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Security in defect detection: A new one-pixel attack for fooling DNNs.
- Source :
- Journal of King Saud University - Computer & Information Sciences; Sep2023, Vol. 35 Issue 8, pN.PAG-N.PAG, 1p
- Publication Year :
- 2023
-
Abstract
- 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 T eaching and L earning-based M oth- F lame O ptimization (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. [ABSTRACT FROM AUTHOR]
- Subjects :
- DEEP learning
DIGITAL twins
DIFFERENTIAL evolution
DATA security
MACHINE learning
Subjects
Details
- Language :
- English
- ISSN :
- 13191578
- Volume :
- 35
- Issue :
- 8
- Database :
- Supplemental Index
- Journal :
- Journal of King Saud University - Computer & Information Sciences
- Publication Type :
- Academic Journal
- Accession number :
- 172846536
- Full Text :
- https://doi.org/10.1016/j.jksuci.2023.101689