High Capacity Reversible Data Hiding in Encrypted Images by Patch-Level Sparse Representation

TLDR

Reversible data hiding in encrypted images has attracted attention, and prior work shows that exploiting image redundancy—such as representing patches linearly with atoms from an over‑complete dictionary—yields superior performance. The paper proposes using patch‑level sparse representation to better exploit neighbor pixel correlation for data hiding. The method compresses highly similar local patches via sparse coding, embeds the residual errors and the learned dictionary into the encrypted image, thereby creating a large hiding capacity for secret data. Extensive experiments demonstrate that the proposed method significantly outperforms the state‑of‑the‑art methods in terms of embedding rate and image quality.

Abstract

Reversible data hiding in encrypted images has attracted considerable attention from the communities of privacy security and protection. The success of the previous methods in this area has shown that a superior performance can be achieved by exploiting the redundancy within the image. Specifically, because the pixels in the local structures (like patches or regions) have a strong similarity, they can be heavily compressed, thus resulting in a large hiding room. In this paper, to better explore the correlation between neighbor pixels, we propose to consider the patch-level sparse representation when hiding the secret data. The widely used sparse coding technique has demonstrated that a patch can be linearly represented by some atoms in an over-complete dictionary. As the sparse coding is an approximation solution, the leading residual errors are encoded and self-embedded within the cover image. Furthermore, the learned dictionary is also embedded into the encrypted image. Thanks to the powerful representation of sparse coding, a large vacated room can be achieved, and thus the data hider can embed more secret messages in the encrypted image. Extensive experiments demonstrate that the proposed method significantly outperforms the state-of-the-art methods in terms of the embedding rate and the image quality.