## 1 Introduction
Along with the detection results, the main information generated during the detection process is recorded in the blockchain, securing the transparency and reliability of the detection results through the integrity of the blockchain. The proposed system forms cooperation between hostile ICPs through the blockchain to derive a common consensus, and ICPs will establish a consortium for pan-service deepfake detection with the blockchain network as a notary. 2. This study aims to propose an integrated voting method that mixes the deep-learning-based detection results of the ICP consortium with the detection results obtained through collective intelligence voting to improve the accuracy of deepfake detection results. Each ICP independently builds a deep-learning-based detection model to derive detection results, and the results of several ICPs are aggregated to produce the detection results of the consortium. Then, random users are selected and voted on to derive deepfake detection results based on collective intelligence. These two detection results are combined to produce the final detection result. This proposed method has higher accuracy and stability than individual deepfake detection models. 3. To improve the accuracy of the collective intelligence voting process based on user participation in a blockchain environment, this study introduced a user reputation management and compensation system, referring to several previous studies. Each user's reputation is continuously updated by reflecting the user's past behavior, and weight based on this reputation is applied to the user's voting results. In addition, the reward system for friendly behavior in the system induces the normal behavior of the voting group and supplements the accuracy of the final voting result. 4. In this study, several recently announced deepfake detection models were applied to independent nodes to implement prototypes of the proposed system and conduct experiments. The experimental results show that the proposed system outperformsexisting single deepfake detection models in terms of accuracy. In addition, the integrated voting method used in the proposed system provides high stability. In this paper, we propose a deepfake detection system using collective intelligence and deep learning in a blockchain environment. The proposed system integrates the prediction results of independent deep-learning nodes to solve the problem of detection reliability of individual independent detectors, which is a problem of previous research [3]. Through the ensemble of independently operated deep-learning model nodes, each node pursues complementary model development and has a node weight that complements the accuracy and reliability of detection results. Another study [4; 5] used a blockchain to check and track content forgery and tampering, but its limitations are clear because it is only detection based on metadata. Through the blockchain, we establish an organic cooperation system of service providers-blockchain network-users. We do not simply use the blockchain as a database, but we derive cooperation for meaningful agreement and detection in mutually hostile business relationships. In this paper, we design a detection cooperative system based on deep learning and collective intelligence in a blockchain environment that has not been attempted in previous studies. In the proposed system, the user's participation in the platform can be realized through collective intelligence, and the opinions of the participating groups can be reflected in automated detection through deep learning. The deep-learning model guarantees effective profits from investing each other's resources through consensus and cooperation through blockchains in situations where they operate individually and suppresses situations where technologies are exposed. This study is structured as follows. Section 2 describes the relevant techniques and research utilized in the study, and Section 3 provides detailed information on the structure, components, and operation method of the proposed system.