Muhammad Shafique

FAdeML: Understanding the Impact of Pre-Processing Noise Filtering on Adversarial Machine Learning

2018 · arXiv (Cornell University)

Deep neural networks (DNN)-based machine learning (ML) algorithms have recently emerged as the leading ML paradigm particularly for the task of classification due to their superior capability of learning efficiently from large datasets. The discovery …

Probabilistic Analysis of Targeted Attacks Using Transform-Domain Adversarial Examples

2020 · IEEE Access

In the past decade, Deep Neural Networks (DNNs) have achieved breakthrough collaborations in developing smart intelligent systems within the field of computer vision, natural language processing, autonomous systems, etc. Recent research has revealed that stability …

RoHNAS: A Neural Architecture Search Framework with Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule Networks

2022 · arXiv (Cornell University)

Neural Architecture Search (NAS) algorithms aim at finding efficient Deep Neural Network (DNN) architectures for a given application under given system constraints. DNNs are computationally-complex as well as vulnerable to adversarial attacks. In order to …

Impact of Leader’s State of Core Self-Evaluation on Task Complexity: A Quantitative Analysis

2023 · Review of Education Administration and Law

This study aims to explore the association between a leader's State of Core Self-Evaluation and the complexity of tasks assigned to them. Previous research on this topic has established a strong foundation for the conceptual …

ODDR: Outlier Detection & Dimension Reduction Based Defense Against Adversarial Patches

2025

Adversarial attacks present a significant challenge to the dependable deployment of machine learning models, with patch-based attacks being particularly potent. These attacks introduce adversarial perturbations in localized regions of an image, deceiving even well-trained models. …

A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends

2024 · arXiv (Cornell University)

In today's digital age, Convolutional Neural Networks (CNNs), a subset of Deep Learning (DL), are widely used for various computer vision tasks such as image classification, object detection, and image segmentation. There are numerous types …