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fluid/adversarial/README.md
浏览文件 @ 090cf436
......@@ -59,9 +59,11 @@ The structure of Advbox module are as follows:
└── README.md
**advbox.attack**
Advbox implements several popular adversarial attacks which search adversarial examples. Each attack method uses a distance measure(L1, L2, etc.) to quantify the size of adversarial perturbations. Advbox is easy to craft adversarial example as some attack methods could perform internal hyperparameter tuning to find the minimum perturbation.
**advbox.model**
Advbox implements interfaces to PaddlePaddle. Additionally, other deep learning framworks such as TensorFlow can also be defined and employed. The module is use to compute predictions and gradients for given inputs in a specific framework.
**advbox.adversary**
......@@ -83,15 +85,16 @@ Benchmarks on a vanilla CNN model.
> MNIST
| adversarial attacks | fooling rate (non-targeted) | fooling rate (targeted) | max_epsilon | iterations | comments |
| adversarial attacks | fooling rate (non-targeted) | fooling rate (targeted) | max_epsilon | iterations | Strength |
|:-----:| :----: | :---: | :----: | :----: | :----: |
|L-BFGS| --- | --- | --- | One shot | |
|FGSM| 57.8% | 5.5% | 0.3 | One shot| |
|BIM| 98.4% | --- | 0.1 | 100 | |
|ILCM| --- | 100.0% | 0.1 | 100 | |
|JSMA| --- | --- | 0.1 | 2000 | |
|DeepFool| | --- | 0.02(overshoot) | 100 | |
|L-BFGS| --- | 89.2% | --- | One shot | *** |
|FGSM| 57.8% | 26.55% | 0.3 | One shot| *** |
|BIM| 97.4% | --- | 0.1 | 100 | **** |
|ILCM| --- | 100.0% | 0.1 | 100 | **** |
|JSMA| 96.8% | 90.4%| 0.1 | 2000 | *** |
|DeepFool| 97.7% | 51.3% | --- | 100 | **** |
* The strength (higher for more asterisks) is based on the impression from the reviewed literature.
--
## References
......@@ -102,3 +105,4 @@ Benchmarks on a vanilla CNN model.
* [DeepFool: a simple and accurate method to fool deep neural networks](https://arxiv.org/abs/1511.04599), S. Moosavi-Dezfooli et al., CVPR 2016
* [Foolbox: A Python toolbox to benchmark the robustness of machine learning models] (https://arxiv.org/abs/1707.04131), Jonas Rauber et al., arxiv 2018
* [CleverHans: An adversarial example library for constructing attacks, building defenses, and benchmarking both](https://github.com/tensorflow/cleverhans#setting-up-cleverhans)
* [Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey](https://arxiv.org/abs/1801.00553), Naveed Akhtar, Ajmal Mian, arxiv 2018
fluid/adversarial/advbox/attacks/gradient_method.py
浏览文件 @ 090cf436
......@@ -32,7 +32,12 @@ class GradientMethodAttack(Attack):
super(GradientMethodAttack, self).__init__(model)
self.support_targeted = support_targeted
def _apply(self, adversary, norm_ord=np.inf, epsilons=0.01, steps=100):
def _apply(self,
adversary,
norm_ord=np.inf,
epsilons=0.01,
steps=1,
epsilon_steps=100):
"""
Apply the gradient attack method.
:param adversary(Adversary):
......@@ -41,8 +46,11 @@ class GradientMethodAttack(Attack):
Order of the norm, such as np.inf, 1, 2, etc. It can't be 0.
:param epsilons(list|tuple|int):
Attack step size (input variation).
Largest step size if epsilons is not iterable.
:param steps:
The number of iterator steps.
The number of attack iteration.
:param epsilon_steps:
The number of Epsilons' iteration for each attack iteration.
:return:
adversary(Adversary): The Adversary object.
"""
......@@ -55,7 +63,7 @@ class GradientMethodAttack(Attack):
"This attack method doesn't support targeted attack!")
