Security in defect detection: A new one-pixel attack for fooling DNNs

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12310%2F23%3A43907484" target="_blank" >RIV/60076658:12310/23:43907484 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S1319157823002434?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1319157823002434?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jksuci.2023.101689" target="_blank" >10.1016/j.jksuci.2023.101689</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Security in defect detection: A new one-pixel attack for fooling DNNs

Popis výsledku v původním jazyce

The Industrial 5.0 Model integrates enabling technologies such as deep learning, digital twins, and the meta-universe with new development concepts. However, model and data security may pose challenges for developing zero-defect production and other industrial manufacturing industries. To address this issue, we generate adversarial examples using a one-pixel attack in adversarial machine learning, which can fool the defect detection classification model. The traditional one-pixel attack based on the Differential Evolution (DE) algorithm has limited global search ability. Therefore, we use a novel algorithm called Teaching and Learning-based Moth-Flame Optimization (TLMFO), which enhances the global search performance and improves the attack effectiveness. We evaluate TLMFO on benchmark functions and attacks on Cifar10 and ImageNet datasets, and compare it with MFO and DE. The results show that TLMFO outperforms both MFO and DE in terms of accuracy and speed of convergence. Moreover, TLMFO achieves notably better attack effectiveness than DE under targeted and untargeted attacks on the Cifar10 dataset and under-targeted attacks on the ImageNet dataset. Our research confirms that safety prevention is a link worth considering in developing Industry 5.0.(c) 2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Název v anglickém jazyce

Security in defect detection: A new one-pixel attack for fooling DNNs

Popis výsledku anglicky

The Industrial 5.0 Model integrates enabling technologies such as deep learning, digital twins, and the meta-universe with new development concepts. However, model and data security may pose challenges for developing zero-defect production and other industrial manufacturing industries. To address this issue, we generate adversarial examples using a one-pixel attack in adversarial machine learning, which can fool the defect detection classification model. The traditional one-pixel attack based on the Differential Evolution (DE) algorithm has limited global search ability. Therefore, we use a novel algorithm called Teaching and Learning-based Moth-Flame Optimization (TLMFO), which enhances the global search performance and improves the attack effectiveness. We evaluate TLMFO on benchmark functions and attacks on Cifar10 and ImageNet datasets, and compare it with MFO and DE. The results show that TLMFO outperforms both MFO and DE in terms of accuracy and speed of convergence. Moreover, TLMFO achieves notably better attack effectiveness than DE under targeted and untargeted attacks on the Cifar10 dataset and under-targeted attacks on the ImageNet dataset. Our research confirms that safety prevention is a link worth considering in developing Industry 5.0.(c) 2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of King Saud University-Computer and Information Sciences

ISSN

1319-1578

e-ISSN

2213-1248

Svazek periodika

35

Číslo periodika v rámci svazku

8

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

16

Strana od-do

—

Kód UT WoS článku

001080358700001

EID výsledku v databázi Scopus

2-s2.0-85172465788