## 6 Conclusions  
This study proposed a system to prevent social confusion caused by manipulated video content in the information hyper-connected era through the development of ICT technology and the Fourth Industrial Revolution. The proposed system detects deepfake videos shared on the ICP service and predicts whether they were deepfakes. Furthermore, it presents a solution to the reliability and fairness of the detection subject, which is a problem of various studies on deep fake detection that have been studied in the past. The proposed system uses a method for detecting deepfake images by ensembling the detection results of collective intelligence and deep-learning models in a blockchain environment. In the proposed method, the blockchain network uses independent ICPs as nodes that drive each deep-learning model for deepfake detection. Through this, it realizes the value sharing of censorship and image information management functions that ICPs with conflicting interests keep to the inside of the service. ICP users participate as voters in user voting based on collective intelligence to supplement the fairness and universality of detection results. Each detection result aggregated through user voting and node model voting is integrated into the blockchain to ensure the reliability and transparency of the final decision. It operates on the basis of smart contracts on the blockchain and guarantees the integrity of the process. As a result of implementing and testing the proposed system in this study, it was confirmed that the method of integrating the detection results of the deep-learning model of the proposed system by counting and integrating user votes showed higher accuracy than the existing individual and independent deepfake detection models. The detection process performed by the system is automated through a blockchain network, which is not controlled by external entities or administrators without authorization, and all users can check the execution process. This ensures the transparency and reliability of the detection process, and the reliability of the detection subject is secured through the voting process. When the three deep-learning models used for the experiment, efficientViT, cross-efficientViT, and efficientNetB4, show AUC of 0.8372, 0.8809, and 0.9012 in the same hardware environment and data environment, the results obtained through the integrated voting of the proposed system are 0.9263. In addition, it was confirmed that the detection process has objectivity and high reliability by showing stable prediction accuracy in the range of 91-95, unlike the individually driven model in the experiment, according to the change of the data group. Through the proposed system, the possibility of organic cooperation between blockchain and artificial intelligence was confirmed, suggesting that the reliability and integrity of artificial intelligence and the effective guarantee of the permanence of version control were possible through blockchain. In addition, by using blockchains, a consortium for a common purpose between companies was created, allowing hostile companies to create common values. In addition, it was possible to build an environment for the effective use of collective intelligence through a blockchain network. Although the proposed system is focused on detecting deepfakes, it is expected to be helpful when applied to decision-making systems in various domains. We will supplement the experimental evidence through the practical development of collective intelligence to overcome the limitations of the experimental environment in future research progress. In addition, beyond aggregating the results of each deep-learning model in black-box form, we will design a large-scale federated learning environment for super-large artificial intelligence by forming a consortium for each machine to collect, learn, and detect data through the blockchain. Conceptualization, H.K. ; Methodology, N.C.; software, N.C.; validation, N.C. and H.K. ; writing--original draft, N.C. and H.K.