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A curated list of awesome adversarial machine learning resources
| Date | Stars |
|---|---|
| 2026-07-31 | 1909 |
| 2026-08-01 | 1909 |
| 2026-08-06 | 1909 |
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# :warning: Deprecated I no longer include up-to-date papers, but the list is still a good reference for starters. # Awesome Adversarial Machine Learning: [](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/1703.08
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:ea3685fbfeb8f8a4, desc:curated list