46 citations · 70 across the 24 of their papers we have counts for
12 papers · 1 filter
DRIFT: Divergent Response in Filtered Transformations for Robust Adversarial Defense
Amira Guesmi, Muhammad Shafique
Deep neural networks remain highly vulnerable to adversarial examples, and most defenses collapse once gradients can be reliably estimated. We identify \emph{gradient consensus} --…
TriQDef: Disrupting Semantic and Gradient Alignment to Prevent Adversarial Patch Transferability in Quantized Neural Networks
Amira Guesmi, Bassem Ouni, Muhammad Shafique
Quantized Neural Networks (QNNs) are increasingly deployed in edge and resource-constrained environments due to their efficiency in computation and memory usage. While shown to dis…
TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization
Amira Guesmi, Bassem Ouni, Muhammad Shafique
Adversarial transferability remains a critical challenge in evaluating the robustness of deep neural networks. In security-critical applications, transferability enables black-box…
Breaking the Limits of Quantization-Aware Defenses: QADT-R for Robustness Against Patch-Based Adversarial Attacks in QNNs
Amira Guesmi, Bassem Ouni, Muhammad Shafique
Quantized Neural Networks (QNNs) have emerged as a promising solution for reducing model size and computational costs, making them well-suited for deployment in edge and resource-c…
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…
Exploring the Interplay of Interpretability and Robustness in Deep Neural Networks: A Saliency-guided Approach
Amira Guesmi, Nishant Suresh Aswani, Muhammad Shafique
Adversarial attacks pose a significant challenge to deploying deep learning models in safety-critical applications. Maintaining model robustness while ensuring interpretability is…
SSAP: A Shape-Sensitive Adversarial Patch for Comprehensive Disruption of Monocular Depth Estimation in Autonomous Navigation Applications
Amira Guesmi, Muhammad Abdullah Hanif, Ihsen Alouani +2
Monocular depth estimation (MDE) has advanced significantly, primarily through the integration of convolutional neural networks (CNNs) and more recently, Transformers. However, con…
Anomaly Unveiled: Securing Image Classification against Adversarial Patch Attacks
Nandish Chattopadhyay, Amira Guesmi, Muhammad Shafique
Adversarial patch attacks pose a significant threat to the practical deployment of deep learning systems. However, existing research primarily focuses on image pre-processing defen…
SAAM: Stealthy Adversarial Attack on Monocular Depth Estimation
Amira Guesmi, Muhammad Abdullah Hanif, Bassem Ouni +1
In this paper, we investigate the vulnerability of MDE to adversarial patches. We propose a novel \underline{S}tealthy \underline{A}dversarial \underline{A}ttacks on \underline{M}D…
AdvART: Adversarial Art for Camouflaged Object Detection Attacks
Amira Guesmi, Ioan Marius Bilasco, Muhammad Shafique +1
Physical adversarial attacks pose a significant practical threat as it deceives deep learning systems operating in the real world by producing prominent and maliciously designed ph…
APARATE: Adaptive Adversarial Patch for CNN-based Monocular Depth Estimation for Autonomous Navigation
Amira Guesmi, Muhammad Abdullah Hanif, Ihsen Alouani +1
In recent times, monocular depth estimation (MDE) has experienced significant advancements in performance, largely attributed to the integration of innovative architectures, i.e.,…
AdvRain: Adversarial Raindrops to Attack Camera-based Smart Vision Systems
Amira Guesmi, Muhammad Abdullah Hanif, Muhammad Shafique
Vision-based perception modules are increasingly deployed in many applications, especially autonomous vehicles and intelligent robots. These modules are being used to acquire infor…