46 citations · 150 across the 32 of their papers we have counts for
5 papers · 1 filter
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…
Attention Deficit is Ordered! Fooling Deformable Vision Transformers with Collaborative Adversarial Patches
Quazi Mishkatul Alam, Bilel Tarchoun, Ihsen Alouani +1
The latest generation of transformer-based vision models has proven to be superior to Convolutional Neural Network (CNN)-based models across several vision tasks, largely attribute…
Fool the Hydra: Adversarial Attacks against Multi-view Object Detection Systems
Bilel Tarchoun, Quazi Mishkatul Alam, Nael Abu-Ghazaleh +1
Adversarial patches exemplify the tangible manifestation of the threat posed by adversarial attacks on Machine Learning (ML) models in real-world scenarios. Robustness against thes…
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.,…