A Review of Adaptive Image Encryption Schemes Utilizing Optimized Chaotic Systems and Deep Learning Models

Abstract

The encryption of images is very important in keeping visual information secret and unaltered, particularly in the case of secure transmission and storage applications. AES is one of the most extensively used cryptographic algorithms, however it does not fully use picture properties like high redundancy, strong pixel correlation, and enormous data volume. This paper discusses the different approaches to image encryption systems and highlights chaos-based techniques as the main focus. It goes on to explain how chaotic encryption methods in both the spatial and frequency domains are reviewed, with special mention of permutation–diffusion architectures, which are considered very effective at raising security levels. Not only this, but hybrid systems that employ chaos, as well as the use of wavelet transforms, genetic algorithms, neural networks, cellular automata, elliptic curves, blockchain, and DL, among others, are comprehensively covered. Among the most current DL-based image encryption methods, those such as the style-transfer and chaotic Neural-network techniques with strong nonlinearity and dynamic key production are also mentioned. Nevertheless, there remain some issues with the availability of research that compares two different chaotic maps and standardized cryptographic techniques in an exhaustive manner.