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structured.py
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structured.py
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## test_attack.py -- sample code to test attack procedure
##
## Copyright (C) 2016, Nicholas Carlini <[email protected]>.
##
## This program is licenced under the BSD 2-Clause licence,
## contained in the LICENCE file in this directory.
import tensorflow as tf
import numpy as np
import time
import random
import os
from utils.setup_cifar import CIFAR, CIFARModel
from utils.setup_mnist import MNIST, MNISTModel
from utils.setup_inception import ImageNet, InceptionModel
from utils.l2_ADMM_attack_v5 import ADMML2
from utils.l2_LADMMST_attack_v3 import LADMMSTL2
from PIL import Image
def show(img, name = "output.png"):
fig = img.squeeze()
np.save(name,fig)
fig = (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)
def generate_data(data, model, samples, targeted=True, target_num=9, start=0, inception=False, seed=3, handpick=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
inception: if targeted and inception, randomly sample 100 targets intead of 1000
"""
random.seed(seed)
inputs = []
targets = []
labels = []
true_ids = []
sample_set = []
data_d = data.test_data
labels_d = data.test_labels
if handpick:
if inception:
deck = list(range(0, 1500))
else:
deck = list(range(0, 10000))
random.shuffle(deck)
print('Handpicking')
while (len(sample_set) < samples):
rand_int = deck.pop()
pred = model.model.predict(data_d[rand_int:rand_int + 1])
if inception:
pred = np.reshape(pred, (labels_d[0:1].shape))
if (np.argmax(pred, 1) == np.argmax(labels_d[rand_int:rand_int + 1], 1)):
sample_set.append(rand_int)
print('Handpicked')
else:
sample_set = random.sample(range(0, 10000), samples)
for i in sample_set:
if targeted:
if inception:
seq = random.sample(range(1, 1001), target_num)
else:
seq = range(labels_d.shape[1])
for j in seq:
if (j == np.argmax(labels_d[start + i])) and (inception == False):
continue
inputs.append(data_d[start + i])
targets.append(np.eye(labels_d.shape[1])[j])
labels.append(labels_d[start + i])
true_ids.append(start + i)
else:
inputs.append(data_d[start + i])
targets.append(labels_d[start + i])
labels.append(labels_d[start + i])
true_ids.append(start + i)
inputs = np.array(inputs)
targets = np.array(targets)
labels = np.array(labels)
true_ids = np.array(true_ids)
return inputs, targets, labels, true_ids
def l1_l2_li_computation(args, data, model, adv, inception, inputs, targets, labels, true_ids):
r_best = []
d_best_l0 = []
d_best_l1 = []
d_best_l2 = []
d_best_linf = []
r_average = []
d_average_l0 = []
d_average_l1 = []
d_average_l2 = []
d_average_linf = []
r_worst = []
d_worst_l0 = []
d_worst_l1 = []
d_worst_l2 = []
d_worst_linf = []
if (args['show']):
if not os.path.exists(str(args['save']) + "/" + str(args['dataset']) + "/" + str(args['attack'])):
os.makedirs(str(args['save']) + "/" + str(args['dataset']) + "/" + str(args['attack']))
for i in range(0, len(inputs), args['target_number']):
pred = []
for j in range(i, i + args['target_number']):
if inception:
pred.append(np.reshape(model.model.predict(adv[j:j + 1]), (data.test_labels[0:1].shape)))
else:
pred.append(model.model.predict(adv[j:j + 1]))
dist_l0 = 1e10
dist_l1 = 1e10
dist_l2 = 1e10
dist_linf = 1e10
dist_l0_index = 1e10
dist_l1_index = 1e10
