Novel image scrambling method based on chaotic technology

Abstract

The invention discloses a novel image scrambling method based on a chaotic technology, for controlling a cyclic shift and a rotation angle of an image through chaotic sequences after quantification processing. First of all, through combination with feature values of R, G and B different layers of a color image, three initial values of a chaotic system are generated, and three chaotic sequences are generated through the independent chaotic system; according to maximum values of the length and the width of a plaintext image, defining a variable w and performing quantification processing on the chaotic sequences; and finally, through pairwise combination of the three chaotic sequences, cyclic shift bit numbers and rotation angles of different layers and different row pixels are controlled, and finally a scrambled color image is obtained. According to the invention, the scrambled image has quite good randomness, conventional image attacks can be fended off, and the limitation that pixels, at the same positions, of different image layers have quite high correlation after a conventional scrambling image algorithm is encrypted is also broken.

Description

technical field [0001] The invention relates to the technical field of image encryption, in particular to a novel image scrambling method based on chaos technology. Background technique [0002] Image scrambling refers to confusing the pixels of an image, also called pixel shuffling. Diffusion and confusion are relatively common techniques in today's image encryption technology. Most of the current image encryption methods combine diffusion and obfuscation to encrypt images. Now the image is mainly encrypted in the four directions of pixel domain, chroma domain, frequency domain and bit plane. Researchers mainly process images by optimizing diffusion to make them random and hide the original image information, ignoring the importance of obfuscation algorithms to a certain extent, and these scrambling algorithms are often similar. For example, in the pixel domain, orthogonal transformation, knight tour transformation, Arnold cat mapping, ergodic array algorithm, etc. are u...

AI Technical Summary

Problems solved by technology

The traversal processing through the above two artificial methods will reduce the randomness of the generated sequence, resulting in a relatively high correlation between adjacent pixels in the scrambled image, the scrambling effect is not ideal, and multiple scrambling is required In order to get a better effect, this greatly increases the complexity of the algorithm and the time of image scrambling

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