.png){kind=icon}
Return
A Visually Meaningful Image Encryption Scheme Based on Lossless Compression SPIHT Coding
.png){kind=icon}
PRE
AI
DOI：10.1109/TSC.2023.3258144.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
With the popularity of social networks and the increase of cloud platform applications, service computing has also developed. Therefore, the protection of information even privacy uploaded to the cloud server has become critical. Recently, some researchers have proposed encryption schemes of visual meaningful image by using compressive sensing. However, these schemes generally cannot hide the large-size of plain image into the small-size of cover image and cannot recover the original plain image lossless. To solve above problems, this paper proposed a visually meaningful image encryption scheme based on lossless compression set partitioning in hierarchical trees (SPIHT) coding. The sender encrypts the plain image into the cipher image through the proposed encryption scheme, and then uploads the cipher image to the cloud server which is assumed as semi-honest trusted. Authorized receiver can completely decrypt the plain image after downloading the cipher image. In addition, even if the cipher image is attacked by attacker in the cloud server, the final decrypted image is still readable. Experimental results show that the proposed scheme is not only completely reversible and can hide the large-size of plain image into the small-size of cover image, but also superior to other schemes in visual quality and anti-attack performance.
Keywords:
Image encryption
lossless compression
privacy preserving
reversibility
services computing
set partitioning in hierarchical trees
visual quality
Journal
IF:
5.8
Papers:
2.2K
Citations:
6.5K
Organization
Cited Papers
Image encryption and hiding algorithm based on compressive sensing and random numbers insertion.png)
SIGNAL PROCESSING
SIGNAL PROCESSINGIF3.6
PREAI
ShareSaveShareSaveA visually secure image encryption scheme based on semi-tensor product compressed sensingSIGNAL PROCESSING
SIGNAL PROCESSINGIF3.6
PREAI
ShareSave
View more