## 4 Experiment and Results
### Experiment  
The experiments in this study were conducted under environmental assumptions. The assumptions were as follows. The network peer node, the ICP node, submits the _seed_ to the network within normal time for smooth operation of RANDAO for voter election. In addition, the node model calculates the prediction result for the agenda content within a normal time limit and submits it to the blockchain network. In addition, the node model calculates the prediction result for the agenda content within a normal time limit of 1440 min and submits it to the blockchain network. In the experiment, the video of the dataset was divided into frames and used for each feedback epoch. In the experiment, the voting accuracy of user voting by system clients was assumed to be 92.5 \(\pm\) 2.5%p. The experimental environment consisted of consensus through an ordering service, 100 voters, three peer nodes, and a node model in a Hyperledger Fabric blockchain network environment. Table 5 shows a comparison between the area under the receiver operating characteristic (AUROC) of each deep-learning model and the AUC through the integrated voting process of the proposed system. Each node model was tested through a combined dataset and environment for the experiment in this study. In the experiment, each node sequentially occupied hardware resources for detection and decision making. Figure 12 shows the ROC curve and confusion matrix of the integrated voting of the proposed system. Through the ROC curve and confusion matrix, high accuracy is shown in the final decision through integrated voting when it is an actual deep fake video. In the proposed system, when the average accuracy of the node model was 87% or higher, \(VI^{node}\) of \(VI^{value}\) in Equation (7) for voting collection was 26.1044. Each node model is assumed to be given a node weight proportional to its accuracy by the reliability evaluation. The minimum value for exceeding the threshold-73 of \(VI^{voter}\) for the agreement of integrated voting is 46.8957. It is resistant to up to 16 malicious voters based on 100 voters in an experimental environment, assuming a voting accuracy of 92.5 \(\pm\) 2.5%p. Figure 13 shows the AUC change of the independent deep-learning model and the proposed system during each feedback epoch. The above figure shows the AUC change according to the data set of the final aggregation model of integrated voting and when the deep-learning models \(\alpha\), \(\beta\), and \(\gamma\) of each node constituting integrated voting are \begin{table}
\begin{tabular}{c c c} \hline
**Num** & **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). 3 [[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.