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首页> 外文期刊>Mathematical Problems in Engineering >Local Negative Base Transform and Image Scrambling
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Local Negative Base Transform and Image Scrambling

机译:局部负基变换和图像加扰

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摘要

Scrambling transform is an important tool for image encryption and hiding. A new class of scrambling algorithms is obtained by exploiting negative integer as the base of number representation to express the natural numbers. Unlike Arnold transform, the proposed scrambling transform is one-dimensional and nonlinear, and an image can be shuffled by using the proposed transform to rearrange the rows and columns of the image separately or to permute the pixels of the image after scanned into a sequence of pixels; it can be also applied to shuffle certain part region of an image. Firstly, the transformation algorithm for converting nonnegative integers in base B to the corresponding integers in base -B is given in this paper, which is the computational core of scrambling transform and the basis of studying scrambling transform. Then, the three kinds of transforms are introduced, that is, negative base transform (abbreviated as NBT), modular negative base transform (MNBT), and local negative base transform (LNBT) with three parameters, where NBT is an injection and MNBT a surjection and LNBT a bijection. The minimum transform periods of LNBT are calculated for some different values of the three parameters, and the algorithm for calculating the inverse transform of LNBT is given. The image scrambled by LBNT can be recovered by the transform period or the inverse transform. Numerical experiments show that LNBT is an efficient scrambling transform and a strong operation of confusing gray values of pixels in the application of image encryption. Therefore, the proposed transform is a novel tool for information hiding and encryption of two-dimensional image and one-dimensional audio.
机译:加扰变换是图像加密和隐藏的重要工具。通过利用负整数作为数字表示的基础来表达自然数,获得了一类新的加扰算法。与Arnold变换不同,建议的加扰变换是一维且是非线性的,并且可以通过使用建议的变换分别重新排列图像的行和列或在扫描成序列后排列图像的像素来对图像进行混洗。像素;它也可以应用于混洗图像的某些部分区域。首先,给出了将基数为B的非负整数转换为基数为-B的对应整数的变换算法,它是加扰变换的计算核心,也是研究加扰变换的基础。然后,介绍了三种变换,即负基变换(缩写为NBT),模块化负基变换(MNBT)和具有三个参数的局部负基变换(LNBT),其中NBT是注入,而MNBT是射影和LNBT射影。针对三个参数的一些不同值,计算了LNBT的最小变换周期,并给出了计算LNBT逆变换的算法。可以通过变换周期或逆变换来恢复由LBNT加扰的图像。数值实验表明,LNBT是一种有效的加扰变换,在图像加密应用中具有很强的混淆像素灰度值的作用。因此,提出的变换是一种用于隐藏和加密二维图像和一维音频的新颖工具。

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