Multidimensional chaotic signals generation using deep learning and its application in image encryption

Zhou S., Tao Z., Erkan U., TOKTAŞ A., Ho-Ching Iu H., Zhang Y., ...More

Engineering Applications of Artificial Intelligence, vol.156, 2025 (SCI-Expanded, Scopus) identifier identifier

  • Publication Type: Article / Article
  • Volume: 156
  • Publication Date: 2025
  • Doi Number: 10.1016/j.engappai.2025.111017
  • Journal Name: Engineering Applications of Artificial Intelligence
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Keywords: Applied artificial intelligence, Chaotic systems, Deep learning, Image encryption, Implemented artificial intelligence
  • Ankara University Affiliated: Yes

Abstract

In this paper, we propose a novel artificial intelligence implemented approach to generate multi-dimensional chaotic signals using the Long- and Short-Term Time-Series Network (LSTNet) for a newly contrived Two-Stage pixel/bit level Scrambling and Dynamic Diffusion (TSSDD) color image encryption. Initially, we employ the hyperchaotic Lorenz and Chen chaotic systems to produce chaotic signals. Subsequently, the LSTNet model is trained to predict these produced multi-dimensional chaotic sequences and then it generates new multi-dimensional chaotic signals. Through analysis involving phase diagrams, largest Lyapunov exponent (LE), 0–1 test, Permutation Entropy (PE), Sample Entropy (SE), Correlation Dimension (CD) and National Institute of Standards and Technology (NIST), we observe that these applied artificial intelligence signals exhibit high chaotic states and randomness. Finally, we apply these signals to demonstrate the proposed TSSDD color image encryption wherein simulation experiments indicate competitive performance against common attacks.