_adv_cache_folder, adv_log_folder, predictions_folder]:
if os.path.isdir(folder):
#os.rmdir(folder)
shutil.rmtree(folder)
for folder in [X_adv_cache_folder, adv_log_folder, predictions_folder]:
if not os.path.isdir(folder):
os.makedirs(folder)
predictions_fpath = os.path.join(predictions_folder, "legitimate.npy")
np.save(predictions_fpath, Y_pred, allow_pickle=False)
if FLAGS.clip >= 0:
epsilon = FLAGS.clip
print("Clip the adversarial perturbations by +-%f" % epsilon)
max_clip = np.clip(X_test + epsilon, 0, 1)
min_clip = np.clip(X_test - epsilon, 0, 1)
for attack_string in attack_string_list:
attack_log_fpath = os.path.join(adv_log_folder, "%s_%s.log" % (task_id, attack_string))
attack_name, attack_params = parse_params(attack_string)
print("\nRunning attack: %s %s" % (attack_name, attack_params))
if 'targeted' in attack_params:
targeted = attack_params['targeted']
print("targeted value: %s" % targeted)
if targeted == 'next':
Y_test_target = Y_test_target_next
elif targeted == 'most':
Y_test_target = Y_test_target_most
elif targeted == 'll':
Y_test_target = Y_test_target_ll
elif targeted is False:
attack_params['targeted'] = False
Y_test_target = Y_test.copy()
else:
targeted = False
attack_params['targeted'] = False
Y_test_target = Y_test.copy()
x_adv_fname = "%s_%s.pickle" % (task_id, attack_string)
x_adv_fpath = os.path.join(X_adv_cache_folder, x_adv_fname)
X_test_adv, aux_info = maybe_generate_adv_examples(sess, model, x, y, X_test, Y_test_target, attack_name, attack_params, use_cache=x_adv_fpath, verbose=FLAGS.verbose, attack_log_fpath=attack_log_fpath)
if FLAGS.clip > 0:
# This is L-inf clipping.
X_test_adv = np.clip(X_test_adv, min_clip, max_clip)
X_test_adv_list.append(X_test_adv)
if isinstance(aux_info, float):
duration = aux_info
else:
duration = aux_info['duration']
dur_per_sample = duration / len(X_test_adv)
# 5.0 Output predictions.
# Y_test_adv_pred = model.predict(X_test_adv)
# predictions_fpath = os.path.join(predictions_folder, "%s.npy"% attack_string)
# np.save(predictions_fpath, Y_test_adv_pred, allow_pickle=False)
# 5.1 Evaluate the adversarial examples being discretized to uint8.
print("\n---Attack (uint8): %s" % attack_string)
# All data should be discretized to uint8.
X_test_adv_discret = reduce_precision_py(X_test_adv, 256)
X_test_adv_discretized_list.append(X_test_adv_discret)
Y_test_adv_discret_pred = model.predict(X_test_adv_discret)
Y_test_adv_discretized_pred_list.append(Y_test_adv_discret_pred)
rec = evaluate_adversarial_examples2(X_test, Y_test, X_test_adv_discret, Y_test_target.copy(), targeted, Y_test_adv_discret_pred, attack_string)
confidences = rec['confidence_scores']
preds = np.argmax(Y_test_adv_discret_pred,axis=1)
k = 0
confidence_scores = ""
preds_after_attack = ""
mean = 0
for pred in preds:
preds_after_attack += str(pred) + ","
if pred == FLAGS.label_index:
confidence_scores += str(float("nan")) + ","
else:
try:
confidence_scores += str(confidences[k]) + ","
mean += float(confidences[k])
except:
confidence_scores += str(float("nan")) + ","
k += 1
mean /= len(preds)
rec['confidence_scores'] = confidence_scores.rstrip(",")
rec['dataset_name'] = FLAGS.dataset_name
rec['model_name'] = FLAGS.model_name
rec['attack_string'] = attack_string
rec['original_label_index'] = FLAGS.label_index
rec['random'] = True if FLAGS.random_image != 0 else False
rec['duration_per_sample'] = dur_per_sample
rec['discretization'] = True
