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

2025-02-16

AI Summary

Existing reversible data hiding in encrypted images (RDH-EI) for cloud environments suffers from high computational complexity, low embedding capacity, and severe ciphertext expansion. To address these bottlenecks, this paper proposes a spatial-preserving RDH-EI method based on block-level secret sharing and redundancy mining. We introduce two novel embedding mechanisms: direct spatial shifting—enabling high-capacity and robust embedding—and image-shrinking shifting—actively compressing ciphertext size while preserving reversibility, achieving an average 32% reduction. Through an integrated design encompassing shared redundancy analysis, spatial-domain modeling, collaborative encryption, and reversible embedding, our approach uniquely eliminates redundant shares and suppresses data expansion. Experiments demonstrate state-of-the-art performance in both embedding capacity and ciphertext expansion ratio. The method is particularly suitable for security- and storage-efficiency-critical applications such as medical imaging and cloud storage.

Technology Category

• Data Mining & Knowledge Management: Data Compression
• Computer Vision: Image and Video Retrieval
• Machine Learning: Privacy

Application Category

• Security and Privacy: Applications of cryptography
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Economics, Online Markets and Human Computation: The sharing economy

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.

Problem

• Reduces computational complexity in RDH-EI
• Increases embedding rates securely
• Minimizes data expansion in encrypted images

Innovation

• Block-based secret sharing analysis
• Direct space-vacating method
• Image-shrinking-based space-vacating method