if not isinstance(epsilons, Iterable):
epsilons = np.linspace(epsilons, epsilons + 1e-10, num=steps)
epsilons = np.linspace(0, epsilons, num=epsilon_steps)
pre_label = adversary.original_label
min_, max_ = self.model.bounds()
......@@ -65,20 +73,23 @@ class GradientMethodAttack(Attack):
self.model.channel_axis() == adversary.original.shape[0] or
self.model.channel_axis() == adversary.original.shape[-1])
for epsilon in epsilons[:]:
step = 1
adv_img = adversary.original
for epsilon in epsilons[:steps]:
for i in range(steps):
if epsilon == 0.0:
continue
if adversary.is_targeted_attack:
gradient = -self.model.gradient(adv_img, adversary.target_label)
gradient = -self.model.gradient(adv_img,
adversary.target_label)
else:
gradient = self.model.gradient(adv_img,
adversary.original_label)
if norm_ord == np.inf:
gradient_norm = np.sign(gradient)
else:
gradient_norm = gradient / self._norm(gradient, ord=norm_ord)
gradient_norm = gradient / self._norm(
gradient, ord=norm_ord)
adv_img = adv_img + epsilon * gradient_norm * (max_ - min_)
adv_img = np.clip(adv_img, min_, max_)
......@@ -113,7 +124,7 @@ class FastGradientSignMethodTargetedAttack(GradientMethodAttack):
Paper link: https://arxiv.org/abs/1412.6572
"""
def _apply(self, adversary, epsilons=0.03):
def _apply(self, adversary, epsilons=0.01):
return GradientMethodAttack._apply(
self,
adversary=adversary,
......@@ -144,7 +155,7 @@ class IterativeLeastLikelyClassMethodAttack(GradientMethodAttack):
Paper link: https://arxiv.org/abs/1607.02533
"""
def _apply(self, adversary, epsilons=0.001, steps=1000):
def _apply(self, adversary, epsilons=0.01, steps=1000):
return GradientMethodAttack._apply(
self,
adversary=adversary,
......
fluid/adversarial/mnist_tutorial_fgsm.py 已删除 100644 → 0
浏览文件 @ 95661d47
"""
FGSM demos on mnist using advbox tool.
"""
import matplotlib.pyplot as plt
import paddle.v2 as paddle
import paddle.fluid as fluid
from advbox.adversary import Adversary
from advbox.attacks.gradient_method import FGSM
from advbox.models.paddle import PaddleModel
def cnn_model(img):
"""
Mnist cnn model
Args:
img(Varaible): the input image to be recognized
Returns:
Variable: the label prediction
"""
# conv1 = fluid.nets.conv2d()
conv_pool_1 = fluid.nets.simple_img_conv_pool(
input=img,
num_filters=20,
filter_size=5,
pool_size=2,
pool_stride=2,
act='relu')
conv_pool_2 = fluid.nets.simple_img_conv_pool(
input=conv_pool_1,
num_filters=50,
filter_size=5,
pool_size=2,
pool_stride=2,
act='relu')
logits = fluid.layers.fc(input=conv_pool_2, size=10, act='softmax')
return logits
def main():
"""
Advbox demo which demonstrate how to use advbox.
"""
IMG_NAME = 'img'
LABEL_NAME = 'label'
img = fluid.layers.data(name=IMG_NAME, shape=[1, 28, 28], dtype='float32')
# gradient should flow
img.stop_gradient = False
label = fluid.layers.data(name=LABEL_NAME, shape=[1], dtype='int64')
logits = cnn_model(img)
cost = fluid.layers.cross_entropy(input=logits, label=label)
avg_cost = fluid.layers.mean(x=cost)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
BATCH_SIZE = 1
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=500),
batch_size=BATCH_SIZE)
feeder = fluid.DataFeeder(
feed_list=[IMG_NAME, LABEL_NAME],
place=place,
program=fluid.default_main_program())
fluid.io.load_params(
exe, "./mnist/", main_program=fluid.default_main_program())
# advbox demo
m = PaddleModel(fluid.default_main_program(), IMG_NAME, LABEL_NAME,
logits.name, avg_cost.name, (-1, 1))
att = FGSM(m)
for data in train_reader():
# fgsm attack
adversary = att(Adversary(data[0][0], data[0][1]))
if adversary.is_successful():
plt.imshow(adversary.target, cmap='Greys_r')
plt.show()
# np.save('adv_img', adversary.target)
break
if __name__ == '__main__':
main()
fluid/adversarial/mnist_tutorial_jsma.py 已删除 100644 → 0
浏览文件 @ 95661d47
"""
FGSM demos on mnist using advbox tool.
"""
import matplotlib.pyplot as plt
import paddle.v2 as paddle
import paddle.fluid as fluid
import numpy as np
from advbox import Adversary
from advbox.attacks.saliency import SaliencyMapAttack
from advbox.models.paddle import PaddleModel
def cnn_model(img):
"""
Mnist cnn model
Args:
img(Varaible): the input image to be recognized
Returns:
Variable: the label prediction
"""
# conv1 = fluid.nets.conv2d()
conv_pool_1 = fluid.nets.simple_img_conv_pool(
input=img,
num_filters=20,
filter_size=5,
pool_size=2,
pool_stride=2,
act='relu')
conv_pool_2 = fluid.nets.simple_img_conv_pool(
input=conv_pool_1,
num_filters=50,
filter_size=5,
pool_size=2,
pool_stride=2,
act='relu')
logits = fluid.layers.fc(input=conv_pool_2, size=10, act='softmax')
return logits
def main():
"""
Advbox demo which demonstrate how to use advbox.