dist_l2_index = 1e10
dist_linf_index = 1e10
for k, j in enumerate(range(i, i + args['target_number'])):
if (np.argmax(pred[k], 1) == np.argmax(targets[j:j + 1], 1)):
if (np.sum(np.abs(adv[j] - inputs[j])) < dist_l1):
dist_l1 = np.sum(np.abs(adv[j] - inputs[j]))
dist_l1_index = j
if (np.amax(np.abs(adv[j] - inputs[j])) < dist_linf):
dist_linf = np.amax(np.abs(adv[j] - inputs[j]))
dist_linf_index = j
if ((np.sum((adv[j] - inputs[j]) ** 2) ** .5) < dist_l2):
dist_l2 = (np.sum((adv[j] - inputs[j]) ** 2) ** .5)
dist_l2_index = j
if np.array(np.nonzero(np.where(np.abs(adv[j]-inputs[j]) < 1e-7, 0, adv[j]-inputs[j]))).shape[1] < dist_l0:
dist_l0 = np.array(np.nonzero(np.where(np.abs(adv[j]-inputs[j]) < 1e-7, 0, adv[j]-inputs[j]))).shape[1]
dist_l0_index = j
if (dist_l1_index != 1e10):
d_best_l2.append((np.sum((adv[dist_l2_index] - inputs[dist_l2_index]) ** 2) ** .5))
d_best_l1.append(np.sum(np.abs(adv[dist_l1_index] - inputs[dist_l1_index])))
d_best_linf.append(np.amax(np.abs(adv[dist_linf_index] - inputs[dist_linf_index])))
d_best_l0.append(np.array(np.nonzero(np.where(np.abs(adv[dist_l0_index] - inputs[dist_l0_index]) < 1e-7, 0,
adv[dist_l0_index] - inputs[dist_l0_index]))).shape[1])
r_best.append(1)
else:
r_best.append(0)
rand_int = np.random.randint(i, i + args['target_number'])
if inception:
pred_r = np.reshape(model.model.predict(adv[rand_int:rand_int + 1]), (data.test_labels[0:1].shape))
else:
pred_r = model.model.predict(adv[rand_int:rand_int + 1])
if (np.argmax(pred_r, 1) == np.argmax(targets[rand_int:rand_int + 1], 1)):
r_average.append(1)
d_average_l2.append(np.sum((adv[rand_int] - inputs[rand_int]) ** 2) ** .5)
d_average_l1.append(np.sum(np.abs(adv[rand_int] - inputs[rand_int])))
d_average_linf.append(np.amax(np.abs(adv[rand_int] - inputs[rand_int])))
d_average_l0.append(np.array(np.nonzero(np.where(np.abs(adv[rand_int] - inputs[rand_int]) < 1e-6, 0,
adv[rand_int] - inputs[rand_int]))).shape[1])
else:
r_average.append(0)
dist_l0 = 0
dist_l0_index = 1e10
dist_l1 = 0
dist_l1_index = 1e10
dist_linf = 0
dist_linf_index = 1e10
dist_l2 = 0
dist_l2_index = 1e10
for k, j in enumerate(range(i, i + args['target_number'])):
if (np.argmax(pred[k], 1) != np.argmax(targets[j:j + 1], 1)):
r_worst.append(0)
dist_l0_index = 1e10
dist_l1_index = 1e10
dist_l2_index = 1e10
dist_linf_index = 1e10
break
else:
if (np.sum(np.abs(adv[j] - inputs[j])) > dist_l1):
dist_l1 = np.sum(np.abs(adv[j] - inputs[j]))
dist_l1_index = j
if (np.amax(np.abs(adv[j] - inputs[j])) > dist_linf):
dist_linf = np.amax(np.abs(adv[j] - inputs[j]))
dist_linf_index = j
if ((np.sum((adv[j] - inputs[j]) ** 2) ** .5) > dist_l2):
dist_l2 = (np.sum((adv[j] - inputs[j]) ** 2) ** .5)
dist_l2_index = j
if np.array(np.nonzero(np.where(np.abs(adv[j]-inputs[j]) < 1e-6, 0, adv[j]-inputs[j]))).shape[1] > dist_l0:
dist_l0 = np.array(np.nonzero(np.where(np.abs(adv[j]-inputs[j]) < 1e-6, 0, adv[j]-inputs[j]))).shape[1]
dist_l0_index = j
if (dist_l1_index != 1e10):
d_worst_l2.append((np.sum((adv[dist_l2_index] - inputs[dist_l2_index]) ** 2) ** .5))
d_worst_l1.append(np.sum(np.abs(adv[dist_l1_index] - inputs[dist_l1_index])))
d_worst_linf.append(np.amax(np.abs(adv[dist_linf_index] - inputs[dist_linf_index])))
d_worst_l0.append(np.array(np.nonzero(np.where(np.abs(adv[dist_l0_index] - inputs[dist_l0_index]) < 1e-6, 0,
adv[dist_l0_index] - inputs[dist_l0_index]))).shape[1])
