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Hiding data in dual images based on turtle shell matrix with high embedding capacity and reversibility

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Abstract

Reversible data hiding (RDH) is a technology that embeds secret data into a carrier where both the secret data and the carrier can be recovered without any data loss. Inspired by dual images technology, this article proposes to employ a high capacity RDH scheme that is based on turtle shell (TS). We start by constructing a newly designed TS-based reference matrix. Then, two meaningful shadows will be generated after hiding the secret data in the cover image with the reference matrix’s help. Meanwhile, the location conflict problem is solved. On the decoder side, when both shadows are gathered, the data extraction and image recovery can be accomplished using the orientation relationship between two stego pixels that are located at the same coordinates in the two shadows and the reference matrix. Moreover, we introduce a security enhancement technology that improves the security of data extraction. The experiment shows that compared with other state-of-the-art RDH schemes, a higher embedding capacity is achieved by this method, and a good visual quality is retained. Simultaneously, the proposed scheme is effective against attacks on pixel value difference histograms (PDH) and regular singular (RS) analysis.

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Abbreviations

RDH:

Reversible data hiding.

DE:

Different expansion.

HS:

Histogram shifting.

PVO:

Pixel value ordering.

TS:

Turtle shell.

ER:

Embedding ratio.

PSNR:

Peak signal-to-noise ratio.

PDH:

Pixel value difference histograms.

RS:

Regular singular.

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Acknowledgements

This work is supported by Scientific Research Foundation for the Introduction of Talent at Xiamen University of Technology ( YKJ21007R ) and the Open Fund of Engineering Research Center for Software Testing and Evaluation of Fujian Province.

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Correspondence to Chin-Chen Chang.

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Xie, XZ., Chang, CC. Hiding data in dual images based on turtle shell matrix with high embedding capacity and reversibility. Multimed Tools Appl 80, 36567–36584 (2021). https://doi.org/10.1007/s11042-021-11368-z

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  • DOI: https://doi.org/10.1007/s11042-021-11368-z

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