"""
IMG_NAME = 'img'
LABEL_NAME = 'label'
img = fluid.layers.data(name=IMG_NAME, shape=[1, 28, 28], dtype='float32')
# gradient should flow
img.stop_gradient = False
label = fluid.layers.data(name=LABEL_NAME, shape=[1], dtype='int64')
logits = cnn_model(img)
cost = fluid.layers.cross_entropy(input=logits, label=label)
avg_cost = fluid.layers.mean(x=cost)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
BATCH_SIZE = 1
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=500),
batch_size=BATCH_SIZE)
feeder = fluid.DataFeeder(
feed_list=[IMG_NAME, LABEL_NAME],
place=place,
program=fluid.default_main_program())
fluid.io.load_params(
exe, "./mnist/", main_program=fluid.default_main_program())
# advbox demo
m = PaddleModel(fluid.default_main_program(), IMG_NAME, LABEL_NAME,
logits.name, avg_cost.name, (-1, 1))
attack = SaliencyMapAttack(m)
total_num = 0
success_num = 0
for data in train_reader():
total_num += 1
# adversary.set_target(True, target_label=target_label)
jsma_attack = attack(Adversary(data[0][0], data[0][1]))
if jsma_attack is not None and jsma_attack.is_successful():
# plt.imshow(jsma_attack.target, cmap='Greys_r')
# plt.show()
success_num += 1
print('original_label=%d, adversary examples label =%d' %
(data[0][1], jsma_attack.adversarial_label))
# np.save('adv_img', jsma_attack.adversarial_example)
print('total num = %d, success num = %d ' % (total_num, success_num))
if total_num == 100:
break
if __name__ == '__main__':
main()
fluid/adversarial/tutorials/__init__.py 0 → 100644
浏览文件 @ 090cf436
"""
A set of tutorials for generating adversarial examples with advbox.
"""
\ No newline at end of file
fluid/adversarial/fluid_mnist.py → fluid/adversarial/tutorials/mnist_model.py
浏览文件 @ 090cf436
......@@ -30,8 +30,9 @@ def mnist_cnn_model(img):
pool_size=2,
pool_stride=2,
act='relu')
fc = fluid.layers.fc(input=conv_pool_2, size=50, act='relu')
logits = fluid.layers.fc(input=conv_pool_2, size=10, act='softmax')
logits = fluid.layers.fc(input=fc, size=10, act='softmax')
return logits
......@@ -60,7 +61,10 @@ def main():
paddle.dataset.mnist.train(), buf_size=500),
batch_size=BATCH_SIZE)
# use CPU
place = fluid.CPUPlace()
# use GPU
# place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
feeder = fluid.DataFeeder(feed_list=[img, label], place=place)
exe.run(fluid.default_startup_program())
......@@ -74,9 +78,11 @@ def main():
feed=feeder.feed(data),
fetch_list=[avg_cost, batch_acc, batch_size])
pass_acc.add(value=acc, weight=b_size)
pass_acc_val = pass_acc.eval()[0]
print("pass_id=" + str(pass_id) + " acc=" + str(acc[0]) +
" pass_acc=" + str(pass_acc.eval()[0]))
if loss < LOSS_THRESHOLD and pass_acc > ACC_THRESHOLD:
" pass_acc=" + str(pass_acc_val))
if loss < LOSS_THRESHOLD and pass_acc_val > ACC_THRESHOLD:
# early stop
break
print("pass_id=" + str(pass_id) + " pass_acc=" + str(pass_acc.eval()[
......
fluid/adversarial/tutorials/mnist_tutorial_bim.py 0 → 100644
浏览文件 @ 090cf436
"""
BIM tutorial on mnist using advbox tool.
BIM method iteratively take multiple small steps while adjusting the direction after each step.
It only supports non-targeted attack.
"""
import sys
sys.path.append("..")
import matplotlib.pyplot as plt
import paddle.fluid as fluid
import paddle.v2 as paddle
from advbox.adversary import Adversary
from advbox.attacks.gradient_method import BIM
from advbox.models.paddle import PaddleModel
from tutorials.mnist_model import mnist_cnn_model
def main():
"""
Advbox demo which demonstrate how to use advbox.
