3 citations · 7 across the 5 of their papers we have counts for
7 papers
Mitigating Gradient-based Adversarial Attacks via Denoising and Compression
Rehana Mahfuz, Rajeev Sahay, Aly El Gamal
Gradient-based adversarial attacks on deep neural networks pose a serious threat, since they can be deployed by adding imperceptible perturbations to the test data of any network,…
A Deep Ensemble-based Wireless Receiver Architecture for Mitigating Adversarial Attacks in Automatic Modulation Classification
Rajeev Sahay, Christopher G. Brinton, David J. Love
Deep learning-based automatic modulation classification (AMC) models are susceptible to adversarial attacks. Such attacks inject specifically crafted wireless interference into tra…
Frequency-based Automated Modulation Classification in the Presence of Adversaries
Rajeev Sahay, Christopher G. Brinton, David J. Love
Automatic modulation classification (AMC) aims to improve the efficiency of crowded radio spectrums by automatically predicting the modulation constellation of wireless RF signals.…
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…