2022

Branytskyi, Vladyslav; Golovianko, Mariia; Gryshko, Svitlana; Malyk, Diana; Terziyan, Vagan; Tuunanen, Tuure

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.