r_worst.append(1)
if (args['show']):
for j in range(i, i + args['target_number']):
target_id = np.argmax(targets[j:j + 1], 1)
label_id = np.argmax(labels[j:j + 1], 1)
prev_id = np.argmax(np.reshape(model.model.predict(inputs[j:j + 1]), (data.test_labels[0:1].shape)), 1)
adv_id = np.argmax(np.reshape(model.model.predict(adv[j:j + 1]), (data.test_labels[0:1].shape)), 1)
suffix = "id{}_seq{}_lbl{}_prev{}_adv{}_{}_l1_{:.3f}_l2_{:.3f}_linf_{:.3f}".format(
true_ids[i],
target_id,
label_id,
prev_id,
adv_id,
adv_id == target_id,
np.sum(np.abs(adv[j] - inputs[j])),
np.sum((adv[j] - inputs[j]) ** 2) ** .5,
np.amax(np.abs(adv[j] - inputs[j])))
show(inputs[j:j + 1], str(args['save']) + "/" + str(args['dataset']) + "/" + str(
args['attack']) + "/original_{}.png".format(suffix))
show(adv[j:j + 1], str(args['save']) + "/" + str(args['dataset']) + "/" + str(
args['attack']) + "/adversarial_{}.png".format(suffix))
show(adv[j:j + 1]-inputs[j:j + 1], str(args['save']) + "/" + str(args['dataset']) + "/" + str(
args['attack']) + "/diff_{}.png".format(suffix))
print('best_case_L0_mean', np.mean(d_best_l0))
print('best_case_L1_mean', np.mean(d_best_l1))
print('best_case_L2_mean', np.mean(d_best_l2))
print('best_case_Linf_mean', np.mean(d_best_linf))
print('best_case_prob', np.mean(r_best))
print('average_case_L0_mean', np.mean(d_average_l0))
print('average_case_L1_mean', np.mean(d_average_l1))
print('average_case_L2_mean', np.mean(d_average_l2))
print('average_case_Linf_mean', np.mean(d_average_linf))
print('average_case_prob', np.mean(r_average))
print('worst_case_L0_mean', np.mean(d_worst_l0))
print('worst_case_L1_mean', np.mean(d_worst_l1))
print('worst_case_L2_mean', np.mean(d_worst_l2))
print('worst_case_Linf_mean', np.mean(d_worst_linf))
print('worst_case_prob', np.mean(r_worst))
def l0_computation(args, data, model, adv, inception, inputs, targets, labels, true_ids):
r_best = []
d_best_l1 = []
r_average = []
d_average_l1 = []
r_worst = []
d_worst_l1 = []
if args['show']:
if not os.path.exists(str(args['save']) + "/" + str(args['dataset']) + "/" + str(args['attack'])):
os.makedirs(str(args['save']) + "/" + str(args['dataset']) + "/" + str(args['attack']))
for i in range(0, len(inputs), args['target_number']):
pred = []
for j in range(i, i + args['target_number']):
if inception:
pred.append(np.reshape(model.model.predict(adv[j:j + 1]), (data.test_labels[0:1].shape)))
else:
pred.append(model.model.predict(adv[j:j + 1]))
dist_l1 = 1e10
dist_l1_index = 1e10
for k, j in enumerate(range(i, i + args['target_number'])):
if np.argmax(pred[k], 1) == np.argmax(targets[j:j + 1], 1):
#if np.array(np.nonzero(adv[j]-inputs[j])).shape[1] < dist_l1:
if np.array(np.nonzero(np.where(np.abs(adv[j]-inputs[j]) < 1e-6, 0, adv[j]-inputs[j]))).shape[1] < dist_l1:
dist_l1 = np.array(np.nonzero(np.where(np.abs(adv[j]-inputs[j]) < 1e-6, 0, adv[j]-inputs[j]))).shape[1]
#abc = np.array(adv[j]-inputs[j])
#print(np.nonzero(np.where(adv[j] - inputs[j] < 1e-8, 0, adv[j] - inputs[j])))
dist_l1_index = j
if dist_l1_index != 1e10:
d_best_l1.append(np.array(np.nonzero(np.where(np.abs(adv[dist_l1_index]-inputs[dist_l1_index]) < 1e-6, 0,
adv[dist_l1_index]-inputs[dist_l1_index]))).shape[1])
r_best.append(1)
else:
r_best.append(0)
rand_int = np.random.randint(i, i + args['target_number'])
if inception:
