DeepRobust: a Platform for Adversarial Attacks and Defenses

Authors

  • Yaxin Li Michigan State University
  • Wei Jin Michigan State University
  • Han Xu Michigan State University
  • Jiliang Tang Michigan State University

Keywords:

Adversarial Learning, Attack, Defense, Security

Abstract

DeepRobust is a PyTorch platform for generating adversarial examples and building robust machine learning models for different data domains. Users can easily evaluate the attack performance against different defense methods with DeepRobust and get performance analyzing visualization. In this paper, we introduce the functions of DeepRobust with detailed instructions. We believe that DeepRobust is a useful tool to measure deep learning model robustness and to find the suitable countermeasures against adversarial attacks. The platform is kept updated and can be found at https://github.com/DSE-MSU/DeepRobust. More details of instruction can be found in the documentation at https://deeprobust.readthedocs.io/en/latest/.

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Published

2021-05-18

How to Cite

Li, Y., Jin, W., Xu, H., & Tang, J. (2021). DeepRobust: a Platform for Adversarial Attacks and Defenses. Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 16078-16080. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/18017