# Advanced Deep Learning Methods to Generate and Discriminate Fake Images of Egyptian Monuments
## 3 Methodology  
### Data Collection  
This study developed research data using various complementary information sources to build a broad and accurate database of Egyptian monumental structures. The primary data sources included the following:  
1. Crowdsourced Collections: Wikimedia Commons, the world's largest crowdsourced media repository, delivers historically documented images that the public has verified for accuracy;
2. Research Repositories: [PERSON]'s Egypt Monuments Dataset (222 MB) provides a curated collection of monument pictures [2]. The final combined dataset comprised approximately 5000 photos, according to the target research requirements, from diverse historical periods and geographical locations. In addition, multiple Egyptian architectural features, such as pyramids, temples, obelisks, sphinxes, and additional elements, were represented. The original image resolution varied between 256 \(\times\) 256 pixels and 512 \(\times\) 512 pixels. Multiple approaches have been implemented to ensure dataset quality control. First, a verification process was implemented while developing the image database to confirm that every source image originated from reputable public-domain repositories and confirmed metadata, such as licensing information or explicit rights statements. Additionally, reverse image searches were conducted using tools such as Google Images and TinEye to cross-reference and rule out potential copyright restrictions and ambiguous usage terms. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Contributions** & **Identified Gaps** & **Future Research Directions** \\ \hline \multirow{6}{*}{[PERSON] et al. [7]} & Collection of Generation of Chinese Architectural Heritage datasets, creation of domain-specific datasets, implementation of style transfer techniques. & Evaluation metrics of the model were limited, and there was no focus on Egyptian monuments, whereas the architectural detail required more thorough protective measures. & Cross-cultural architectural generation Egyptian-specific adaptations Enhanced evaluation frameworks. \\ \hline \multirow{6}{*}{[PERSON] et al. [7]} & StyleGAN for Chinese calligraphy, cultural artifact generation, and detail preservation techniques. & The style transfer applications involved 2D art but not architecture. Application to Egyptian cultural heritage was included, so there was limited understanding of hierarchical features. & Extension to 3D architectural forms, Egyptian heteroglymic integration, and multi-scale feature representation. \\ \hline \multirow{6}{*}{[PERSON] [16]} & Reconstruction from fragmentary evidence, archaeological application of GANs, historical accuracy evaluation. & Limited dataset size. No Egyptian monument focus. & The GAN system excels at completing Egyptian monument fragments while attracting expert archaeologists to improve available archaeological datasets for reconstruction. \\ \hline \multirow{6}{*}{[PERSON] et al. [19]} & A 3D modeling of Egyptian monuments, photogrammetric techniques, archival quality documentation. & No generative approaches. Limited to existing monuments only. No integration with text descriptions. & The GAN algorithm performs 3D reconstruction alongside creative damage repair operations under text directive. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cont. The dataset has been cleaned of any images that were duplicates or nearly duplicates using perceptual hashing techniques. The method employed for comparison was a representation of each image as a hash, which allowed for easy computation of visual likeness. Images that had hash values lower than a specific threshold of resemblance set to T were identified as duplicates and removed without preserving diversity and quality within the dataset [22]. Removing overfitting to needless patterns increases StyleGAN output generation while maintaining dataset integrity improves algorithm performance [12]. Third, a group of images (n = 500) was subjected to manual review to ensure that they maintained quality requirements.