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

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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 AttacksLiu et al., ICLR 2017
Towards Evaluating the Robustness of Neural NetworksN. 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

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