## 5 Discussions and Limitations  
In this paper, we propose a system for detecting deep fake content in an environment where ICP services are provided to users. The proposed system is constructed through a blockchain, and the final prediction is performed by integrating collective intelligence-based voting and the prediction results through the deep-learning model. We considered how to use collective intelligence-based voting significantly throughout the paper. This paper attempts to solve the problem of individual information (manipulation of information, private use of results, possibility of incitement) and independence (dependence on other people's opinions, bias of results by minority opinions) [33] that occur in voting through collective intelligence. Each problem was solved through the integration of the prediction results of the deep-learning model by the machine, the voting process, and contribution weighting in the blockchain environment. In addition, by implementing collective intelligence in a blockchain environment, it transparently solves the distributed environment (the way to solve the problem should not be concentrated in one place) and the economic cohesion problem that can occur in incentive-based collective intelligence optimization. From an environmental point of view, the blockchain allows collective intelligence and deep learning, which are the methods that make up the system, to work complementarily, complementing its reliability and transparency. This solves the social costs caused by the non-trust relationship between the existing companies and the absence of the cooperation system. As a result, agreements are drawn based on meaningful results and transparency of the process at low levels of mutual trust, and complementary partnerships are established. Deepfake detection through deep learning uses detection and prediction results from individual detection subjects when used in terms of enterprise and service. The proposed system solves the problem of the total trust of the detection subject that occurs in this dependent detection environment. By designing a blockchain-based ensemble network based on a deep-learning model configured as an independent node as a peer, we overcome the problem of deep learning by utilizing the blockchain in a way that has not been attempted before. We anticipate that these attempts may be in line with the web3.0 framework where users participate in blockchain and manage data independently and explore new possibilities for blockchain. We have limitations in constructing a collective intelligence experimental environment in implementing and experimenting with the proposed system. Collective intelligence can be influenced by the background of the times and social trends, and it can change according to the individual characteristics of the participating group. Therefore, unlike implementing collective intelligence, experimentation needs to be developed through actual users and platforms. In order to construct the experimental environment, we assume voting figures by referring to the percentage of user opinions that are not malicious to the system in wiki and crowdsourcing, which are collective intelligence-based platforms. However, there is a limit to reflecting the characteristics of collective intelligence to determine whether a problem is right or wrong in the proposed system. In future research, we will collect long-term data for the reliability of collective intelligence to improve the system. Data will be collected in various ways by adjusting conflicting opinion rates within the participating group and by variables such as the age and gender of the group. Through this, this study intends to  
Figure 14: Graph of weight change of node model according to feedback epoch. supplement the basis of the user voting participation rate and the positive (+) direction voting success rate presented in this paper.