"""
TOTAL_NUM = 500
IMG_NAME = 'img'
LABEL_NAME = 'label'
img = fluid.layers.data(name=IMG_NAME, shape=[1, 28, 28], dtype='float32')
# gradient should flow
img.stop_gradient = False
label = fluid.layers.data(name=LABEL_NAME, shape=[1], dtype='int64')
logits = mnist_cnn_model(img)
cost = fluid.layers.cross_entropy(input=logits, label=label)
avg_cost = fluid.layers.mean(x=cost)
# use CPU
place = fluid.CPUPlace()
# use GPU
# place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
BATCH_SIZE = 1
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=128 * 10),
batch_size=BATCH_SIZE)
test_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.test(), buf_size=128 * 10),
batch_size=BATCH_SIZE)
fluid.io.load_params(
exe, "./mnist/", main_program=fluid.default_main_program())
# advbox demo
m = PaddleModel(
fluid.default_main_program(),
IMG_NAME,
LABEL_NAME,
logits.name,
avg_cost.name, (-1, 1),
channel_axis=1)
attack = BIM(m)
attack_config = {"epsilons": 0.1, "steps": 100}
# use train data to generate adversarial examples
total_count = 0
fooling_count = 0
for data in train_reader():
total_count += 1
adversary = Adversary(data[0][0], data[0][1])
# BIM non-targeted attack
adversary = attack(adversary, **attack_config)
if adversary.is_successful():
fooling_count += 1
print(
'attack success, original_label=%d, adversarial_label=%d, count=%d'
% (data[0][1], adversary.adversarial_label, total_count))
# plt.imshow(adversary.target, cmap='Greys_r')
# plt.show()
# np.save('adv_img', adversary.target)
else:
print('attack failed, original_label=%d, count=%d' %
(data[0][1], total_count))
if total_count >= TOTAL_NUM:
print(
"[TRAIN_DATASET]: fooling_count=%d, total_count=%d, fooling_rate=%f"
% (fooling_count, total_count,
float(fooling_count) / total_count))
break
# use test data to generate adversarial examples
total_count = 0
fooling_count = 0
for data in test_reader():
total_count += 1
adversary = Adversary(data[0][0], data[0][1])
# BIM non-targeted attack
adversary = attack(adversary, **attack_config)
if adversary.is_successful():
fooling_count += 1
print(
'attack success, original_label=%d, adversarial_label=%d, count=%d'
% (data[0][1], adversary.adversarial_label, total_count))
# plt.imshow(adversary.target, cmap='Greys_r')
# plt.show()
# np.save('adv_img', adversary.target)
else:
print('attack failed, original_label=%d, count=%d' %
(data[0][1], total_count))
if total_count >= TOTAL_NUM:
print(
"[TEST_DATASET]: fooling_count=%d, total_count=%d, fooling_rate=%f"
% (fooling_count, total_count,
float(fooling_count) / total_count))
break
print("bim attack done")
if __name__ == '__main__':
main()
fluid/adversarial/tutorials/mnist_tutorial_deepfool.py 0 → 100644
浏览文件 @ 090cf436
"""
DeepFool tutorial on mnist using advbox tool.
Deepfool is a simple and accurate adversarial attack method.
It supports both targeted attack and non-targeted attack.
"""
import sys
sys.path.append("..")
import matplotlib.pyplot as plt
import paddle.fluid as fluid
import paddle.v2 as paddle
from advbox.adversary import Adversary
from advbox.attacks.deepfool import DeepFoolAttack
from advbox.models.paddle import PaddleModel
from tutorials.mnist_model import mnist_cnn_model
def main():
"""
Advbox demo which demonstrate how to use advbox.
