Awesome Adversarial Machine Learning:

A curated list of awesome adversarial machine learning resources, inspired by awesome-computer-vision.

Table of Contents

• Blogs
• Papers
• Talks

Blogs

• Breaking Linear Classifiers on ImageNet, A. Karpathy et al.
• Breaking things is easy, N. Papernot & I. Goodfellow et al.

Papers

• Intriguing properties of neural networks, C. Szegedy et al., arxiv 2014
• Explaining and Harnessing Adversarial Examples, I. Goodfellow et al., ICLR 2015
• Adversarial Examples In The Physical World, A. Kurakin et al., ICLR workshop 2017
• 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
• The Limitations of Deep Learning in Adversarial Settings, N. Papernot et al., ESSP 2016
• Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples, N. Papernot et al., arxiv 2016
• Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples, N. Papernot et al., arxiv 2016
• Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images, A. Nguyen et al., CVPR 2015
• DeepFool: a simple and accurate method to fool deep neural networks, S. Moosavi-Dezfooli et al., CVPR 2016
• Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks, N. Papernot et al., SSP 2016
• [Towards Evaluating the Robustness of Neural Networks]
• Adversarial Examples For Generative Models
• [Delving into Transferable Adversarial Examples and Black-box Attacks]
• [SoK: Towards the Science of Security and Privacy in Machine Learning]
• [Learning Adversary-Resistant Deep Neural Networks]

Talks

• Do Statistical Models Understand the World?, I. Goodfellow, 2015

Licenses

License

To the extent possible under law, Yen-Chen Lin has waived all copyright and related or neighboring rights to this work.