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A novel data hiding scheme based on multidimensional reference tensors

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Abstract

The protection of transmitted messages from being eavesdropped on the internet by a third party has always been an important research topic. The image steganography, which utilizes natural images to hide secret messages, is a widely used approach. The security of image steganography depends primarily on the undetectability of the transmitted stego-images. However, very few methods can handle customized adjustments to both the security level and embedding capacity simultaneously. Therefore, this study proposes a novel data hiding scheme utilizing a multidimensional reference tensor (MRT) with \(N\) dimensions and a side length of \(M\) to achieve these adjustments. The proposed method can freely adjust the embedding capacity from 1 bit per pixel (bpp) to 8 bpp, reduce the overall memory usage by up to nearly 99% based on a scalable index encoding (SIE) technique, and customize the level of security by simply altering the number of dimensions or the side length while maintaining a balance between the embedding capacity and visual imperceptibility. Based on experimental results, we demonstrated that the method could achieve a peak signal-to-noise ratio (PSNR) of 46.38 dB with an embedding capacity of 2 bpp, which is equivalent to the performance of state-of-the-art methods. Furthermore, compared with existing steganographic methods, the embedded message through the MRT is less susceptible to hacking owing to the factorial number of solutions for the MRT, which in this case is \(M\) to the power of \(N\).

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Data Availability

The experimental data and results that support the findings of this study are available in the USC-SIPI Image Database [33] with the following URL: https://sipi.usc.edu/database/.

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Acknowledgements

This research was partially supported by the National Science and Technology Council, Taiwan, R.O.C. (Grant Nos. NSTC 112-2221-E-005-048, NSTC 111-2221-E-005-048, NSTC 112-2634-F-005-001-MBK, and NSTC 111-2634-F-005-001).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Chung Hsing University, No. 145, Xingda Rd., South District, Taichung, 40227, Taiwan

    Jason Lin, Kuan-Hung Liu & Cih-Lian Lin

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  1. Jason Lin
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  2. Kuan-Hung Liu
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  3. Cih-Lian Lin
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Correspondence to Jason Lin.

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Lin, J., Liu, KH. & Lin, CL. A novel data hiding scheme based on multidimensional reference tensors. Multimed Tools Appl 83, 25695–25712 (2024). https://doi.org/10.1007/s11042-023-16526-z

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