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https://github.com/yenchenlin/awesome-adversarial-machine-learning

A curated list of awesome adversarial machine learning resources
https://github.com/yenchenlin/awesome-adversarial-machine-learning

List: awesome-adversarial-machine-learning

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A curated list of awesome adversarial machine learning resources

Lists

README 

        
# :warning: Deprecated

I no longer include up-to-date papers, but the list is still a good reference for starters.



# Awesome Adversarial Machine Learning: [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)

A curated list of awesome adversarial machine learning resources, inspired by [awesome-computer-vision](https://github.com/jbhuang0604/awesome-computer-vision).



## Table of Contents



 - [Blogs](#blogs)

 - [Papers](#papers)

 - [Talks](#talks)



## Blogs

 * [Breaking Linear Classifiers on ImageNet](http://karpathy.github.io/2015/03/30/breaking-convnets/), A. Karpathy et al.

 * [Breaking things is easy](http://www.cleverhans.io/security/privacy/ml/2016/12/16/breaking-things-is-easy.html), N. Papernot & I. Goodfellow et al.

 * [Attacking Machine Learning with Adversarial Examples](https://blog.openai.com/adversarial-example-research/), N. Papernot, I. Goodfellow, S. Huang, Y. Duan, P. Abbeel, J. Clark.

 * [Robust Adversarial Examples](https://blog.openai.com/robust-adversarial-inputs/), Anish Athalye.

 * [A Brief Introduction to Adversarial Examples](http://people.csail.mit.edu/madry/lab/blog/adversarial/2018/07/06/adversarial_intro/), A. Madry et al.

 * [Training Robust Classifiers (Part 1)](http://people.csail.mit.edu/madry/lab/blog/adversarial/2018/07/11/robust_optimization_part1/), A. Madry et al.

 * [Adversarial Machine Learning Reading List](https://nicholas.carlini.com/writing/2018/adversarial-machine-learning-reading-list.html), N. Carlini

 * [Recommendations for Evaluating Adversarial Example Defenses](https://nicholas.carlini.com/writing/2018/evaluating-adversarial-example-defenses.html), N. Carlini



 

## Papers

### General

 * [Intriguing properties of neural networks](https://arxiv.org/abs/1312.6199), C. Szegedy et al., arxiv 2014

 * [Explaining and Harnessing Adversarial Examples](https://arxiv.org/abs/1412.6572), I. Goodfellow et al., ICLR 2015

 * [Motivating the Rules of the Game for Adversarial Example Research](https://arxiv.org/abs/1807.06732), J. Gilmer et al., arxiv 2018

 * [Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning](https://arxiv.org/abs/1712.03141), B. Biggio, Pattern Recognition 2018



### Attack

**Image Classification**



 * [DeepFool: a simple and accurate method to fool deep neural networks](https://arxiv.org/abs/1511.04599), S. Moosavi-Dezfooli et al., CVPR 2016

 * [The Limitations of Deep Learning in Adversarial Settings](https://arxiv.org/abs/1511.07528), N. Papernot et al., ESSP 2016

 * [Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples](https://arxiv.org/abs/1605.07277), N. Papernot et al., arxiv 2016

 * [Adversarial Examples In The Physical World](https://arxiv.org/pdf/1607.02533v3.pdf), A. Kurakin et al., ICLR workshop 2017 

 * [Delving into Transferable Adversarial Examples and Black-box Attacks](https://arxiv.org/abs/1611.02770) Liu et al., ICLR 2017

 * [Towards Evaluating the Robustness of Neural Networks](https://arxiv.org/abs/1608.04644) N. Carlini et al., SSP 2017

 * [Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples](https://arxiv.org/abs/1602.02697), N. Papernot et al., Asia CCS 2017

 * [Privacy and machine learning: two unexpected allies?](http://www.cleverhans.io/privacy/2018/04/29/privacy-and-machine-learning.html), I. Goodfellow et al.