pred_r = np.reshape(model.model.predict(adv[rand_int:rand_int + 1]), (data.test_labels[0:1].shape))
else:
pred_r = model.model.predict(adv[rand_int:rand_int + 1])
if np.argmax(pred_r, 1) == np.argmax(targets[rand_int:rand_int + 1], 1):
r_average.append(1)
d_average_l1.append(np.array(np.nonzero(np.where(np.abs(adv[rand_int]-inputs[rand_int]) < 1e-6, 0,
adv[rand_int]-inputs[rand_int]))).shape[1])
else:
r_average.append(0)
dist_l1 = 0
dist_l1_index = 1e10
for k, j in enumerate(range(i, i + args['target_number'])):
if (np.argmax(pred[k], 1) != np.argmax(targets[j:j + 1], 1)):
r_worst.append(0)
dist_l1_index = 1e10
break
else:
if np.array(np.nonzero(np.where(np.abs(adv[j]-inputs[j]) < 1e-6, 0, adv[j]-inputs[j]))).shape[1] > dist_l1:
dist_l1 = np.array(np.nonzero(np.where(np.abs(adv[j]-inputs[j]) < 1e-6, 0, adv[j]-inputs[j]))).shape[1]
dist_l1_index = j
if dist_l1_index != 1e10:
d_worst_l1.append(np.array(np.nonzero(np.where(np.abs(adv[dist_l1_index]-inputs[dist_l1_index]) < 1e-6, 0,
adv[dist_l1_index]-inputs[dist_l1_index]))).shape[1])
r_worst.append(1)
if args['show']:
for j in range(i, i + args['batch_size']):
target_id = np.argmax(targets[j:j + 1], 1)
label_id = np.argmax(labels[j:j + 1], 1)
prev_id = np.argmax(np.reshape(model.model.predict(inputs[j:j + 1]),
(data.test_labels[0:1].shape)), 1)
adv_id = np.argmax(np.reshape(model.model.predict(adv[j:j + 1]), (data.test_labels[0:1].shape)), 1)
suffix = "id{}_seq{}_lbl{}_prev{}_adv{}_{}_l1_{:.3f}_l2_{:.3f}_linf_{:.3f}".format(
true_ids[i],
target_id,
label_id,
prev_id,
adv_id,
adv_id == target_id,
np.sum(np.abs(adv[j] - inputs[j])),
np.sum((adv[j] - inputs[j]) ** 2) ** .5,
np.amax(np.abs(adv[j] - inputs[j])))
show(inputs[j:j + 1], str(args['save']) + "/" + str(args['dataset']) + "/" + str(
args['attack']) + "/original_{}.png".format(suffix))
show(adv[j:j + 1], str(args['save']) + "/" + str(args['dataset']) + "/" + str(
args['attack']) + "/adversarial_{}.png".format(suffix))
show(adv[j:j + 1]-inputs[j:j + 1], str(args['save']) + "/" + str(args['dataset']) + "/" + str(
args['attack']) + "/diff_{}.png".format(suffix))
print('best_case_L0_mean', np.mean(d_best_l1))
print('best_case_prob', np.mean(r_best))
print('average_case_L0_mean', np.mean(d_average_l1))
print('average_case_prob', np.mean(r_average))
print('worst_case_L0_mean', np.mean(d_worst_l1))
print('worst_case_prob', np.mean(r_worst))
def main(args):
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.7)
with tf.Session() as sess:
if args['dataset'] == 'mnist':
data, model = MNIST(), MNISTModel("models/mnist", sess)
handpick = False
inception = False
if args['dataset'] == "cifar":
data, model = CIFAR(), CIFARModel("models/cifar", sess)
handpick = True
inception = False
if args['dataset'] == "imagenet":
data, model = ImageNet(args['seed_imagenet']), InceptionModel(sess)
handpick = True
inception = True
if args['adversarial'] != "none":
model = MNISTModel("models/mnist_cwl2_admm" + str(args['adversarial']), sess)
if args['temp'] and args['dataset'] == 'mnist':
model = MNISTModel("models/mnist-distilled-" + str(args['temp']), sess)
if args['temp'] and args['dataset'] == 'cifar':
model = CIFARModel("models/cifar-distilled-" + str(args['temp']), sess)
inputs, targets, labels, true_ids = generate_data(data, model, samples=args['numimg'], targeted=True,
start=0, inception=inception, handpick=handpick, seed=args['seed'])
#print(true_ids)
