Cargando…

An intermediate significant bit (ISB) watermarking technique using neural networks

Prior research studies have shown that the peak signal to noise ratio (PSNR) is the most frequent watermarked image quality metric that is used for determining the levels of strength and weakness of watermarking algorithms. Conversely, normalised cross correlation (NCC) is the most common metric use...

Descripción completa

Detalles Bibliográficos
Autores principales: Zeki, Akram, Abubakar, Adamu, Chiroma, Haruna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4920804/
https://www.ncbi.nlm.nih.gov/pubmed/27386317
http://dx.doi.org/10.1186/s40064-016-2371-6
_version_ 1782439439705833472
author Zeki, Akram
Abubakar, Adamu
Chiroma, Haruna
author_facet Zeki, Akram
Abubakar, Adamu
Chiroma, Haruna
author_sort Zeki, Akram
collection PubMed
description Prior research studies have shown that the peak signal to noise ratio (PSNR) is the most frequent watermarked image quality metric that is used for determining the levels of strength and weakness of watermarking algorithms. Conversely, normalised cross correlation (NCC) is the most common metric used after attacks were applied to a watermarked image to verify the strength of the algorithm used. Many researchers have used these approaches to evaluate their algorithms. These strategies have been used for a long time, however, which unfortunately limits the value of PSNR and NCC in reflecting the strength and weakness of the watermarking algorithms. This paper considers this issue to determine the threshold values of these two parameters in reflecting the amount of strength and weakness of the watermarking algorithms. We used our novel watermarking technique for embedding four watermarks in intermediate significant bits (ISB) of six image files one-by-one through replacing the image pixels with new pixels and, at the same time, keeping the new pixels very close to the original pixels. This approach gains an improved robustness based on the PSNR and NCC values that were gathered. A neural network model was built that uses the image quality metrics (PSNR and NCC) values obtained from the watermarking of six grey-scale images that use ISB as the desired output and that are trained for each watermarked image’s PSNR and NCC. The neural network predicts the watermarked image’s PSNR together with NCC after the attacks when a portion of the output of the same or different types of image quality metrics (PSNR and NCC) are obtained. The results indicate that the NCC metric fluctuates before the PSNR values deteriorate.
format Online
Article
Text
id pubmed-4920804
institution National Center for Biotechnology Information
language English
publishDate 2016
publisher Springer International Publishing
record_format MEDLINE/PubMed
spelling pubmed-49208042016-07-06 An intermediate significant bit (ISB) watermarking technique using neural networks Zeki, Akram Abubakar, Adamu Chiroma, Haruna Springerplus Research Prior research studies have shown that the peak signal to noise ratio (PSNR) is the most frequent watermarked image quality metric that is used for determining the levels of strength and weakness of watermarking algorithms. Conversely, normalised cross correlation (NCC) is the most common metric used after attacks were applied to a watermarked image to verify the strength of the algorithm used. Many researchers have used these approaches to evaluate their algorithms. These strategies have been used for a long time, however, which unfortunately limits the value of PSNR and NCC in reflecting the strength and weakness of the watermarking algorithms. This paper considers this issue to determine the threshold values of these two parameters in reflecting the amount of strength and weakness of the watermarking algorithms. We used our novel watermarking technique for embedding four watermarks in intermediate significant bits (ISB) of six image files one-by-one through replacing the image pixels with new pixels and, at the same time, keeping the new pixels very close to the original pixels. This approach gains an improved robustness based on the PSNR and NCC values that were gathered. A neural network model was built that uses the image quality metrics (PSNR and NCC) values obtained from the watermarking of six grey-scale images that use ISB as the desired output and that are trained for each watermarked image’s PSNR and NCC. The neural network predicts the watermarked image’s PSNR together with NCC after the attacks when a portion of the output of the same or different types of image quality metrics (PSNR and NCC) are obtained. The results indicate that the NCC metric fluctuates before the PSNR values deteriorate. Springer International Publishing 2016-06-24 /pmc/articles/PMC4920804/ /pubmed/27386317 http://dx.doi.org/10.1186/s40064-016-2371-6 Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Research
Zeki, Akram
Abubakar, Adamu
Chiroma, Haruna
An intermediate significant bit (ISB) watermarking technique using neural networks
title An intermediate significant bit (ISB) watermarking technique using neural networks
title_full An intermediate significant bit (ISB) watermarking technique using neural networks
title_fullStr An intermediate significant bit (ISB) watermarking technique using neural networks
title_full_unstemmed An intermediate significant bit (ISB) watermarking technique using neural networks
title_short An intermediate significant bit (ISB) watermarking technique using neural networks
title_sort intermediate significant bit (isb) watermarking technique using neural networks
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4920804/
https://www.ncbi.nlm.nih.gov/pubmed/27386317
http://dx.doi.org/10.1186/s40064-016-2371-6
work_keys_str_mv AT zekiakram anintermediatesignificantbitisbwatermarkingtechniqueusingneuralnetworks
AT abubakaradamu anintermediatesignificantbitisbwatermarkingtechniqueusingneuralnetworks
AT chiromaharuna anintermediatesignificantbitisbwatermarkingtechniqueusingneuralnetworks
AT zekiakram intermediatesignificantbitisbwatermarkingtechniqueusingneuralnetworks
AT abubakaradamu intermediatesignificantbitisbwatermarkingtechniqueusingneuralnetworks
AT chiromaharuna intermediatesignificantbitisbwatermarkingtechniqueusingneuralnetworks