ScholarWorks@경상국립대학교

초록

As the rapid expansion of future mobility systems increases, along with the demand for fast and accurate X-ray security inspections, deep neural network (DNN)-based systems have gained significant attention for detecting prohibited items by constructing high-quality datasets and enhancing detection performance. While Generative AI has been widely explored across various fields, its application in DNN-based X-ray security inspection remains largely underexplored. The accessibility of commercial Generative AI raises safety concerns about the creation of new prohibited items, highlighting the need to integrate synthetic X-ray images into DNN training to improve detection performance, adapt to emerging threats, and investigate its impact on object detection. To address this, we propose a novel machine learning framework that enhances DNN-based X-ray security inspection by integrating real-world X-ray images with Generative AI images utilizing a commercial text-to-image model, improving dataset diversity and detection accuracy. Our proposed framework provides an effective solution to mitigate potential security threats posed by Generative AI, significantly improving the reliability of DNN-based X-ray security inspection systems, as verified through comprehensive evaluations.

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