Search Robust and Adaptable Architecture

Abstract

The vulnerability of deep neural networks poses a significant challenge to their application in security-sensitive domains. In this paper, we propose the Search Robust and Adaptable Architecture (SRAA) to identify the robust architecture. Unlike previous NAS-based approaches that utilize a single network search space, we introduce a novel dual-input ensemble search space, enabling the searched structures to exhibit good robustness under different attacks. The results demonstrate that the optimal SRAA model excels in complex tasks, such as Imagenet, and exhibits superior performance against strong attacks, such as PGD. Remarkably, our NAS-based model surpasses hand-designed models in terms of adversarial accuracy under strong attacks for the first time. Furthermore, the experimental results on CIFAR10/100 and IMAGENET datasets highlight the comprehensive improvement achieved by SRAA over previous state-of-the-art (SOTA) models and baseline approaches in terms of accuracy against diverse attack scenarios.