Neural architecture search (NAS) has emerged as one successful technique to find robust deep neural network (DNN) architectures. However, most existing robustness evaluations in NAS only consider $l_{\infty}$ norm-based adversarial noises. In order to improve the robustness of DNN models against multiple types of noises, it is necessary to consider a comprehensive evaluation in NAS for robust architectures. But with the increasing number of types of robustness evaluations, it also becomes more time-consuming to find comprehensively robust architectures. To alleviate this problem, we propose a novel efficient search of comprehensively robust neural architectures via multi-fidelity evaluation (ES-CRNA-ME). Specifically, we first search for comprehensively robust architectures under multiple types of evaluations using the weight-sharing-based NAS method, including different $l_{p}$ norm attacks, semantic adversarial attacks, and composite adversarial attacks. In addition, we reduce the number of robustness evaluations by the correlation analysis, which can incorporate similar evaluations and decrease the evaluation cost. Finally, we propose a multi-fidelity online surrogate during optimization to further decrease the search cost. On the basis of the surrogate constructed by low-fidelity data, the online high-fidelity data is utilized to finetune the surrogate. Experiments on CIFAR10 and CIFAR100 datasets show the effectiveness of our proposed method.

翻译：神经架构搜索（NAS）已成为发现鲁棒深度神经网络（DNN）架构的成功技术之一。然而，现有NAS中的鲁棒性评估大多仅考虑基于$l_{\infty}$范数的对抗噪声。为提升DNN模型对多种噪声类型的鲁棒性，有必要在NAS中引入面向鲁棒架构的全方位评估。但随着鲁棒性评估类型数量的增加，寻找全面鲁棒架构的计算耗时也随之增长。为解决该问题，我们提出一种新颖的基于多保真度评估的全方位鲁棒神经架构高效搜索方法（ES-CRNA-ME）。具体而言，我们首先采用基于权重共享的NAS方法，在包含不同$l_{p}$范数攻击、语义对抗攻击及复合对抗攻击的多类型评估下搜索全面鲁棒架构。此外，我们通过相关性分析减少鲁棒性评估次数，该方法能够合并相似评估从而降低评估成本。最后，我们在优化过程中提出一种多保真度在线代理模型以进一步降低搜索成本。基于低保真度数据构建的代理模型，我们利用在线高保真度数据对其进行微调。在CIFAR10和CIFAR100数据集上的实验验证了所提方法的有效性。