## 3 Dataset  
In the field of image synthesis and data augmentation for X-ray imaging, the dataset is the most important factor in producing high-quality images for various applications, such as detection [41] and layer separation [42]. These applications require complex datasets to successfully identify hidden prohibited images and recognize prohibited objects occluded within X-ray images. Although it is challenging to obtain such specific datasets, deep learning enables the creation of complex datasets and their associated ground truths. However, the effectiveness of these synthesised images still heavily depends on the availability of a real dataset that includes diverse objects in multiple positions and of high quality. However, there are two popular X-ray datasets: GDX-ray [43] and SIXray [44]. GDX-ray consists of greyscale images, whereas pseudo-colour images are needed for image synthesis in X-ray imaging. SIXray has over a million images, but only 8,929 of them contain prohibited items. Due to the limitations of existing datasets, our experiment utilised a Korean dataset [45], which contained 35 types of hazardous products relevant to ports and airports. There were 570 different types of objects in this collection, all carefully categorized into 35 different groups. The dataset was created using four different scanning techniques, and the results of each technique were carefully categorized into separate files: \"Single Default\", \"Single Other\", \"Multiple Category\", and \"Multiple Others\". This structured approach ensured the creation of a comprehensive and diverse dataset, significantly enhancing its inclusiveness and practical applicability. Single Default: This dataset captured a single sample of the relevant item, excluding any other items. The item was scanned from various angles. The data set represented 15% of the total data for the target image, considering different angles and entry directions of the luggage. Single Other: This dataset included a relevant item captured from various angles, along with general non-hazardous objects such as clothes, cables, chargers, pens, and glasses. However, these additional items were not classified as part of our specific object \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **[Ref.]** & **Year** & **Dataset** \\ \hline Custom CT-GAN & [PERSON] [38] & 2018 & Private dataset \\ GAN and KNN & [PERSON] [35] & 2019 & GDX-ray \\ SAGAN and CycleGAN & [PERSON] [34] & 2020 & GDX-ray and SIXray \\ DCGAN and CycleGAN & [PERSON] [2] & 2020 & SIXray \\ Improved self-attention & & & \\ GAN (SAGAN) & [PERSON] [37] & 2020 & Private dataset \\ SCAB-XWGAN-GP & [PERSON] [36] & 2022 & Private dataset \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative analysis of X-ray security image generative models. classes. The dataset represented 15% of the total data for the target image, considering the longitudinal direction. Multiple Categories: This dataset captured a single instance of the relevant item alongside other hazardous objects. These images constituted the majority of the dataset, accounting for 40% of the total data. Simultaneous images were generated with other samples of the same item. For instance, if the target item was a gun, the images could also include other sharp tools or hazardous items at the same time. Multiple Others: This dataset comprised multiple instances of the relevant item alongside general non-hazardous objects. These images constituted 30% of the entire dataset. All four types of data were generated by inserting the items into bags, backpacks, suitcases, and baskets as shown in Figure 1.  
This dataset already included detailed bounding boxes and segmentation data in 140,000 images, offering two levels of precision to specify their precise location and shape. These segmentation data were essential for our next step: cropping the objects from the image by extracting the objects from their backgrounds. Figure 2 showcases the real images extracted from the original dataset and the different edges used in our experiment.