46 citations · 70 across the 24 of their papers we have counts for
11 papers · 1 filter
Do Not Leave a Gap: Hallucination-Free Object Concealment in Vision-Language Models
Amira Guesmi, Muhammad Shafique
Vision-language models (VLMs) have recently shown remarkable capabilities in visual understanding and generation, but remain vulnerable to adversarial manipulations of visual conte…
PatchBlock: A Lightweight Defense Against Adversarial Patches for Embedded EdgeAI Devices
Nandish Chattopadhyay, Abdul Basit, Amira Guesmi +3
Adversarial attacks pose a significant challenge to the reliable deployment of machine learning models in EdgeAI applications, such as autonomous driving and surveillance, which re…
Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks
Amira Guesmi, Bassem Ouni, Muhammad Shafique
Quantized neural networks (QNNs) are increasingly used for efficient deployment of deep learning models on resource-constrained platforms, such as mobile devices and edge computing…
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…
ODDR: Outlier Detection & Dimension Reduction Based Defense Against Adversarial Patches
Nandish Chattopadhyay, Amira Guesmi, Muhammad Abdullah Hanif +2
Adversarial attacks present a significant challenge to the dependable deployment of machine learning models, with patch-based attacks being particularly potent. These attacks intro…
Physical Adversarial Attacks For Camera-based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook
Amira Guesmi, Muhammad Abdullah Hanif, Bassem Ouni +1
In this paper, we present a comprehensive survey of the current trends focusing specifically on physical adversarial attacks. We aim to provide a thorough understanding of the conc…
DAP: A Dynamic Adversarial Patch for Evading Person Detectors
Amira Guesmi, Ruitian Ding, Muhammad Abdullah Hanif +2
Patch-based adversarial attacks were proven to compromise the robustness and reliability of computer vision systems. However, their conspicuous and easily detectable nature challen…
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…
Defensive Approximation: Securing CNNs using Approximate Computing
Amira Guesmi, Ihsen Alouani, Khaled Khasawneh +4
In the past few years, an increasing number of machine-learning and deep learning structures, such as Convolutional Neural Networks (CNNs), have been applied to solving a wide rang…