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A non-targeted adversarial attack method, which won the first place in NIPS 2017 non-targeted adversarial attacks competition
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This repository contains the code for the top-1 submission to NIPS 2017: Non-targeted Adversarial Attacks Competition.


We propose a momentum iterative method to generate more transferable adversarial examples. We summarize our algorithm in Boosting Adversarial Attacks with Momentum (CVPR 2018, Spotlight).

Basically, the update rule of momentum iterative method is:



If you use momentum iterative method for attacks in your research, please consider citing

  title={Boosting Adversarial Attacks with Momentum},
  author={Dong, Yinpeng and Liao, Fangzhou and Pang, Tianyu and Su, Hang and Zhu, Jun and Hu, Xiaolin and Li, Jianguo},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},



We use the ensemble of eight models in our submission, many of which are adversarially trained models. The models can be downloaded here.

If you want to attack other models, you can replace the model definition part to your own models.


We also implement this method in Cleverhans.

Targeted Attacks

Please find the targeted attacks at

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