A Novel Image Encryption Technique Using EEG S‐Box and Dynamic Permutation for IoT Applications

ABSTRACT This paper introduces a new image encryption method using the S‐Box obtained from EEG datasets utilizing correlation and diffusion. A modified algorithm has been proposed for extracting binary information from EEG datasets, which is subsequently utilized for generating 64‐bit keys. The generated keys and the proposed S‐Box, along with the Lehmer random number generator‐based dynamic permutation, are used for the encryption of grayscale test images. The generated 64‐bit keys are tested using NIST SP 800‐22 analysis for their randomness. The strength of the S‐Box is estimated using parameters like bijectivity, nonlinearity, and strict avalanche criterion. The strength of the proposed encryption algorithm is evaluated using a chi‐square test, peak signal‐to‐noise ratio, number of pixels change rate (NPCR), mean squared error, unified average changing intensity (UACI), and so on. The proposed design has been successfully implemented on a Virtex‐7 FPGA, and an ASIC implementation has also been completed using 45‐nm technology. When compared to the LUT–CLA–QTL architecture, it has been observed that there is almost a 40.21% reduction in the area occupied by the design on a silicon chip using 45‐nm technology. This work gives a secure encryption of the data along with reduced hardware resources applicable for the resource constraint devices in IoT applications.