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Title: GitHub - bawed/AdvBox: AdvBox是一款由百度安全实验室研发，在百度大范围使用的AI模型安全工具箱，目前原生支持PaddlePaddle、PyTorch、Caffe2、MxNet、Keras以及TensorFlow平台，方便广大开发者和安全工程师可以使用自己熟悉的框架。 AdvBox同时支持GraphPipe,屏蔽了底层使用的深度学习平台，用户可以通过几个命令就可以对PaddlePaddle、PyTorch、Caffe2、MxNet、CNTK、ScikitLearn以及TensorFlow平台生成的模型文件进行黑盒攻击。

Open Graph Title: GitHub - bawed/AdvBox: AdvBox是一款由百度安全实验室研发，在百度大范围使用的AI模型安全工具箱，目前原生支持PaddlePaddle、PyTorch、Caffe2、MxNet、Keras以及TensorFlow平台，方便广大开发者和安全工程师可以使用自己熟悉的框架。 AdvBox同时支持GraphPipe,屏蔽了底层使用的深度学习平台，用户可以通过几个命令就可以对PaddlePaddle、PyTorch、Caffe2、MxNet、CNTK、ScikitLearn以及TensorFlow平台生成的模型文件进行黑盒攻击。

X Title: GitHub - bawed/AdvBox: AdvBox是一款由百度安全实验室研发，在百度大范围使用的AI模型安全工具箱，目前原生支持PaddlePaddle、PyTorch、Caffe2、MxNet、Keras以及TensorFlow平台，方便广大开发者和安全工程师可以使用自己熟悉的框架。 AdvBox同时支持GraphPipe,屏蔽了底层使用的深度学习平台，用户可以通过几个命令就可以对PaddlePaddle、PyTorch、Caffe2、MxNet、CNTK、ScikitLearn以及TensorFlow平台生成的模型文件进行黑盒攻击。

Description: AdvBox是一款由百度安全实验室研发，在百度大范围使用的AI模型安全工具箱，目前原生支持PaddlePaddle、PyTorch、Caffe2、MxNet、Keras以及TensorFlow平台，方便广大开发者和安全工程师可以使用自己熟悉的框架。 AdvBox同时支持GraphPipe,屏蔽了底层使用的深度学习平台，用户可以通过几个命令就可以对PaddlePaddle、PyTorch、Caffe2、MxNet、CNTK、ScikitLearn以及TensorFlow平台生成的模型文件进行黑盒攻击。 - bawed/AdvBox

Open Graph Description: AdvBox是一款由百度安全实验室研发，在百度大范围使用的AI模型安全工具箱，目前原生支持PaddlePaddle、PyTorch、Caffe2、MxNet、Keras以及TensorFlow平台，方便广大开发者和安全工程师可以使用自己熟悉的框架。 AdvBox同时支持GraphPipe,屏蔽了底层使用的深度学习平台，用户可以通过几个命令就可以对PaddlePaddle、PyTorch、C...

X Description: AdvBox是一款由百度安全实验室研发，在百度大范围使用的AI模型安全工具箱，目前原生支持PaddlePaddle、PyTorch、Caffe2、MxNet、Keras以及TensorFlow平台，方便广大开发者和安全工程师可以使用自己熟悉的框架。 AdvBox同时支持GraphPipe,屏蔽了底层使用的深度学习平台，用户可以通过几个命令就可以对PaddlePaddle、PyTorch、C...

