4 citations · 8 across the 5 of their papers we have counts for
5 papers
Navigating Threats: A Survey of Physical Adversarial Attacks on LiDAR Perception Systems in Autonomous Vehicles
Amira Guesmi, Muhammad Shafique
Autonomous vehicles (AVs) rely heavily on LiDAR (Light Detection and Ranging) systems for accurate perception and navigation, providing high-resolution 3D environmental data that i…
DefensiveDR: Defending against Adversarial Patches using Dimensionality Reduction
Nandish Chattopadhyay, Amira Guesmi, Muhammad Abdullah Hanif +2
Adversarial patch-based attacks have shown to be a major deterrent towards the reliable use of machine learning models. These attacks involve the strategic modification of localize…
Adversarial Attack on Radar-based Environment Perception Systems
Amira Guesmi, Ihsen Alouani
Due to their robustness to degraded capturing conditions, radars are widely used for environment perception, which is a critical task in applications like autonomous vehicles. More…
Defending with Errors: Approximate Computing for Robustness of Deep Neural Networks
Amira Guesmi, Ihsen Alouani, Khaled N. Khasawneh +4
Machine-learning architectures, such as Convolutional Neural Networks (CNNs) are vulnerable to adversarial attacks: inputs crafted carefully to force the system output to a wrong l…
ROOM: Adversarial Machine Learning Attacks Under Real-Time Constraints
Amira Guesmi, Khaled N. Khasawneh, Nael Abu-Ghazaleh +1
Advances in deep learning have enabled a wide range of promising applications. However, these systems are vulnerable to Adversarial Machine Learning (AML) attacks; adversarially cr…