Open AccessA compressed secret image sharing method with shadow image verification capability
Author(s) -
Guo Zheng Yang,
Lin Tao Liu,
Xue Hu Yan
Publication year - 2020
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2020237
Subject(s) - image sharing , computer science , secret sharing , lossless compression , grayscale , homomorphic secret sharing , theoretical computer science , image (mathematics) , shadow (psychology) , bitwise operation , computer security , cryptography , computer vision , artificial intelligence , data compression , psychology , psychotherapist , programming language
Secret image sharing (SIS) is an important research direction in information hiding and data security transmission. Since the generated shadow images (shares) are always noise-like, it is difficult to distinguish the fake share from the unauthorized participant before recovery. Even more serious is that an attacker with a fake share can easily collect shares of other honest participants. As a result, it is significant to verify the shares, before being taken out for recovery. Based on two mainstream methods of SIS, such as polynomial-based SIS and visual secret sharing(VSS), this paper proposed a novel compressed SIS with the ability of shadow image verification. Considering that the randomness of the sharing phase of polynomial-based SIS can be utilized, one out of shares of (2, 2)-threshold random-grid VSS is embedded into all shares of polynomial-based SIS by a XOR operation as the verification information, while the other binary share is private for verification. Before recovery, each participant must extract the binary share from the grayscale share to perform XOR operation with the private share, and the original binary image can be recovered only with the true share. The proposed scheme also has the characteristics of shadow image verification, pixel compression, loss tolerance and lossless recovery. Through experiments and comparative analysis of related research results, the effectiveness and advantages of the method are verified.