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Title: models/PaddleCV/adversarial at develop · 17MachineLearning/models · GitHub

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Description: Pre-trained and Reproduced Deep Learning Models （『飞桨』官方模型库，包含多种学术前沿和工业场景验证的深度学习模型） - models/PaddleCV/adversarial at develop · 17MachineLearning/models

Open Graph Description: Pre-trained and Reproduced Deep Learning Models （『飞桨』官方模型库，包含多种学术前沿和工业场景验证的深度学习模型） - 17MachineLearning/models

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please update your installationhttp://www.paddlepaddle.org/docs/develop/documentation/en/build_and_install/pip_install_en.html
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PaddlePaddlehttps://github.com/PaddlePaddle/Paddle
https://github.com/17MachineLearning/models/tree/develop/PaddleCV/adversarial#overview
Szegedy et al.https://arxiv.org/abs/1312.6199
Foolboxhttps://github.com/bethgelab/foolbox
CleverHanshttps://github.com/tensorflow/cleverhans
https://github.com/17MachineLearning/models/tree/develop/PaddleCV/adversarial#usage
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https://github.com/17MachineLearning/models/tree/develop/PaddleCV/adversarial#structure
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L-BFGShttps://arxiv.org/abs/1312.6199
FGSMhttps://arxiv.org/abs/1412.6572
BIMhttps://arxiv.org/abs/1607.02533
ILCMhttps://arxiv.org/abs/1607.02533
MI-FGSMhttps://arxiv.org/pdf/1710.06081.pdf
JSMAhttps://arxiv.org/pdf/1511.07528
DeepFoolhttps://arxiv.org/abs/1511.04599
https://github.com/17MachineLearning/models/tree/develop/PaddleCV/adversarial#testing
https://github.com/17MachineLearning/models/tree/develop/PaddleCV/adversarial#references
Intriguing properties of neural networkshttps://arxiv.org/abs/1312.6199
Explaining and Harnessing Adversarial Exampleshttps://arxiv.org/abs/1412.6572
Adversarial Examples In The Physical Worldhttps://arxiv.org/pdf/1607.02533v3.pdf
Boosting Adversarial Attacks with Momentumhttps://arxiv.org/abs/1710.06081
The Limitations of Deep Learning in Adversarial Settingshttps://arxiv.org/abs/1511.07528
DeepFool: a simple and accurate method to fool deep neural networkshttps://arxiv.org/abs/1511.04599
Foolbox: A Python toolbox to benchmark the robustness of machine learning modelshttps://arxiv.org/abs/1707.04131
CleverHans: An adversarial example library for constructing attacks, building defenses, and benchmarking bothhttps://github.com/tensorflow/cleverhans#setting-up-cleverhans
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Surveyhttps://arxiv.org/abs/1801.00553
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