## 5 Supplementary Experimental Studies with PV-ISM
### The RVF-10K Dataset  
For additional experiments, the Real vs. Fake Faces (RVF-10K) dataset was utilized--a curated collection of 10,000 facial images specifically designed for binary classification tasks distinguishing authentic human faces from synthetically generated ones. This dataset addresses the growing need for robust deepfake detection systems in an era where photorealistic face generation has become increasingly accessible through advanced generative models. The RVF-10K dataset maintains a balanced distribution across both training and validation splits to ensure unbiased model evaluation. The complete dataset structure is organized as follows:  
* Training set: 7000 images (3500 real, 3500 fake). * Validation set: 3000 images (1500 real, 1500 fake). * Total: 10,000 images with perfect class balance (50% real, 50% fake). The dataset draws from two distinct and well-established sources to ensure high quality and diversity. Authentic images: Sourced from NVIDIA Research's Flickr-Faces-HQ (FFHQ) dataset, these images represent authentic human faces captured in natural settings. The FFHQ dataset is renowned for its high resolution, diverse demographic representation, and careful curation process that removes low-quality or problematic images [75]. Synthetic images: These images were generated using the StyleGAN architecture from [PERSON]'s 1 Million Fake Face dataset [76]. They demonstrate state-of-the-art generative capabilities, producing photorealistic faces that challenge traditional detection methods. StyleGAN-generated content was selected to ensure that the dataset reflects current deepfake generation techniques. The dataset encompasses a wide range of facial characteristics and imaging conditions. Authentic images exhibit natural variations in lighting, pose, expression, age, and ethnicity, reflecting the diversity found in authentic photography. Synthetic images demonstrate sophisticated generation quality, with consistent lighting and composition, while maintaining a photorealistic appearance that poses genuine classification challenges. The sample images in Figure 13 from the dataset illustrate the quality and diversity of both classes. Real faces display natural imperfections, varied lighting conditions, and authentic expressions. In contrast, synthetic faces show the high-fidelity output of modern generative models, with subtle artifacts that require sophisticated identification detection methods. As with the CIFAKE dataset classification, the same data augmentation techniques were applied. In addition, no limit was set for training epoch count, and an early stopping mechanism was utilized with a patience of ten epochs. Differently from the CIFAKE dataset, consecutive dense layers were adjusted for performance improvement. The training split ratio was set to 0.2 for all hyperparameter evaluations. The results shown in Table 5 demonstrate that the proposed performance is susceptible to architectural choices, including image size, patch size, transformer depth, MLP layer dimensions, and batch size. Substantial gains in classification performance are observed as these parameters are scaled appropriately. Starting with an image size of 224 \(\times\) 224, increasing the depth of the transformer blocks and the size of the MLP layers led to notable improvements. For instance, moving from a shallow ViT with four transformer blocks and small MLP layers (accuracy = 69.30%, F1 = 63.20) to deeper models with more expressive MLPs and finer patch sizes resulted in a consistent rise in accuracy and F1 score. Notably, using a 256 \(\times\) 256 image resolution with a patch size of 16, 32 transformer blocks, and an MLP of (2048, 1024), the model achieved its best performance, with an accuracy of 81.70%, precision of 82.82%, recall of 80.00%, and F1 score of 81.38. Figure 14 shows the overall training progress, demonstrating the accuracy and loss over epochs.