## 4 Experiment and Results
### Experiment
\begin{table}
\begin{tabular}{c c c} \hline
**Node** & **Node Model** & **Official AUC** \\ \hline \(\alpha\) & Efficient Vision Transformer [25]1 & 0.919 \\ \(\beta\) & Cross Efficient Vision Transformer [25]2 & 0.951 \\ \(\gamma\) & Ensemble EfficientNet B4/B4 ST/B4 Att/B4 AttST [26]3 & 0.944 \\ \hline \end{tabular} \({}^{1}\)[[https://github.com/davide-cocomini/Combining-EfficientNet-and-Vision-Transformers-for-Video-Deepfake-Detection/tree/main/efficient-vit/](https://github.com/davide-cocomini/Combining-EfficientNet-and-Vision-Transformers-for-Video-Deepfake-Detection/tree/main/efficient-vit/)]([https://github.com/davide-cocomini/Combining-EfficientNet-and-Vision-Transformers-for-Video-Deepfake-Detection/tree/main/efficient-vit/](https://github.com/davide-cocomini/Combining-EfficientNet-and-Vision-Transformers-for-Video-Deepfake-Detection/tree/main/efficient-vit/)) (accessed on 5 January 2023). 2 [[https://github.com/davide-cocomini/Combining-EfficientNet-and-Vision-Transformers-for-Video-Deepfake-Detection/tree/main/cross-efficient-vit/](https://github.com/davide-cocomini/Combining-EfficientNet-and-Vision-Transformers-for-Video-Deepfake-Detection/tree/main/cross-efficient-vit/)]([https://github.com/davide-cocomini/Combining-EfficientNet-and-Vision-Transformers-for-Video-Deepfake-Detection/tree/main/cross-efficient-vit/](https://github.com/davide-cocomini/Combining-EfficientNet-and-Vision-Transformers-for-Video-Deepfake-Detection/tree/main/cross-efficient-vit/)) (accessed on 5 January 2023). 3 [[https://github.com/polimi-ispl/icpr2020](https://github.com/polimi-ispl/icpr2020) dfdc/]([https://github.com/polimi-ispl/icpr2020](https://github.com/polimi-ispl/icpr2020) dfdc/) (accessed on 5 January 2023).  
\end{table} 
Table 3: Deepfake detection model of ICP node for experiment. \begin{table}
\begin{tabular}{c c c c} \hline
**Number** & **Name** & **Volume** \\ \hline
1 & DFDC [22]1 & 124 k videos \\
2 & Faceforensics++ [23]2 & 3000 videos \\ \hline \end{tabular} \({}^{1}\) [[https://ai.facebook.com/datasets/dfdc/](https://ai.facebook.com/datasets/dfdc/)]([https://ai.facebook.com/datasets/dfdc/](https://ai.facebook.com/datasets/dfdc/)) (accessed on 5 January 2023). 2 [[https://github.com/ondyari/FaceForensics/](https://github.com/ondyari/FaceForensics/)]([https://github.com/ondyari/FaceForensics/](https://github.com/ondyari/FaceForensics/)) (accessed on 5 January 2023).  
\end{table} 
Table 4: Deepfake dataset for the experiment. independently driven. The final aggregation value is a sum of the user vote aggregation result of Equation (5) and the nodevote mixing result of Equation (6). The user vote result value \(VI^{user}\) has a maximum value of 70, and the nodevote result value \(VI^{node}\) has a maximum value range of 30. The graph shows that the proposed model shows high and stable predictions on average. This shows that stable predictions can be made by integrating user votes and the results of each node model according to node reliability. Since the video contents registered in the ICP are unpredictable and a very large amount of data is uploaded, those with average stable predictions and small changes can have a high safety level for incident response. The proposed detection process shows an AUC in the range of 91-95 in response to changes in the data set through experiments. Through this, it is confirmed that the detection process of the proposed system performs objective and neutral stable prediction because the vibration width of the AUC according to the data fluctuation is smaller than that of the individual deep-learning model. Figure 14 shows the change in the node weight value of the node model during each feedback epoch for the node reliability evaluation. As the experiment proceeds with the node model fixed, the node weight converges similarly to the accuracy distribution of each model. EfficientViT, cross-EfficientViT, and efficientNetB4 were assigned as the three nodes in the experimental environment. The hardware and environmental performance of each node were identical.