Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A challenge to explore adversarial robustness of neural networks on CIFAR10.
| Date | Stars |
|---|---|
| 2026-07-31 | 511 |
| 2026-08-04 | 511 |
| 2026-08-05 | 511 |
| 2026-08-06 | 511 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# CIFAR10 Adversarial Examples Challenge Recently, there has been much progress on adversarial *attacks* against neural networks, such as the [cleverhans](https://github.com/tensorflow/cleverhans) library and the code by [Carlini and Wagner](https://github.com/carlini/nn_robust_attacks). We now complement these advances by proposing an *attack challenge* for the [CIFAR10 dataset](https://www.cs.toronto.edu/~kriz/cifar.html) which follows the format of [our earlier MNIST challenge](https://github.com/MadryLab/mnist_challenge). We have trained a robust network, and the objective is to find a set of adversarial examples on which this network achieves only a low accuracy. To train an adversarially-robust network, we followed the approach from our recent paper: **Towards Deep Learning Models Resistant to Adversarial Attacks** <br> *Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, Adrian Vladu* <br> https://arxiv.org/abs/1706.06083. As part of the challenge, we release both the training code and the network architecture, but keep the network weights secret. We invite any researcher to submit attacks against our model (see the detailed instructions below). We will maintain a leaderboard of the best attacks for the next two months and then publish our secret network weights. Analogously to our MNIST challenge, the goal of this challenge is to clarify the state-of-the-art for adversarial robustness on CIFAR10. Moreover, we hope that future work on defense mechanisms will adopt a similar challenge format in order to improve reproducibility and empirical comparisons. **Update 2017-12-10**: We released our secret model. You can download it by running `python fetch_model.py secret`. As of Dec 10 we are no longer accepting black-box challenge submissions. We have set up a leaderboard for white-box attacks on the (now released) secret model. The submission format is the same as before. We plan to continue evaluating submissions and maintaining the leaderboard for the foreseeable future. ## Black-Box Leaderboard (Original Challenge) | Attack | Submitted by | Accuracy | Submission Date | | -------------------------------------- | ------------- | -------- | ---- | | PGD on the cross-entropy loss for the<br> adversarially trained public network | (initial entry) | **63.39%** | Jul 12, 2017 | | PGD on the [CW](https://github.com/carlini/nn_robust_attacks) loss for the<br> adversarially trained public network | (initial entry) | 64.38% | Jul 12, 2017 | | FGSM on the [CW](https://github.com/carlini/nn_robust_attacks) loss for the<br> adversarially trained public network | (initial entry) | 67.25% | Jul 12, 2017 | | FGSM on the [CW](https://github.com/carlini/nn_robust_attacks) loss for the<br> naturally trained public network | (initial entry) | 85.23% | Jul 12, 2017 | ## White-Box Leaderboard | Attack | Submitted by | Accuracy | Submission Date | | -------------------------------------- | ------------- | -------- | ---- | | Guided Local Attack | Siyuan Yi | **43.95%** | Aug 2, 2021 | | [EWR-PGD](https://github.com/liuye6666/EWR-PGD) | Ye Liu | 43.96% | Sep 8, 2020 | | [PGD attack with Output Diversified Initialization](https://arxiv.org/abs/2003.06878) | Yusuke Tashiro | 43.99% | Feb 15, 2020 | | [MultiTargeted](https://arxiv.org/abs/1910.09338) | Sven Gowal | 44.03% | Aug 28, 2019 | | [FAB: Fast Adaptive Boundary Attack](https://github.com/fra31/fab-attack) | Francesco Croce | 44.51% | Jun 7, 2019 | | [Distributionally Adversarial Attack](https://github.com/tianzheng4/Distributionally-Adversarial-Attack) | Tianhang Zheng | 44.71% | Aug 21, 2018 | | 20-step PGD on the cross-entropy loss<br> with 10 random restarts | Tianhang Zheng | 45.21% | Aug 24, 2018 | | 20-step PGD on the cross-entropy loss | (i
Excerpt of 11,525 characters
Read on GitHub
Dimitris Tsipras
21
7
2
Ludwig Schmidt · Stanford University, Anthropic · United States
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:3429ccceb817cb6f, llm:Description: 'A challenge to explore adversarial robustness of neural networks on CIFAR10.' Repository focuses on adversarial robustness for neural networks on CIFAR-10 dataset.
matched fp:3429ccceb817cb6f, llm:Description: 'A challenge to explore adversarial robustness of neural networks on CIFAR10.' Repository focuses on adversarial robustness for neural networks on CIFAR-10 dataset.
matched fp:3429ccceb817cb6f, llm:Description: 'A challenge to explore adversarial robustness of neural networks on CIFAR10.' Repository focuses on adversarial robustness for neural networks on CIFAR-10 dataset.