2 citations · 3 across the 5 of their papers we have counts for
5 papers
Bounding-box Watermarking: Defense against Model Extraction Attacks on Object Detectors
Satoru Koda, Ikuya Morikawa
Deep neural networks (DNNs) deployed in a cloud often allow users to query models via the APIs. However, these APIs expose the models to model extraction attacks (MEAs). In this at…
X-Detect: Explainable Adversarial Patch Detection for Object Detectors in Retail
Omer Hofman, Amit Giloni, Yarin Hayun +4
Object detection models, which are widely used in various domains (such as retail), have been shown to be vulnerable to adversarial attacks. Existing methods for detecting adversar…
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…
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…
First to Possess His Statistics: Data-Free Model Extraction Attack on Tabular Data
Masataka Tasumi, Kazuki Iwahana, Naoto Yanai +5
Model extraction attacks are a kind of attacks where an adversary obtains a machine learning model whose performance is comparable with one of the victim model through queries and…