rec['prediction_after_attack'] = preds_after_attack.rstrip(",")
rec['number_of_images'] = FLAGS.nb_examples
rec['mean_confidence'] = mean
to_csv.append(rec)
from utils.output import write_to_csv
attacks_evaluation_csv_fpath = os.path.join(FLAGS.result_folder,"evaluation.csv")
fieldnames = ['dataset_name', 'model_name', 'attack_string', 'original_label_index', 'random', 'duration_per_sample', 'discretization', 'success_rate', 'mean_confidence', 'confidence_scores', 'mean_l2_dist', 'mean_li_dist', 'mean_l0_dist_value', 'mean_l0_dist_pixel', 'prediction_after_attack', 'number_of_images']
write_to_csv(to_csv, attacks_evaluation_csv_fpath, fieldnames)
if FLAGS.visualize is True:
from datasets.visualization import show_imgs_in_rows2
if FLAGS.test_mode or FLAGS.balance_sampling:
selected_idx_vis = range(Y_test.shape[1])
else:
#selected_idx_vis = get_first_n_examples_id_each_class(Y_test, 1)
#selected_idx_vis = selected_idx
selected_idx_vis = [i for i in range(FLAGS.nb_examples)]
legitimate_examples = X_test[selected_idx_vis]
rows = [legitimate_examples]
rows += map(lambda x: x[selected_idx_vis], X_test_adv_list)
img_fpath = os.path.join(FLAGS.result_folder, '%s_attacks_%s_examples.png' % (task_id, attack_string_hash))
show_imgs_in_rows2(rows, dataset.num_channels, img_fpath)
print('\n===Adversarial image examples are saved in ', img_fpath)
print(Y_test_adv_discretized_pred_list)
"""rows = [legitimate_examples]
def main(argv=None):
# 0. Select a dataset.
from datasets import MNISTDataset, CIFAR10Dataset, ImageNetDataset
from datasets import get_correct_prediction_idx, evaluate_adversarial_examples, calculate_mean_confidence, calculate_accuracy
if FLAGS.dataset_name == "MNIST":
dataset = MNISTDataset()
elif FLAGS.dataset_name == "CIFAR-10":
dataset = CIFAR10Dataset()
elif FLAGS.dataset_name == "ImageNet":
dataset = ImageNetDataset()
# 1. Load a dataset.
print ("\n===Loading %s data..." % FLAGS.dataset_name)
if FLAGS.dataset_name == 'ImageNet':
if FLAGS.model_name == 'inceptionv3':
img_size = 299
else:
img_size = 224
X_test_all, Y_test_all = dataset.get_test_data(img_size, 0, 200)
else:
X_test_all, Y_test_all = dataset.get_test_dataset()
# 2. Load a trained model.
sess = load_tf_session()
keras.backend.set_learning_phase(0)
# Define input TF placeholder
x = tf.placeholder(tf.float32, shape=(None, dataset.image_size, dataset.image_size, dataset.num_channels))
y = tf.placeholder(tf.float32, shape=(None, dataset.num_classes))
with tf.variable_scope(FLAGS.model_name):
"""
Create a model instance for prediction.
The scaling argument, 'input_range_type': {1: [0,1], 2:[-0.5, 0.5], 3:[-1, 1]...}
"""
model = dataset.load_model_by_name(FLAGS.model_name, logits=False, input_range_type=1)
model.compile(loss='categorical_crossentropy',optimizer='sgd', metrics=['acc'])
# 3. Evaluate the trained model.
# TODO: add top-5 accuracy for ImageNet.
print ("Evaluating the pre-trained model...")
Y_pred_all = model.predict(X_test_all)
mean_conf_all = calculate_mean_confidence(Y_pred_all, Y_test_all)
accuracy_all = calculate_accuracy(Y_pred_all, Y_test_all)
print('Test accuracy on raw legitimate examples %.4f' % (accuracy_all))
print('Mean confidence on ground truth classes %.4f' % (mean_conf_all))
# 4. Select some examples to attack.
import hashlib
from datasets import get_first_example_id_each_class
# Filter out the misclassified examples.
correct_idx = get_correct_prediction_idx(Y_pred_all, Y_test_all)
if FLAGS.test_mode:
