纯像素重排也能实现强敏感性，打破传统加密瓶颈
A Content-Aware Pure Permutation with Intrinsic Avalanche Effect: Breaking the Diffusion-Permutation Dichotomy
- 基于边缘三角剖分构建内容感知重排模式
- 单像素修改导致全局重排，NPCR达97.10%
- 适合脆弱水印与非盲隐写，安全更强
像素重排是图像处理、加密及数据隐藏中的基础操作，传统观点认为仅靠重排无法产生差分敏感性——改变一个像素只会移动其位置，不会引发级联效应。本文提出三角形内容感知重排（TCA）算法，利用Canny检测边缘并进行Delaunay三角剖分，生成依赖图像几何结构的独特分区。由于三角剖分对几何变化高度敏感，单个像素的改动会彻底改变边图，进而导致完全不同的重排模式。实验在50张图像上验证，平均14.81次迭代即实现NPCR=97.10%、UACI=20.06%，而传统方法接近零。图像复杂度影响迭代次数（6.4~30.7），低PSNR（11.93 dB）与极低相关性（~10^-3）表明优异统计特性。尽管因三角剖分较慢，但为更强安全性的合理牺牲。该方法适用于参考式加密、脆弱水印与非盲隐写。
原文摘要 · Abstract (English)
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.
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