Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
The development and investigation of content protection models for online courses is an urgent task in light of the growing popularity of online education and the need to protect content from unauthorised access and distribution. The purpose of this study is to create effective and reliable models for protecting the content of online courses. To achieve the goal, methods of analysis of existing content protection systems and mathematical modelling were used. Models based on various security approaches, including cryptography methods, watermarks, and access rights management systems, have been developed and tested. The main result of the research was the creation of tools to protect the content of online courses, which includes a comprehensive protection system using cryptographic algorithms and watermarks. An access rights management system has also been developed, which allows restricting access to content only for authorised users. The results obtained confirm the effectiveness of using a combined content protection system and can significantly reduce the risk of unauthorised access and distribution of online course content. The practical significance of the study lies in the possibility of applying the developed models for protecting the content of online courses in practical conditions. They can be used in various educational organisations providing online courses, and in other areas where protection of digital content from unauthorised access and distribution is required. The use of the developed content protection models will improve the security level of online courses and reduce the risks of leakage of confidential information. Moreover, the developed models can be used as an example in the development of similar digital content protection systems in other areas. In general, this research has a high practical significance and can be useful for everyone involved in the creation, storage, and distribution of digital content.
The development and investigation of content protection models for online courses is an urgent task in light of the growing popularity of online education and the need to protect content from unauthorised access and distribution. The purpose of this study is to create effective and reliable models for protecting the content of online courses. To achieve the goal, methods of analysis of existing content protection systems and mathematical modelling were used. Models based on various security approaches, including cryptography methods, watermarks, and access rights management systems, have been developed and tested. The main result of the research was the creation of tools to protect the content of online courses, which includes a comprehensive protection system using cryptographic algorithms and watermarks. An access rights management system has also been developed, which allows restricting access to content only for authorised users. The results obtained confirm the effectiveness of using a combined content protection system and can significantly reduce the risk of unauthorised access and distribution of online course content. The practical significance of the study lies in the possibility of applying the developed models for protecting the content of online courses in practical conditions. They can be used in various educational organisations providing online courses, and in other areas where protection of digital content from unauthorised access and distribution is required. The use of the developed content protection models will improve the security level of online courses and reduce the risks of leakage of confidential information. Moreover, the developed models can be used as an example in the development of similar digital content protection systems in other areas. In general, this research has a high practical significance and can be useful for everyone involved in the creation, storage, and distribution of digital content.
In recent years, digital watermarking has improved the accuracy and resistance of watermarked images against many assaults, such as various noises and random dosage characteristics. Because, based on the most recent assault, all existing watermarking research techniques have an acceptable level of resistance. The deep learning approach is one of the most remarkable methods for guaranteeing maximal resistance in the watermarking system's digital image processing. In the digital watermarking technique, a smaller amount of calculation time with high robustness has recently become a difficult challenge. In this research study, the light weight convolution neural network (LW-CNN) technique is introduced and implemented for the digital watermarking scheme, which has more resilience than any other standard approaches. Because of the LW-CNN framework's feature selection, the calculation time has been reduced. Furthermore, we have demonstrated the robustness of two distinct assaults, collusion and geometric type. This research work has reduced the calculation time and made the system more resistant to current assaults.
In recent years, there has been an increasing research interest in image de-noising due to an emphasis on sparse representation. When sparse representation theory is compared to transform domain-based image de-noising, the former indicates that the images have more information. It contains structural characteristics that are quite similar to the structure of dictionary-based atoms. This structure and the dictionary-based method is highly unsuccessful. However, image representation assumes that the noise lack such a feature. The dual-tree complex wavelet transform incorporates an increase in transform data density to reduce the effects of sparse data. This technique has been developed to decrease the image noise by selecting the best-predicted threshold value derived from wavelet coefficients. For our experiment, Discrete Cosine Transform (DCT) and Complex Wavelet Transform (CWT) are used to examine how the suggested technique compares the conventional DCT and CWT on sets of realistic images. As for image quality measures, DT-CWT has leveraged superior results. In terms of processing time, DT-CWT gave better results with a wider PSNR range. Further, the proposed model is tested with a standard digital image named Lena and multimedia sensor images for the denoising algorithm. The suggested denoising technique has delivered minimal effect on the MSE value.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.