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An Optimized Dwt Based Approach In Video Watermarking With Multiple Watermarks Using Oppositional Krill Herd Algorithm

Authors: T. Shankar*1 and G.Yamuna2;

An Optimized Dwt Based Approach In Video Watermarking With Multiple Watermarks Using Oppositional Krill Herd Algorithm

Abstract

In video watermarking applications, there is a need to extract the watermark without using the original data because of the huge storage of the cover data. In this paper, we have intended to present an efficient multiple video watermarking using optimized three levels DWT. Basically, the system consists of three modules such as (i) selecting optimal wavelet coefficients using Oppositional Krill Herd Algorithm (OKHA) (ii) Watermark embedding process and (iii) Watermark extraction process. A hybrid combination of the Krill Herd with Oppositional-Based Learning (OBL) has been used to select the optimal wavelet co-efficient OBL is used to improvise the performance of Krill Herd algorithm, while optimizing the co-efficient of standard DWT model. It is then followed by encryption of watermark based on Arnold transform. After, that we encrypt the watermark based on the Arnold transform and different types of media (gray image and colour image) are used for the watermark. Finally, the original video is obtained with the help of extraction process. The robustness of this technique is tested by various attacks such as: noising, compression, and image-processing attacks

Keywords

Video Watermarking, DWT, Oppositional Krill Herd Algorithm, Opposition-based learning, multiple watermarking.

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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