All Outputs (4)

Not So Robust after All: Evaluating the Robustness of Deep Neural Networks to Unseen Adversarial Attacks (2024)
Journal Article
Garaev, R., Rasheed, B., & Khan, A. M. (2024). Not So Robust after All: Evaluating the Robustness of Deep Neural Networks to Unseen Adversarial Attacks. Algorithms, 17, Article 162. https://doi.org/10.3390/a17040162

Deep neural networks (DNNs) have gained prominence in various applications, but remain vulnerable to adversarial attacks that manipulate data to mislead a DNN. This paper aims to challenge the efficacy and transferability of two contemporary defense... Read More about Not So Robust after All: Evaluating the Robustness of Deep Neural Networks to Unseen Adversarial Attacks.

Structure Estimation of Adversarial Distributions for Enhancing Model Robustness: A Clustering-Based Approach (2023)
Journal Article
Rasheed, B., Khan, A., & Masood Khattak, A. (2023). Structure Estimation of Adversarial Distributions for Enhancing Model Robustness: A Clustering-Based Approach. Applied Sciences, 13(19), Article 10972. https://doi.org/10.3390/app131910972

In this paper, we propose an advanced method for adversarial training that focuses on leveraging the underlying structure of adversarial perturbation distributions. Unlike conventional adversarial training techniques that consider adversarial example... Read More about Structure Estimation of Adversarial Distributions for Enhancing Model Robustness: A Clustering-Based Approach.

Overhead Based Cluster Scheduling of Mixed Criticality Systems on Multicore Platform (2023)
Journal Article
Ali, A., Khattak, A. M., Iqbal, S., Alfandi, O., Hayat, B., Siddiqi, M. H., & Khan, A. (2023). Overhead Based Cluster Scheduling of Mixed Criticality Systems on Multicore Platform. IEEE Access, 11, 142341-142359. https://doi.org/10.1109/ACCESS.2023.3330973

The cluster-based technique is gaining focus for scheduling tasks of mixed-criticality (MC) real-time multicore systems. In this technique, the cores of the MC system are distributed in groups known as clusters. When all cores are distributed in clus... Read More about Overhead Based Cluster Scheduling of Mixed Criticality Systems on Multicore Platform.

A Disjoint Samples-Based 3D-CNN With Active Transfer Learning for Hyperspectral Image Classification (2022)
Journal Article
Ahmad, M., Ghous, U., Hong, D., Khan, A. M., Yao, J., Wang, S., & Chanussot, J. (2022). A Disjoint Samples-Based 3D-CNN With Active Transfer Learning for Hyperspectral Image Classification. IEEE transactions on geoscience and remote sensing : a publication of the IEEE Geoscience and Remote Sensing Society, 60, 1-16. https://doi.org/10.1109/TGRS.2022.3209182

Convolutional neural networks (CNNs) have been extensively studied for hyperspectral image classification (HSIC). However, CNNs are critically attributed to a large number of labeled training samples, which outlays high costs in terms of time and res... Read More about A Disjoint Samples-Based 3D-CNN With Active Transfer Learning for Hyperspectral Image Classification.