Open AccessMedical Image Watermarking Based on Secret Sharing and Integer Wavelet Transform
Author(s) -
Gaidaa Saeed Mahdi,
NoorA Yousif,
Abeer Fadhil Shimal
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1963/1/012159
Subject(s) - computer science , shadow (psychology) , steganography , pooling , image sharing , peak signal to noise ratio , digital watermarking , image (mathematics) , integer (computer science) , artificial intelligence , secret sharing , computer vision , file size , noise (video) , algorithm , cryptography , psychology , psychotherapist , programming language , operating system
In telemedicine, the medical data are shared and distributed between the whole world with different specialists and for many purposes through an unsafe medium. So protecting the medical data during the transmissions becomes an important issue. Many image secret sharing schemes with steganography have been proposed. Unfortunately, each of these schemes has one or more drawbacks. First, the large size of a stego images. Second, the visual quality of the stego images evaluated by the peak signal-to-noise ratio (PSNR) is degraded too much. To overcome such drawbacks, a new scheme based on secret sharing and IWT is proposed in this paper. The new scheme can optimize both the size of the stego images and its quality. The proposal scheme involves a dispersion of medical image into shadow images using Lin and Thien’s technique. The size of each shadow size is reduces to 1/k from the overall of the secret image size and k is the number of shadows. After that, the shadows are embedded in a host image by using Integer wavelet transform (IWT) technique. In the reconstruction the secret medical image is reconstruct by pooling at least k shadows The experimental results of the proposed algorithm are shown for many medical images the effectiveness of analyzed with the help of the peak signal-to-noise ratio and normalized correlation.