Title
Meaningful Encryption: Generating Visually Meaningful Encrypted Images By Compressive Sensing And Reversible Color Transformation
Abstract
Recently, compressive sensing (CS) and visual security (VS) have caught researchers attention in information security field. However, the measurement matrix is often reused in CS, which makes it vulnerable to chosen plaintext attack (CPA). In addition, when generating meaningful cipher images, the size of the carrier image is usually not less than the size of the plain image. In order to overcome these drawbacks, a new visually secure image encryption scheme using CS and reversible color transformation is proposed. The algorithm consists of two stages: compression and embedding. In the first stage, chaotic sequence is used to generate different structurally random matrices. When CS is performed, a random number is added during the process of sampling. By choosing different random numbers, different measurement matrices can be used to compress and encrypt the same image in different order. In the second stage, block pairing, color transformation and block replacement are employed to obtain a meaningful image. Different from the block replacement between two similar images, this paper first attempts to replace the block of the carrier image with a compressed noise-like image block. Thus, the carrier image can be smaller than the plain image, which saves the bandwidth of transmission. Both theoretical analysis and experimental results show that the proposed encryption scheme has good encryption performance, can effectively resist common attacks, and is suitable for meaningful image encryption.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2955570
IEEE ACCESS
Keywords
DocType
Volume
Encryption, Compressed sensing, Image coding, Ciphers, Cats, Image color analysis, Image encryption, compressive sensing, structurally random matrix, image camouflage, visually secure
Journal
7
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Ping Ping100.68
Jie Fu200.34
Yingchi Mao300.68
Feng Xu444869.80
Jerry Gao521.76