**Reinforcement Learning**



* [Adversarial attacks on neural network policies](https://arxiv.org/abs/1702.02284), S. Huang et al, ICLR workshop 2017

* [Tactics of Adversarial Attacks on Deep Reinforcement Learning Agents](https://arxiv.org/abs/1703.06748), Y. Lin et al, IJCAI 2017

* [Delving into adversarial attacks on deep policies](https://arxiv.org/abs/1705.06452), J. Kos et al., ICLR workshop 2017



**Segmentation & Object Detection**



* [Adversarial Examples for Semantic Segmentation and Object Detection](https://arxiv.org/pdf/1703.08603.pdf), C. Xie, ICCV 2017



**VAE-GAN**



* [Adversarial examples for generative models](https://arxiv.org/abs/1702.06832), J. Kos et al. arxiv 2017



**Speech Recognition**



* [Audio Adversarial Examples: Targeted Attacks on Speech-to-Text](https://arxiv.org/abs/1801.01944), N. Carlini et al., arxiv 2018



**Questiona Answering System**



* [Adversarial Examples for Evaluating Reading Comprehension Systems](https://arxiv.org/abs/1707.07328), R. Jia et al., EMNLP 2017



### Defence



**Adversarial Training**



* [Adversarial Machine Learning At Scale](https://arxiv.org/pdf/1611.01236.pdf), A. Kurakin et al., ICLR 2017

* [Ensemble Adversarial Training: Attacks and Defenses](https://arxiv.org/abs/1705.07204), F. Tramèr et al., arxiv 2017



**Defensive Distillation**

* [Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks](https://arxiv.org/pdf/1511.04508.pdf), N. Papernot et al., SSP 2016

* [Extending Defensive Distillation](https://arxiv.org/abs/1705.05264), N. Papernot et al., arxiv 2017



**Generative Model**

* [PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples](https://arxiv.org/abs/1710.10766), Y. Song et al., ICLR 2018

* [Detecting Adversarial Attacks on Neural Network Policies with Visual Foresight](https://arxiv.org/abs/1710.00814), Y. Lin et al., NIPS workshop 2017



### Regularization

 * [Distributional Smoothing with Virtual Adversarial Training](https://arxiv.org/abs/1507.00677), T. Miyato et al., ICLR 2016

 * [Adversarial Training Methods for Semi-Supervised Text Classification](https://arxiv.org/abs/1605.07725), T. Miyato et al., ICLR 2017



### Others

 * [Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images](https://arxiv.org/abs/1412.1897), A. Nguyen et al., CVPR 2015

 

## Talks

 * [Do Statistical Models Understand the World?](https://www.youtube.com/watch?v=Pq4A2mPCB0Y), I. Goodfellow, 2015

 * [Classifiers under Attack](https://www.usenix.org/conference/enigma2017/conference-program/presentation/evans), David Evans, 2017

  * [Adversarial Examples in Machine Learning](https://www.usenix.org/conference/enigma2017/conference-program/presentation/papernot), Nicolas Papernot, 2017

  * [Poisoning Behavioral Malware Clustering](http://pralab.diee.unica.it/en/node/1121), Biggio. B, Rieck. K, Ariu. D, Wressnegger. C, Corona. I. Giacinto, G. Roli. F, 2014

  * [Is Data Clustering in Adversarial Settings Secure?](http://pralab.diee.unica.it/en/node/955), BBiggio. B, Pillai. I, Rota Bulò. S, Ariu. D, Pelillo. M, Roli. F, 2015

  * [Poisoning complete-linkage hierarchical clustering](http://pralab.diee.unica.it/en/node/1089), Biggio. B, Rota Bulò. S, Pillai. I, Mura. M, Zemene Mequanint. E, Pelillo. M, Roli. F, 2014

  * [Is Feature Selection Secure against Training Data Poisoning?](https://pralab.diee.unica.it/en/node/1191), Xiao. H, Biggio. B, Brown. G, Fumera. G, Eckert. C, Roli. F, 2015

  * [Adversarial Feature Selection Against Evasion Attacks](https://pralab.diee.unica.it/en/node/1188), 	Zhang. F, Chan. PPK, Biggio. B, Yeung. DS, Roli. F, 2016



## Licenses

License



[![CC0](http://i.creativecommons.org/p/zero/1.0/88x31.png)](http://creativecommons.org/publicdomain/zero/1.0/)



To the extent possible under law, [Yen-Chen Lin](http://yclin.me/) has waived all copyright and related or neighboring rights to this work.