3 papers
cs.LG2024
Improving Adversarial Training using Vulnerability-Aware Perturbation Budget
Olukorede Fakorede, Modeste Atsague, Jin Tian
Adversarial Training (AT) effectively improves the robustness of Deep Neural Networks (DNNs) to adversarial attacks. Generally, AT involves training DNN models with adversarial exa…
cs.LG2023
Vulnerability-Aware Instance Reweighting For Adversarial Training
Olukorede Fakorede, Ashutosh Kumar Nirala, Modeste Atsague +1
Adversarial Training (AT) has been found to substantially improve the robustness of deep learning classifiers against adversarial attacks. AT involves obtaining robustness by inclu…
cs.LG2023
Improving Adversarial Robustness with Hypersphere Embedding and Angular-based Regularizations
Olukorede Fakorede, Ashutosh Nirala, Modeste Atsague +1
Adversarial training (AT) methods have been found to be effective against adversarial attacks on deep neural networks. Many variants of AT have been proposed to improve its perform…