Securing images with a diffusion mechanism based on Fractional Brownian Motion

In this work, an image encryption model is proposed incorporating an intertwining logistic map, neural network based confusion algorithm and Fractional Brownian Motion (FBM) based diffusion process. Using an intertwining logistic map in the encryption model leads to a better distribution of random numbers compared to the logistic map which have the blank window observed in bifurcation diagram and further the neural network model based confusion algorithm increases the key sensitivity in the model. Finally, the use of FBM based diffusion process in the model changes the pixels in a way that any minor change in a pixel leads to a change in a large number of pixels in the cipher image. The present encryption model is shown to possess an enhanced image security and better NPCR, UACI scores. The correlation between pixels in cipher image is observed to be negligible. The performance indices of the proposed model are shown to improve in comparison with other models based on dynamic random growth method and also block scrambling and dynamic index based diffusion.