3 citations · 11 across the 19 of their papers we have counts for
5 papers · 1 filter
Bringing Multi-Modal Multi-Task Federated Foundation Models to Education Domain: Prospects and Challenges
Kasra Borazjani, Naji Khosravan, Rajeev Sahay +2
Multi-modal multi-task (M3T) foundation models (FMs) have recently shown transformative potential in artificial intelligence, with emerging applications in education. However, thei…
Non-Intrusive Detection of Adversarial Deep Learning Attacks via Observer Networks
Kirthi Shankar Sivamani, Rajeev Sahay, Aly El Gamal
Recent studies have shown that deep learning models are vulnerable to specifically crafted adversarial inputs that are quasi-imperceptible to humans. In this letter, we propose a n…
Ensemble Noise Simulation to Handle Uncertainty about Gradient-based Adversarial Attacks
Rehana Mahfuz, Rajeev Sahay, Aly El Gamal
Gradient-based adversarial attacks on neural networks can be crafted in a variety of ways by varying either how the attack algorithm relies on the gradient, the network architectur…
A Computationally Efficient Method for Defending Adversarial Deep Learning Attacks
Rajeev Sahay, Rehana Mahfuz, Aly El Gamal
The reliance on deep learning algorithms has grown significantly in recent years. Yet, these models are highly vulnerable to adversarial attacks, which introduce visually impercept…
Combatting Adversarial Attacks through Denoising and Dimensionality Reduction: A Cascaded Autoencoder Approach
Rajeev Sahay, Rehana Mahfuz, Aly El Gamal
Machine Learning models are vulnerable to adversarial attacks that rely on perturbing the input data. This work proposes a novel strategy using Autoencoder Deep Neural Networks to…