Selective scalable secret image sharing with verification

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• 作者：Jung-San Lee ; You-Ren Chen
• 关键词：Scalable secret image sharing ; Salient map ; Meaningful shadow ; Verification ; ROI
• 刊名：Multimedia Tools and Applications
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：76
• 期：1
• 页码：1-11
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems;
• 出版者：Springer US
• ISSN：1573-7721
• 卷排序：76

文摘

Scalable secret image sharing (SSIS) is a new secret image sharing technique. The feature of scalability refers to the fact that the revealed secret information is proportional to the number of gathered shadows. Once all of the valid shadows are collected, the complete secret can be revealed easily. The kernel of secret information, however, may be leaked out with a few shadows collected in the existing SSIS mechanisms. This is because researchers seldom concerned about secret distribution. Thus, we introduce the Salient map to develop a new method, in which the ROIs (region of interesting) of secret image can be revealed progressively. Additionally, we introduce the concepts of meaningful shadow and verification to SSIS. To the best of our knowledge, this is the first SSIS that employs a meaningful shadow. The leading adoption can greatly help reduce the attention of attackers in order to enhance the security, while the second concept can avoid malicious behaviors from outside attackers or dishonest members.