# Only select the first example of each class.
correct_and_selected_idx = get_first_example_id_each_class(Y_test_all[correct_idx])
selected_idx = [ correct_idx[i] for i in correct_and_selected_idx ]
else:
selected_idx = correct_idx[:FLAGS.nb_examples]
from utils.output import format_number_range
selected_example_idx_ranges = format_number_range(sorted(selected_idx))
print ( "Selected %d examples." % len(selected_idx))
print ( "Selected index in test set (sorted): %s" % selected_example_idx_ranges )
X_test, Y_test, Y_pred = X_test_all[selected_idx], Y_test_all[selected_idx], Y_pred_all[selected_idx]
accuracy_selected = calculate_accuracy(Y_pred, Y_test)
mean_conf_selected = calculate_mean_confidence(Y_pred, Y_test)
print('Test accuracy on selected legitimate examples %.4f' % (accuracy_selected))
print('Mean confidence on ground truth classes, selected %.4f\n' % (mean_conf_selected))
task = {}
task['dataset_name'] = FLAGS.dataset_name
task['model_name'] = FLAGS.model_name
task['accuracy_test'] = accuracy_all
task['mean_confidence_test'] = mean_conf_all
task['test_set_selected_length'] = len(selected_idx)
task['test_set_selected_idx_ranges'] = selected_example_idx_ranges
task['test_set_selected_idx_hash'] = hashlib.sha1(str(selected_idx).encode('utf-8')).hexdigest()
task['accuracy_test_selected'] = accuracy_selected
task['mean_confidence_test_selected'] = mean_conf_selected
task_id = "%s_%d_%s_%s" % \
(task['dataset_name'], task['test_set_selected_length'], task['test_set_selected_idx_hash'][:5], task['model_name'])
FLAGS.result_folder = os.path.join(FLAGS.result_folder, task_id)
if not os.path.isdir(FLAGS.result_folder):
os.makedirs(FLAGS.result_folder)
from utils.output import save_task_descriptor
save_task_descriptor(FLAGS.result_folder, [task])
# 5. Generate adversarial examples.
from attacks import maybe_generate_adv_examples, parse_attack_string
from defenses.feature_squeezing.squeeze import reduce_precision_np
attack_string_hash = hashlib.sha1(FLAGS.attacks.encode('utf-8')).hexdigest()[:5]
sample_string_hash = task['test_set_selected_idx_hash'][:5]
from attacks import get_next_class, get_least_likely_class
Y_test_target_next = get_next_class(Y_test)
Y_test_target_ll = get_least_likely_class(Y_pred)
X_test_adv_list = []
attack_string_list = filter(lambda x:len(x)>0, FLAGS.attacks.lower().split(';'))
to_csv = []
X_adv_cache_folder = os.path.join(FLAGS.result_folder, 'adv_examples')
adv_log_folder = os.path.join(FLAGS.result_folder, 'adv_logs')
predictions_folder = os.path.join(FLAGS.result_folder, 'predictions')
for folder in [X_adv_cache_folder, adv_log_folder, predictions_folder]:
if not os.path.isdir(folder):
os.makedirs(folder)
predictions_fpath = os.path.join(predictions_folder, "legitimate.npy")
np.save(predictions_fpath, Y_pred, allow_pickle=False)
for attack_string in attack_string_list:
attack_log_fpath = os.path.join(adv_log_folder, "%s_%s.log" % (task_id, attack_string))
attack_name, attack_params = parse_attack_string(attack_string)
print ( "\nRunning attack: %s %s" % (attack_name, attack_params))
if 'targeted' in attack_params:
targeted = attack_params['targeted']
if targeted == 'next':
Y_test_target = Y_test_target_next
elif targeted == 'll':
Y_test_target = Y_test_target_ll
else:
targeted = False
attack_params['targeted'] = False
Y_test_target = Y_test.copy()
x_adv_fname = "%s_%s.pickle" % (task_id, attack_string)
x_adv_fpath = os.path.join(X_adv_cache_folder, x_adv_fname)
X_test_adv, aux_info = maybe_generate_adv_examples(sess, model, x, y, X_test, Y_test_target, attack_name, attack_params, use_cache = x_adv_fpath, verbose=FLAGS.verbose, attack_log_fpath=attack_log_fpath)
X_test_adv_list.append(X_test_adv)
if isinstance(aux_info, float):
duration = aux_info
else:
print (aux_info)
duration = aux_info['duration']
dur_per_sample = duration / len(X_test_adv)
