6 papers
Rethinking Generative Reconstruction Attacks against Graph Neural Network Models
Adebayo Keji, Sayanton Dibbo
The application of graph data in numerous disciplines raises the need for gathering and analyzing huge volumes of data, some of which is private and sensitive. The non-Euclidean na…
Quantum-Resilient Decentralized AI Economies: Proof-of-Useful-Work and Post-Quantum Security
Connor Barbaccia, Sudip Vhaduri, Sayanton Dibbo
Proof-of-Work blockchains secure consensus through hash puzzles, producing no external value. In this research, we propose a decentralized AI economy where nodes are rewarded for u…
Are Neuro-Inspired Multi-Modal Vision-Language Models Resilient to Membership Inference Privacy Leakage?
David Amebley, Sayanton Dibbo
In the age of agentic AI, the growing deployment of multi-modal models (MMs) has introduced new attack vectors that can leak sensitive training data in MMs, causing privacy leakage…
On the Evaluation of Spiking Neural Network Configurations for Network Intrusion Detection
Raj Patel, David Amebley, Taye Akinrele +3
Network intrusion detection is a core component of modern cybersecurity infrastructure, yet the deep learning models that dominate the field are computationally demanding, motivati…
Do We Really Need Quantum Machine Learning?: A Multidimensional Empirical Study
Sudip Vhaduri, Ryan Gammon, Sayanton Dibbo
The rapid growth of computer vision and increasingly complex image recognition tasks has exposed fundamental computational limitations of classical machine learning models, motivat…
Beyond Attack Success Rate: A Multi-Metric Evaluation of Adversarial Transferability in Medical Imaging Models
Emily Curl, Kofi Ampomah, Md Erfan +1
While deep learning systems are becoming increasingly prevalent in medical image analysis, their vulnerabilities to adversarial perturbations raise serious concerns for clinical de…