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    Multidimensional chaotic signals generation using deep learning and its application in image encryption
    (Elsevier Ltd, 15 September 2025) Zhou, Shuang; Tao, Zhiji; Erkan, Uğur; Toktaş, Abdurrahim; Ho-Ching Iu, Herbert; Zhang, Yingqian
    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.
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    Novel hyperchaotic system: Implementation to audio encryption
    (Elsevier Ltd, 2025) Zhou, Shuang; Erkan, Uğur; Toktas, Abdurrahim; Yin, Yanli; Zhang, Yingqian
    To overcome the limitations of existing low-dimensional chaotic systems, particularly their vulnerability to degradation, this study introduces a novel family of discrete hyper-chaotic systems, designed using a one-dimensional quadratic map. The dynamic behavior of the systems is analysed using Lyapunov exponents and sample entropy to evaluate their complexity and robustness. The results demonstrate that the proposed systems exhibit higher ergodicity, greater Lyapunov exponents and better randomness compared to existing chaotic systems. Exploiting these systems, a novel fractal K-means audio encryption (FKM-AE) algorithm is proposed, integrating fractal algorithms with the K-means grouping approach. Simulations reveal that the proposed method effectively reduces the correlation of audio messages across adjacent time intervals and robustly resists various attacks, demonstrating its high performance. © 2025 Elsevier Ltd