## 6 Conclusions  
This study introduced PV-ISM, a Vision Transformer-based framework for distinguishing between real and AI-generated images. Beyond strong performance on the CIFAKE dataset, the results underscore the architectural advantage of patch-wise tokenization and attention-based modeling for capturing subtle generative artifacts. Architecturally, PV-ISM diverges from conventional ViT models through several design choices. First, it integrates custom dense and dropout layers not only after but also within the transformer stack, improving non-linearity and regularization. Second, the model replaces the typical sigmoid-activated single-neuron output with a dual-logit strategy, outputting raw values per class before applying softmax externally. This offers greater  
Figure 16: Accuracy and loss values vs. epoch. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **ACC (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** \\ \hline ResNet50 & 88.98 & 83.5 & 93.77 & 88.34 \\ ResNet101 & 90.94 & 86.46 & 94.97 & 90.52 \\ PV-ISM & 93.16 & 88.80 & 99.09 & 93.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison between ResNet50, ResNet101, and PV-ISM on Art Images dataset. flexibility in thresholding and interpretability. Importantly, PV-ISM achieves high performance without relying on pre-trained backbones or transfer learning, which makes it lightweight, fast, and adaptable for deployment in practical scenarios. To evaluate generalizability, two additional experiments were conducted using the Real vs. Fake Faces (RVF-10K) and Art Images datasets. The RVF-10K dataset tested the model's ability to detect facial deepfakes in realistic, high-resolution scenarios, while the Art Images dataset assessed PV-ISM's robustness in stylistically abstract domains. In both cases, the model maintained strong classification performance, supporting its applicability across varying visual distributions and use cases. These findings extend the potential impact of PV-ISM beyond technical benchmarks. As generative models continue to evolve, their misuse in journalism, forensics, and digital media poses growing risks. Robust detection systems like PV-ISM are therefore crucial for preserving visual trust in increasingly synthetic digital ecosystems. Future work could investigate the scalability of PV-ISM to high-resolution datasets and real-time applications, explore its integration with multimodal data (e.g., video or audio), and advance interpretability tools to better understand attention mechanisms in transformer-based architectures. O.C. : Conceptualization, methodology, investigation, formal analysis, resources, data curation, visualization, and writing--review and editing; Y.D. : data curation, supervision, project administration, and writing--review and editing. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The CIFAR dataset [39] is publicly available in the Kaggle machine learning repository ([[https://www.kaggle.com/datasets/birdy654/cifake-real-and-ai-generated-synthetic-images](https://www.kaggle.com/datasets/birdy654/cifake-real-and-ai-generated-synthetic-images)]([https://www.kaggle.com/datasets/birdy654/cifake-real-and-ai-generated-synthetic-images](https://www.kaggle.com/datasets/birdy654/cifake-real-and-ai-generated-synthetic-images)), accessed on 12 April 2025). The RVF-10K dataset [76] is publicly available in the Kaggle machine learning repository ([[https://www.kaggle.com/c/deepfake-detection-challenge/discussion/121173](https://www.kaggle.com/c/deepfake-detection-challenge/discussion/121173)]([https://www.kaggle.com/c/deepfake-detection-challenge/discussion/121173](https://www.kaggle.com/c/deepfake-detection-challenge/discussion/121173)), accessed on 1 June 2025).