Ensemble Reversible Data Hiding

TL;DR

This paper introduces an ensemble reversible data hiding strategy that partitions the host into subunits, allowing for optimized parameter use and combining multiple algorithms to improve rate-distortion performance.

Contribution

It proposes a novel segmented embedding approach enabling the use of different RDH algorithms within subhosts, enhancing efficiency and performance.

Findings

Ensemble RDH outperforms traditional methods in most cases.

Partitioning host improves parameter optimization.

Combining algorithms enhances rate-distortion trade-off.

Abstract

The conventional reversible data hiding (RDH) algorithms often consider the host as a whole to embed a secret payload. In order to achieve satisfactory rate-distortion performance, the secret bits are embedded into the noise-like component of the host such as prediction errors. From the rate-distortion optimization view, it may be not optimal since the data embedding units use the identical parameters. This motivates us to present a segmented data embedding strategy for efficient RDH in this paper, in which the raw host could be partitioned into multiple subhosts such that each one can freely optimize and use the data embedding parameters. Moreover, it enables us to apply different RDH algorithms within different subhosts, which is defined as ensemble. Notice that, the ensemble defined here is different from that in machine learning. Accordingly, the conventional operation corresponds to a special case of the proposed work. Since it is a general strategy, we combine some state-of-the-art algorithms to construct a new system using the proposed embedding strategy to evaluate the rate-distortion performance. Experimental results have shown that, the ensemble RDH system could outperform the original versions in most cases, which has shown the superiority and applicability.