Image integrity verification via reversible predictive hiding and elliptic curve Diffie-Hellman. (30th October 2019)

• Record Type: Journal Article
• Authors: Agarwal, Siddharth; Ranjani, J. Jennifer
• Is Part Of: International journal of innovative computing and applications. Volume 10:Number 3/4(2019)
• Page Start: 154
• Page End: 163
• Publication Date: 2019-10-30
• Subjects: reversible data hiding -- elliptic cryptography -- Diffie-Hellman -- image encryption -- integrity verification; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

This paper presents a medical image integrity verification algorithm which will be able to detect any changes made in the pixel value or the size of a medical image. At the same time, it also provides a secure way of transmitting images over the public domain. This algorithm can not only ensure integrity of the medical image, but also checks if the sender of the image is authentic, making it useful for archiving medical images and remote medical diagnosis. This algorithm essentially has three modules: hashing, data embedding and image encryption. Initially, hash signatures are extracted from the image and are embedded inside the medical image. In the embedding phase, the image is divided into blocks of uneven size. In the raster scanning order, the blocks are embedded with multiple pixels depending on its smoothness. At the receiver end, the signatures are extracted and it can be used to verify the integrity of the medical images. The effectiveness of the embedding algorithm is verified in terms of peak signal to noise ratio, structural similarity index metrics, correlation coefficient. L 2 norm is used for verifying the integrity of the images.