Accelerating randomized image secret sharing with GPU: contrast enhancement and secure reconstruction using progressive and convolutional approaches

M. Raviraja Holla, D. Suma, Alwyn R. Pais

Research output: Contribution to journalArticlepeer-review

Abstract

Image Secret Sharing (ISS) is a cryptographic technique used to distribute secret images among multiple users. However, current Visual Secret Sharing (VSS) schemes produce a halftone image with only 50% contrast when reconstructing the original image. To overcome this limitation, the Randomized Image Secret Sharing (RISS) scheme was introduced. RISS achieves a higher contrast of 70% when extracting the secret image but comes with a high computational cost. This research paper presents a novel approach called Graphics Processing Unit (GPU)-based Randomized Image Secret Sharing (GRISS), which utilizes data parallelism within the RISS pipeline. The proposed technique also incorporates an Autoencoder-based Single Image Super-Resolution (ASISR) to enhance the contrast of the recovered image. The performance of GRISS is evaluated against RISS, and the contrast of the ASISR images is compared to current benchmark models. The results demonstrate that GRISS outperforms state-of-the-art models in both efficiency and effectiveness.

Original languageEnglish
Pages (from-to)43761-43776
Number of pages16
JournalMultimedia Tools and Applications
Volume83
Issue number15
DOIs
Publication statusPublished - 05-2024

All Science Journal Classification (ASJC) codes

  • Software
  • Media Technology
  • Hardware and Architecture
  • Computer Networks and Communications

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