"""
TOTAL_NUM = 500
IMG_NAME = 'img'
LABEL_NAME = 'label'
img = fluid.layers.data(name=IMG_NAME, shape=[1, 28, 28], dtype='float32')
# gradient should flow
img.stop_gradient = False
label = fluid.layers.data(name=LABEL_NAME, shape=[1], dtype='int64')
logits = mnist_cnn_model(img)
cost = fluid.layers.cross_entropy(input=logits, label=label)
avg_cost = fluid.layers.mean(x=cost)
# use CPU
place = fluid.CPUPlace()
# use GPU
# place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
BATCH_SIZE = 1
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=128 * 10),
batch_size=BATCH_SIZE)
test_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.test(), buf_size=128 * 10),
batch_size=BATCH_SIZE)
fluid.io.load_params(
exe, "./mnist/", main_program=fluid.default_main_program())
# advbox demo
m = PaddleModel(
fluid.default_main_program(),
IMG_NAME,
LABEL_NAME,
logits.name,
avg_cost.name, (-1, 1),
channel_axis=1)
attack = DeepFoolAttack(m)
attack_config = {"iterations": 100, "overshoot": 9}
# use train data to generate adversarial examples
total_count = 0
fooling_count = 0
for data in train_reader():
total_count += 1
adversary = Adversary(data[0][0], data[0][1])
# DeepFool non-targeted attack
adversary = attack(adversary, **attack_config)
# DeepFool targeted attack
# tlabel = 0
# adversary.set_target(is_targeted_attack=True, target_label=tlabel)
# adversary = attack(adversary, **attack_config)
if adversary.is_successful():
fooling_count += 1
print(
'attack success, original_label=%d, adversarial_label=%d, count=%d'
% (data[0][1], adversary.adversarial_label, total_count))
# plt.imshow(adversary.target, cmap='Greys_r')
# plt.show()
# np.save('adv_img', adversary.target)
else:
print('attack failed, original_label=%d, count=%d' %
(data[0][1], total_count))
if total_count >= TOTAL_NUM:
print(
"[TRAIN_DATASET]: fooling_count=%d, total_count=%d, fooling_rate=%f"
% (fooling_count, total_count,
float(fooling_count) / total_count))
break
# use test data to generate adversarial examples
total_count = 0
fooling_count = 0
for data in test_reader():
total_count += 1
adversary = Adversary(data[0][0], data[0][1])
# DeepFool non-targeted attack
adversary = attack(adversary, **attack_config)
# DeepFool targeted attack
# tlabel = 0
# adversary.set_target(is_targeted_attack=True, target_label=tlabel)
# adversary = attack(adversary, **attack_config)
if adversary.is_successful():
fooling_count += 1
print(
'attack success, original_label=%d, adversarial_label=%d, count=%d'
% (data[0][1], adversary.adversarial_label, total_count))
# plt.imshow(adversary.target, cmap='Greys_r')
# plt.show()
# np.save('adv_img', adversary.target)
else:
print('attack failed, original_label=%d, count=%d' %
(data[0][1], total_count))
if total_count >= TOTAL_NUM:
print(
"[TEST_DATASET]: fooling_count=%d, total_count=%d, fooling_rate=%f"
% (fooling_count, total_count,
float(fooling_count) / total_count))
break
print("deelfool attack done")
if __name__ == '__main__':
main()
fluid/adversarial/tutorials/mnist_tutorial_fgsm.py 0 → 100644
浏览文件 @ 090cf436
"""
FGSM tutorial on mnist using advbox tool.
FGSM method is non-targeted attack while FGSMT is targeted attack.
"""
import sys
sys.path.append("..")
import matplotlib.pyplot as plt
import numpy as np
import paddle.fluid as fluid
import paddle.v2 as paddle
from advbox.adversary import Adversary
from advbox.attacks.gradient_method import FGSM
from advbox.attacks.gradient_method import FGSMT
from advbox.models.paddle import PaddleModel
from tutorials.mnist_model import mnist_cnn_model
def main():
"""
Advbox demo which demonstrate how to use advbox.
