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Related Experiment Video

Updated: May 6, 2026

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
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Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

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APVCPC: An Adaptive Predicted Value Computation and Pixel Classification Framework for Reversible Data Hiding in

Yaomin Wang1, Wenguang He1, Gangqiang Xiong1

  • 1School of Biomedical Engineering, Guangdong Medical University, Dongguan 523808, China.

Sensors (Basel, Switzerland)
|March 14, 2026
PubMed
Summary

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EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
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This study introduces a new method for reversible data hiding in encrypted images (RDHEI) for secure cloud data management. The APVCPC framework significantly boosts embedding capacity and data fidelity for sensor data.

Area of Science:

  • Computer Science
  • Information Security
  • Data Compression

Background:

  • Internet of Things (IoT) and mobile sensing systems necessitate secure data management.
  • Reversible data hiding in encrypted images (RDHEI) is crucial for secure cloud-based sensor data.
  • Existing RDHEI methods face challenges balancing embedding capacity and data fidelity.

Purpose of the Study:

  • To propose an advanced RDHEI framework, APVCPC, optimizing embedding capacity and data fidelity for sensor data.
  • To enhance prediction accuracy and spatial redundancy utilization in heterogeneous image regions.
  • To support separable data extraction and decryption for flexible access control.

Main Methods:

  • Developed an Adaptive Predicted Value Computation and Pixel Classification (APVCPC) framework.
Keywords:
data hidinghigh capacityimage encryptionmultimedia security

Related Experiment Videos

Last Updated: May 6, 2026

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
07:05

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

2.0K
  • Implemented a context-aware prediction engine with adaptive optimal estimation function selection based on texture complexity.
  • Utilized a content-driven pixel classification paradigm to categorize pixels into loadable and non-loadable sets.

    • Main Results:

    • Achieved a superior average embedding rate exceeding 2.0 bits per pixel (bpp).
    • Ensured perfect reversibility of original visual assets.
    • Demonstrated significant performance improvements over state-of-the-art techniques in capacity and security.

    Conclusions:

    • The APVCPC framework effectively addresses the limitations of conventional RDHEI methods.
    • APVCPC offers enhanced security and efficiency for cloud-based sensor data management.
    • The proposed scheme provides a robust solution for secure sensing scenarios with flexible access control.