Towards robust neural networks via a global and monotonically decreasing robustness training strategy

Zhen LIANG; Taoran WU; Wanwei LIU; Bai XUE; Wenjing YANG; Ji WANG; Zhengbin PANG

doi:10.1631/FITEE.2300059

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Towards robust neural networks via a global and monotonically decreasing robustness training strategy

Regular Papers | Updated：2023-10-25

- Towards robust neural networks via a global and monotonically decreasing robustness training strategy
  Cover Article
- 基于全局和单调递减鲁棒性策略的鲁棒神经网络训练方法
- Frontiers of Information Technology & Electronic Engineering Vol. 24, Issue 10, Pages: 1375-1389(2023)
- Affiliations：
  
  1.Institute for Quantum Information & State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha 410073, China
  2.State Key Laboratory of Computer Science Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
  3.School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100190, China
  4.College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, China
  5.Key Laboratory of Software Engineering for Complex Systems, National University of Defense Technology, Changsha 410073, China
- Author bio：
  
  †E-mail: liangzhen@nudt.edu.cn
  ‡Corresponding author
- Funds：
- DOI：10.1631/FITEE.2300059
  CLC： TP311; TP183
- Received：01 February 2023，
  
  Accepted：26 April 2023，
  
  Published：0 October 2023
- Accepted：
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Zhen LIANG, Taoran WU, Wanwei LIU, et al. Towards robust neural networks via a global and monotonically decreasing robustness training strategy[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(10): 1375-1389.
DOI：

Zhen LIANG, Taoran WU, Wanwei LIU, et al. Towards robust neural networks via a global and monotonically decreasing robustness training strategy[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(10): 1375-1389. DOI： 10.1631/FITEE.2300059.

摘要

深度神经网络的鲁棒性引发了学术界和工业界的高度关注，特别是在安全攸关领域。相比于验证神经网络的鲁棒性是否成立，本文关注点在于给定扰动前提下的鲁棒神经网络训练。现有的代表性训练方法——区间边界传播（IBP）和CROWN-IBP——在较小扰动下表现良好，但在较大扰动下性能显著下降，本文称之为衰退风险。具体来说，衰退风险是指与较小扰动情况相比，IBP系列训练方法在较大扰动情况下不能提供预期的鲁棒神经网络的现象。为了缓解这种衰退风险，我们提出一种全局的、单调递减的鲁棒神经网络训练策略，该策略在每个训练轮次考虑多个扰动（全局鲁棒性训练策略），并将其相应的鲁棒性损失以单调递减的权重进行组织（单调递减鲁棒性训练策略）。实验证明，所提策略在较小扰动下能够保持原有算法的性能，在较大扰动下的衰退风险得到很大程度改善。值得注意的是，与原有训练方法相比，所提训练策略保留了更多的模型准确度，这意味着该训练策略更加平衡地考虑了模型的鲁棒性和准确性。

Abstract

Robustness of deep neural networks (DNNs) has caused great concerns in the academic and industrial communities

especially in safety-critical domains. Instead of verifying whether the robustness property holds or not in certain neural networks

this paper focuses on training robust neural networks with respect to given perturbations. State-of-the-art training methods

interval bound propagation (IBP) and CROWN-IBP

perform well with respect to small perturbations

but their performance declines significantly in large perturbation cases

which is termed "drawdown risk" in this paper. Specifically

drawdown risk refers to the phenomenon that IBP-family training methods cannot provide expected robust neural networks in larger perturbation cases

as in smaller perturbation cases. To alleviate the unexpected drawdown risk

we propose a global and monotonically decreasing robustness training strategy that takes multiple perturbations into account during each training epoch (global robustness training)

and the corresponding robustness losses are combined with monotonically decreasing weights (monotonically decreasing robustness training). With experimental demonstrations

our presented strategy maintains performance on small perturbations and the drawdown risk on large perturbations is alleviated to a great extent. It is also noteworthy that our training method achieves higher model accuracy than the original training methods

which means that our presented training strategy gives more balanced consideration to robustness and accuracy.

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