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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…
Transferability Ranking of Adversarial Examples
Mosh Levy, Guy Amit, Yuval Elovici +1
Adversarial transferability in black-box scenarios presents a unique challenge: while attackers can employ surrogate models to craft adversarial examples, they lack assurance on wh…
WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models
Yonatan Bitton, Nitzan Bitton Guetta, Ron Yosef +4
While vision-and-language models perform well on tasks such as visual question answering, they struggle when it comes to basic human commonsense reasoning skills. In this work, we…