Copyright Protection and Illegal Distributor Identification for Video-on-demand Applications using Forensic Watermarking

In the direct-to-home (DTH) environment video-on-demand (VOD) applications are tremendously popular due to its inexpensive and convenient nature. In VOD approach legal customers can connect their set-top boxes (STB) to the Internet and can access or record the available content. Due to the easy transmission of the highest quality digital data to the customers by the pay-per-view approach, the data are highly at risk. The data can be vulnerable for illegal distribution of duplicate copies and they are prone to unnecessary modifications which creates a financial loss to the information creators. So it is necessary to authenticate the owner as well as the illegal distributor to reduce the digital piracy which is the motivation for this work. This paper presents a forensic watermarking scheme for protecting copyrights, and for identifying the illegal distributor who distributes the legal copy in the illegal fashion though it is copyright violation. In this paper, two watermarks are embedded in the video that is on-demand, where one watermark is the owner’s information and another watermark is related to the unique information of the STB. This work is also suitable for the biomedical domain, where one watermark can be the patient information and another watermark will be the health center information, in order to secure the patient information and the hospital information.

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Publication Details

Journal
International Journal of Intelligent Systems and Applications
Published
2025-04-08
DOI
https://doi.org/10.5815/ijisa.2025.02.03
Primary Topic
Advanced Steganography and Watermarking Techniques
Type
article
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article

Copyright Protection and Illegal Distributor Identification for Video-on-demand Applications using Forensic Watermarking

V. Masilamani, Ayesha Shaik
International Journal of Intelligent Systems and Applications
Advanced Steganography and Watermarking Techniques
article

Copyright Protection and Illegal Distributor Identification for Video-on-demand Applications using Forensic Watermarking

V. Masilamani, Ayesha Shaik
article en

Abstract

In the direct-to-home (DTH) environment video-on-demand (VOD) applications are tremendously popular due to its inexpensive and convenient nature. In VOD approach legal customers can connect their set-top boxes (STB) to the Internet and can access or record the available content. Due to the easy transmission of the highest quality digital data to the customers by the pay-per-view approach, the data are highly at risk. The data can be vulnerable for illegal distribution of duplicate copies and they are prone to unnecessary modifications which creates a financial loss to the information creators. So it is necessary to authenticate the owner as well as the illegal distributor to reduce the digital piracy which is the motivation for this work. This paper presents a forensic watermarking scheme for protecting copyrights, and for identifying the illegal distributor who distributes the legal copy in the illegal fashion though it is copyright violation. In this paper, two watermarks are embedded in the video that is on-demand, where one watermark is the owner’s information and another watermark is related to the unique information of the STB. This work is also suitable for the biomedical domain, where one watermark can be the patient information and another watermark will be the health center information, in order to secure the patient information and the hospital information.

International Journal of Intelligent Systems and ApplicationsVol. 17(2)
Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram (IN), Vellore Institute of Technology University (IN)
Openalex Percentile: Top 94%
Advanced Steganography and Watermarking Techniques
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Copyright Protection and Illegal Distributor Identification for Video-on-demand Applications using Forensic Watermarking — V. Masilamani, Ayesha Shaik · International Journal of Intelligent Systems and Applications (2025) | TGRS Research Map | TGRS