Vijayanth Tummala - Researcher in Cybersecurity and Human-AI Interaction domain
Evaluating The Impact of Cyberattacks On AI-based Machine Vision Systems: A Case Study of Threaded Fasteners
Jan 21, 2026
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Abstract
AI-driven machine vision systems are becoming essential in mechanical engineering applications such as fastener classification, yet their increasing connectivity exposes them to adversarial cyberattacks. Model evasion attacks like FGSM can subtly alter input images and cause misclassification, raising concerns about reliability in automated manufacturing.This talk focuses on the role of Explainable AI and human-in-the-loop strategies in detecting and mitigating such attacks. In the presented case study, an EfficientNet-B0 fastener classification model is examined using Grad-CAM visualizations to determine whether shifts inactivation patterns can reveal adversarial manipulation. The study evaluates how FGSM-generated images affect model accuracy and confidence while assessing the XAI system's ability to highlight abnormal regions of attention and the potential for human-in-the-loop approaches to be utilized with XAI techniques as a practical path to strengthening the resilience of AI-based machine vision systems in manufacturing.
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