# 5.0 Output predictions.
Y_test_adv_pred = model.predict(X_test_adv)
predictions_fpath = os.path.join(predictions_folder, "%s.npy"% attack_string)
np.save(predictions_fpath, Y_test_adv_pred, allow_pickle=False)
# 5.1. Evaluate the quality of adversarial examples
print ("\n---Attack: %s" % attack_string)
rec = evaluate_adversarial_examples(X_test, X_test_adv, Y_test_target.copy(), targeted, Y_test_adv_pred)
print ("Duration per sample: %.1fs" % dur_per_sample)
rec['dataset_name'] = FLAGS.dataset_name
rec['model_name'] = FLAGS.model_name
rec['attack_string'] = attack_string
rec['duration_per_sample'] = dur_per_sample
rec['discretization'] = False
to_csv.append(rec)
# 5.2 Adversarial examples being discretized to uint8.
print ("\n---Attack (uint8): %s" % attack_string)
X_test_adv_discret = reduce_precision_np(X_test_adv, 256)
Y_test_adv_discret_pred = model.predict(X_test_adv_discret)
rec = evaluate_adversarial_examples(X_test, X_test_adv_discret, Y_test_target.copy(), targeted, Y_test_adv_discret_pred)
rec['dataset_name'] = FLAGS.dataset_name
rec['model_name'] = FLAGS.model_name
rec['attack_string'] = attack_string
rec['duration_per_sample'] = dur_per_sample
rec['discretization'] = True
to_csv.append(rec)
from utils.output import write_to_csv
attacks_evaluation_csv_fpath = os.path.join(FLAGS.result_folder,
"%s_attacks_%s_evaluation.csv" % \
(task_id, attack_string_hash))
fieldnames = ['dataset_name', 'model_name', 'attack_string', 'duration_per_sample', 'discretization', 'success_rate', 'mean_confidence', 'mean_l2_dist', 'mean_li_dist', 'mean_l0_dist_value', 'mean_l0_dist_pixel']
write_to_csv(to_csv, attacks_evaluation_csv_fpath, fieldnames)
if FLAGS.visualize is True:
from datasets.visualization import show_imgs_in_rows
if FLAGS.test_mode:
selected_idx_vis = range(Y_test.shape[1])
else:
selected_idx_vis = get_first_example_id_each_class(Y_test)
legitimate_examples = X_test[selected_idx_vis]
rows = [legitimate_examples]
rows += map(lambda x:x[selected_idx_vis], X_test_adv_list)
img_fpath = os.path.join(FLAGS.result_folder, '%s_attacks_%s_examples.png' % (task_id, attack_string_hash) )
show_imgs_in_rows(rows, img_fpath)
print ('\n===Adversarial image examples are saved in ', img_fpath)
# TODO: output the prediction and confidence for each example, both legitimate and adversarial.
# 6. Evaluate defense techniques.
if FLAGS.defense == 'feature_squeezing':
"""
Test the accuracy with feature squeezing filters.
"""
from defenses.feature_squeezing.robustness import calculate_squeezed_accuracy_new
# Calculate the accuracy of legitimate examples for only once.
csv_fpath = "%s_%s_robustness.csv" % (task_id, attack_string_hash)
print ("Saving robustness test results at %s" % csv_fpath)
csv_fpath = os.path.join(FLAGS.result_folder, csv_fpath)
calculate_squeezed_accuracy_new(model, Y_test, X_test, attack_string_list, X_test_adv_list, csv_fpath)
# 7. Detection experiment.
# All data should be discretized to uint8.
X_test_adv_discretized_list = [ reduce_precision_np(X_test_adv, 256) for X_test_adv in X_test_adv_list]
del X_test_adv_list
if FLAGS.detection == 'feature_squeezing':
from utils.detection import evalulate_detection_test, get_detection_train_test_set
# 7.1 Prepare the dataset for detection.
X_detect_train, Y_detect_train, X_detect_test, Y_detect_test, test_idx, failed_adv_idx = \
get_detection_train_test_set(X_test_all, Y_test, X_test_adv_discretized_list, predict_func=model.predict)
