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A high capacity multi-image steganography technique based on golden ratio and non-subsampled contourlet transform

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Abstract

Hiding sensitive information in a host image (or 2D signal) is a challenging task. Several image steganography techniques have been proposed in recent years, which either have low embedding capacity, or the embedded images are vulnerable. The proposed technique, which is based on Golden Ratio and Non-Subsampled Contourlet Transform (GRNSCT) model provides both high embedding capacity as well as the confidentiality of the embedded images. The high embedding capacity is achieved via a combination of mosaic process and two level NSCT (Non-Subsampled Contourlet Transform), while confidentiality is attained via double layer encryption based on shuffling method of a deck of cards. Several types of security evaluation metrics, such as, key sensitivity, histogram, and information entropy, are utilized to assess the robustness of the embedded images. The experimental results demonstrate that the proposed multi-image steganography technique achieves 24 bpp (bits per pixel) embedding capacity, or 300% payload with PSNR up to 42.38 dB (decibels), which is better than the existing techniques.

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Notes

  1. USC–SIPI image database is available at http://sipi.usc.edu/database/. Accessed 18 May 2021

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Acknowledgements

The authors would like thank everyone, who has contributed anyhow in the preparation of this manuscript.

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Authors and Affiliations

  1. Department of Mathematics and Astronomy, University of Lucknow, Lucknow, 226 007, India

    Abdul Gaffar, Anand B. Joshi & Sonali Singh

  2. Department of Mathematics, CIPET, Lucknow, 226 008, India

    Keerti Srivastava

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  1. Abdul Gaffar
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  2. Anand B. Joshi
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  3. Sonali Singh
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  4. Keerti Srivastava
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Correspondence to Anand B. Joshi.

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This work was supported by UGC (University Grants Commission), India under grant No. [415024]

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Gaffar, A., Joshi, A.B., Singh, S. et al. A high capacity multi-image steganography technique based on golden ratio and non-subsampled contourlet transform. Multimed Tools Appl 81, 24449–24476 (2022). https://doi.org/10.1007/s11042-022-12246-y

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