Abstract: Digital medical image has a potential to be manipulated by unauthorized persons due to advanced communication technology. Verifying integrity and authenticity have become important issues on the medical image. This paper proposed a self-embedding watermark using a spiral block mapping for tamper detection and restoration. The block-based coding with the size of 3×3 was applied to perform self-embedding watermark with two authentication bits and seven recovery bits. The authentication bits are obtained from a set of condition between sub-block and block image, and the parity bits of each sub-block. The authentication bits and the recovery bits are embedded in the least significant bits using the proposed spiral block mapping. The recovery bits are embedded into different sub-blocks based on a spiral block mapping. The watermarked images were tested under various tampered images such as blurred image, unsharp-masking, copy-move, mosaic, noise, removal, and sharpening. The experimental results show that the scheme achieved a PSNR value of about 51.29 dB and a SSIM value of about 0.994 on the watermarked image. The scheme showed tamper localization with accuracy of 93.8%. In addition, the proposed scheme does not require external information to perform recovery bits. The proposed scheme was able to recover the tampered image with a PSNR value of 40.45 dB and a SSIM value of 0.994.

Ferda Ernawan, Afrig Aminuddin, Danakorn Nincarean, Mohd Faizal Ab Razak and Ahmad Firdaus, “Three Layer Authentications with a Spiral Block Mapping to Prove Authenticity in Medical Images” International Journal of Advanced Computer Science and Applications(IJACSA), 13(4), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130425

@article{Ernawan2022,
title = {Three Layer Authentications with a Spiral Block Mapping to Prove Authenticity in Medical Images},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130425},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130425},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {4},
author = {Ferda Ernawan and Afrig Aminuddin and Danakorn Nincarean and Mohd Faizal Ab Razak and Ahmad Firdaus}
}