"""
TOTAL_NUM = 500
IMG_NAME = 'img'
LABEL_NAME = 'label'
img = fluid.layers.data(name=IMG_NAME, shape=[1, 28, 28], dtype='float32')
# gradient should flow
img.stop_gradient = False
label = fluid.layers.data(name=LABEL_NAME, shape=[1], dtype='int64')
logits = mnist_cnn_model(img)
cost = fluid.layers.cross_entropy(input=logits, label=label)
avg_cost = fluid.layers.mean(x=cost)
# use CPU
place = fluid.CPUPlace()
# use GPU
# place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
BATCH_SIZE = 1
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=128 * 10),
batch_size=BATCH_SIZE)
test_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.test(), buf_size=128 * 10),
batch_size=BATCH_SIZE)
fluid.io.load_params(
exe, "./mnist/", main_program=fluid.default_main_program())
# advbox demo
m = PaddleModel(
fluid.default_main_program(),
IMG_NAME,
LABEL_NAME,
logits.name,
avg_cost.name, (-1, 1),
channel_axis=1)
attack = FGSM(m)
# attack = FGSMT(m)
attack_config = {"epsilons": 0.3}
# use train data to generate adversarial examples
total_count = 0
fooling_count = 0
for data in train_reader():
total_count += 1
adversary = Adversary(data[0][0], data[0][1])
# FGSM non-targeted attack
adversary = attack(adversary, **attack_config)
# FGSMT targeted attack
# tlabel = 0
# adversary.set_target(is_targeted_attack=True, target_label=tlabel)
# adversary = attack(adversary, **attack_config)
if adversary.is_successful():
fooling_count += 1
print(
'attack success, original_label=%d, adversarial_label=%d, count=%d'
% (data[0][1], adversary.adversarial_label, total_count))
# plt.imshow(adversary.target, cmap='Greys_r')
# plt.show()
# np.save('adv_img', adversary.target)
else:
print('attack failed, original_label=%d, count=%d' %
(data[0][1], total_count))
if total_count >= TOTAL_NUM:
print(
"[TRAIN_DATASET]: fooling_count=%d, total_count=%d, fooling_rate=%f"
% (fooling_count, total_count,
float(fooling_count) / total_count))
break
# use test data to generate adversarial examples
total_count = 0
fooling_count = 0
for data in test_reader():
total_count += 1
adversary = Adversary(data[0][0], data[0][1])
# FGSM non-targeted attack
adversary = attack(adversary, **attack_config)
# FGSMT targeted attack
# tlabel = 0
# adversary.set_target(is_targeted_attack=True, target_label=tlabel)
# adversary = attack(adversary, **attack_config)
if adversary.is_successful():
fooling_count += 1
print(
'attack success, original_label=%d, adversarial_label=%d, count=%d'
% (data[0][1], adversary.adversarial_label, total_count))
# plt.imshow(adversary.target, cmap='Greys_r')
# plt.show()
# np.save('adv_img', adversary.target)
else:
print('attack failed, original_label=%d, count=%d' %
(data[0][1], total_count))
if total_count >= TOTAL_NUM:
print(
"[TEST_DATASET]: fooling_count=%d, total_count=%d, fooling_rate=%f"
% (fooling_count, total_count,
float(fooling_count) / total_count))
break
print("fgsm attack done")
if __name__ == '__main__':
main()
fluid/adversarial/tutorials/mnist_tutorial_ilcm.py 0 → 100644
浏览文件 @ 090cf436
"""
ILCM tutorial on mnist using advbox tool.
ILCM method extends "BIM" to support targeted attack.
"""
import sys
sys.path.append("..")
import matplotlib.pyplot as plt
import paddle.fluid as fluid
import paddle.v2 as paddle
from advbox.adversary import Adversary
from advbox.attacks.gradient_method import ILCM
from advbox.models.paddle import PaddleModel
from tutorials.mnist_model import mnist_cnn_model
def main():
"""
Advbox demo which demonstrate how to use advbox.
"""
TOTAL_NUM = 500
IMG_NAME = 'img'
LABEL_NAME = 'label'
img = fluid.layers.data(name=IMG_NAME, shape=[1, 28, 28], dtype='float32')
# gradient should flow
img.stop_gradient = False
label = fluid.layers.data(name=LABEL_NAME, shape=[1], dtype='int64')
logits = mnist_cnn_model(img)
cost = fluid.layers.cross_entropy(input=logits, label=label)
avg_cost = fluid.layers.mean(x=cost)
# use CPU
place = fluid.CPUPlace()
# use GPU
# place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
BATCH_SIZE = 1
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=128 * 10),
batch_size=BATCH_SIZE)
test_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.test(), buf_size=128 * 10),
batch_size=BATCH_SIZE)
fluid.io.load_params(
exe, "./mnist/", main_program=fluid.default_main_program())
# advbox demo
m = PaddleModel(
fluid.default_main_program(),
IMG_NAME,
LABEL_NAME,
logits.name,
avg_cost.name, (-1, 1),
channel_axis=1)
attack = ILCM(m)
attack_config = {"epsilons": 0.1, "steps": 100}
# use train data to generate adversarial examples
total_count = 0
fooling_count = 0
for data in train_reader():
total_count += 1
adversary = Adversary(data[0][0], data[0][1])
tlabel = 0
adversary.set_target(is_targeted_attack=True, target_label=tlabel)
# ILCM targeted attack
adversary = attack(adversary, **attack_config)
if adversary.is_successful():
fooling_count += 1
print(
'attack success, original_label=%d, adversarial_label=%d, count=%d'
% (data[0][1], adversary.adversarial_label, total_count))
