3DSLCM-CNN: 3D sinusoidal logistic chaotic map and CNN for image encryption

A new approach for image encryption is introduced in this article with a deep network guided key map/image for diffusion. The limitations in the selection or generation of key images for diffusion in the existing chaotic map-based methods are focused on in this work. The sinusoidal logistic map is modified and extended to three dimensions in the proposed work for confusion. A variant of the sinusoidal logistic function is utilized to generate a random sequence and resized to the size of 2 m × 2 n and normalized to the range of pixel intensity of plaintext image having size m × n . The size of key map is reduced equal to the size of plaintext image using a deep network with dilated convolution. The plaintext image is scrambled using row-column shuffling and split into two image parts, odd and even images. Both the images are then reshaped and then confused using the proposed chaotic map and then combined into a single image. The confused image is diffused with the deep network-guided key map at the bit level using XOR logic operation. To enhance the security, the diffused image is further confused using the chaotic maps. The overall process is reversed in decryption to retrieve the plaintext image. The performance of the proposed encryption is measured and compared with the existing methods using standard measures such as key sensitivity, information entropy, correlation coefficient, NPCR, and UACI, which shows that the proposed method improves randomness to secure the information from attacks.