Code division multiplexing and machine learning based reversible data hiding scheme for medical image

Document Type

Article

Publication Date

1-1-2019

Abstract

In this paper, a new reversible data hiding (RDH) scheme based on Code Division Multiplexing (CDM) and machine learning algorithms for medical image is proposed. The original medical image is firstly converted into frequency domain with integer-to-integer wavelet transform (IWT) algorithm, and then the secret data are embedded into the medium frequency subbands of medical image robustly with CDM and machine learning algorithms. According to the orthogonality of different spreading sequences employed in CDM algorithm, the secret data are embedded repeatedly, most of the elements of spreading sequences are mutually canceled, and the proposed method obtained high data embedding capacity at low image distortion. Simultaneously, the to-be-embedded secret data are represented by different spreading sequences, and only the receiver who has the spreading sequences the same as the sender can extract the secret data and original image completely, by which the security of the RDH is improved effectively. Experimental results show the feasibility of the proposed scheme for data embedding in medical image comparing with other state-of-the-art methods.

Identifier

85060783467 (Scopus)

Publication Title

Security and Communication Networks

External Full Text Location

https://doi.org/10.1155/2019/4732632

e-ISSN

19390122

ISSN

19390114

Volume

2019

Grant

J18KA331

Fund Ref

National Natural Science Foundation of China

This document is currently not available here.

Share

COinS