Universal Adversarial Training


  • Ali Shafahi University of Maryland
  • Mahyar Najibi University of Maryland
  • Zheng Xu University of Maryland
  • John Dickerson University of Maryland
  • Larry S. Davis University of Maryland
  • Tom Goldstein University of Maryland




Standard adversarial attacks change the predicted class label of a selected image by adding specially tailored small perturbations to its pixels. In contrast, a universal perturbation is an update that can be added to any image in a broad class of images, while still changing the predicted class label. We study the efficient generation of universal adversarial perturbations, and also efficient methods for hardening networks to these attacks. We propose a simple optimization-based universal attack that reduces the top-1 accuracy of various network architectures on ImageNet to less than 20%, while learning the universal perturbation 13× faster than the standard method.

To defend against these perturbations, we propose universal adversarial training, which models the problem of robust classifier generation as a two-player min-max game, and produces robust models with only 2× the cost of natural training. We also propose a simultaneous stochastic gradient method that is almost free of extra computation, which allows us to do universal adversarial training on ImageNet.




How to Cite

Shafahi, A., Najibi, M., Xu, Z., Dickerson, J., Davis, L. S., & Goldstein, T. (2020). Universal Adversarial Training. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 5636-5643. https://doi.org/10.1609/aaai.v34i04.6017



AAAI Technical Track: Machine Learning