Privacy-preserved data hiding using compressive sensing and fuzzy C-means clustering

Ming Li, Lanlan Wang and Haiju Fan

International Journal of Distributed Sensor Networks, 2020, vol. 16, issue 2, 1550147720908748

Abstract: Nowadays, digital images are confronted with notable privacy and security issues, and many research works have been accomplished to countermeasure these risks. In this article, a novel scheme for data hiding in encrypted domain is proposed using fuzzy C-means clustering and compressive sensing technologies to protect privacy of the host image. The original image is preprocessed first to generate multiple highly correlated classes with fuzzy C-means clustering algorithm. Then, all classes are further divided into two parts according to proper threshold. One is encrypted by stream cipher, and the other is encrypted and compressed simultaneously with compressive sensing technology for easy data embedding by information hider. The receiver can extract additional data and recover the original image with data-hiding key and encryption key. Experiments and analysis demonstrate that the proposed scheme can achieve higher embedding rate about additional data and better visual quality of recovered image than other state-of-the-art schemes.

Keywords: Encryption; data hiding; fuzzy C-means; compressive sensing; privacy (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:16:y:2020:i:2:p:1550147720908748

DOI: 10.1177/1550147720908748

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