  Traditional permutation schemes mostly focus on random scrambling of pixels,
often neglecting the intrinsic image information that could enhance diffusion
in image encryption algorithms. This paper introduces PermutEx, a
feature-extraction-based permutation method that utilizes inherent image
features to scramble pixels effectively. Unlike random permutation schemes,
PermutEx extracts the spatial frequency and local contrast features of the
image and ranks each pixel based on this information, identifying which pixels
are more important or information-rich based on texture and edge information.
In addition, a unique permutation key is generated using the Logistic-Sine Map
based on chaotic behavior. The ranked pixels are permuted in conjunction with
this unique key, effectively permuting the original image into a scrambled
version. Experimental results indicate that the proposed method effectively
disrupts the correlation in information-rich areas within the image resulting
in a correlation value of 0.000062. The effective scrambling of pixels,
resulting in nearly zero correlation, makes this method suitable to be used as
diffusion in image encryption algorithms.
