1.5k citations · 2.3k across the 133 of their papers we have counts for
12 papers · 1 filter
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…
Latent SHAP: Toward Practical Human-Interpretable Explanations
Ron Bitton, Alon Malach, Amiel Meiseles +5
Model agnostic feature attribution algorithms (such as SHAP and LIME) are ubiquitous techniques for explaining the decisions of complex classification models, such as deep neural n…
Seeds Don't Lie: An Adaptive Watermarking Framework for Computer Vision Models
Jacob Shams, Ben Nassi, Ikuya Morikawa +3
In recent years, various watermarking methods were suggested to detect computer vision models obtained illegitimately from their owners, however they fail to demonstrate satisfacto…
Attacking Object Detector Using A Universal Targeted Label-Switch Patch
Avishag Shapira, Ron Bitton, Dan Avraham +3
Adversarial attacks against deep learning-based object detectors (ODs) have been studied extensively in the past few years. These attacks cause the model to make incorrect predicti…
Improving Interpretability via Regularization of Neural Activation Sensitivity
Ofir Moshe, Gil Fidel, Ron Bitton +1
State-of-the-art deep neural networks (DNNs) are highly effective at tackling many real-world tasks. However, their wide adoption in mission-critical contexts is hampered by two ma…
A Transferable and Automatic Tuning of Deep Reinforcement Learning for Cost Effective Phishing Detection
Orel Lavie, Asaf Shabtai, Gilad Katz
Many challenging real-world problems require the deployment of ensembles multiple complementary learning models to reach acceptable performance levels. While effective, applying th…
Evaluating the Security of Aircraft Systems
Edan Habler, Ron Bitton, Asaf Shabtai
The sophistication and complexity of cyber attacks and the variety of targeted platforms have been growing in recent years. Various adversaries are abusing an increasing range of p…
A Survey of MulVAL Extensions and Their Attack Scenarios Coverage
David Tayouri, Nick Baum, Asaf Shabtai +1
Organizations employ various adversary models in order to assess the risk and potential impact of attacks on their networks. Attack graphs represent vulnerabilities and actions an…
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…
AnoMili: Spoofing Prevention and Explainable Anomaly Detection for the 1553 Military Avionic Bus
Efrat Levy, Nadav Maman, Asaf Shabtai +1
MIL-STD-1553, a standard that defines a communication bus for interconnected devices, is widely used in military and aerospace avionic platforms. Due to its lack of security mechan…
Evaluating the Security of Open Radio Access Networks
Dudu Mimran, Ron Bitton, Yehonatan Kfir +5
The Open Radio Access Network (O-RAN) is a promising RAN architecture, aimed at reshaping the RAN industry toward an open, adaptive, and intelligent RAN. In this paper, we conducte…
Adversarial Machine Learning Threat Analysis and Remediation in Open Radio Access Network (O-RAN)
Edan Habler, Ron Bitton, Dan Avraham +6
O-RAN is a new, open, adaptive, and intelligent RAN architecture. Motivated by the success of artificial intelligence in other domains, O-RAN strives to leverage machine learning (…