# plt.imshow(adversary.target, cmap='Greys_r')
# plt.show()
# np.save('adv_img', adversary.target)
else:
print('attack failed, original_label=%d, count=%d' %
(data[0][1], total_count))
if total_count >= TOTAL_NUM:
print(
"[TRAIN_DATASET]: fooling_count=%d, total_count=%d, fooling_rate=%f"
% (fooling_count, total_count,
float(fooling_count) / total_count))
break
# use test data to generate adversarial examples
total_count = 0
fooling_count = 0
for data in test_reader():
total_count += 1
adversary = Adversary(data[0][0], data[0][1])
tlabel = 0
adversary.set_target(is_targeted_attack=True, target_label=tlabel)
# ILCM targeted attack
adversary = attack(adversary, **attack_config)
if adversary.is_successful():
fooling_count += 1
print(
'attack success, original_label=%d, adversarial_label=%d, count=%d'
% (data[0][1], adversary.adversarial_label, total_count))
# plt.imshow(adversary.target, cmap='Greys_r')
# plt.show()
# np.save('adv_img', adversary.target)
else:
print('attack failed, original_label=%d, count=%d' %
(data[0][1], total_count))
if total_count >= TOTAL_NUM:
print(
"[TEST_DATASET]: fooling_count=%d, total_count=%d, fooling_rate=%f"
% (fooling_count, total_count,
float(fooling_count) / total_count))
break
print("ilcm attack done")
if __name__ == '__main__':
main()
fluid/adversarial/tutorials/mnist_tutorial_jsma.py 0 → 100644
浏览文件 @ 090cf436
"""
JSMA tutorial on mnist using advbox tool.
JSMA method supports both targeted attack and non-targeted attack.
"""
import sys
sys.path.append("..")
import matplotlib.pyplot as plt
import paddle.fluid as fluid
import paddle.v2 as paddle
from advbox.adversary import Adversary
from advbox.attacks.saliency import JSMA
from advbox.models.paddle import PaddleModel
from tutorials.mnist_model import mnist_cnn_model
def main():
"""
Advbox demo which demonstrate how to use advbox.
"""
TOTAL_NUM = 500
IMG_NAME = 'img'
LABEL_NAME = 'label'
img = fluid.layers.data(name=IMG_NAME, shape=[1, 28, 28], dtype='float32')
# gradient should flow
img.stop_gradient = False
label = fluid.layers.data(name=LABEL_NAME, shape=[1], dtype='int64')
logits = mnist_cnn_model(img)
cost = fluid.layers.cross_entropy(input=logits, label=label)
avg_cost = fluid.layers.mean(x=cost)
# use CPU
place = fluid.CPUPlace()
# use GPU
# place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
BATCH_SIZE = 1
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=128 * 10),
batch_size=BATCH_SIZE)
test_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.test(), buf_size=128 * 10),
batch_size=BATCH_SIZE)
fluid.io.load_params(
exe, "./mnist/", main_program=fluid.default_main_program())
# advbox demo
m = PaddleModel(
fluid.default_main_program(),
IMG_NAME,
LABEL_NAME,
logits.name,
avg_cost.name, (-1, 1),
channel_axis=1)
attack = JSMA(m)
attack_config = {
"max_iter": 2000,
"theta": 0.1,
"max_perturbations_per_pixel": 7
}
# use train data to generate adversarial examples
total_count = 0
fooling_count = 0
for data in train_reader():
total_count += 1
adversary = Adversary(data[0][0], data[0][1])
# JSMA non-targeted attack
adversary = attack(adversary, **attack_config)
# JSMA targeted attack
# tlabel = 0
# adversary.set_target(is_targeted_attack=True, target_label=tlabel)
# adversary = attack(adversary, **attack_config)
# JSMA may return None
if adversary is not None and adversary.is_successful():
fooling_count += 1
print(
'attack success, original_label=%d, adversarial_label=%d, count=%d'
% (data[0][1], adversary.adversarial_label, total_count))
# plt.imshow(adversary.target, cmap='Greys_r')
# plt.show()
# np.save('adv_img', adversary.target)
else:
print('attack failed, original_label=%d, count=%d' %
(data[0][1], total_count))
if total_count >= TOTAL_NUM:
print(
"[TRAIN_DATASET]: fooling_count=%d, total_count=%d, fooling_rate=%f"
% (fooling_count, total_count,
float(fooling_count) / total_count))
break
# use test data to generate adversarial examples
total_count = 0
fooling_count = 0
for data in test_reader():
total_count += 1
adversary = Adversary(data[0][0], data[0][1])
# JSMA non-targeted attack
adversary = attack(adversary, **attack_config)
# JSMA targeted attack
# tlabel = 0
# adversary.set_target(is_targeted_attack=True, target_label=tlabel)
# adversary = attack(adversary, **attack_config)
# JSMA may return None
if adversary is not None and adversary.is_successful():
fooling_count += 1
print(
'attack success, original_label=%d, adversarial_label=%d, count=%d'
% (data[0][1], adversary.adversarial_label, total_count))
# plt.imshow(adversary.target, cmap='Greys_r')
# plt.show()
# np.save('adv_img', adversary.target)
else:
print('attack failed, original_label=%d, count=%d' %
(data[0][1], total_count))
if total_count >= TOTAL_NUM:
print(
"[TEST_DATASET]: fooling_count=%d, total_count=%d, fooling_rate=%f"
% (fooling_count, total_count,
float(fooling_count) / total_count))
break
print("jsma attack done")
if __name__ == '__main__':
main()
fluid/adversarial/tutorials/mnist_tutorial_lbfgs.py 0 → 100644
浏览文件 @ 090cf436
"""
LBFGS tutorial on mnist using advbox tool.
