<p>The exponential growth of image data, driven by the Internet of Things (IoT), highlights the critical need for lightweight yet robust security measures to safeguard sensitive visual information across diverse applications. One-dimensional (1D) chaotic maps offer promising security solutions, but their effectiveness is limited by a smaller chaotic range. This limitation restricts their applicability in cryptographic fields that require extensive dynamical behavior for enhanced security. This paper proposes a novel adaptive image encryption algorithm utilizing Fibonacci Chaotification Model (FCM)-based chaotic maps with an infinitely tunable control parameter. The encryption scheme follows a three-tier structure consisting of diffusion-shuffling-diffusion, where bitwise operations and pixel permutation are strategically combined to ensure a high degree of confusion and diffusion within a single round, offering both robustness and computational efficiency. The encryption scheme has been comprehensively evaluated for six FCM maps, including Logistic, Sine, Chebyshev, Quadratic, Simple Quadratic, and Singer maps across a spectrum of performance and security metrics. These metrics include NPCR, UACI, execution time, correlation analysis, PSNR, SSIM, and MSE, and robustness assessments against attacks like noise interference and cropping. The findings demonstrate that the enciphered images exhibit strong security characteristics, with NPCR values exceeding <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$99\%$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mn>99</mn> <mo>%</mo> </mrow> </math></EquationSource> </InlineEquation>, UACI values approaching the ideal of <InlineEquation ID="IEq2"> <EquationSource Format="TEX">$33\%$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mn>33</mn> <mo>%</mo> </mrow> </math></EquationSource> </InlineEquation>, and entropy values close to <InlineEquation ID="IEq3"> <EquationSource Format="TEX">$8$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mn>8</mn> </mrow> </math></EquationSource> </InlineEquation>, indicating high randomness. Additionally, the encrypted images exhibit low SSIM values and high MSE, and a PSNR value close to <InlineEquation ID="IEq4"> <EquationSource Format="TEX">$8$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mn>8</mn> </mrow> </math></EquationSource> </InlineEquation> dB, collectively confirming effective distortion and minimal residual similarity to the original images. Furthermore, the results indicate the remarkable performance of the proposed method compared to numerous state-of-the-art alternatives, specifically in terms of encryption and decryption speeds, rendering the proposed scheme highly suitable for safeguarding lightweight real-time IoT applications. Additionally, the proposed scheme demonstrates strong resilience to attacks such as noise, sharpening, low-pass filtering, histogram equalization, and cropping, as evidenced by smaller MSE and higher PSNR and SSIM values, indicating the scheme’s ability to successfully retrieve the original image from the enciphered ones despite various distortions. Thus, the extensive evaluation results underscore the effectiveness of the proposed encryption scheme, affirming its reliability and suitability for low-end IoT applications.</p>

Adaptive image encryption for securing IoT applications using FCM-based chaotic maps

期刊论文
发表日期：

2025年8月13日

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Mir Nazish,
M. Tariq Banday

摘要

The exponential growth of image data, driven by the Internet of Things (IoT), highlights the critical need for lightweight yet robust security measures to safeguard sensitive visual information across diverse applications. One-dimensional (1D) chaotic maps offer promising security solutions, but their effectiveness is limited by a smaller chaotic range. This limitation restricts their applicability in cryptographic fields that require extensive dynamical behavior for enhanced security. This paper proposes a novel adaptive image encryption algorithm utilizing Fibonacci Chaotification Model (FCM)-based chaotic maps with an infinitely tunable control parameter. The encryption scheme follows a three-tier structure consisting of diffusion-shuffling-diffusion, where bitwise operations and pixel permutation are strategically combined to ensure a high degree of confusion and diffusion within a single round, offering both robustness and computational efficiency. The encryption scheme has been comprehensively evaluated for six FCM maps, including Logistic, Sine, Chebyshev, Quadratic, Simple Quadratic, and Singer maps across a spectrum of performance and security metrics. These metrics include NPCR, UACI, execution time, correlation analysis, PSNR, SSIM, and MSE, and robustness assessments against attacks like noise interference and cropping. The findings demonstrate that the enciphered images exhibit strong security characteristics, with NPCR values exceeding $99\%$ $99 %$ , UACI values approaching the ideal of $33\%$ $33 %$ , and entropy values close to $8$ $8$ , indicating high randomness. Additionally, the encrypted images exhibit low SSIM values and high MSE, and a PSNR value close to $8$ $8$ dB, collectively confirming effective distortion and minimal residual similarity to the original images. Furthermore, the results indicate the remarkable performance of the proposed method compared to numerous state-of-the-art alternatives, specifically in terms of encryption and decryption speeds, rendering the proposed scheme highly suitable for safeguarding lightweight real-time IoT applications. Additionally, the proposed scheme demonstrates strong resilience to attacks such as noise, sharpening, low-pass filtering, histogram equalization, and cropping, as evidenced by smaller MSE and higher PSNR and SSIM values, indicating the scheme’s ability to successfully retrieve the original image from the enciphered ones despite various distortions. Thus, the extensive evaluation results underscore the effectiveness of the proposed encryption scheme, affirming its reliability and suitability for low-end IoT applications.