AADMIP: Adversarial Attacks and Defenses Modeling in Industrial Processes

AADMIP: Adversarial Attacks and Defenses Modeling in Industrial Processes

Vitaliy Pozdnyakov, Aleksandr Kovalenko, Ilya Makarov, Mikhail Drobyshevskiy, Kirill Lukyanov

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Demo Track. Pages 8776-8779. https://doi.org/10.24963/ijcai.2024/1030

The development of the smart manufacturing trend includes the integration of Artificial Intelligence technologies into industrial processes. One example of such implementation is deep learning models that diagnose the current state of a technological process. Recent studies have demonstrated that small data perturbations, named adversarial attacks, can significantly affect the correct predictions of such models. This fact is critical in industrial systems, where AI-based decisions can be made to manage physical equipment. In this work, we present a system which can help to evaluate the robustness of technological process diagnosis models to adversarial attacks, as well as consider protection options. We briefly review the system's modules and also consider some useful applications. Our demo video is available at: http://tinyurl.com/3by9zcj5
Keywords:
Machine Learning: ML: Adversarial machine learning
Machine Learning: ML: Evaluation
Multidisciplinary Topics and Applications: MDA: Real-time systems
Multidisciplinary Topics and Applications: MDA: Security and privacy