60 citations · 138 across the 21 of their papers we have counts for
5 papers · 1 filter
Data-Driven Subsampling in the Presence of an Adversarial Actor
Abu Shafin Mohammad Mahdee Jameel, Ahmed P. Mohamed, Jinho Yi +2
Deep learning based automatic modulation classification (AMC) has received significant attention owing to its potential applications in both military and civilian use cases. Recent…
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…
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…
A Sampling Theory Perspective of Graph-based Semi-supervised Learning
Aamir Anis, Aly El Gamal, Salman Avestimehr +1
Graph-based methods have been quite successful in solving unsupervised and semi-supervised learning problems, as they provide a means to capture the underlying geometry of the data…