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Beyond Adversarial Learning — 深度学习系统的数据流攻击

1.

Beyond Adversarial Learning —
深度学习系统的数据流攻击
李康
乔治亚大学
Collaborators: Qixue Xiao, Yufei Chen, Deyue Zhang,
and many others from Qihoo 360
360 智能安全研究院

2.

⾃自我介绍
360 智能安全研究团队负责⼈
Disekt、SecDawgs CTF 战队创始⼈
xCTF 和蓝莲花战队启蒙导师
2016 年 DARPA Cyber Grand Challenge 决赛获奖者
disekt
CTF Team
360

8.

⼈工智能深度学习的“成功应用”
⼈脸识别监测迟到早退学⽣跑客换课分析学⽣抬头率关注度
http://www.caigou.com.cn/news/2017102759.shtml

⼈工智能深度学习的“成功应用”
利⽤机器学习刷帖刷评价
“Leverage deep learning language models (Recurrent Neural
Networks or RNNs) to automate the generation of fake online
reviews for products and services"
https://www.schneier.com/blog/archives/2017/09/
new_techniques_.html

10.

⼈工智能深度学习的“成功应用”
利用机器学习破解图片验证码
Defeating Captcha with Learning
https://deepmlblog.wordpress.com/2016/01/03/how-to-breaka-captcha-system/
I’m not a human: Breaking the Google reCAPTCHA
https://www.blackhat.com/docs/asia-16/materials/asia-16Sivakorn-Im-Not-a-Human

11.

AI 手写笔迹模仿
Deep Learning-based
Handwriting Generation Machine
— LiChuang@CFCA
https://2018.qconbeijing.com/presentation/407

12.

对抗⼈工智能的需求

13.

如何攻击⼀个⼈工智能人脸/猫脸识别系统
https://www.pyimagesearch.com/2016/06/20/detecting-cats-in-images-with-opencv/

14.

如何攻击⼀个⼈工智能人脸/猫脸识别系统
https://www.pyimagesearch.com/2016/06/20/detecting-cats-in-images-with-opencv/
https://www.wired.com/story/tried-to-beat-face-id-and-failed-so-far/

15.

对抗性机器学习 Adversarial Machine Learning
https://medium.com/self-driving-cars/adversarial-tra c-signs-fd16b7171906

16.

Beyond Adversarial Machine Learning
对抗样本之外的攻击方法

17.

通过利用软件洞来攻击⼈工智能系统
奇虎360 安全团队在2017年夏天发现的⼈工智能框架漏洞 CVEs

18.

深度学习应用的分层实现
Program Logic
Model
深度学习应用层
Torch theano
TensorFlow
Caffe
深度学习框架层
框架依赖层
GNU LibC
NumPy
Data

19.

20.

基于框架的深度学习应用 (CAFFE 样 )
./build/examples/cpp_classi cation/classi cation.bin \
models/bvlc_reference_caffenet/deploy.prototxt \
models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \
data/ilsvrc12/imagenet_mean.binaryproto \
data/ilsvrc12/synset_words.txt \
examples/images/cat.jpg
Output:
---------- Prediction for examples/images/cat.jpg ---------0.3134 - "n02123045 tabby, tabby cat"
0.2380 - "n02123159 tiger cat"
0.1235 - "n02124075 Egyptian cat"
0.1003 - "n02119022 red fox, Vulpes vulpes"
0.0715 - "n02127052 lynx, catamount"
https://github.com/BVLC/caffe/tree/master/examples/cpp_classi cation

21.

22.

# bullog [Original Image]
./classi cation.bin models/bvlc_reference_ca enet/deploy.prototxt
models/bvlc_reference_ca enet/bvlc_reference_ca enet.ca emodel
data/ilsvrc12/imagenet_mean.binaryproto
data/ilsvrc12/synset_words.txt
./poc_samples/bulldog
---------- Prediction for ./poc_samples/bulldog —————
0.5111 - "n02108915 French bulldog"
# Threat Example 2 -- Evasion attack
# bulldog_sh [ cause classi cation to misclassify to an arbitrary ca
# [Here the classi cation produced a class "Flying Pig" made up b
./classi cation.bin models/bvlc_reference_ca enet/deploy.prototx
models/bvlc_reference_ca enet/bvlc_reference_ca enet.ca emod
data/ilsvrc12/imagenet_mean.binaryproto
data/ilsvrc12/synset_words.txt
./poc_samples/bulldog_fp
---------- Prediction for ./poc_samples/bulldog_fp ---------0.98 - "n03770679 yingpig”
# Threat Example 3 -- Exploitation Attack
# Threat Example 1 -- DoS attack
# bulldog_crash [ cause classi cation binary to crash]
./classi cation.bin models/bvlc_reference_ca enet/deploy.prototxt
models/bvlc_reference_ca enet/bvlc_reference_ca enet.ca emodel
data/ilsvrc12/imagenet_mean.binaryproto
data/ilsvrc12/synset_words.txt
./poc_samples/bulldog_crash
---------- Prediction for ./poc_samples/bulldog_crash ---------Segmentation fault (core dumped)
# bulldog_sh [ cause classi cation to generate a local shell]
./classi cation.bin models/bvlc_reference_ca enet/deploy.prototx
models/bvlc_reference_ca enet/bvlc_reference_ca enet.ca emod
data/ilsvrc12/imagenet_mean.binaryproto
data/ilsvrc12/synset_words.txt
./poc_samples/bulldog_sh
---------- Prediction for ./poc_samples/bulldog_sh ---------$ uname -a
Linux ctf-box 4.4.0-31-generic #50~14.04.1-Ubuntu SMP
Wed Jul 13 01:07:32 UTC 2016 x86_64 x86_64 x86_64 GNU/Linux
$ exit

