Short Video Copyright Storage Algorithm Based on Blockchain and Expression Recognition

Yang Yang, Dingguo Yu
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引用次数: 4

Abstract

Blockchain technology is widely used in the field of digital right protection technology. The traditional digital right protection scheme is not only inefficient and highly centralized but also has the risk of being modified. Due to its own characteristics, blockchain cannot completely store all the original files of digital resources. In this paper, a convolutional neural network algorithm based on visual priority rule is proposed (CNNVP). This algorithm can recognize facial expressions in the original files of digital resources (for short video of face class). The algorithm extracts facial expression features accurately and makes these features form log files that can represent the original files of digital resources. Then, the paper proposes a short video copyright storage algorithm based on blockchain and facial expression recognition and stores the log file into the blockchain. The above methods not only improve the efficiency of short video copyright storage, reduce the degree of storage centralization, and eliminate the risk that copyright is easy to be modified. Moreover, the computing operation of deep learning technology on short video not only ensures the privacy of storage certificate information but also ensures the possibility of blockchain storage of video information. Experiments show that the algorithm proposed in this paper is more efficient than the traditional copyright storage method. Moreover, the algorithm proposed in this paper can provide technical support to the media resource management department.
基于区块链和表情识别的短视频版权存储算法
区块链技术被广泛应用于数字版权保护技术领域。传统的数字版权保护方案不仅效率低、集中度高,而且存在被修改的风险。由于区块链自身的特点,它不能完全存储数字资源的所有原始文件。提出了一种基于视觉优先级规则的卷积神经网络算法。该算法可以识别数字资源原始文件中的面部表情(用于人脸类短视频)。该算法准确提取面部表情特征,并将这些特征形成日志文件,可以代表数字资源的原始文件。然后,提出了一种基于区块链和面部表情识别的短视频版权存储算法,并将日志文件存储到区块链中。以上方法不仅提高了短视频版权存储的效率,降低了存储集中化程度,还消除了版权容易被修改的风险。此外,深度学习技术对短视频的计算操作,既保证了存储证书信息的私密性,又保证了视频信息区块链存储的可能性。实验表明,本文提出的算法比传统的版权存储方法效率更高。此外,本文提出的算法可以为媒体资源管理部门提供技术支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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