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Found 4 papers, 0 papers with code
X-Detect: Explainable Adversarial Patch Detection for Object Detectors in Retail
X-Detect was evaluated in both the physical and digital space using five different attack scenarios (including adaptive attacks) and the COCO dataset and our new Superstore dataset.
YolOOD: Utilizing Object Detection Concepts for Multi-Label Out-of-Distribution Detection
In this research, we propose YolOOD - a method that utilizes concepts from the object detection domain to perform OOD detection in the multi-label classification task.
Seeds Don't Lie: An Adaptive Watermarking Framework for Computer Vision Models
In this paper, we present an adaptive framework to watermark a protected model, leveraging the unique behavior present in the model due to a unique random seed initialized during the model training.
First to Possess His Statistics: Data-Free Model Extraction Attack on Tabular Data
Whereas model extraction is more challenging on tabular data due to normalization, TEMPEST no longer needs initial samples that previous attacks require; instead, it makes use of publicly available statistics to generate query samples.
