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<p>This paper proposes a secure image encryption method based on bit-reversal transformation, Cellular Automata Rule 60, and a chaotic sine map to ensure high security and randomness of image data. The encryption algorithm uses a two-round XOR operation to promote confusion and diffusion, thereby significantly altering pixel correlations and rendering the encrypted image highly resistant to statistical and characteristic attacks. The algorithm’s performance is compared based on several statistical parameters, including Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Number of Pixel Change Rate (NPCR), Unified Average Changing Intensity (UACI), information entropy, and correlation coefficient. Experimental results indicate higher MSE (Peppers: 105.9416, Airplane: 106.0059), lower PSNR (Peppers: 27.8801 dB, Airplane: 27.8755 dB), superior NPCR (Peppers: 99.6119%, Airplane: 99.6215%), and strong UACI values (Peppers: 32.2553%, Airplane: 32.5939%), ensuring significant distortion and higher sensitivity to small fluctuations. Entropy values of around 8 (Peppers: 7.9969, Airplane: 7.9965) reflect exceptional randomness, and correlation coefficients at or around zero reflect reduced pixel correlation. Furthermore, the approach successfully breaks the Diehard and NIST tests. Relatively, it demonstrates its superiority over current algorithms in terms of security and effectiveness. This makes the method suitable for secure image transmission, cloud storage, and protection of sensitive visual data.</p>

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Lca-sm-iot: lightweight image encryption based on rule 60 cellular automata and sine map for IoT devices

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月4日

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  • Biswarup Yogi,
  • Trisha Mallick,
  • Satyabrata Roy

摘要

This paper proposes a secure image encryption method based on bit-reversal transformation, Cellular Automata Rule 60, and a chaotic sine map to ensure high security and randomness of image data. The encryption algorithm uses a two-round XOR operation to promote confusion and diffusion, thereby significantly altering pixel correlations and rendering the encrypted image highly resistant to statistical and characteristic attacks. The algorithm’s performance is compared based on several statistical parameters, including Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Number of Pixel Change Rate (NPCR), Unified Average Changing Intensity (UACI), information entropy, and correlation coefficient. Experimental results indicate higher MSE (Peppers: 105.9416, Airplane: 106.0059), lower PSNR (Peppers: 27.8801 dB, Airplane: 27.8755 dB), superior NPCR (Peppers: 99.6119%, Airplane: 99.6215%), and strong UACI values (Peppers: 32.2553%, Airplane: 32.5939%), ensuring significant distortion and higher sensitivity to small fluctuations. Entropy values of around 8 (Peppers: 7.9969, Airplane: 7.9965) reflect exceptional randomness, and correlation coefficients at or around zero reflect reduced pixel correlation. Furthermore, the approach successfully breaks the Diehard and NIST tests. Relatively, it demonstrates its superiority over current algorithms in terms of security and effectiveness. This makes the method suitable for secure image transmission, cloud storage, and protection of sensitive visual data.