发表机构
Amirkabir University of Technology (Tehran Polytechnic); Iran University of Science and Technology（阿米尔卡比尔理工大学（德黑兰理工）; 伊朗科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出TCA算法，通过内容感知三角剖分实现纯像素置换，打破扩散-置换二分法，仅靠像素重定位即可产生差分敏感性，在50张图像实验中获NPCR=97.10%等优异安全性能。
AI 中文摘要
像素置换是图像处理、图像加密和数据隐藏（包括水印和隐写术）中的基础工具，它在不改变像素值的前提下重新排列像素。文献中一个常见假设是，仅置换无法产生差分敏感性；改变一个像素只会将该像素在输出中重新定位，不会产生雪崩效应。本文对此提出挑战，引入三角内容感知置换（Triangular Content-Aware Permutation，TCA）算法。该方法使用Canny提取边缘点，并对边缘和角点应用Delaunay三角剖分，形成独特的划分。由于三角剖分对图像几何结构高度敏感，改变单个像素会改变边缘图，进而导致完全不同的三角剖分和全局置换模式。与缺乏差分敏感性的经典基于维度的置换及先进的内容感知方法（2025-2026年）不同，TCA仅通过像素重定位就将像素变化率（NPCR）从接近零提升至97.10%。对50张图像的实验显示，TCA平均迭代14.81次时，达到NPCR=97.10%、统一平均变化强度（UACI）=20.06%，证明纯置换可产生显著的差分敏感性，而传统方法的NPCR维持在接近零的水平。迭代阈值根据内容复杂度在6.4至30.7之间变化，低峰值信噪比（PSNR=11.93 dB）和接近零的相关性（~10^-3）证实其优越的统计性能。尽管由于三角剖分，TCA比经典方法速度更慢，但这是为更强安全性做出的刻意权衡。鉴于其模式具有非解析、内容依赖的特性，TCA适用于基于参考的加密、脆弱水印和非盲隐写术。
英文摘要
Pixel permutation is a fundamental tool in image processing, image encryption, and data hiding (including watermarking and steganography) that rearranges pixels without changing their values. A common assumption in the literature is that permutation alone cannot create differential sensitivity; changing one pixel merely relocates that pixel in the output, producing no avalanche effect. This paper challenges this by introducing the Triangular Content-Aware Permutation (TCA) algorithm. The method extracts edge points using Canny and applies Delaunay Triangulation to edges and corners, creating a unique partition. Since triangulation is highly sensitive to image geometry, changing a single pixel alters the edge map, resulting in a completely different triangulation and global permutation pattern. Unlike classical dimension-based permutations and advanced content-aware methods (2025-2026), which lack differential sensitivity, TCA increases NPCR from near-zero to 97.10% solely through pixel relocation. Experiments on 50 images show that TCA, with an average of 14.81 iterations, achieves NPCR = 97.10% and UACI = 20.06%, proving pure permutation can create significant differential sensitivity. Conventional methods maintain near-zero NPCR. The iteration threshold varies from 6.4 to 30.7 based on content complexity. Low PSNR (11.93 dB) and near-zero correlation (~10^-3) confirm superior statistical performance. Although slower than classical methods due to triangulation, this is a deliberate trade-off for stronger security. Given the non-analytic, content-dependent nature of the pattern, TCA is ideal for reference-based encryption, fragile watermarking, and non-blind steganography.
Comments12 pages, 5 figures
