Meaningful Secret Image Sharing Scheme with High Visual Quality Based on Natural Steganography

Yuyuan Sun, Yuliang Lu, Jinrui Chen, Weiming Zhang and Xuehu Yan
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Yuyuan Sun: National University of Defense Technology, Hefei 230037, China
Yuliang Lu: National University of Defense Technology, Hefei 230037, China
Jinrui Chen: National University of Defense Technology, Hefei 230037, China
Weiming Zhang: School of Information Science and Technology, University of Science and Technology of China, Hefei 230026, China
Xuehu Yan: National University of Defense Technology, Hefei 230037, China

Mathematics, 2020, vol. 8, issue 9, 1-17

Abstract: The ( k , n ) -threshold Secret Image Sharing scheme (SISS) is a solution to image protection. However, the shadow images generated by traditional SISS are noise-like, easily arousing deep suspicions, so that it is significant to generate meaningful shadow images. One solution is to embed the shadow images into meaningful natural images and visual quality should be considered first. Limited by embedding rate, the existing schemes have made concessions in size and visual quality of shadow images, and few of them take the ability of anti-steganalysis into consideration. In this paper, a meaningful SISS that is based on Natural Steganography (MSISS-NS) is proposed. The secret image is firstly divided into n small-sized shadow images with Chinese Reminder Theorem, which are then embedded into RAW images to simulate the images with higher I S O parameters with NS. In MSISS-NS, the visual quality of shadow images is improved significantly. Additionally, as the payload of cover images with NS is larger than the size of small-sized shadow images, the scheme performs well not only in visual camouflage, but also in other aspects, like lossless recovery, no pixel expansion, and resisting steganalysis.

Keywords: meaningful secret image sharing; Chinese Reminder Theorem; Natural Steganography; small-sized shadow images; steganalysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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