Scholarlink

<p>This paper proposes a novel multi-layer image encryption model that integrates Type-2 Fuzzy Logic (T2-FL) with optimized chaotic maps to address the critical challenge of achieving robust security without compromising efficiency. Moving beyond traditional static methods vulnerable to statistical attacks, our model employs a T2-FL system to dynamically modify the encryption process to an image’s content by analyzing pixel intensity distributions. The corresponding intelligence implements a three-stage hybrid diffusion-confusion strategy: first, an intensity-based XOR operation applies different key modifications to dark, mid-tone, and bright pixels; second, a value-dependent transformation performs bit rotation, reversal, or nibble swapping; and finally, a chaotic mixing layer ensures deep diffusion. The T2-FL system dynamically optimizes the chaotic sequences generated by a 2D coupled logistic map with channel-specific initial conditions for RGB components in real-time. Security and performance analyses confirm the model’s superiority, demonstrating near-ideal value, exceptional differential attack resistance (NPCR <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\approx \)</EquationSource> <EquationSource Format="MATHML"><math> <mo>≈</mo> </math></EquationSource> </InlineEquation> 99.82%, UACI <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\approx \)</EquationSource> <EquationSource Format="MATHML"><math> <mo>≈</mo> </math></EquationSource> </InlineEquation> 33.62% ), as well as negligible correlation coefficients, all while maintaining high computational.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Type-2 fuzzy logic-controlled multi-layer image encryption model with hybrid diffusion–confusion strategies based on optimized 2D chaotic maps

  • 期刊论文

  • 发表日期：

    2026年1月16日

查看全文
  • Shadman Rahman Kareem,
  • Mardan Ameen Pirdawood

摘要

This paper proposes a novel multi-layer image encryption model that integrates Type-2 Fuzzy Logic (T2-FL) with optimized chaotic maps to address the critical challenge of achieving robust security without compromising efficiency. Moving beyond traditional static methods vulnerable to statistical attacks, our model employs a T2-FL system to dynamically modify the encryption process to an image’s content by analyzing pixel intensity distributions. The corresponding intelligence implements a three-stage hybrid diffusion-confusion strategy: first, an intensity-based XOR operation applies different key modifications to dark, mid-tone, and bright pixels; second, a value-dependent transformation performs bit rotation, reversal, or nibble swapping; and finally, a chaotic mixing layer ensures deep diffusion. The T2-FL system dynamically optimizes the chaotic sequences generated by a 2D coupled logistic map with channel-specific initial conditions for RGB components in real-time. Security and performance analyses confirm the model’s superiority, demonstrating near-ideal value, exceptional differential attack resistance (NPCR \(\approx \) ≈ 99.82%, UACI \(\approx \) ≈ 33.62% ), as well as negligible correlation coefficients, all while maintaining high computational.