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In recent years, the rapid advancement of artificial intelligence (AI) in the video sector has captured widespread attention. Despite the proliferation of AI video generation tools targeted at general consumers, there remains a gap in addressing the specialized requirements and advanced functionalities sought by industry professionals. This study aims to identify and overcome the barriers hindering the adoption of AI video tools in the Chinese video industry, thereby facilitating their seamless integration. The research unfolded in two phases employing a comprehensive methodology. Initially, we delineated the industry’s video usage standards, drawing upon both established norms and insights gleaned from seasoned industry experts through focus group interviews. These insights informed the formulation of semi-structured interview questions. Subsequently, in-depth semi-structured interviews were conducted with ten Chinese industry experts, leading to the identification of eight primary adoption barriers: innovation, market demand, technological maturity, cross-disciplinary collaboration, ethics and privacy, public acceptance, data security and copyright, and global perspectives and localization. In the second phase, a detailed questionnaire survey involving 401 Chinese industry practitioners validated these factors. A data analysis underscored the significant impact of these eight factors on industry adoption, particularly emphasizing technological maturity. Furthermore, exhaustive examinations and discussions were undertaken for each identified barrier. The findings of this study theoretically bridge the gaps in understanding the impediments to the application of AI video generation tools in the video industry. They offer valuable insights into the current application landscape and furnish pertinent suggestions for advancing technology promotion and development in the future. Ultimately, this research aspires to augment the integration and utilization of AI technology within the Chinese video production industry, thereby propelling its progress and development forward.
In recent years, the rapid advancement of artificial intelligence (AI) in the video sector has captured widespread attention. Despite the proliferation of AI video generation tools targeted at general consumers, there remains a gap in addressing the specialized requirements and advanced functionalities sought by industry professionals. This study aims to identify and overcome the barriers hindering the adoption of AI video tools in the Chinese video industry, thereby facilitating their seamless integration. The research unfolded in two phases employing a comprehensive methodology. Initially, we delineated the industry’s video usage standards, drawing upon both established norms and insights gleaned from seasoned industry experts through focus group interviews. These insights informed the formulation of semi-structured interview questions. Subsequently, in-depth semi-structured interviews were conducted with ten Chinese industry experts, leading to the identification of eight primary adoption barriers: innovation, market demand, technological maturity, cross-disciplinary collaboration, ethics and privacy, public acceptance, data security and copyright, and global perspectives and localization. In the second phase, a detailed questionnaire survey involving 401 Chinese industry practitioners validated these factors. A data analysis underscored the significant impact of these eight factors on industry adoption, particularly emphasizing technological maturity. Furthermore, exhaustive examinations and discussions were undertaken for each identified barrier. The findings of this study theoretically bridge the gaps in understanding the impediments to the application of AI video generation tools in the video industry. They offer valuable insights into the current application landscape and furnish pertinent suggestions for advancing technology promotion and development in the future. Ultimately, this research aspires to augment the integration and utilization of AI technology within the Chinese video production industry, thereby propelling its progress and development forward.
Deepfakes have become widespread and have continued to develop rapidly in recent years. In addition to the use of deepfakes in movies and for humorous purposes, this technology has also begun to pose a threat to many companies and politicians. Deepfake detection is critical to the prevention of this threat. In this study, a Choquet fuzzy integral-based deepfake detection method is proposed to increase overall performance by combining the results obtained from different deepfake detection methods. Three different deepfake detection models were used in the study: XceptionNet, which has better performance in detecting real images/videos; EfficientNet, which has better performance in detecting fake videos; and a model based on their hybrid uses. The proposed method based on the Choquet fuzzy integral aims to eliminate the shortcomings of these methods by using each of the other methods. As a result, a higher performance was achieved with the proposed method than found when all three methods were used individually. As a result of the testing and validation studies carried out on FaceForensics++, DFDC, Celeb-DF, and DeepFake-TIMIT datasets, the individual performance levels of the algorithms used were 81.34%, 82.78%, and 79.15% on average, according to the AUC curve, while the level of 97.79% was reached with the proposed method. Considering that the average performance of the three methods across all datasets is 81.09%, it can be seen that an improvement of approximately 16.7% is achieved. In the FaceForensics++ dataset, in which individual algorithms are more successful, the performance of the proposed method reaches the highest AUC value, 99.8%. It can be seen that the performance rates can be increased by changing the individual methods discussed in the proposed method. We believe that the proposed method will inspire researchers and will be further developed.
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