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Binary nonogram puzzle based data hiding technique for data security

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Abstract

Data security is one of the critical challenges for digital information. The existing steganography algorithms hide confidential data in the cover media to preserve the presence of hidden data. One type of steganography algorithm is a puzzle-based algorithm such as sudoku, magic cube, and basic nonogram puzzle. These puzzles depend on an auxiliary reference matrix for communication with the receiver. However, using a reference matrix is prone to a statistical histogram attack because image data hiding pixels are fixed. The challenging task here is to design a secured secret data technique that hides the secret data bits in the cover using a suitable puzzle without communicating the reference matrix with the receiver. This paper proposes a new Binary Nonogram Puzzle (BNP) technique. The BNP is based on two elements: searching the BNP indexes based on LSB of the cover pixel and hiding the secret data in random pixel based on the index information. The first row and column for each block are considered an index for the remaining block elements used for data hiding. We also identify the position to hide the secret data that calculates vertical and horizontal summation to indicate a suitable index with the specified rule. The effectiveness of the BNP was tested on the standard dataset images (USC-SIPI image database) and evaluated using Peak signal-to-noise ratio (PSNR) and Mean Square Error (MSE). The results showed that BNP achieves high PSNR and low MSE compared to the existing puzzles (Magic Cube and Basic Nonogram puzzle) and guarantees a better stego image quality.

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Acknowledgments

We would also like to thank Prof Dr. Masri Ayob for their valuable inputs.

Funding

This work was supported by Universiti Kebangsaan Malaysia under research LRGS-1-2019-UKM-UKM-2-7.

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Correspondence to Mohammad Kamrul Hasan.

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Kamil, S., Abdullah, S.N.H.S., Hasan, M.K. et al. Binary nonogram puzzle based data hiding technique for data security. Multimed Tools Appl 83, 4951–4992 (2024). https://doi.org/10.1007/s11042-023-15618-0

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