## 1 Introduction  
Currently, Internet content providers (ICPs), such as video hosting services like YouTube, have developed rapidly through the Fourth Industrial Revolution, the spread of smartphones, high levels of information, and the improvement of the mobile service environment. Due to the development of ICP, video media and content are widely shared, and as video content is consumed by many users, social influence through video is growing [1]. Deepfake videos with manipulated content or characters, on the other hand, can be used to cause social confusion or political attacks. If they spread through indiscriminate sharing, the resulting damage is uncontrollable [2]. ICPs such as YouTube and Facebook, which are currently widely used, are censoring video content in their own way. However, to guarantee freedom of expression, ICP only responds to content copyright infringement and does not respond to manipulated images. In the era of hyperconnectivity society due to the Fourth Industrial Revolution, social confusion due to manipulated video content, such as deepfake videos, will accelerate. To prevent this, deepfake detection technology through a fair and reliable detection entity is required. Most of the existing studies to detect deepfake images have focused on determining whether a single image has been manipulated in a local environment through an independent deep-learning model. As a study to detect deepfake images in the form of a platform or system, the study by [PERSON] et al. [3] developed an independent platform to provide the result by determining whether manipulation was performed through the deep-learning model of the service server for the videos that users individually accessed and registered. However, this has limitations in that the user must fully trust the detection subject and must own and upload the original video. [PERSON] [4] and [PERSON] [5] suggested a plan to integrate and manage video content registered in various services such as YouTube. The above study used a blockchain as a database to find manipulated or unauthorized content through video metadata-based use paths and revision history tracking. However, this assumes an environment in which the original video of the manipulated content is pre-registered and has limitations in tracking the modified items in the original. This study aims to propose a system in which various ICPs and users mutually cooperate to detect deepfakes and ensure fair and reliable detection results and detection subjects. The proposed system integrates the collective intelligence-based voting and prediction results of deep-learning models in a blockchain environment for fair deepfake detection results. In addition, the detection system of each ICP that performs deepfake detection through a blockchain is flexibly integrated, preventing the decision-making authority of the detection result from being monopolized. The detection process, results, and detection model information are recorded on the blockchain to ensure integrity and persistence. In the proposed system, the fairness of the detection results is supplemented by adding a voting process based on collective intelligence to the deepfake decision process. Collective intelligence-based user voting, which is integrated through voting weights set based on users' reputations, is integrated with the results of the deep-learning model to supplement the accuracy of the final detection results. The main contributions of this study are summarized as follows:  
1. In most studies, fairness and transparency of the detecting subject have not been considered important in building a system for detecting deepfakes, degrading the reliability of the system and detection results. This study uses blockchain technology to improve the fairness and stability of deepfake detection subjects by conducting a detection process based on an automated and verifiable smart contract.