Artificial intelligence techniques for encrypt images based on the chaotic system implemented on field-programmable gate array

Abstract

Image encryption is an important issue in protecting the content of images and in the area of information security. This article proposes a novel method for image encryption and decryption using the structure of the artificial neural network (ANN)-based chua chaotic system (CCS). This structure was efficiently designed on a field-programmable gate array (FPGA) chip utilizing the xilinx system generator (XSG) tool with the IEEE-754-1985 32-bit floating-point number format. For ANN-based CCS design, a multilayer feed forward neural network (FFNN) structure with three inputs and three outputs was created. This structure consists of one hidden layer with four neurons, each of which has a Tangent Sigmoid activation function. The training of ANN-based CCS yielded a 3.602e-13 mean square error (MSE) value. After successfully training the ANN-based CCS, the design was carried out on FPGA, utilizing the ANN structure's bias and weight values as a reference. The xilinx vivado (2017.4) design suite was used to synthesis and test the ANN-based CCS on the FPGA. The histogram, correlation coefficient, and entropy are used to perform security analysis on various images. Finally, FPGA hardware co-simulation using a Xilinx Artix7 xc7a100t-1csg324 chip was utilized to verify that the encryption and decryption of the images were successful.