Kevin Li

RobArch: Designing Robust Architectures against Adversarial Attacks

ShengYun Peng
Weilin Xu
Cory Cornelius
Rahul Duggal
Duen Horng Chau
Jason Martin
arXiv:2301.03110 (Arxiv), 2023


Adversarial Training is the most effective approach for improving the robustness of Deep Neural Networks (DNNs). However, compared to the large body of research in optimizing the adversarial training process, there are few investigations into how architecture components affect robustness, and they rarely constrain model capacity. Thus, it is unclear where robustness precisely comes from. In this work, we present the first large-scale systematic study on the robustness of DNN architecture components under fixed parameter budgets. Through our investigation, we distill 18 actionable robust network design guidelines that empower model developers to gain deep insights. We demonstrate these guidelines’ effectiveness by introducing the novel Robust Architecture (RobArch) model that instantiates the guidelines to build a family of top-performing models across parameter capacities against strong adversarial attacks. RobArch achieves the new state-of-the-art AutoAttack accuracy on the RobustBench ImageNet leaderboard. The code is available at




  title={RobArch: Designing Robust Architectures against Adversarial Attacks},
  author={Sheng-Hsuan Peng and Weilin Xu and Cory Cornelius and Kevin Li and Rahul Duggal and Duen Horng Chau and Jason Martin},