3 papers
cs.LG2024
Standard-Deviation-Inspired Regularization for Improving Adversarial Robustness
Olukorede Fakorede, Modeste Atsague, Jin Tian
Adversarial Training (AT) has been demonstrated to improve the robustness of deep neural networks (DNNs) against adversarial attacks. AT is a min-max optimization procedure where i…
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…