LBFGS method only supports targeted attack.
"""
import sys
sys.path.append("..")
import matplotlib.pyplot as plt
import paddle.fluid as fluid
import paddle.v2 as paddle
from advbox.adversary import Adversary
from advbox.attacks.lbfgs import LBFGS
from advbox.models.paddle import PaddleModel
from tutorials.mnist_model import mnist_cnn_model
def main():
"""
Advbox demo which demonstrate how to use advbox.
"""
TOTAL_NUM = 500
IMG_NAME = 'img'
LABEL_NAME = 'label'
img = fluid.layers.data(name=IMG_NAME, shape=[1, 28, 28], dtype='float32')
# gradient should flow
img.stop_gradient = False
label = fluid.layers.data(name=LABEL_NAME, shape=[1], dtype='int64')
logits = mnist_cnn_model(img)
cost = fluid.layers.cross_entropy(input=logits, label=label)
avg_cost = fluid.layers.mean(x=cost)
# use CPU
place = fluid.CPUPlace()
# use GPU
# place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
BATCH_SIZE = 1
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=128 * 10),
batch_size=BATCH_SIZE)
test_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.test(), buf_size=128 * 10),
batch_size=BATCH_SIZE)
fluid.io.load_params(
exe, "./mnist/", main_program=fluid.default_main_program())
# advbox demo
m = PaddleModel(
fluid.default_main_program(),
IMG_NAME,
LABEL_NAME,
logits.name,
avg_cost.name, (-1, 1),
channel_axis=1)
attack = LBFGS(m)
attack_config = {"epsilon": 0.001, }
# use train data to generate adversarial examples
total_count = 0
fooling_count = 0
for data in train_reader():
total_count += 1
adversary = Adversary(data[0][0], data[0][1])
# LBFGS targeted attack
tlabel = 0
adversary.set_target(is_targeted_attack=True, target_label=tlabel)
adversary = attack(adversary, **attack_config)
if adversary.is_successful():
fooling_count += 1
print(
'attack success, original_label=%d, adversarial_label=%d, count=%d'
% (data[0][1], adversary.adversarial_label, total_count))
# plt.imshow(adversary.target, cmap='Greys_r')
# plt.show()
# np.save('adv_img', adversary.target)
else:
print('attack failed, original_label=%d, count=%d' %
(data[0][1], total_count))
if total_count >= TOTAL_NUM:
print(
"[TRAIN_DATASET]: fooling_count=%d, total_count=%d, fooling_rate=%f"
% (fooling_count, total_count,
float(fooling_count) / total_count))
break
# use test data to generate adversarial examples
total_count = 0
fooling_count = 0
for data in test_reader():
total_count += 1
adversary = Adversary(data[0][0], data[0][1])
# LBFGS targeted attack
tlabel = 0
adversary.set_target(is_targeted_attack=True, target_label=tlabel)
adversary = attack(adversary, **attack_config)
if adversary.is_successful():
fooling_count += 1
print(
'attack success, original_label=%d, adversarial_label=%d, count=%d'
% (data[0][1], adversary.adversarial_label, total_count))
# plt.imshow(adversary.target, cmap='Greys_r')
# plt.show()
# np.save('adv_img', adversary.target)
else:
print('attack failed, original_label=%d, count=%d' %
(data[0][1], total_count))
if total_count >= TOTAL_NUM:
print(
"[TEST_DATASET]: fooling_count=%d, total_count=%d, fooling_rate=%f"
% (fooling_count, total_count,
float(fooling_count) / total_count))
break
print("lbfgs attack done")
if __name__ == '__main__':
main()
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