Modular Architecture for Artificial Intelligence Integration in Chaos- and Hyperchaos-Based Image Encryption Algorithms

This paper presents an architecture for integrating artificial intelligence (AI) functionalities into a chaos-based image encryption algorithm employing a Fibonacci matrix. The proposed approach aims to introduce dynamic behavior into the encryption process by generating the parameters of a chaotic or hyperchaotic system and the Fibonacci matrix based on numerical characteristics of the input image. The methodology incorporates two main innovations: a modular architecture for integrating AI models to automatically generate the parameters of the dynamical system and an AI-assisted procedure for determining the values of the Fibonacci Q-matrix. The algorithm is implemented in MATLAB and experimentally evaluated using AI models from four platforms: OpenAI, Gemini, Claude, and DeepSeek. The obtained results demonstrate values close to the theoretical reference values, with an average information entropy of 7.9966 and a mean absolute correlation coefficient of 0.001209. Robustness against different attacks is also investigated. The deviations of the overall mean values from their theoretical references are 0.000731 for NPCR and 0.002063 for UACI. Furthermore, the PSNR decreases by an average of 3.7747 dB when the salt-and-pepper noise density increases from 2% to 5%, and by 3.5577 dB when the data-cut level increases from 10% to 25%. The obtained results demonstrate the applicability of the proposed AI integration architecture for dynamically configuring chaos-based image encryption algorithms.