Current Issue : October-December Volume : 2024 Issue Number : 4 Articles : 5 Articles
This study aims to design and implement deepfake video detection using VGG-16 in combination with long short-term memory (LSTM). In contrast to other studies, this study compares VGG-16, VGG-19, and the newest model, ResNet-101, including LSTM. All the models were tested using Celeb-DF video dataset. The result showed that the VGG-16 model with 15 epochs and 32 batch sizes had the highest performance. The results showed that the VGG-16 model with 15 epochs and 32 batch sizes exhibited the highest performance, with 96.25% accuracy, 93.04% recall, 99.20% specificity, and 99.07% precision. In conclusion, this model can be implemented practically....
The aim of this paper is to shape the identity of digital video advertising in the new digital media landscape, focusing on whether and to what extent popular digital ads on social media are differentiated from traditional television ads in the context of the convergence of traditional and new electronic media. Content analysis was preferred for the study of popular advertising content in social media. In this respect, the differentiation of digital video advertising from its television counterpart is examined in terms of the properties and effects of the transmission medium itself on the advertising content and of the features of the digital advertising content itself. Out of the findings emerged that digital video advertising is still in a phase of adaptation/transition, consisting of only a potential breakthrough in the contemporary media environment, since it has not yet exhausted the possibilities offered by the internet and Web 2.0. Currently, digital advertising only partially incorporates and exploits the advantages of enriched, multimedia, interactive, and personalized content, characteristics that would potentially differentiate it to a greater extent from advertising shown in traditional media, especially television....
Recently, live streaming technology has been widely utilized in areas such as online gaming, e-healthcare, and video conferencing. The increasing network and computational resources required for live streaming increase the cost of content providers and Internet Service Providers (ISPs), which may lead to increased latency or even unavailability of live streaming services.The current research primarily focuses on providing high-quality services by assessing the resource status of network nodes individually. However, the role assignment within nodes and the interconnectivity among nodes are often overlooked. To fill this gap, we propose a hierarchical game theory-based live video transmission framework to coordinate the heterogeneity of live tasks and nodes and to improve the resource utilization of nodes and the service satisfaction of users. Secondly, the service node roles are set as producers who are closer to the live streaming source and provide content, consumers who are closer to the end users and process data, and silent nodes who do not participate in the service process, and a non-cooperative game-based role competition algorithm is designed to improve the node resource utilization. Furthermore, a matching-based optimal path algorithm for media services is designed to establish optimal matching associations among service nodes to optimize the service experience. Finally, extensive simulation experiments show that our approach performs better in terms of service latency and bandwidth....
Short video platforms have gradually become a new engine for agricultural development under the Chinese rural revitalization strategy. The phenomenon of the short video platform empowering agriculture has essential theoretical and practical significance for promoting agricultural modernization and improving farmers' living standards. Using Douyin as a case study, this paper discusses how short video platforms empower agriculture through case analysis. Research finds that short video platforms can effectively increase agricultural product sales and shape rural areas' image. However, problems include uneven content, lack of e-commerce live broadcast talents, and reduced competitive advantages. Therefore, it is necessary to strengthen the supervision of short video content and cultivate live e-commerce broadcast talents through agricultural Multi-Channel Network organizations. This paper has significant reference value for using short video platforms to empower agriculture, solve problems in developing e-commerceassisted agriculture, and improve farmers' living standards. At the same time, it also provides essential decision-making references for policymakers and helps promote the implementation of rural revitalization strategies....
The exponential growth of video-sharing platforms, exemplified by platforms like YouTube and Netflix, has made videos available to everyone with minimal restrictions. This proliferation, while offering a variety of content, at the same time introduces challenges, such as the increased vulnerability of children and adolescents to potentially harmful material, notably explicit content. Despite the efforts in developing content moderation tools, a research gap still exists in creating comprehensive solutions capable of reliably estimating users’ ages and accurately classifying numerous forms of inappropriate video content. This study is aimed at bridging this gap by introducing VideoTransformer, which combines the power of two existing models: AgeNet and MobileNetV2. To evaluate the effectiveness of the proposed approach, this study utilized a manually annotated video dataset collected from YouTube, covering multiple categories, including safe, real violence, drugs, nudity, simulated violence, kissing, pornography, and terrorism. In contrast to existing models, the proposed VideoTransformer model demonstrates significant performance improvements, as evidenced by two distinct accuracy evaluations. It achieves an impressive accuracy rate of (96.89%) in a 5-fold cross-validation setup, outperforming NasNet (92.6%), EfficientNet-B7 (87.87%), GoogLeNet (85.1%), and VGG-19 (92.83%). Furthermore, in a single run, it maintains a consistent accuracy rate of 90%. Additionally, the proposed model attains an F1-score of 90.34%, indicating a well-balanced trade-off between precision and recall. These findings highlight the potential of the proposed approach in advancing content moderation and enhancing user safety on video-sharing platforms. We envision deploying the proposed methodology in real-time video streaming to effectively mitigate the spread of inappropriate content, thereby raising online safety standards....
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