Elliptic curve cryptographic image encryption using Henon map and Hopfield chaotic neural network

In this paper, a new chaotic image encryption and authentication model based on Elliptic Curves is proposed. The Elliptic Curve Diffie-Hellman (ECDH) is used to generate a reliable session key prior to encryption. The scheme uses the Henon map for the initial shuffling of the pixels and the Hopfield chaotic neural network to compute random chaotic values. The chaotic matrix is XORed with henon shuffled image. The model encrypts the scrambled image using improved ElGamal encoding to obtain the cipher image. The model employs a low computational cost digital signature to check for the authenticity of the received encrypted images before decryption. The proposed model makes use of a large key space to resist brute force attacks, and produces randomized cipher images with high average Shannon’s entropies, 7.9994 for grayscale and 7.9993 for color images, and lower adjacent pixel correlation to resist statistical attacks and ensure a good quality encryption. The model can withstand the chosen-plaintext and known-plaintext attacks, and attains average Number of Pixel Change Rate (NPCR) and Unified Average Change Intensity (UACI) values of (99.63%, 33.345%) for grayscale and (99.625%, 33.34%) for color images signifying its effectiveness against differential attacks. The ability to recover decipherable images after masking up to 75% of cipher image indicates the robustness of the proposed scheme against the occlusion attacks.