## 3 Deepfake Detection System through Collective Intelligence and Deep Learning
### Integrated Vote
#### 3.3.3 Composition of Integrated Vote
The voting process requests node model voting from service nodes, which are peer nodes in the network, with branching through the system's main chaincode. The service node decides on the voting agenda for a limited time through a deep-learning model running in each locality. The node model is aggregated in the nodevote mixer along with the node weight generated through the node reliability evaluation in Equation (4). Equation (6) shows the aggregation of node model votes through the nodevode mixer. \[VI^{\mathit{node}}=\sum_{\mathit{node}}^{m}\left\{\frac{\sum_{f=0}^{k}F[C]_{f} [0,\ 0.1]}{k}\times NW[m]\right\} \tag{6}\]  
Equation (6) shows the process of integrating votes for content composed of \(k\) frames with \(m\) node models. \(F[C]_{f}\) refers to the prediction result of each node model and indicates whether there is a deep fake for each frame of content \(C\) submitted as a voting agenda. The prediction result of the node model uses the average result of all frames. Node model votes aggregated in the nodevote mixer are merged with user votes through the voting collector. Equation (7) shows voting integration through voting collectors. In the integration stage, which is the end stage of integrated voting, the user voting value aggregated through user voting and the forecasting result of node model voting are integrated. \[V^{\mathit{value}}=VI^{\mathit{voter}}+VI^{\mathit{node}}=\left\{ \begin{array}[ ]{ll}\mathit{opposition},&V^{\mathit{value}}<27\\ \mathit{dispute},&27\ \leq\ V^{\mathit{value}}\leq 73\\ \mathit{agreement},&73<V^{\mathit{value}}\end{array} \right. \tag{7}\]  
The integration result, \(V^{\mathit{value}}\), represents positive or negative votes to the voting agenda according to the threshold of 50 \(\pm\) 23, and results below the threshold give a hold status to the voting agenda contents. The pending content is notified of each service so that the re-voting process can be performed by the service. Table 1 shows the compensation policy according to participation in integrated voting for network users (clients). Incentives are provided according to the compensation policy to improve the accuracy of collective intelligence through the categorization of participation in integrated voting for the system self-purification of client users and compensation. A client who participates in integrated voting, a system-friendly act, and submits a vote in the same direction as the voting result can obtain an increase in reputation raw value and a self-purificator (SP) token, a cryptocurrency within Hyperledger Fabric, as a reward. Rewards are given to participants who voted that there would be no problem when there is no problem with the content and to the participant who voted that there would be a problem when there is indeed a problem with the content and the object. Reputation raw, which is the base value of a user's reputation score, changes through system contributions. Here, system contribution behavior is defined as follows:  
* Registering the contents in each ICP and registering the content information in the system through the ICP node. * Participating in integrated voting-user voting to ensure smooth operation of the proposed system. * Prediction accepted in the compensation range according to Table 1.
* Appeal and approval of suspected deepfake content.