# 7.2 Enumerate all specified detection methods.
# Take Feature Squeezing as an example.
csv_fname = "%s_attacks_%s_detection_two_filters_%s_raw_adv.csv" % (task_id, attack_string_hash, FLAGS.detection)
detection_csv_fpath = os.path.join(FLAGS.result_folder, csv_fname)
to_csv = []
from defenses.feature_squeezing.detection import FeatureSqueezingDetector
from sklearn.metrics import roc_curve, auc
fsd = FeatureSqueezingDetector(model, task_id, attack_string_hash)
# TODO: Automatically get the suitable squeezers through robustness test with legitimate examples.
# squeezers_name = fsd.select_squeezers(X_test, Y_test, accuracy_preserved=0.9)
if FLAGS.dataset_name == "MNIST":
squeezers_name = ['median_smoothing_2', 'median_smoothing_3', 'binary_filter']
elif FLAGS.dataset_name == "CIFAR-10":
squeezers_name = ["bit_depth_6", 'median_smoothing_1_2', 'median_smoothing_2_1','median_smoothing_2']
elif FLAGS.dataset_name == "ImageNet":
squeezers_name = ["bit_depth_5", 'median_smoothing_1_2', 'median_smoothing_2_1','median_smoothing_2']
# best_metrics = fsd.view_adv_propagation(X_test, X_test_adv_list[0], squeezers_name)
# best_metrics = [[len(model.layers)-1, 'none', 'kl_f'], [len(model.layers)-1, 'none', 'l1'], [len(model.layers)-1, 'none', 'l2'], \
# [len(model.layers)-1, 'unit_norm', 'l1'], [len(model.layers)-1, 'unit_norm', 'l2']]
best_metrics = [[len(model.layers)-1, 'none', 'l1']]
for layer_id, normalizer_name, metric_name in best_metrics:
fsd.set_config(layer_id, normalizer_name, metric_name, squeezers_name)
print ("===Detection config: Layer-%d, Metric-%s, Norm-%s" % (layer_id, metric_name, normalizer_name))
csv_fpath = "%s_distances_%s_%s_layer_%d.csv" % (task_id, metric_name, normalizer_name, layer_id)
csv_fpath = os.path.join(FLAGS.result_folder, csv_fpath)
fsd.output_distance_csv([X_test_all] + X_test_adv_discretized_list, ['legitimate'] + attack_string_list, csv_fpath)
# continue
threshold = fsd.train(X_detect_train, Y_detect_train)
Y_detect_pred, distances = fsd.test(X_detect_test)
accuracy, tpr, fpr = evalulate_detection_test(Y_detect_test, Y_detect_pred)
fprs, tprs, thresholds = roc_curve(Y_detect_test, distances)
roc_auc = auc(fprs, tprs)
print ("ROC-AUC: %.2f, Accuracy: %.2f, TPR: %.2f, FPR: %.2f, Threshold: %.2f." % (roc_auc, accuracy, tpr, fpr, threshold))
ret = {}
ret['threshold'] = threshold
ret['accuracy'] = accuracy
ret['fpr'] = fpr
ret['tpr'] = tpr
ret['roc_auc'] = roc_auc
# index of false negatives
fn_idx = np.where((Y_detect_test == True) & (Y_detect_pred == False))
# index in Y_detect.
fn_idx_Y_test = np.array(test_idx)[fn_idx]
nb_failed_as_negative = len(fn_idx_Y_test) - len(set(fn_idx_Y_test) - set(failed_adv_idx))
print ("%d/%d failed adv. examples in false negatives." % (nb_failed_as_negative, len(fn_idx_Y_test)))
ret['fn'] = len(fn_idx_Y_test)
ret['failed_adv_as_fn'] = nb_failed_as_negative
tp_idx = np.where((Y_detect_test == True) & (Y_detect_pred == True))
tp_idx_Y_test = np.array(test_idx)[tp_idx]
nb_failed_as_positive = len(tp_idx_Y_test) - len(set(tp_idx_Y_test) - set(failed_adv_idx))
print ("%d/%d failed adv. examples in true positives." % (nb_failed_as_positive, len(tp_idx_Y_test)))
ret['layer_id'] = layer_id
ret['normalizer'] = normalizer_name
ret['distance_metric'] = metric_name
to_csv.append(ret)
fieldnames = ['layer_id', 'distance_metric', 'normalizer', 'roc_auc', 'accuracy', 'tpr', 'fpr', 'threshold', 'failed_adv_as_fn', 'fn']
write_to_csv(to_csv, detection_csv_fpath, fieldnames)