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35210a04
编写于
1月 06, 2018
作者:
G
gx_wind
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adversarial/advbox/tutorials/tutorial_model.py
adversarial/advbox/tutorials/tutorial_model.py
+0
-32
adversarial/mnist_fgsm.py
adversarial/mnist_fgsm.py
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adversarial/advbox/tutorials/tutorial_model.py
已删除
100644 → 0
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2b3ba40e
################################################################################
#
# Copyright (c) 2017 Baidu.com, Inc. All Rights Reserved
#
################################################################################
"""
A pure Paddlepaddle implementation of a neural network.
"""
from
__future__
import
absolute_import
from
__future__
import
division
from
__future__
import
print_function
from
__future__
import
unicode_literals
import
paddle.v2
as
paddle
import
paddle.v2.fluid
as
fluid
from
advbox
import
Model
def
main
():
"""
example main function
"""
model_dir
=
"./mnist_model"
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
program
,
feed_var_names
,
fetch_vars
=
fluid
.
io
.
load_inferfence_model
(
model_dir
,
exe
)
print
(
program
)
if
__name__
==
"__main__"
:
main
()
adversarial/mnist_fgsm.py
已删除
100644 → 0
浏览文件 @
2b3ba40e
"""
This attack was originally implemented by Goodfellow et al. (2015) with the
infinity norm (and is known as the "Fast Gradient Sign Method"). This is therefore called
the Fast Gradient Method.
Paper link: https://arxiv.org/abs/1412.6572
"""
import
numpy
as
np
import
paddle.v2
as
paddle
import
paddle.v2.fluid
as
fluid
BATCH_SIZE
=
50
PASS_NUM
=
1
EPS
=
0.3
CLIP_MIN
=
-
1
CLIP_MAX
=
1
PASS_NUM
=
1
def
mnist_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
():
"""
Generate adverserial example and evaluate accuracy on mnist using FGSM
"""
images
=
fluid
.
layers
.
data
(
name
=
'pixel'
,
shape
=
[
1
,
28
,
28
],
dtype
=
'float32'
)
# The gradient should flow
images
.
stop_gradient
=
False
label
=
fluid
.
layers
.
data
(
name
=
'label'
,
shape
=
[
1
],
dtype
=
'int64'
)
predict
=
mnist_cnn_model
(
images
)
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
predict
,
label
=
label
)
avg_cost
=
fluid
.
layers
.
mean
(
x
=
cost
)
# Cal gradient of input
params_grads
=
fluid
.
backward
.
append_backward_ops
(
avg_cost
,
parameter_list
=
[
'pixel'
])
# data batch
train_reader
=
paddle
.
batch
(
paddle
.
reader
.
shuffle
(
paddle
.
dataset
.
mnist
.
train
(),
buf_size
=
500
),
batch_size
=
BATCH_SIZE
)
accuracy
=
fluid
.
evaluator
.
Accuracy
(
input
=
predict
,
label
=
label
)
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
accuracy
.
reset
(
exe
)
#exe.run(fluid.default_startup_program())
feeder
=
fluid
.
DataFeeder
(
feed_list
=
[
images
,
label
],
place
=
place
)
for
pass_id
in
range
(
PASS_NUM
):
fluid
.
io
.
load_params
(
exe
,
"./mnist/"
,
main_program
=
fluid
.
default_main_program
())
for
data
in
train_reader
():
# cal gradient and eval accuracy
ps
,
acc
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
feeder
.
feed
(
data
),
fetch_list
=
[
params_grads
[
0
][
1
]]
+
accuracy
.
metrics
)
labels
=
[]
for
idx
,
_
in
enumerate
(
data
):
labels
.
append
(
data
[
idx
][
1
])
# generate adversarial example
batch_num
=
ps
.
shape
[
0
]
new_data
=
[]
for
i
in
range
(
batch_num
):
adv_img
=
np
.
reshape
(
data
[
0
][
0
],
(
1
,
28
,
28
))
+
EPS
*
np
.
sign
(
ps
[
i
])
adv_img
=
np
.
clip
(
adv_img
,
CLIP_MIN
,
CLIP_MAX
)
#adv_imgs.append(adv_img)
t
=
(
adv_img
,
data
[
0
][
1
])
new_data
.
append
(
t
)
# predict label
predict_label
,
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
feeder
.
feed
(
new_data
),
fetch_list
=
[
predict
])
adv_labels
=
np
.
argmax
(
predict_label
,
axis
=
1
)
batch_accuracy
=
np
.
mean
(
np
.
equal
(
labels
,
adv_labels
))
print
"pass_id="
+
str
(
pass_id
)
+
" acc="
+
str
(
acc
)
+
" adv_acc="
+
str
(
batch_accuracy
)
if
__name__
==
"__main__"
:
main
()
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