Abstract
Reversible data hiding (RDH) allows carrying secret information in cover media without introducing permanent distortion. For a RDH method, the important performance measurements are embedding capacity and image quality. Since embedding capacity is an important requirement in the field of data hiding, it is necessary to consider the security of data embedding in RDH applications. In general, RDH algorithms usually prefer data embedding in simple image regions with low local complexity. As a result, image degradation is alleviated at the cost of poor embedding security. In this study, a novel RDH method is proposed to embed data into complex image regions, wherein the data hiding becomes more secure in defending against modern steganalysis. To measure regional local complexity, the harmonic mean of directional local variances is employed to combine directional pixel differences. To embed data into complex regions instead of smooth regions, multiple histogram modification is adopted and updated for optimized data embedding with higher complexity. Experiment results show that embedding security is significantly improved with a considerable amount of payload and well-preserved image quality.
Highlights
Reversible data hiding (RDH) refers to embedded messages into digital contents such as images, videos, and documents of the cover media which are completely recovered after data extraction [1]
Several RDH techniques have been proposed for histogram modification (HM) [2, 3], difference expansion (DE) [4,5,6], prediction-error expansion (PEE) [7,8,9,10], deep neural networks (DNN) [11, 12], and multiple histogram modification (MHM) [3, 12, 13], and so forth
MHM has attracted considerable attention owing to its potential in revealing spatial redundancy in natural images, while DNN suggests a new research direction since DNN serves as a higher performance predictor for predictionbased RDH methods
Summary
Reversible data hiding (RDH) refers to embedded messages into digital contents such as images, videos, and documents of the cover media which are completely recovered after data extraction [1]. Several RDH techniques have been proposed for histogram modification (HM) [2, 3], difference expansion (DE) [4,5,6], prediction-error expansion (PEE) [7,8,9,10], deep neural networks (DNN) [11, 12], and multiple histogram modification (MHM) [3, 12, 13], and so forth. In the aforementioned RDH methods, embedding performance is usually represented by embedding capacity and image quality since the methods embed data in pixels from the flat regions. The technique of MHM is exploited to embed data into complex image regions with the inspiration of Li et al.’s [3] and Hong et al.’s work [21].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
