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utils.py
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## Copyright (C) 2017-2018, IBM Corp.
## Copyright (C) 2017, Lily Weng <[email protected]>
## and Huan Zhang <[email protected]>
## Copyright (C) 2016, Nicholas Carlini <[email protected]>.
##
## This program is licenced under the Apache 2.0 licence,
## contained in the LICENCE file in this directory.
import numpy as np
import random
from PIL import Image
def linf_dist(x, y):
return np.linalg.norm(x.flatten() - y.flatten(), ord=np.inf)
def l2_dist(x, y):
return np.linalg.norm(x.flatten() - y.flatten(), ord=2)
def l1_dist(x, y):
return np.linalg.norm(x.flatten() - y.flatten(), ord=1)
def l0_dist(x, y):
return np.linalg.norm(x.flatten() - y.flatten(), ord=0)
def show(img, name = "output.png"):
"""
Show MNSIT digits in the console.
"""
np.save('img', img)
fig = np.around((img + 0.5)*255)
fig = fig.astype(np.uint8).squeeze()
pic = Image.fromarray(fig)
# pic.resize((512,512), resample=PIL.Image.BICUBIC)
pic.save(name)
remap = " .*#"+"#"*100
img = (img.flatten()+.5)*3
return
if len(img) != 784: return
print("START")
for i in range(28):
print("".join([remap[int(round(x))] for x in img[i*28:i*28+28]]))
def generate_data(data, samples, targeted=True, random_and_least_likely = False, skip_wrong_label = True, start=0, ids = None,
target_classes = None, target_type = 0b1111, predictor = None, imagenet=False, remove_background_class=False):
"""
Generate the input data to the attack algorithm.
data: the images to attack
samples: number of samples to use
targeted: if true, construct targeted attacks, otherwise untargeted attacks
start: offset into data to use
ids: true IDs of images in the dataset, if given, will use these images
target_classes: a list of list of labels for each ids
inception: if targeted and inception, randomly sample 100 targets instead of 1000
"""
inputs = []
targets = []
true_labels = []
true_ids = []
information = []
target_candidate_pool = np.eye(data.test_labels.shape[1])
target_candidate_pool_remove_background_class = np.eye(data.test_labels.shape[1] - 1)
print('generating labels...')
if ids is None:
ids = range(samples)
else:
ids = ids[start:start+samples]
if target_classes:
target_classes = target_classes[start:start+samples]
start = 0
total = 0
n_correct = 0
for i in ids:
total += 1
if targeted:
predicted_label = -1 # unknown
if random_and_least_likely:
# if there is no user specified target classes
if target_classes is None:
original_predict = np.squeeze(predictor(np.array([data.test_data[start+i]])))
num_classes = len(original_predict)
predicted_label = np.argmax(original_predict) + int(imagenet and remove_background_class)
least_likely_label = np.argmin(original_predict)
top2_label = np.argsort(original_predict)[-2]
start_class = 1 if (imagenet and not remove_background_class) else 0
random_class = predicted_label
new_seq = [least_likely_label, top2_label, predicted_label]
while random_class in new_seq:
random_class = random.randint(start_class, start_class + num_classes - 1)
new_seq[2] = random_class
true_label = np.argmax(data.test_labels[start+i])
seq = []
if true_label != predicted_label and skip_wrong_label:
seq = []
else:
if target_type & 0b10000:
for c in range(num_classes):
if c != predicted_label:
seq.append(c)
information.append('class'+str(c))
else:
if target_type & 0b0100:
# least
seq.append(new_seq[0])
information.append('least')
if target_type & 0b0001:
# top-2
seq.append(new_seq[1])
information.append('top2')
if target_type & 0b0010:
# random
seq.append(new_seq[2])
information.append('random')
else:
# use user specified target classes
seq = target_classes[total - 1]
information.extend(len(seq) * ['user'])
else:
if imagenet:
if remove_background_class:
seq = random.sample(range(0,1000), 10)
else:
seq = random.sample(range(1,1001), 10)
information.extend(data.test_labels.shape[1] * ['random'])
else:
seq = range(data.test_labels.shape[1])
information.extend(data.test_labels.shape[1] * ['seq'])
is_correct = np.argmax(data.test_labels[start+i]) == predicted_label
print("[DATAGEN][L1] no = {}, true_id = {}, true_label = {}, predicted = {}, correct = {}, seq = {}, info = {}".format(total, start + i,
np.argmax(data.test_labels[start+i]), predicted_label, is_correct, seq, [] if len(seq) == 0 else information[-len(seq):]))
if is_correct:
n_correct += 1
for j in seq:
# skip the original image label
# if (j == np.argmax(data.test_labels[start+i])):
if (j == np.argmax(data.test_labels[start+i]) - int(imagenet and remove_background_class)):
continue
inputs.append(data.test_data[start+i])
if remove_background_class:
targets.append(target_candidate_pool_remove_background_class[j])
else:
targets.append(target_candidate_pool[j])
true_labels.append(data.test_labels[start+i])
if remove_background_class:
true_labels[-1] = true_labels[-1][1:]
true_ids.append(start+i)
else:
inputs.append(data.test_data[start+i])
if remove_background_class:
# shift target class by 1
print(np.argmax(data.test_labels[start+i]))
print(np.argmax(data.test_labels[start+i][1:1001]))
targets.append(data.test_labels[start+i][1:1001])
else:
targets.append(data.test_labels[start+i])
true_labels.append(data.test_labels[start+i])
if remove_background_class:
true_labels[-1] = true_labels[-1][1:]
true_ids.append(start+i)
information.extend(['original'])
inputs = np.array(inputs)
targets = np.array(targets)
true_labels = np.array(true_labels)
true_ids = np.array(true_ids)
print('labels generated')
print('top-1 accuracy:', n_correct / float(samples))
return inputs, targets, true_labels, true_ids, information