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

TL;DR

The paper tackles the challenge of reversible data hiding over encrypted images by revealing an intrinsic correlation in block-based secret sharing and introducing two space-vacating strategies. It then presents two RDH-EI schemes: a high-capacity scheme that directly creates embedding space, and a size-reduced scheme that minimizes data expansion by discarding unnecessary shares. Through theoretical analysis and extensive experiments, the high-capacity scheme achieves the highest embedding capacity with robust performance, while the size-reduced scheme significantly reduces encrypted-image size with competitive embedding rates. The results demonstrate practical viability for secure cloud storage and medical imaging, offering a flexible trade-off between embedding rate and data expansion. Overall, the work advances RDH-EI by leveraging intrinsic block correlations to design efficient, secure, and scalable schemes.

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.

Figures (9)

• Figure 1: Shape of the MED predictor, where $x$ is the value to be predicted.
• Figure 2: An example of the proposed high-capacity scheme.
• Figure 3: An example of the size-reduced scheme.
• Figure 4: Six test images: (a) Man; (b) Jetplane; (c) Peppers; (d) Boat; (e) Goldhill; (f) Baboon.
• Figure 5: Simulation experiments of $(4,4)$-threshold. (a) Image Baboon; (b)-(e) four encrypted images with size $4 \times 4$; (f)-(i) four marked encrypted images; (j) recovered image with $PSNR \rightarrow +\infty$; (k) image Jetplane; (l)-(o) four encrypted images with size $8 \times 8$; (p)-(s) Four marked encrypted images; (t) recovered image with $PSNR \rightarrow +\infty$.