Large-Capacity Reversible Data Hiding Over Encrypted Images via Pixel Correlation Recovery

Zhongyun Hua, Jianhui Zou, Yifeng Zheng, Zhili Zhou, Fei Peng, Qing Liao

Abstract: Cloud services have been commonly leveraged to store and manage the exponential growth of images, yet this also comes with critical data privacy concerns. Reversible data hiding over encrypted images (RDH-EI) techniques can embed data into encrypted images and support lossless recovery, which can provide an effective solution for securely managing private images in the cloud. However, existing schemes generally suffer from low embedding capacity. Moreover, most of them rely on a single cloud server, which introduces a single point of failure. In this paper, we first propose a pixel correlation recovery (PCR) technique for restoring the pixel correlation excessively disrupted during encryption. Using the PCR technique, we develop a secure $(r, n)$-threshold RDH-EI scheme with large embedding capacity and avoidance of single point of failure. In our scheme, a content owner encrypts a confidential image into $n$ shares and distributes them across $n$ independent cloud servers. We design a new encoding method enabling each cloud server to efficiently encode the share, preserving capacity for data embedding. An authorized receiver can later extract the embedded data and reconstruct the confidential image from $r$ shares. Experiments demonstrate that our scheme achieves significantly larger embedding capacity over state-of-the-art schemes.

External IDs:dblp:journals/tdsc/HuaZZZPL26