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February 17, 20260 citationsOpen Access

Probability Distribution Tree-Based Dishonest-Participant-Resistant Visual Secret Sharing Using Linearly Polarized Shares

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SJShuvroo JadidAhababLRLaxmisha Rai

Key Points

  • This research aims to develop a visual secret sharing scheme that resists manipulation by dishonest participants.
  • Proposed a dishonest-participant-resistant visual secret sharing scheme using linearly polarized shares.
  • Employed probability distribution trees to embed secret and fake images into polarized shares.
  • Utilized binary image binarization and probability-guided polarization assignment for efficiency.
  • Analyzed performance through PSNR, SSIM, and VIF metrics.
  • Authorized reconstructions exhibit high visual fidelity and recognizability.
  • Unauthorized reconstructions yield significantly degraded visual outputs.
  • Demonstrated effective suppression of information leakage and strong resistance against dishonest attempts.

Abstract

With the rapid growth of data transmission and visual encryption technologies, Visual Secret Sharing (VSS) has become an important technique for image-based information protection. However, many existing VSS schemes remain vulnerable to dishonest participants who attempt to recover secret images through unauthorized stacking or manipulation of shares. To address this issue, this paper proposes a dishonest-participant-resistant VSS scheme based on linearly polarized shares and Probability Distribution Trees (PDTs). The proposed method embeds both secret and fake images into polarized shares, such that any unauthorized stacking of ordinary shares produces a visually plausible fake image or random noise, while only stacking that includes the master share under a predefined optical ordering reveals the true secret image. Binary image binarization and probability-guided polarization assignment are employed to improve computational efficiency and increase uncertainty against adaptive attacks. In addition to visual inspection and contrast analysis, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and visual information fidelity (VIF) are used as complementary metrics to distinguish authorized reconstructions from unauthorized and partial ones. Experimental results show that authorized reconstructions achieve high visual fidelity and perceptual recognizability, whereas unauthorized and partial reconstructions yield significantly degraded or misleading outputs, demonstrating effective suppression of information leakage and strong resistance against dishonest behavior. Consequently, the proposed scheme enhances security and practical usability compared with existing polarization-based VSS approaches.

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Cite This Study

JadidAhabab et al. (2026) studied this question.

synapsesocial.com/papers/6994058c4e9c9e835dfd6874https://doi.org/10.3390/a19020153
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