Adversarial Weight Prediction Networks for Defense of Industrial FDC Systems
Zhenqin Yin, Lingjian Ye, Zhiqiang Ge · IEEE Transactions on Industrial Informatics · 2024
In recent years, more and more open environment have led to confidential links and data exposure, which seriously threatens the security of industrial systems. Adversarial attacks can easily fool machine learning models by adding tiny perturbations to input data. Industrial fault detection and classification (FDC) system is an indispensable part of ensuring production safety, but it is also not immune to the impact of adversarial risks. Once it is under attack, the disastrous consequences that may be caused to the industrial system are unimaginable. Adversarial training is among the most effective defense methods to protect those data-driven intelligent systems. This article studies a novel reweighted adversarial training approach called adversarial weight prediction networks. By assigning more appropriate weights to different data samples, we can make better use of the limited model capacity of the industrial FDC system. Particularly, predicting weights through a synchronized network overcomes the limitations of insufficient information and nontransferability of existing statistical methods. Performance evaluation on three industrial cases containing structured and image data shows the superior generalization and stability of our proposed method.