You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
95 lines
2.4 KiB
95 lines
2.4 KiB
"""
|
|
FGSM demos on mnist using advbox tool.
|
|
"""
|
|
import paddle.v2 as paddle
|
|
import paddle.v2.fluid as fluid
|
|
import matplotlib.pyplot as plt
|
|
import numpy as np
|
|
|
|
from advbox.models.paddle import PaddleModel
|
|
from advbox.attacks.gradientsign import GradientSignAttack
|
|
|
|
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 = GradientSignAttack(m)
|
|
for data in train_reader():
|
|
# fgsm attack
|
|
adv_img = att(data)
|
|
plt.imshow(n[0][0], cmap='Greys_r')
|
|
plt.show()
|
|
#np.save('adv_img', adv_img)
|
|
break
|
|
|
|
if __name__ == '__main__':
|
|
main()
|