Adversarial Attacks and Defences Competition

To accelerate research on adversarial examples and robustness of machine learning classifiers, Google Brain organized a NIPS 2017 competition that encouraged researchers to develop new methods to generate adversarial examples as well as to develop new ways to defend against them. In this chapter, we describe the structure and organization of the competition and the solutions developed by several of the top-placing teams.

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[17]  François Chollet,et al.  Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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[19]  Jian Sun,et al.  Identity Mappings in Deep Residual Networks , 2016, ECCV.

[20]  Yanjun Qi,et al.  Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks , 2017, NDSS.

[21]  Ananthram Swami,et al.  Practical Black-Box Attacks against Machine Learning , 2016, AsiaCCS.

[22]  Dan Boneh,et al.  Ensemble Adversarial Training: Attacks and Defenses , 2017, ICLR.

[23]  Alan L. Yuille,et al.  Mitigating adversarial effects through randomization , 2017, ICLR.

[24]  Jun Zhu,et al.  Boosting Adversarial Attacks with Momentum , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[25]  Dale Schuurmans,et al.  Learning with a Strong Adversary , 2015, ArXiv.

[26]  Ian S. Fischer,et al.  Adversarial Transformation Networks: Learning to Generate Adversarial Examples , 2017, ArXiv.

[27]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[28]  Dawn Xiaodong Song,et al.  Adversarial Example Defenses: Ensembles of Weak Defenses are not Strong , 2017, ArXiv.

[29]  Martin Wattenberg,et al.  Adversarial Spheres , 2018, ICLR.

[30]  Dawn Xiaodong Song,et al.  Delving into Transferable Adversarial Examples and Black-box Attacks , 2016, ICLR.

[31]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[32]  Matthias Bethge,et al.  Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models , 2017, ICLR.

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[35]  Patrick D. McDaniel,et al.  Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples , 2016, ArXiv.

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[37]  Li Chen,et al.  Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression , 2017, ArXiv.

[38]  Samy Bengio,et al.  Adversarial examples in the physical world , 2016, ICLR.

[39]  Geoffrey E. Hinton,et al.  Matrix capsules with EM routing , 2018, ICLR.

[40]  Samy Bengio,et al.  Adversarial Machine Learning at Scale , 2016, ICLR.

[41]  David Wagner,et al.  Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods , 2017, AISec@CCS.

[42]  Joan Bruna,et al.  Intriguing properties of neural networks , 2013, ICLR.

[43]  Xiaolin Hu,et al.  Defense Against Adversarial Attacks Using High-Level Representation Guided Denoiser , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.