Neural Architectures Search (NAS) becomes more and more popular over these years. However, NAS-generated models tends to suffer greater vulnerability to various malicious attacks. Lots of robust NAS methods leverage adversarial training to enhance the robustness of NAS-generated models, however, they neglected the nature accuracy of NAS-generated models. In our paper, we propose a novel NAS method, Robust Neural Architecture Search (RNAS). To design a regularization term to balance accuracy and robustness, RNAS generates architectures with both high accuracy and good robustness. To reduce search cost, we further propose to use noise examples instead adversarial examples as input to search architectures. Extensive experiments show that RNAS achieves state-of-the-art (SOTA) performance on both image classification and adversarial attacks, which illustrates the proposed RNAS achieves a good tradeoff between robustness and accuracy.
翻译：神经架构搜索（NAS）在近年来越来越受欢迎。然而，NAS生成的模型往往更容易受到各种恶意攻击的影响。许多鲁棒性NAS方法利用对抗训练来增强NAS生成的模型的鲁棒性，但是他们忽略了NAS生成的模型的自然准确性。在本文中，我们提出了一种新颖的NAS方法，鲁棒神经架构搜索（RNAS）。为了设计一个正则化项来平衡准确性和鲁棒性，RNAS生成具有高准确性和良好鲁棒性的架构。为了降低搜索成本，我们进一步建议使用噪声样本而不是对抗样本作为输入来搜索架构。大量实验证明，RNAS在图像分类和对抗攻击方面均实现了最先进的性能，这说明所提出的RNAS在准确性和鲁棒性之间实现了良好的平衡。
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