Robust Proxy: Improving Adversarial Robustness by Robust Proxy Learning

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Recently, it has been widely known that deep neural networks are highly vulnerable and easily broken by adversarial attacks. To mitigate the adversarial vulnerability, many defense algorithms have been proposed. Recently, to improve adversarial robustness, many works try to enhance feature representation by imposing more direct supervision on the discriminative feature. However, existing approaches lack an understanding of learning adversarially robust feature representation. In this paper, we propose a novel training framework called Robust Proxy Learning. In the proposed method, the model explicitly learns robust feature representations with robust proxies. To this end, firstly, we demonstrate that we can generate class-representative robust features by adding class-wise robust perturbations. Then, we use the class representative features as robust proxies. With the class-wise robust features, the model explicitly learns adversarially robust features through the proposed robust proxy learning framework. Through extensive experiments, we verify that we can manually generate robust features, and our proposed learning framework could increase the robustness of the DNNs.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2023-06
Language
English
Article Type
Article
Citation

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, v.18, pp.4021 - 4033

ISSN
1556-6013
DOI
10.1109/TIFS.2023.3288672
URI
http://hdl.handle.net/10203/310957
Appears in Collection
EE-Journal Papers(저널논문)
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