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<p>This study presents an advanced AI-Adaptive Hilbert and Logistic Image Encryption Mechanism (HL-IEM) that combines an Adaptive Feed-Forward Neural Network (FNN), a Logistic Chaotic Map, and Hilbert Curve Block Scrambling to achieve robust, lightweight, and content-aware image security. The FNN operates on image features such as mean intensity, variance, entropy, and edge density, and accordingly generates optimised chaotic parameters that control both pixel-level diffusion via the Logistic Map and adaptive block-level permutation via the Hilbert Curve. This collaboration between AI-driven chaotic control and nonlinear permutation–diffusion ensures strong key sensitivity, high randomness, and complete decorrelation of adjacent pixels. Experimental evaluation across standard test images (<i>Peppers</i>, <i>Tree</i>, <i>Airplane</i>, and <i>House</i>) demonstrates near-ideal information entropy (7.9488–7.9932), correlation coefficients close to zero after encryption, NPCR above 99.83%, and UACI around 32.90 to 33.78%. The MSE values range from 8125 to 8205, and the PSNR values range from 9.03 to 9.08 dB, indicating very high image quality. The encryption and decryption times are around 1&#xa0;second for <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(1024\times 1024\)</EquationSource> </InlineEquation> images, indicating that the technique can be used in real-time scenarios in IoT and multimedia environments. The proposed method increases computational load only slightly and has been confirmed to be quite resistant to statistical, differential, and brute-force attacks. These findings establish the AI-adaptive HL-IEM as a highly secure, efficient, and scalable image encryption solution suitable for both civilian and defence-grade communication systems.</p>

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HL-IEM: a Hilbert-Logistic dual-layer chaotic encryption mechanism for lightweight and secure image communication

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月11日

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  • Biswarup Yogi,
  • Raj Majumdar,
  • Pritha Ghosh,
  • Lokesh Sharma,
  • Satyabrata Roy

摘要

This study presents an advanced AI-Adaptive Hilbert and Logistic Image Encryption Mechanism (HL-IEM) that combines an Adaptive Feed-Forward Neural Network (FNN), a Logistic Chaotic Map, and Hilbert Curve Block Scrambling to achieve robust, lightweight, and content-aware image security. The FNN operates on image features such as mean intensity, variance, entropy, and edge density, and accordingly generates optimised chaotic parameters that control both pixel-level diffusion via the Logistic Map and adaptive block-level permutation via the Hilbert Curve. This collaboration between AI-driven chaotic control and nonlinear permutation–diffusion ensures strong key sensitivity, high randomness, and complete decorrelation of adjacent pixels. Experimental evaluation across standard test images (Peppers, Tree, Airplane, and House) demonstrates near-ideal information entropy (7.9488–7.9932), correlation coefficients close to zero after encryption, NPCR above 99.83%, and UACI around 32.90 to 33.78%. The MSE values range from 8125 to 8205, and the PSNR values range from 9.03 to 9.08 dB, indicating very high image quality. The encryption and decryption times are around 1 second for \(1024\times 1024\) images, indicating that the technique can be used in real-time scenarios in IoT and multimedia environments. The proposed method increases computational load only slightly and has been confirmed to be quite resistant to statistical, differential, and brute-force attacks. These findings establish the AI-adaptive HL-IEM as a highly secure, efficient, and scalable image encryption solution suitable for both civilian and defence-grade communication systems.