## 2 Related Work  
In the field of X-ray security screening, the lack of sufficient image datasets for training convolutional neural networks (CNNs) present a major challenge. Addressing this issue, [PERSON] et al. [34] made a noteworthy contribution by proposing an innovative data augmentation method for enhancing X-ray image datasets. Their research began with the generation of a diverse range of prohibited item images using an improved self-attention generative adversarial network (SAGAN). Subsequently, a CycleGAN-based technique was applied to convert natural images of items into X-ray-style images, significantly enriching the diversity in terms of item shape and pose. These images were then synthesised with background images to create realistic X-ray security checking scenes. The effectiveness of this augmented dataset was validated using two single-shot multibox detector (SSD) models, and the findings revealed that the performance of the SSD model trained with the augmented dataset surpassed that of the model trained with the original dataset. [PERSON]'s work not only tackled the critical issue of insufficient training data in X-ray security screening but also highlighted the potential of advanced generative models in generating diverse and realistic training datasets. Also, [PERSON] [35] proposed a novel data augmentation technique using generative adversarial networks (GANs). This process involved extracting prohibited items from X-ray images using a K-Nearest Neighbour (KNN) matting scheme, categorizing them based on poses estimated through a space rectangular coordinate system, and then generating realistic images with an improved GAN model. The effectiveness of these generated images was validated through a cross-validation scheme using a CNN model, showing their successful classification and indicating their potential to augment the existing dataset for CNN training. The study also focused on optimizing the GAN model, including adjustments to the architecture, loss function (CT-GAN), and specific parameter settings to enhance the quality of generated X-ray images. [PERSON] et al. [2] suggested exploring the use of generative adversarial networks (GANs) for image augmentation to improve the performance of these algorithms on an imbalanced X-ray dataset. Specifically, they employed Deep Convolutional GAN (DCGAN) for generating new X-ray images of threat objects and CycleGAN for translating camera images of threats into X-ray images. These synthesised images were then combined with background X-ray images to augment the dataset. The authors trained various Faster R-CNN models using different augmentation approaches, including both image transformation and image synthesis and assessed their performance on a practical X-ray image dataset. The results demonstrated that image synthesis effectively reduced the false-positive rate by up to 19.9% when combined with conventional image augmentation while maintaining a high true-positive rate of about 94%. This study highlighted the potential of GAN-based image synthesis in addressing the imbalance problem in X-ray security image datasets. [PERSON] [36] tackled the scarcity of pseudocolour X-ray image datasets for prohibited item detection in security inspections by introducing a data augmentation method, DA-PIX. This approach utilised a generative adversarial network model, SCAB-XWGAN-GP, to generate high-quality dual-energy X-ray images. These synthetic images were then composited with real data to simulate realistic overlapping conditions, significantly enhancing dataset robustness, and improving the performance of object detection models in identifying prohibited items in X-ray scans. [PERSON] et al. [37] proposed an innovative method to augment the dataset of X-ray images of prohibited items. They utilised an improved self-attention generative adversarial network (SAGAN) for this purpose. Initially, they collected a preliminary set of X-ray images representing various prohibited items.