Deep CNN chaotic key generator for multi-parameter elliptic curves over cybersecurity image encryption application.

A chaotic image encryption technique is introduced relying on efficient non-linear substitution boxes (NL-S boxes) based on Deep convolutional neural networks chaotic key generator and new multi-parameter-multi-prime elliptic curves (Deep-CNN-CKG MP-MP-EC). Deep convolutional neural networks are used in this image encryption algorithm to generate an unexpected key stream. Image encryption system can benefit from the unexpected key stream sequence. In the presented method, a new cascaded 2D sine-cosine cross-chaotic map (cascaded 2D [Formula: see text] map), and this chaotic map is used to generate the multi-parameter and the multi-prime for the elliptic curve's generation. All the elliptic curves tack their parameters based on the pseudo-random sequences produced by the proposed cascaded 2D [Formula: see text] map. Furthermore, the points produced by the proposed cascaded 2D [Formula: see text] map and the generated elliptic curves are utilized to generate dynamic NL-S boxes and dynamic permutation tables. The updated NL-S boxes can be established by changing the shared elliptic curve key and the secure chaotic parameters. A reliable image encryption approach is designed based on the proposed Deep-CNN-CKG MP-MP-EC and the generated strong S-boxes. Finally, security analysis and simulation results confirm the reliability of the suggested encryption scheme, and it can be used as a robust encryption scheme.