if args['attack'] == 'ADMM':
attack = ADMML2(sess, model, batch_size=args['batch_size'], max_iterations=args['maxiter'],
confidence=args['conf'], binary_search_steps=args['iteration_steps'], ro=args['ro'],
abort_early=args['abort_early'])
if args['attack'] == 'structured':
attack = LADMMSTL2(sess, model, batch_size=args['batch_size'], max_iterations=args['maxiter'],
confidence=args['conf'], binary_search_steps=args['iteration_steps'], ro=args['ro'],
abort_early=args['abort_early'],retrain=args['retrain'])
timestart = time.time()
adv = attack.attack(inputs, targets)
timeend = time.time()
print("Took", timeend - timestart, "seconds to run", len(inputs), "samples.\n")
if args['train']:
np.save('labels_train.npy', labels)
np.save(str(args['attack']) + '_train.npy', adv)
if (args['conf'] != 0):
model = MNISTModel("models/mnist-distilled-100", sess)
l1_l2_li_computation(args, data, model, adv, inception, inputs, targets, labels, true_ids)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("-d", "--dataset", choices=["mnist", "cifar", "imagenet"], default="imagenet",
help="dataset to use")
parser.add_argument("-n", "--numimg", type=int, default=2, help="number of images to attack")
parser.add_argument("-b", "--batch_size", type=int, default=18, help="batch size")
parser.add_argument("-m", "--maxiter", type=int, default=50000, help="max iterations per bss")
#parser.add_argument("-m1", "--maxiter_1", type=int, default=1000, help="max iterations per bss")
parser.add_argument("-is", "--iteration_steps", type=int, default=1, help="number of iteration L2ADMM")
parser.add_argument("-ro", "--ro", type=int, default=2, help="value of ro")
parser.add_argument("-bs", "--binary_steps", type=int, default=4, help="number of bss")
parser.add_argument("-ae", "--abort_early", action='store_true', default=True,
help="abort binary search step early when losses stop decreasing")
parser.add_argument("-cf", "--conf", type=int, default=0, help='Set attack confidence for transferability tests')
parser.add_argument("-imgsd", "--seed_imagenet", type=int, default=1,
help='random seed for pulling images from ImageNet test set')
parser.add_argument("-sd", "--seed", type=int, default=2225,
help='random seed for pulling images from data set')
parser.add_argument("-sh", "--show", action='store_true', default=True,
help='save original and adversarial images to save directory')
parser.add_argument("-s", "--save", default="./saves", help="save directory")
parser.add_argument("-a", "--attack_name",
choices=["ADMM", "structured"],
default="structured",
help="attack algorithm")
parser.add_argument("-re", "--retrain", default=True, help="retrain or not")
parser.add_argument("-tn", "--target_number", type=int, default=9, help="number of targets for one input")
parser.add_argument("-tr", "--train", action='store_true', default=False,
help="save adversarial images generated from train set")
parser.add_argument("-tp", "--temp", type=int, default=0,
help="attack defensively distilled network trained with this temperature")
parser.add_argument("-adv", "--adversarial", choices=["none", "l2", "l1", "en", "l2l1", "l2en"], default="none",
help="attack network adversarially trained under these examples")
parser.add_argument("-be", "--beta", type=float, default=1e-4, help='beta hyperparameter')
args = vars(parser.parse_args())
print(args)
main(args)