5 citations · 13 across the 9 of their papers we have counts for
6 papers · 1 filter
DeSparsify: Adversarial Attack Against Token Sparsification Mechanisms in Vision Transformers
Oryan Yehezkel, Alon Zolfi, Amit Baras +2
Vision transformers have contributed greatly to advancements in the computer vision domain, demonstrating state-of-the-art performance in diverse tasks (e.g., image classification,…
QuantAttack: Exploiting Dynamic Quantization to Attack Vision Transformers
Amit Baras, Alon Zolfi, Yuval Elovici +1
In recent years, there has been a significant trend in deep neural networks (DNNs), particularly transformer-based models, of developing ever-larger and more capable models. While…
YolOOD: Utilizing Object Detection Concepts for Multi-Label Out-of-Distribution Detection
Alon Zolfi, Guy Amit, Amit Baras +4
Out-of-distribution (OOD) detection has attracted a large amount of attention from the machine learning research community in recent years due to its importance in deployed systems…
Phantom Sponges: Exploiting Non-Maximum Suppression to Attack Deep Object Detectors
Avishag Shapira, Alon Zolfi, Luca Demetrio +2
Adversarial attacks against deep learning-based object detectors have been studied extensively in the past few years. Most of the attacks proposed have targeted the model's integri…
Adversarial Mask: Real-World Universal Adversarial Attack on Face Recognition Model
Alon Zolfi, Shai Avidan, Yuval Elovici +1
Deep learning-based facial recognition (FR) models have demonstrated state-of-the-art performance in the past few years, even when wearing protective medical face masks became comm…
The Translucent Patch: A Physical and Universal Attack on Object Detectors
Alon Zolfi, Moshe Kravchik, Yuval Elovici +1
Physical adversarial attacks against object detectors have seen increasing success in recent years. However, these attacks require direct access to the object of interest in order…