## 5 Supplementary Experimental Studies with PV-ISM
### The Art Images Dataset  
The dataset employed in this experiment is sourced from various artists and GAN projects [77]. This curated dataset enables the development and evaluation of models for distinguishing between authentic human-created art and artificially synthesized images, including those generated by generative adversarial networks (GANs) and other AI-based image synthesis techniques. The dataset comprises a total of 21,642 images, evenly divided into two categories. * Real: This class contains 10,821 authentic networks from WikiArt, a comprehensive and publicly accessible art database. The artworks span a diverse range of artistic styles, periods, and genres, contributing to the representational richness of the real class. * Fake: This class includes 10,821 AI-generated images created using various generative models, including GANs. These synthetic images were curated to emulate the visual properties of real artworks, introducing variability in style and technique to challenge classification algorithms and promote generalization. Each image in the dataset has been resized to 256 \(\times\) 256 pixels, providing uniformity and reducing preprocessing overhead during model training. Figure 15 illustrates sample images from both categories. The dataset is intentionally balanced with an equal number of real and fake images, facilitating fair evaluation in binary classification tasks without additional class-balancing techniques. The real artworks encompass many artistic traditions, while the fake class includes outputs from multiple GAN architectures, enhancing the dataset's variability and suitability for training robust models. All images are stored in a high-resolution format and preprocessed to a standard size, making them directly compatible with deep learning pipelines. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **ACC (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** \\ \hline ResNet50 & 67.27 & 69.16 & 62.33 & 65.57 \\ ResNet101 & 66.93 & 67.96 & 64.07 & 65.96 \\ PV-ISM & 81.70 & 82.82 & 80.00 & 81.38 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison between ResNet50, ResNet101, and PV-ISM on face dataset. Figure 14: Accuracy and loss values vs. epoch. Table 7 presents the performance metrics for various configurations of the Vision Transformer (ViT) architecture, where the input image size (IS), patch size (PS), number of transformer blocks (TBs), and MLP layer dimensions were systematically varied. The performance is reported in terms of accuracy (ACC), precision, recall, and F1 score. The evaluation process of this experiment was conducted similarly to the previous experiment, including base data augmentation as well as an early stopping mechanism with a patience of ten. Consecutive dense layers were also adjusted for performance improvement. The split ratio of test-to-training was adjusted to 0.2 for all hyperparameter evaluations, with a batch size of 64. Initial results using an image size of 224 by 224 pixels and a patch size of 14 showed modest performance when only four transformer blocks were used. This configuration achieved an accuracy of 78.42 percent and an F1 score of 79.20 percent. Increasing the number of transformer blocks to eight and expanding the multi-layer perceptron to include larger hidden units resulted in a significant performance boost. Under this setting, the model achieved an F1 score of 89.60 percent, illustrating the positive influence of greater depth and model capacity. Transitioning to a higher image resolution of 256 by 256 pixels and a patch size of 16 led to further improvements. A configuration with eight transformer blocks and a relatively small multi-layer perceptron still matched the previous F1 score of 89.60 percent. This indicates that increased input resolution enhances feature representation, even with reduced feed-forward capacity. The most notable performance gains were observed in configurations that used high-resolution inputs and deeper transformer networks.