23.

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26.

27.

深度学习系统的数据流攻击

28.

看⼀个经典的⼈工智能应用
https://www.cs.toronto.edu/~ranzato/publications/taigman_cvpr14.pdf

29.

数据流维度攻击实例
AI 人脸识别会认成谁

30.

数据流维度攻击实例
为什么会这样

31.

深度学习系统的一个隐含假设
MNIST
Deep Learning Model Input Requirement
(pixel x pixel)
MNIST
http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf
ImageNet
28x28
ImageNet
AlexNet
227x227
GoogleNet,VGG
224x224
ResNet
224x224
https://github.com/BVLC/caffe/tree/master/examples/cpp_classi cation
NVIDIA PX DAVE-2
NVIDIA
DAVE-2Self-Driving
https://images.nvidia.com/content/tegra/automotive/images/2016/solutions/pdf/end-to-end-dl-using-px.pdf
200x66

32.

如果输⼊的维度与模型的维度不匹配会怎样

33.

NVIDIA ⾃动驾驶系统视觉模型和推荐的输入系统
NVIDIA Sample Self-Driving
Models
NVIDIAPX2
DAVE-2Self-Driving
200x66
https://images.nvidia.com/content/tegra/automotive/images/2016/solutions/pdf/end-to-end-dl-using-px.pdf
NVIDIA Recommended Ecosystem
Camera Vendors
Filr
A310
A615
320x240
640x480
Leopard
LI-AR0231
1920x1208
SF3326-100
SEKONIX
1920x1208
https://developer.nvidia.com/drive/ecosystem

34.

深度学习系统中的数据流处
深度学习图⽚识别应用
猫
维度缩减
深度学习模型
类别信息

35.

维度变换在深度学习系统中的使用
1 def read_tensor_from_image_file(file_name, input_height=299, input_width=299,
TensorFlow
Example
2
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4
5
6
7
8
9
10
11
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13
14
15
16
17
18
19
20
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22
23
24
input_mean=0, input_std=255):
input_name = "file_reader"
output_name = "normalized"
file_reader = tf.read_file(file_name, input_name)
if file_name.endswith(".png"):
image_reader = tf.image.decode_png(file_reader, channels = 3,
name='png_reader')
elif file_name.endswith(".gif"):
image_reader = tf.squeeze(tf.image.decode_gif(file_reader,
name='gif_reader'))
elif file_name.endswith(".bmp"):
image_reader = tf.image.decode_bmp(file_reader, name='bmp_reader')
else:
image_reader = tf.image.decode_jpeg(file_reader, channels = 3,
name='jpeg_reader')
float_caster = tf.cast(image_reader, tf.float32)
dims_expander = tf.expand_dims(float_caster, 0);
resized = tf.image.resize_bilinear(dims_expander, [input_height, input_width])
normalized = tf.divide(tf.subtract(resized, [input_mean]), [input_std])
sess = tf.Session()
result = sess.run(normalized)
return result

36.

维度变换在深度学习系统中的使用
DeepDetect Example
1
2
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4
5
6
7
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15
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int read_file(const std::string &fname)
{
cv::Mat img = cv::imread(fname,_bw ? CV_LOAD_IMAGE_GRAYSCALE :
CV_LOAD_IMAGE_COLOR);
if (img.empty())
{
LOG(ERROR) << "empty image";
return -1;
}
_imgs_size.push_back(std::pair<int,int>(img.rows,img.cols));
cv::Size size(_width,_height);
cv::Mat rimg;
cv::resize(img,rimg,size,0,0,CV_INTER_CUBIC);
_imgs.push_back(rimg);
return 0;
}

37.

常用维度变换算法

38.

维度变换和差值算法
Scaling is supposed to preserve the visual features of an image
and thus does not change its semantic meaning.

39.

维度变换和差值算法
差值算法猜测新的点上像素的值

40.

常用维度变换和差值算法
双线性差值
Value at (x,y) =
Sum of the value at each spot multiplied
by the area of the rectangle divided by the
total area of all four rectangles
https://en.wikipedia.org/wiki/Bilinear_interpolation
f(i+u,j+v) = (1-u)(1-v)f(i,j) + (1-u)vf(i,j+1) + u(1-v)f(i+1,j) + uvf(i+1,j+1)