An Improved and Secure Visual Secret Sharing (VSS) scheme for Medical Images

被引:0
作者
Mhala, Nikhil C. [1 ]
Pais, Alwyn R. [1 ]
机构
[1] Natl Inst Technol Karnataka, Dept Comp Sci & Engn, Informat Secur Res Lab, Mangalore, India
来源
2019 11TH INTERNATIONAL CONFERENCE ON COMMUNICATION SYSTEMS & NETWORKS (COMSNETS) | 2019年
关键词
histopathological images; HVS; data hiding; Super-resolution; visual secret sharing; CRYPTOGRAPHY; SCALE;
D O I
10.1109/comsnets.2019.8711327
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Nowadays, medical information is being shared over the communication networks due to ease of technology. The patient's medical information has to be securely communicated over the network for automatic diagnosis. Most of the communication networks are prone to attacks from an intruder thus compromising the security of patients data. Therefore, there is a need to transmit medical images securely over the network. Visual secret sharing scheme can be used to transmit the medical images over the network securely. Visual Secret Sharing (VSS) scheme generates multiple shares to share secret information among n participants. To recover the secret information, all shares should be stacked together. In our previous work [9], we proposed a VSS based technique to recover secret images with the contrast of 70-80% known as Randomized Visual Secret Sharing (RVSS) scheme. However, RVSS scheme suffers from problems like 1) Generation of blocking artifacts in the recovered images. 2) It recovers medical images with a maximum contrast of 30-40%, hence it is not suitable for medical images. In this paper, we propose a modified RVSS scheme to recover the medical images with improved contrast. The proposed scheme introduces the idea of using super-resolution concept to improve the contrast of reconstructed medical images. The reconstruction quality of the medical images is evaluated using Human Visual System (HVS) based parameters. Additionally, the performance of the proposed system is evaluated using the existing Computer Aided Diagnosis (CAD) systems. The experimental results showed that the proposed system is able to reconstruct the secret image with the contrast of almost 85-90% and similarity of almost 77%. Also, the reconstructed images using the proposed system achieves the similar classification accuracy as that of existing CAD systems.
引用
收藏
页码:823 / 828
页数:6
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