Opengraph URL: https://github.com/bawed/AdvBox

X: @github

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advbox	https://github.com/bawed/AdvBox/tree/master/advbox
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advbox_tools.py	https://github.com/bawed/AdvBox/blob/master/advbox_tools.py
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README	https://github.com/bawed/AdvBox
Apache-2.0 license	https://github.com/bawed/AdvBox
	https://github.com/bawed/AdvBox#advbox
	https://github.com/bawed/AdvBox/blob/master/pic/logo.png
GraphPipe	https://oracle.github.io/graphpipe
	https://github.com/bawed/AdvBox/blob/master/pic/GraphPipe.png
	https://github.com/bawed/AdvBox#白盒攻击算法
	https://github.com/bawed/AdvBox#黑盒攻击算法
	https://github.com/bawed/AdvBox#防护算法
	https://github.com/bawed/AdvBox/blob/master/pic/%E9%92%88%E5%AF%B9%E5%9B%BE%E5%83%8F%E5%88%86%E7%B1%BB%E6%A8%A1%E5%9E%8B%E7%9A%84%E5%AF%B9%E6%8A%97%E6%A0%B7%E6%9C%AC.png
0.3	https://github.com/bawed/AdvBox/blob/master/doc/RELEASE.cn.md
	https://github.com/bawed/AdvBox#安装
	https://github.com/bawed/AdvBox#部署advbox代码
	https://github.com/bawed/AdvBox#初始化软件环境
	https://github.com/bawed/AdvBox#文档
	https://github.com/bawed/AdvBox#零编码黑盒攻击示例
预训练模型	https://github.com/tensorflow/models
	https://github.com/bawed/AdvBox/blob/master/demo_advbox.png
ONNX	https://onnx.ai/
论文	https://github.com/bawed/AdvBox/blob/master/paper/blackBoxAttack/Simple%20Black-Box%20Adversarial%20Perturbations%20for%20Deep%20Networks.pdf
	https://github.com/bawed/AdvBox#keras示例
tutorials/keras_demo.py	https://github.com/bawed/AdvBox/blob/master/tutorials/keras_demo.py
	https://github.com/bawed/AdvBox/blob/master/pic/keras-demo.png
	https://github.com/bawed/AdvBox#paddlepaddle示例
PaddlePaddle示例	https://github.com/bawed/AdvBox/blob/master/paddle.md
	https://github.com/bawed/AdvBox#学习教程
学习教程	https://github.com/bawed/AdvBox/blob/master/tutorials/README.md
示例1：白盒攻击基于MNIST数据集的CNN模型	https://github.com/bawed/AdvBox/blob/master/tutorials/README.md
示例2：白盒攻击基于CIFAR10数据集的ResNet模型	https://github.com/bawed/AdvBox/blob/master/tutorials/README.md
示例3：白盒攻击caffe下基于MNIST数据集的LeNet模型	https://github.com/bawed/AdvBox/blob/master/tutorials/README.md
示例4：黑盒攻击基于MNIST数据集的CNN模型	https://github.com/bawed/AdvBox/blob/master/tutorials/README.md
示例5：使用FeatureFqueezing加固基于MNIST数据集的CNN模型	https://github.com/bawed/AdvBox/blob/master/tutorials/README.md
示例6：使用GaussianAugmentation加固基于MNIST数据集的CNN模型	https://github.com/bawed/AdvBox/blob/master/tutorials/README.md
示例7：白盒攻击PyTorch下基于MNIST数据集的CNN模型	https://github.com/bawed/AdvBox/blob/master/tutorials/README.md
示例8：白盒攻击PyTorch下基于IMAGENET数据集的AlexNet模型	https://github.com/bawed/AdvBox/blob/master/tutorials/README.md
示例9：白盒攻击MxNet下基于IMAGENET数据集的AlexNet模型	https://github.com/bawed/AdvBox/blob/master/tutorials/README.md
示例10：黑盒攻击graphpipe下的基于tensorflow的squeezenet模型	https://github.com/bawed/AdvBox/blob/master/tutorials/README.md
示例11：黑盒攻击graphpipe下的基于onnx的squeezenet模型	https://github.com/bawed/AdvBox/blob/master/tutorials/README.md
	https://github.com/bawed/AdvBox#典型应用
应用1：白盒攻击人脸识别系统	https://github.com/bawed/AdvBox/blob/master/applications/face_recognition_attack/README.md
	https://github.com/bawed/AdvBox#问题反馈
issues	https://github.com/baidu/AdvBox/issues
	https://github.com/bawed/AdvBox#许可
Apache License 2.0	https://github.com/baidu/AdvBox/blob/master/LICENSE
	https://github.com/bawed/AdvBox#作者
	https://github.com/bawed/AdvBox#参考文献
http://www.paddlepaddle.org/docs/develop/documentation/en/build_and_install/pip_install_en.html	http://www.paddlepaddle.org/docs/develop/documentation/en/build_and_install/pip_install_en.html
http://paddlepaddle.org/docs/0.14.0/documentation/fluid/zh/new_docs/beginners_guide/install/install_doc.html	http://paddlepaddle.org/docs/0.14.0/documentation/fluid/zh/new_docs/beginners_guide/install/install_doc.html
https://github.com/PaddlePaddle/models/tree/develop/fluid/adversarial	https://github.com/PaddlePaddle/models/tree/develop/fluid/adversarial
Intriguing properties of neural networks	https://arxiv.org/abs/1312.6199
Explaining and Harnessing Adversarial Examples	https://arxiv.org/abs/1412.6572
Adversarial Examples In The Physical World	https://arxiv.org/pdf/1607.02533v3.pdf
Boosting Adversarial Attacks with Momentum	https://arxiv.org/abs/1710.06081
The Limitations of Deep Learning in Adversarial Settings	https://arxiv.org/abs/1511.07528
DeepFool: a simple and accurate method to fool deep neural networks	https://arxiv.org/abs/1511.04599
Foolbox: A Python toolbox to benchmark the robustness of machine learning models	https://arxiv.org/abs/1707.04131
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
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