3 papers
cs.LG2026
Explainability-Guided Defense: Attribution-Aware Model Refinement Against Adversarial Data Attacks
Longwei Wang, Mohammad Navid Nayyem, Abdullah Al Rakin +3
The growing reliance on deep learning models in safety-critical domains such as healthcare and autonomous navigation underscores the need for defenses that are both robust to adver…
cs.LG2024
Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning
Longwei Wang, Navid Nayyem, Abdullah Rakin
Adversarial attacks exploit the vulnerabilities of convolutional neural networks by introducing imperceptible perturbations that lead to misclassifications, exposing weaknesses in…
cs.LG2024
Bridging Interpretability and Robustness Using LIME-Guided Model Refinement
Navid Nayyem, Abdullah Rakin, Longwei Wang
This paper explores the intricate relationship between interpretability and robustness in deep learning models. Despite their remarkable performance across various tasks, deep lear…