New Blind Image Moments Watermarking Method Based on Evolutionary Optimization of Embedding Parameters

In image watermarking, the locations where the watermark is embedded in the frequency domain and the embedding strength influence the overall performance of the blind watermarking procedure. The present paper aims to propose a new blind watermarking method based on the dither modulation by developin...

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Main Authors: Chaimae Chekira, Manal Marzouq, Imad Batioua, Hakim El Fadili, Zakia Lakhliai, Khalid Zenkouar
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/10124739/
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author Chaimae Chekira
Manal Marzouq
Imad Batioua
Hakim El Fadili
Zakia Lakhliai
Khalid Zenkouar
author_facet Chaimae Chekira
Manal Marzouq
Imad Batioua
Hakim El Fadili
Zakia Lakhliai
Khalid Zenkouar
author_sort Chaimae Chekira
collection DOAJ
description In image watermarking, the locations where the watermark is embedded in the frequency domain and the embedding strength influence the overall performance of the blind watermarking procedure. The present paper aims to propose a new blind watermarking method based on the dither modulation by developing an automatic selection of the optimum embedding parameters that guarantee high-quality watermarked images and low bit error rates during the extraction process. The proposed automatic search method for the best discrete moments subsets is based on an evolutionary algorithm and adopts a specific coding strategy with a group of genes representing the embedding positions. The second part of the chromosome is reserved for the embedding strength coding, followed by the application of different evolutionary operators on the evolution pool. Our study explores the impact of maximum generation, population size, and cutting-point positions with different crossover and mutation rates. The performances under different attack conditions are evaluated, and a comparative study is established with other conventional selection methods and other discrete transforms. Results show that our proposed optimization algorithm baptized EWIMps achieves the best trade-off between robustness and imperceptibility with a peak signal-to-noise ratio varying from 28.33 dB to 59.87 dB and a normalized cross-correlation value from 0.707 to 1.
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spelling doaj.art-3bdb583995fd4cf0b75811e1f842e76f2023-05-25T23:00:37ZengIEEEIEEE Access2169-35362023-01-0111485174854710.1109/ACCESS.2023.327657610124739New Blind Image Moments Watermarking Method Based on Evolutionary Optimization of Embedding ParametersChaimae Chekira0https://orcid.org/0009-0007-9386-1836Manal Marzouq1Imad Batioua2Hakim El Fadili3Zakia Lakhliai4Khalid Zenkouar5Computer Science and Interdisciplinary Physics Laboratory (LIPI), Sidi Mohamed Ben Abdellah University, Fez, MoroccoComputer Science and Interdisciplinary Physics Laboratory (LIPI), Sidi Mohamed Ben Abdellah University, Fez, MoroccoLaboratory of Intelligent Systems and Applications (LSIA), Sidi Mohamed Ben Abdellah University, Fez, MoroccoComputer Science and Interdisciplinary Physics Laboratory (LIPI), Sidi Mohamed Ben Abdellah University, Fez, MoroccoComputer Science and Interdisciplinary Physics Laboratory (LIPI), Sidi Mohamed Ben Abdellah University, Fez, MoroccoLaboratory of Intelligent Systems and Applications (LSIA), Sidi Mohamed Ben Abdellah University, Fez, MoroccoIn image watermarking, the locations where the watermark is embedded in the frequency domain and the embedding strength influence the overall performance of the blind watermarking procedure. The present paper aims to propose a new blind watermarking method based on the dither modulation by developing an automatic selection of the optimum embedding parameters that guarantee high-quality watermarked images and low bit error rates during the extraction process. The proposed automatic search method for the best discrete moments subsets is based on an evolutionary algorithm and adopts a specific coding strategy with a group of genes representing the embedding positions. The second part of the chromosome is reserved for the embedding strength coding, followed by the application of different evolutionary operators on the evolution pool. Our study explores the impact of maximum generation, population size, and cutting-point positions with different crossover and mutation rates. The performances under different attack conditions are evaluated, and a comparative study is established with other conventional selection methods and other discrete transforms. Results show that our proposed optimization algorithm baptized EWIMps achieves the best trade-off between robustness and imperceptibility with a peak signal-to-noise ratio varying from 28.33 dB to 59.87 dB and a normalized cross-correlation value from 0.707 to 1.https://ieeexplore.ieee.org/document/10124739/Data hidingblind image watermarkingorthogonal discrete momentsdiscrete transformsdither modulationevolutionary algorithm
spellingShingle Chaimae Chekira
Manal Marzouq
Imad Batioua
Hakim El Fadili
Zakia Lakhliai
Khalid Zenkouar
New Blind Image Moments Watermarking Method Based on Evolutionary Optimization of Embedding Parameters
IEEE Access
Data hiding
blind image watermarking
orthogonal discrete moments
discrete transforms
dither modulation
evolutionary algorithm
title New Blind Image Moments Watermarking Method Based on Evolutionary Optimization of Embedding Parameters
title_full New Blind Image Moments Watermarking Method Based on Evolutionary Optimization of Embedding Parameters
title_fullStr New Blind Image Moments Watermarking Method Based on Evolutionary Optimization of Embedding Parameters
title_full_unstemmed New Blind Image Moments Watermarking Method Based on Evolutionary Optimization of Embedding Parameters
title_short New Blind Image Moments Watermarking Method Based on Evolutionary Optimization of Embedding Parameters
title_sort new blind image moments watermarking method based on evolutionary optimization of embedding parameters
topic Data hiding
blind image watermarking
orthogonal discrete moments
discrete transforms
dither modulation
evolutionary algorithm
url https://ieeexplore.ieee.org/document/10124739/
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