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An Effective GPGPU Visual Secret Sharing by Contrast-Adaptive ConvNet Super-Resolution

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Abstract

In this paper, we propose an effective secret image sharing model with super-resolution utilizing a Contrast-adaptive Convolution Neural Network (CCNN or CConvNet). The two stages of this model are the share generation and secret image reconstruction. The share generation step generates information embedded shadows (shares) equal to the number of participants. The activities involved in the share generation are to create a halftone image, create shadows, and transforming the image to the wavelet domain using Discrete Wavelet Transformation (DWT) to embed information into the shadows. The reconstruction stage is the inverse of the share generation supplemented with CCNN to improve the reconstructed image’s quality. This work is significant as it exploits the computational power of the General-Purpose Graphics Processing Unit (GPGPU) to perform the operations. The extensive use of memory optimization using GPGPU-constant memory in all the activities brings uniqueness and efficiency to the proposed model. The contrast-adaptive normalization between the CCNN layers in improving the quality during super-resolution impart novelty to our investigation. The objective quality assessment proved that the proposed model produces a high-quality reconstructed image with the SSIM of \((89-99.8\%)\) for the noise-like shares and \((71.6-90\%)\) for the meaningful shares. The proposed technique achieved a speedup of \(800 \times\) in comparison with the sequential model.

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Authors and Affiliations

  1. Information Security Research Lab. Department of Computer Science and Engineering, National Institute of Technology Karnataka, Surathkal, 575025, India

    M. Raviraja Holla & Alwyn R. Pais

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  1. M. Raviraja Holla
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  2. Alwyn R. Pais
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The work is carried out by the first author under the guidance of the second author.

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Correspondence to M. Raviraja Holla.

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Holla, M.R., Pais, A.R. An Effective GPGPU Visual Secret Sharing by Contrast-Adaptive ConvNet Super-Resolution. Wireless Pers Commun 123, 2367–2391 (2022). https://doi.org/10.1007/s11277-021-09245-x

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