Implementing Artificial Intelligence in Chaos-Based Image Encryption Algorithms
Hristina Stoycheva, Stanimir Sadinov, Krasen Kirov Angelov, Panagiotis Gregorios Kogias, Michalis Malamatoudis · 2025
This paper presents a modification of an image encryption algorithm combining chaos and the Fibonacci matrix by integrating artificial intelligence via a Generative Pre-Trained Transformer (GPT). The goal is to improve the robustness of the algorithm by dynamically adapting the parameters of the chaotic system and generating cryptographic keys based on image characteristics. The proposed methodology includes two main innovations: the implementation of GPT for automated generation of the initial parameters of the chaotic system, which allows for greater variability and security in encryption, and the use of GPT for dynamic determination of the Fibonacci Q-matrix, which provides additional complexity and increased resistance to attacks. The method is realized in the MATLAB (R2023a) environment through integration with OpenAI API and the MATLAB–Python interface for requesting GPT models. The efficiency and reliability of the modified algorithm are compared with those of standard chaotic encryption algorithms, and its robustness to various cryptographic attacks is also studied.