Robust Proxy: Improving Adversarial Robustness by Robust Proxy Learning

Abstract

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.

Accepted at IEEE Transactions on Information Forensics and Security (TIFS)

References in corpus (7)

• Explaining and Harnessing Adversarial Examples
• ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models
• Theoretically Principled Trade-off between Robustness and Accuracy
• On Evaluating Adversarial Robustness
• Improving Adversarial Robustness via Promoting Ensemble Diversity
• Robust Pre-Training by Adversarial Contrastive Learning
• Distilling Robust and Non-Robust Features in Adversarial Examples by Information Bottleneck