## 5 Conclusions  
The originality of this model stands out not only due to the results obtained but also because of the innovative methodology employed. The advantages of quantum computing provided by QTL were combined with the global feature extraction capacity of the CaiT, enabling the detection of subtle details in deepfake videos. In particular, the CaiT's ability to detect small-scale forgery details in visual data is considered one of the model's strongest aspects. Additionally, the benefits offered by QTL facilitated a more efficient learning process on large datasets, accelerating the training process and increasing the model's generalization capacity. Despite its strengths, this study also has some limitations. The model was tested only in a simulated quantum environment, and no evaluation was conducted on a real quantum device. This partially limits the generalizability of the results. Furthermore, the DFDC dataset is one of the largest and most diverse datasets used for deepfake detection. However, due to hardware and time constraints, only 50% of the dataset was utilized in this study. For instance, CNN-LSTM-based models in the literature achieved 98.2% accuracy using the full dataset. Utilizing the entire dataset could provide a more comprehensive evaluation of the model's performance and potentially improve its accuracy. A comparative analysis of the proposed QTL-CaiT model against state-of-the-art deepfake detection methods is presented in Table 6. The table highlights the performance of various models, including transformer-based architectures and hybrid approaches, evaluated on different datasets. Notably, the QTL-CaiT model achieves a high accuracy of 90% on the DFDC dataset, showcasing its competitive edge in performance. Compared to other methods, such as CViT and FakeFormer, the proposed model demonstrates both robustness and effectiveness in handling deepfake detection tasks, further emphasizing its potential applicability in diverse scenarios. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Subject** & **Methods** & **Dataset** & **Accuracy** \\ \hline Quantum Transfer Learning and & & & \\ Class-Attention Vision Transformer & QTL and CaiT & DFDC & 90\% \\ Architecture (This Study) & & & \\ \hline Efficient Deepfake Detection Using Vision & CViT & DFDC & 85\% \\ Transformers [35] & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparative analysis of deepfake detection studies using Vision Transformer-based and hybrid models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Subject** & **Methods** & **DFDC Accuracy** & **DFDC ROC AUC** \\ \hline A Hybrid CNN-LSTM model for Video & & & \\ Deepfake Detection by Leveraging Optical & CNN-LSTM & 66.26\% & Unspecified \\ Flow Features [11] & & & \\ \hline Deepfake Video Detection Using & Convolutional Vision & & \\ Convolutional Vision Transformer [12] & Transformer & 91.5\% & 0.91 \\ \hline Deepfake Detection by Analyzing & & & \\ Convolutional Traces [7] & & & \\ \hline Deepfake Detection Using Multi-Attentional & & & \\ Techniques [8] & Multi-attention Mechanisms & 89.5\% & 0.92 \\ \hline Joint Audio-Visual Deepfake Detection [9] & ResNet, Swin Transformer & 92.1\% & Unspecified \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._The proposed QTL-CaiT model demonstrates a total parameter count of approximately 24.7 million, which includes 24 million parameters from the Vision Transformer (CaiT), 686,992 parameters from the fully connected layers, and 86 parameters from the Quantum Neural Network (QNN). During the training process, the total RAM usage was observed to be approximately 12-13 GB. This memory consumption accounts for the storage of model parameters (-98.74 MB), intermediate activations generated during forward and backward passes, optimizer states, and batch data loaded into memory. Each batch, with a size of 32 and input dimensions of 224 \(\times\) 224 pixels, occupied approximately 192 MB of memory.