Minimizing Embedding Distortion with Weighted Bigraph Matching in Reversible Data Hiding
Hanzhou Wu

TL;DR
This paper introduces a novel approach for reversible data hiding that models embedding as a weighted bipartite graph problem, enabling optimal histogram shifting to minimize distortion and improve payload performance.
Contribution
It proposes a new graph-based model for RDH that optimizes embedding with minimal distortion, independent of content characteristics, and demonstrates its effectiveness through integration with existing methods.
Findings
Significantly improves payload-distortion performance in experiments.
Models embedding as a minimum weight maximum matching problem.
Enhances practical applicability of RDH schemes.
Abstract
For a required payload, the existing reversible data hiding (RDH) methods always expect to reduce the embedding distortion as much as possible, such as by utilizing a well-designed predictor, taking into account the carrier-content characteristics, and/or improving modification efficiency etc. However, due to the diversity of natural images, it is actually very hard to accurately model the statistical characteristics of natural images, which has limited the practical use of traditional RDH methods that rely heavily on the content characteristics. Based on this perspective, instead of directly exploiting the content characteristics, in this paper, we model the embedding operation on a weighted bipartite graph to reduce the introduced distortion due to data embedding, which is proved to be equivalent to a graph problem called as \emph{minimum weight maximum matching (MWMM)}. By solving…
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Taxonomy
TopicsAdvanced Steganography and Watermarking Techniques · Chaos-based Image/Signal Encryption · Digital Media Forensic Detection
