Reversible Data Hiding over Encrypted Images via Intrinsic Correlation in Block-Based Secret Sharing

TL;DR

This paper introduces two novel reversible data hiding schemes for encrypted images that improve embedding capacity and reduce data expansion by leveraging intrinsic correlations and space-preserving techniques.

Contribution

It proposes two space-preserving methods and two RDH-EI schemes that enhance capacity and reduce size, addressing computational complexity and data expansion issues.

Findings

High-capacity scheme outperforms existing methods in embedding capacity.

Size-reduced scheme minimizes data expansion effectively.

Both schemes are suitable for medical imaging and cloud storage applications.

Abstract

With the rapid advancements in information technology, reversible data hiding over encrypted images (RDH-EI) has become essential for secure image management in cloud services. However, existing RDH-EI schemes often suffer from high computational complexity, low embedding rates, and excessive data expansion. This paper addresses these challenges by first analyzing the block-based secret sharing in existing schemes, revealing significant data redundancy within image blocks. Based on this observation, we propose two space-preserving methods: the direct space-vacating method and the image-shrinking-based space-vacating method. Using these techniques, we design two novel RDH-EI schemes: a high-capacity RDH-EI scheme and a size-reduced RDH-EI scheme. The high-capacity RDH-EI scheme directly creates embedding space in encrypted images, eliminating the need for complex space-vacating operations and achieving higher and more stable embedding rates. In contrast, the size-reduced RDH-EI scheme minimizes data expansion by discarding unnecessary shares, resulting in smaller encrypted images. Experimental results show that the high-capacity RDH-EI scheme outperforms existing methods in terms of embedding capacity, while the size-reduced RDH-EI scheme excels in minimizing data expansion. Both schemes provide effective solutions to the challenges in RDH-EI, offering promising applications in fields such as medical imaging and cloud storage.