Digital media system design and visual art analysis based on information security

Background: With the growing maturity of Internet technology and 5G transmission technology, digital media systems begin to present more complex scenario applications. Software design security issues are also increasingly evident in digital media systems. This paper analyzes the security issues of d...

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Bibliographic Details
Main Author: Kani Li
Format: Article
Language:English
Published: Elsevier 2024-02-01
Series:Measurement: Sensors
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2665917423003148
Description
Summary:Background: With the growing maturity of Internet technology and 5G transmission technology, digital media systems begin to present more complex scenario applications. Software design security issues are also increasingly evident in digital media systems. This paper analyzes the security issues of digital media system design from the perspective of software protection security. Discussed based on the information security digital media system design and visual art analysis. Methods: This paper discusses based on the software security protection design and visual art analysis, first proposed the media convergence and information security risk assessment research, including media convergence cognition, information security risk management theory, system information security risk assessment methods and based on the media under the digital certificate. Secondly, the information security algorithm based on digital media technology is studied. From the basic knowledge of digital image, a method of image encryption based on the change of pixel value, the common algorithm of digital image watermarking technology and the encryption technology of digital media are summarized. Results: Experiments were carried out on three art painting data sets, including Painting91, Oil Painting and Pandora. Compared with the existing label distribution learning methods on the Painting91style dataset, the algorithms SA-IIS and SA-BFGS achieve the classification accuracy of 65.51 % and 66.52 %, respectively. The classification result of the baseline method of VGGNet is 63.89 % on the OilPainting dataset. On the Pandora-style dataset, the classification accuracy of MSCNN1 method is 71.32 %, and the experimental results of CNNF4 method reach 71.76 %. Conclusion: The method proposed in this paper has good application results in different data sets, and the digital media art design system has high reliability and fault tolerance. From the perspective of information security, different index systems have higher security levels.
ISSN:2665-9174