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Improving Adversarial Training using Vulnerability-Aware Perturbation Budget
Adversarial Training (AT) effectively improves the robustness of Deep Neural Networks (DNNs) to adversarial attacks.
Vulnerability-Aware Instance Reweighting For Adversarial Training
Adversarial Training (AT) has been found to substantially improve the robustness of deep learning classifiers against adversarial attacks.
Improving Adversarial Robustness with Hypersphere Embedding and Angular-based Regularizations
In this paper, we propose integrating HE into AT with regularization terms that exploit the rich angular information available in the HE framework.
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