awesome-adversarial-machine-learning
by yenchenlin
A curated list of awesome adversarial machine learning resources
AI summary
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.
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What's in the list
44 links in 7 sections, with live GitHub stats.activeno commit in 2y
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
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
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
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
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
Papers / Others
- Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images
, A. Nguyen et al., CVPR 2015
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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