Novel Learning Framework with Generative AI X-Ray Images for Deep Neural Network-Based X-Ray Security Inspection of Prohibited Items Detection with You Only Look Once

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Abstract
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.
Funding Information
  • NRF (National Research Foundation of Korea) (RS-2023-00214142, IITP-2025-RS-2022-00156409)
  • National Research Foundation of Korea (RS-2023-00214142, IITP-2025-RS-2022-00156409)
  • IITP (Institute of Information & Coummunications Technology Planning & Evaluation)-ICAN (ICT Challenge and Advanced Network of HRD) (RS-2023-00214142, IITP-2025-RS-2022-00156409)

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