A1 Journal article (refereed)
Digital clones and digital immunity : adversarial training handles both (2022)

Branytskyi, V., Golovianko, M., Gryshko, S., Malyk, D., Terziyan, V., & Tuunanen, T. (2022). Digital clones and digital immunity : adversarial training handles both. International Journal of Simulation and Process Modelling, 18(2), 124-139. https://doi.org/10.1504/ijspm.2022.126106

JYU authors or editors

Publication details

All authors or editorsBranytskyi, Vladyslav; Golovianko, Mariia; Gryshko, Svitlana; Malyk, Diana; Terziyan, Vagan; Tuunanen, Tuure

Journal or seriesInternational Journal of Simulation and Process Modelling



Publication year2022

Publication date05/10/2022


Issue number2

Pages range124-139

PublisherInderscience Publishers

Publication countrySwitzerland

Publication languageEnglish


Publication open accessNot open

Publication channel open access


Smart manufacturing needs digital clones of physical objects (digital twins) and human decision-makers (cognitive clones). The latter requires use of machine learning to capture hidden personalised decision models from humans. Machine learning nowadays is a subject of various adversarial attacks (poisoning, evasion, etc.). Responsible use of machine learning requires digital immunity (the capability of smart systems to operate robustly in adversarial conditions). Both problems (clones and immunity training) have the same backbone solution, which is adversarial training (learning on automatically generated adversarial samples). In this study, we design and experimentally test special algorithms for adversarial samples generation to fit simultaneously both purposes: to better personalise decision models for digital clones and to train digital immunity, thus, ensuring robustness of autonomous decision models. We demonstrate that our algorithms facilitate the desired robustness and accuracy of the training process.

Keywordsmanufacturing engineeringartificial intelligencemachine learningalgorithms

Free keywordsdigital cloning; digital immunity; Industry 40; adversarial machine learning; adversarial example generation; machine learning; generative adversarial networks; process modelling

Contributing organizations

Ministry reportingYes

Reporting Year2022

JUFO rating1

Last updated on 2024-15-06 at 20:25