awesome-adversarial-machine-learning

Adversarial ML resource list

A curated collection of resources on adversarial machine learning to help developers better understand and prepare against attacks on their models.

A curated list of awesome adversarial machine learning resources

GitHub
2k stars
126 watching
285 forks
last commit: almost 4 years ago
Linked from 4 awesome lists


Awesome Adversarial Machine Learning: / Blogs
Breaking Linear Classifiers on ImageNet , A. Karpathy et al
Breaking things is easy , N. Papernot & I. Goodfellow et al
Attacking Machine Learning with Adversarial Examples , N. Papernot, I. Goodfellow, S. Huang, Y. Duan, P. Abbeel, J. Clark
Robust Adversarial Examples , Anish Athalye
A Brief Introduction to Adversarial Examples , A. Madry et al
Training Robust Classifiers (Part 1) , A. Madry et al
Adversarial Machine Learning Reading List , N. Carlini
Recommendations for Evaluating Adversarial Example Defenses , N. Carlini

Awesome Adversarial Machine Learning: / Papers / General
Intriguing properties of neural networks , C. Szegedy et al., arxiv 2014
Explaining and Harnessing Adversarial Examples , I. Goodfellow et al., ICLR 2015
Motivating the Rules of the Game for Adversarial Example Research , J. Gilmer et al., arxiv 2018
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning , B. Biggio, Pattern Recognition 2018

Awesome Adversarial Machine Learning: / Papers / Attack
DeepFool: a simple and accurate method to fool deep neural networks , S. Moosavi-Dezfooli et al., CVPR 2016
The Limitations of Deep Learning in Adversarial Settings , N. Papernot et al., ESSP 2016
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples , N. Papernot et al., arxiv 2016
Adversarial Examples In The Physical World , A. Kurakin et al., ICLR workshop 2017
Delving into Transferable Adversarial Examples and Black-box Attacks Liu et al., ICLR 2017
Towards Evaluating the Robustness of Neural Networks N. Carlini et al., SSP 2017
Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples , N. Papernot et al., Asia CCS 2017
Privacy and machine learning: two unexpected allies? , I. Goodfellow et al
Adversarial attacks on neural network policies , S. Huang et al, ICLR workshop 2017
Tactics of Adversarial Attacks on Deep Reinforcement Learning Agents , Y. Lin et al, IJCAI 2017
Delving into adversarial attacks on deep policies , J. Kos et al., ICLR workshop 2017
Adversarial Examples for Semantic Segmentation and Object Detection , C. Xie, ICCV 2017
Adversarial examples for generative models , J. Kos et al. arxiv 2017
Audio Adversarial Examples: Targeted Attacks on Speech-to-Text , N. Carlini et al., arxiv 2018
Adversarial Examples for Evaluating Reading Comprehension Systems , R. Jia et al., EMNLP 2017

Awesome Adversarial Machine Learning: / Papers / Defence
Adversarial Machine Learning At Scale , A. Kurakin et al., ICLR 2017
Ensemble Adversarial Training: Attacks and Defenses , F. Tramèr et al., arxiv 2017
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks , N. Papernot et al., SSP 2016
Extending Defensive Distillation , N. Papernot et al., arxiv 2017
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples , Y. Song et al., ICLR 2018
Detecting Adversarial Attacks on Neural Network Policies with Visual Foresight , Y. Lin et al., NIPS workshop 2017

Awesome Adversarial Machine Learning: / Papers / Regularization
Distributional Smoothing with Virtual Adversarial Training , T. Miyato et al., ICLR 2016
Adversarial Training Methods for Semi-Supervised Text Classification , T. Miyato et al., ICLR 2017

Awesome Adversarial Machine Learning: / Papers / Others
Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images , A. Nguyen et al., CVPR 2015

Awesome Adversarial Machine Learning: / Talks
Do Statistical Models Understand the World? , I. Goodfellow, 2015
Classifiers under Attack , David Evans, 2017
Adversarial Examples in Machine Learning , Nicolas Papernot, 2017
Poisoning Behavioral Malware Clustering , Biggio. B, Rieck. K, Ariu. D, Wressnegger. C, Corona. I. Giacinto, G. Roli. F, 2014
Is Data Clustering in Adversarial Settings Secure? , BBiggio. B, Pillai. I, Rota Bulò. S, Ariu. D, Pelillo. M, Roli. F, 2015
Poisoning complete-linkage hierarchical clustering , 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? , Xiao. H, Biggio. B, Brown. G, Fumera. G, Eckert. C, Roli. F, 2015
Adversarial Feature Selection Against Evasion Attacks , Zhang. F, Chan. PPK, Biggio. B, Yeung. DS, Roli. F, 2016

Backlinks from these awesome lists: