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...
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SpringerPlus Open Journal
2016
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| Online Access: | http://irep.iium.edu.my/51593/ http://irep.iium.edu.my/51593/ http://irep.iium.edu.my/51593/ http://irep.iium.edu.my/51593/1/Main_file.pdf http://irep.iium.edu.my/51593/4/51593_An_intermediate_significant_bit_%28ISB%29_WOS.pdf |
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iium-515932017-10-20T14:48:33Z http://irep.iium.edu.my/51593/ An intermediate significant bit (ISB) watermarking technique using neural networks Zeki, Akram M. Abubakar, Adamu Chiroma , Haruna QA76 Computer software 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. SpringerPlus Open Journal 2016-06-24 Article PeerReviewed application/pdf en http://irep.iium.edu.my/51593/1/Main_file.pdf application/pdf en http://irep.iium.edu.my/51593/4/51593_An_intermediate_significant_bit_%28ISB%29_WOS.pdf Zeki, Akram M. and Abubakar, Adamu and Chiroma , Haruna (2016) An intermediate significant bit (ISB) watermarking technique using neural networks. SpringerPlus, 5 (1). 868-1-868-25. ISSN 2193-1801 https://springerplus.springeropen.com/articles/10.1186/s40064-016-2371-6 10.1186/s40064-016-2371-6 |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
International Islamic University Malaysia |
| building |
IIUM Repository |
| collection |
Online Access |
| language |
English English |
| topic |
QA76 Computer software |
| spellingShingle |
QA76 Computer software Zeki, Akram M. Abubakar, Adamu Chiroma , Haruna An intermediate significant bit (ISB) watermarking technique using neural networks |
| 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 |
Article |
| author |
Zeki, Akram M. Abubakar, Adamu Chiroma , Haruna |
| author_facet |
Zeki, Akram M. Abubakar, Adamu Chiroma , Haruna |
| author_sort |
Zeki, Akram M. |
| title |
An intermediate significant bit (ISB) watermarking technique using neural networks |
| title_short |
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_sort |
intermediate significant bit (isb) watermarking technique using neural networks |
| publisher |
SpringerPlus Open Journal |
| publishDate |
2016 |
| url |
http://irep.iium.edu.my/51593/ http://irep.iium.edu.my/51593/ http://irep.iium.edu.my/51593/ http://irep.iium.edu.my/51593/1/Main_file.pdf http://irep.iium.edu.my/51593/4/51593_An_intermediate_significant_bit_%28ISB%29_WOS.pdf |
| first_indexed |
2023-09-18T21:13:03Z |
| last_indexed |
2023-09-18T21:13:03Z |
| _version_ |
1777411380437057536 |