## 4 Results and Discussion
Furthermore, upon examining the minor percentage differences, it can be observed that reducing the test set percentage has a slight impact on performance. Since the test and evaluation sets are distinct, increasing the number of training images aids the model in identifying anomalies more effectively. The transformer block, inspired by [PERSON] et al. [66], also demonstrates similar characteristics: a balanced application of transformers improves accuracy, while excessive use may negatively affect performance. For a comprehensive comparison, a state-of-the-art transfer learning model, ResNet50 initialized using ImageNet weights, was introduced. The ResNet50 model, which is trained on a massive image dataset, is particularly designed for classification tasks. In Table 3, a comparison between the ResNet50 transfer learning model and PV-ISM is provided. PV-ISM achieves an accuracy of 96.60%, which is 3.28 percentage points higher than ResNet50's 93.32%. This indicates that, overall, PV-ISM classifies a higher proportion of images correctly. PV-ISM appears to have a slightly reduced rate of incorrect positive classifications based on the precision score, indicating its higher reliability in predicting real images. The most significant improvement is observed in recall, where PV-ISM achieves 96.6%, compared to ResNet50's 90.43%, marking a substantial 6.17 percentage point increase. This shows that PV-ISM is considerably better at identifying all relevant instances, leading to fewer false negatives. The balanced performance, also reflected in the F1 score, suggests that PV-ISM has well-tuned parameters that work effectively in distinguishing AI-generated images from real ones, whereas the ResNet50 model exhibits more variation across metrics. This comparison demonstrates that while transfer learning with ResNet50 provides a strong baseline performance, PV-ISM offers significant improvements. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **ACC (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** \\ \hline ResNet50 & 93.32 & 95.76 & 90.43 & 93.02 \\ PV-ISM & 96.60 & 96.70 & 96.23 & 96.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison between ResNet50 and PV-ISM. The performance of PV-ISM can be investigated in comparison with other studies, as presented in Table 4. When using the same dataset, PV-ISM demonstrates superior performance relative to other studies, even outperforming highly information-dense transfer learning models. Moreover, when considering the duration times for training, the performance gap becomes even more pronounced. While transfer learning models are commonly trained on very large datasets with industrial-grade computational resources, PV-ISM achieves its results without such extensive resources. The best-performing model in our experiments, as presented in the final result in Table 2, was the flattened transformer architecture, which achieved the highest accuracy on the CIFAKE dataset. The accuracy graph, shown in Figure 10, illustrates how the model's accuracy changed during training over 65 epochs. It can be observed that the training accuracy steadily increased and reached approximately 96% by the time the training was over. Also validation accuracy remained high, albeit with some minor fluctuations after the initial few epochs. These findings show that the model maintained good performance on unseen data  
Figure 11 demonstrates the loss rate throughout the training and validation sets which correspond to the accuracy graph. The training loss decreased smoothly as the number of epochs increased, suggesting that the model continued to improve during the training phase. The validation loss also remained low, despite some slight fluctuations. This behavior is typical and indicates that the model was able to generalize to new data without significant overfitting. The small difference between the training and validation loss further supports this conclusion. Figure 12 presents the results through the confusion matrix for the evaluation set.