## 5 Conclusions
The Vision Transformer component required an estimated 10-20 GFLOPs due to its computational complexity, while the fully connected layers added approximately 21.89 MFLOPs. The QNN contributed minimally to computational overhead, emphasizing the model's lightweight and efficient design. Additionally, the average processing time per video was approximately 4 min, making it computationally intensive for large datasets. Together, these components underline the model's efficient use of computational resources, balancing complexity and performance while maintaining robust results in deepfake detection tasks. The main challenges in deepfake detection are the diversity of fake videos and the continuously evolving forgery techniques used to produce them. The proposed method has the potential to address existing gaps in the literature. However, future studies should focus on adapting the model for real-time applications and improving its applicability on low-resource devices. Moreover, testing the model on larger and more diverse datasets could contribute to enhancing its overall performance. In conclusion, this study presents a novel approach to deepfake detection by combining the CaiT and QTL. Unlike existing studies in the literature, it has optimized resource efficiency while introducing a new perspective to deepfake detection. The findings provide a foundation for broader-scale research and applications. This method could guide the development of new models aimed at detecting more sophisticated forgery techniques in the future. Future studies could focus on testing the model on real quantum devices and training it on larger, more diversified datasets. Additionally, optimizing the model for real-time forgery detection, making it usable on low-resource devices, and integrating multimodal (e.g., visual, audio) analyses could open new horizons in the field of deepfake detection. Furthermore, testing the model's robustness against malicious techniques, such as intentional manipulations or deceptive inputs, and improving its security level could be important steps for future research. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Subject** & **Methods** & **Dataset** & **Accuracy** \\ \hline Improved Deepfake Video Detection Using & & & \\ Convolutional Vision Transformer [36] & & & \\ \hline FakeFormer: Vulnerability-Driven Transformers & & & \\ for Deepfake Detection [37] & & & \\ \hline Feature Fusion Vision Transformers for Deepfake & & & \\ Detection [38] & & & \\ \hline Hybrid Vision Transformer for Deepfake & & & \\ Detection [39] & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._  
**Author Contributions:** Conceptualization, B.E.K. and E.U.K. ; methodology, B.E.K. ; software, B.E.K. ; validation, B.E.K., E.U.K. and G.S. ; formal analysis, B.E.K. ; investigation, B.E.K. ; resources, B.E.K. ; data curation, B.E.K. ; writing--original draft preparation, B.E.K. ; writing--review and editing, B.E.K. ; visualization, B.E.K. ; supervision, E.U.K. ; project administration, E.U.K. ; funding acquisition, not applicable. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable. This study did not involve humans or animals. **Informed Consent Statement:** Not applicable. This study did not involve human participants. **Data Availability Statement:** The data presented in this study are available on Kaggle, including the DFDC dataset [17; 18], the supporting dataset [19], and the CelebDFv2 dataset [34]. **Conflicts of Interest:** The authors declare no conflicts of interest.