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

• Main Authors: Yuyuan Sun, Yuliang Lu, Jinrui Chen, Weiming Zhang, Xuehu Yan
• Format: Article
• Language: English
• Published: MDPI AG 2020-08-01
• Series: Mathematics
• Subjects: meaningful secret image sharing; Chinese Reminder Theorem; Natural Steganography; small-sized shadow images; steganalysis
• Online Access: https://www.mdpi.com/2227-7390/8/9/1452

The <inline-formula><math display="inline"><semantics><mrow><mo>(</mo><mi>k</mi><mo>,</mo><mi>n</mi><mo>)</mo></mrow></semantics></math></inline-formula>-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 <i>n</i> small-sized shadow images with Chinese Reminder Theorem, which are then embedded into RAW images to simulate the images with higher <inline-formula><math display="inline"><semantics><mrow><mi>I</mi><mi>S</mi><mi>O</mi></mrow></semantics></math></inline-formula> 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.