5 papers
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…
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…
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…
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…
Beyond Discriminant Patterns: On the Robustness of Decision Rule Ensembles
Xin Du, Subramanian Ramamoorthy, Wouter Duivesteijn +2
Local decision rules are commonly understood to be more explainable, due to the local nature of the patterns involved. With numerical optimization methods such as gradient boosting…