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Reversible data hiding scheme using prediction neural network and adaptive modulation mapping.

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Title: Reversible data hiding scheme using prediction neural network and adaptive modulation mapping.
Authors: Chen, Si-sheng1,2 (AUTHOR), Chang, Chin-Chen2 (AUTHOR), Horng, Ji-hwei3 (AUTHOR) horng@email.nqu.edu.tw
Source: Multimedia Tools & Applications. Mar2025, Vol. 84 Issue 9, p6665-6686. 22p.
Subjects: Reversible data hiding (Computer science), Adaptive modulation, Artificial intelligence, Feature extraction, Image processing
Abstract: Prediction error expansion (PEE) is an attractive approach for reversible data hiding (RDH). The key issue for PEE-based RDH is to improve the prediction accuracy and design a better modulation mapping. This paper proposes a novel prediction error modulation (PEM) scheme, which comprises a prediction neural network and a modulation mapping rule generation algorithm. We use the multi-scale feature extraction (MSFE) module and the residual dense networks (RDN) to construct the prediction neural network. In the proposed RDH scheme, the cover image is divided into two parts, the reference pixel set and the cover pixel set, based on the chessboard pattern. The prediction network serves to predict the values of the cover pixel set using the reference pixel set. An optimization model is set up to adaptively determine the optimal modulation mapping for data hiding based on the specific prediction-error histogram and payload constraint. Experimental results show the superiority of the proposed prediction neural network and the optimized modulation mapping compared with related works. [ABSTRACT FROM AUTHOR]
Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Reversible data hiding scheme using prediction neural network and adaptive modulation mapping.
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  Data: <searchLink fieldCode="AR" term="%22Chen%2C+Si-sheng%22">Chen, Si-sheng</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chang%2C+Chin-Chen%22">Chang, Chin-Chen</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Horng%2C+Ji-hwei%22">Horng, Ji-hwei</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> horng@email.nqu.edu.tw</i>
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  Data: <searchLink fieldCode="JN" term="%22Multimedia+Tools+%26+Applications%22">Multimedia Tools & Applications</searchLink>. Mar2025, Vol. 84 Issue 9, p6665-6686. 22p.
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  Data: <searchLink fieldCode="DE" term="%22Reversible+data+hiding+%28Computer+science%29%22">Reversible data hiding (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Adaptive+modulation%22">Adaptive modulation</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink>
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  Data: Prediction error expansion (PEE) is an attractive approach for reversible data hiding (RDH). The key issue for PEE-based RDH is to improve the prediction accuracy and design a better modulation mapping. This paper proposes a novel prediction error modulation (PEM) scheme, which comprises a prediction neural network and a modulation mapping rule generation algorithm. We use the multi-scale feature extraction (MSFE) module and the residual dense networks (RDN) to construct the prediction neural network. In the proposed RDH scheme, the cover image is divided into two parts, the reference pixel set and the cover pixel set, based on the chessboard pattern. The prediction network serves to predict the values of the cover pixel set using the reference pixel set. An optimization model is set up to adaptively determine the optimal modulation mapping for data hiding based on the specific prediction-error histogram and payload constraint. Experimental results show the superiority of the proposed prediction neural network and the optimized modulation mapping compared with related works. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s11042-024-19162-3
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        Text: English
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      – SubjectFull: Reversible data hiding (Computer science)
        Type: general
      – SubjectFull: Adaptive modulation
        Type: general
      – SubjectFull: Artificial intelligence
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      – SubjectFull: Image processing
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              Text: Mar2025
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