Bi-fidelity evolutionary multiobjective search for adversarially robust deep neural architectures
Deep neural networks have been found vulnerable to adversarial attacks, thus raising potential concerns in security-sensitive contexts. To address this problem, recent research has investigated the adversarial robustness of deep neural networks from the architectural point of view. However, searching for architectures of deep neural networks is computationally expensive, particularly when coupled with an adversarial training process. To meet the above challenge, this paper proposes a bi-fidelity multiobjective neural architecture search approach. First, we formulate the neural architecture search (NAS) problem for enhancing the adversarial robustness of deep neural networks into a multiobjective optimization problem. Specifically, in addition to using low-fidelity estimations as the primary objectives, we leverage the output of a surrogate model trained with high-fidelity evaluations as an auxiliary objective. Secondly, we reduce the computational cost by combining three performance estimation methods, i.e., parameter sharing, low-fidelity evaluation, and surrogate-based predictor. The effectiveness of the proposed approach is confirmed by extensive experiments conducted on CIFAR-10, CIFAR-100 and SVHN datasets.
|WOS Research Area|
Computer Science, Artificial Intelligence
|WOS Accession No|
|ESI Research Field|
Cited Times [WOS]:0
|Document Type||Journal Article|
|Department||Department of Computer Science and Engineering|
1.Department of Computer Science,University of Surrey,Guildford,United Kingdom
2.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,China
3.Faculty of Technology,Bielefeld University,Bielefeld,Germany
Liu，Jia,Cheng，Ran,Jin，Yaochu. Bi-fidelity evolutionary multiobjective search for adversarially robust deep neural architectures[J]. Neurocomputing,2023,550.
Liu，Jia,Cheng，Ran,&Jin，Yaochu.(2023).Bi-fidelity evolutionary multiobjective search for adversarially robust deep neural architectures.Neurocomputing,550.
Liu，Jia,et al."Bi-fidelity evolutionary multiobjective search for adversarially robust deep neural architectures".Neurocomputing 550(2023).
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