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An efficient and adaptive data-hiding scheme based on secure random matrix


Autoři: Mingwu Zhang aff001;  Shaochen Zhang aff001;  Lein Harn aff003
Působiště autorů: School of Computer Sciences, Hubei University of Technology, Wuhan, Hubei Province, China aff001;  Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, Hubei Province, China aff002;  Department of Computer Science Electrical Engineering, University of Missouri-Kansas City, Kansas City, State of Missouri, United States of America aff003
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0222892

Souhrn

There is a vivid research on securely deliver a secret message by using a data hiding technique in digital images. However, most existing solutions of data-hiding need to encrypt secret data first, and embed the encrypt message into cover image, which is not promising any security once the embedding algorithm is leaked. In this paper, an efficient and adaptive data hiding scheme is presented which is based on a new reference matrix named secure reference matrix. The scheme is flexible enough to meet different data hiding capacities and image quality as needed, and we allow the reference matrix to be more secure since it is randomly generated each time and make it much harder for being attack with exhaustive manner by extending the possible solution. We give the detail protocol for our scheme and provide the practical test and performance analysis. Experimental results show that our scheme has a better flexibility and efficiency in embedding process, and the average value of Peak Signal to Noise Ratio (PSNR) for the stego-images which are generated by our scheme have a higher visual quality for different embedding capacities, and our scheme is more efficient compared with related work.

Klíčová slova:

Imaging techniques – Eyes – Mathematical functions – Turtles – Digital imaging – Signal to noise ratio – Boats – Cryptography


Zdroje

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2019 Číslo 10
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