Chaos-based image encryption using Arnold's cat map confusion and Henon map diffusion

Abstract

This research designed an image encryption system that focused on securing teledermatology data in the form of skin disease images. The encryption and decryption process of this system is done on the client side using chaos-based encryption with confusion and diffusion techniques. Arnold's cat map is the chaotic map model used for confusion, while the Henon map is used for diffusion. The initial values of both chaotic maps are obtained from a 30-digit secret key that is generated using Diffie-Hellman key exchange. During Arnold's cat map generation, different p and q values are used for every iteration. On the other side, the precision of the Henon map's x and y values is 10-14. From the tests that have been done, histograms of the encrypted images are relatively flat and distributed through all the gray values. Moreover, the encrypted images have average correlation coefficients of 0.003877 (horizontal), -0.00026 (vertical) and -0.00049 (diagonal) and an average entropy of 7.950304. According to the key sensitivity test, a difference of just one number in the secret key causes big differences, as both results have a similarity index of 0.005337 (0.5%). Meanwhile, in the decryption process, that small key difference cannot be used to restore the encrypted image to its original form and generate another chaotic image with average entropies of 7.964909333 (secret key difference) and 7.994861667 (private key difference).

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