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A Comprehensive Analysis Method for Reversible Data Hiding in Stream-Cipher-Encrypted Images.

Authors :
Yu, Mingji
Yao, Heng
Qin, Chuan
Zhang, Xinpeng
Source :
IEEE Transactions on Circuits & Systems for Video Technology. Oct2022, Vol. 32 Issue 10, p7241-7254. 14p.
Publication Year :
2022

Abstract

Reversible data hiding in encrypted images (RDHEI), an essential branch of reversible data hiding (RDH), has been in development for more than a decade. For most existing stream-cipher-based RDHEI algorithms, encryption schemes are often not the same; thus, these schemes have different effects on the encrypted images. As a result, it is not reasonable to compare the embedding rates (ERs) of RDHEI algorithms directly as we do for plaintext RDH algorithms. However, to our knowledge, many studies focus on the performance of embedding but neglect the influence of the encryption. To compare the performance of stream-cipher-based RDHEI algorithms more reasonably, this paper proposes a novel comprehensive measure to evaluate state-of-the-art RDHEI algorithms. First, the characteristics of the stream-cipher-based RDHEI algorithms are divided into two categories according to the encryption and embedding processes. Next, we use correlation and redundancy to evaluate the influence of encryption schemes and also use ER, computation complexity, visual quality, and algorithm stability to evaluate the embedding schemes. In the end, in order to combine all six indexes into the final evaluation measure, we standardize each index before applying the radar chart. The experimental results show the evaluation results of the recent RDHEI algorithms via a comprehensive analysis and demonstrate the guiding significance of the proposed method for the current RDHEI algorithm selection. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10518215
Volume :
32
Issue :
10
Database :
Academic Search Index
Journal :
IEEE Transactions on Circuits & Systems for Video Technology
Publication Type :
Academic Journal
Accession number :
160693856
Full Text :
https://doi.org/10.1109/TCSVT.2022.3172226