X-Detect: Explainable Adversarial Patch Detection for Object Detectors in Retail

no code implementations • 14 Jun 2023 • Omer Hofman, Amit Giloni, Yarin Hayun, Ikuya Morikawa, Toshiya Shimizu, Yuval Elovici, Asaf Shabtai

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.

Decision Making Object +2

Seeds Don't Lie: An Adaptive Watermarking Framework for Computer Vision Models

no code implementations • 24 Nov 2022 • Jacob Shams, Ben Nassi, Ikuya Morikawa, Toshiya Shimizu, Asaf Shabtai, Yuval Elovici

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.

Model extraction

First to Possess His Statistics: Data-Free Model Extraction Attack on Tabular Data

no code implementations • 30 Sep 2021 • Masataka Tasumi, Kazuki Iwahana, Naoto Yanai, Katsunari Shishido, Toshiya Shimizu, Yuji Higuchi, Ikuya Morikawa, Jun Yajima

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.

Medical Diagnosis Model extraction