A Secret Graph Masquerading Recovery Method Combining Key and Random Orthogonal Tensor Basis

Abstract

The invention discloses a secret image camouflage restoration method combined with a key and a random orthogonal tensor base. Firstly, the secret image and the public image are divided into the same number of small matrix blocks, and the random orthogonal matrix is ​​constructed by using the unit orthogonal matrix generated by the key. Intersecting tensor base; secondly, by obtaining the projection of the dense graph small block on the random orthogonal tensor base to fully and effectively linearly express the dense graph small block, select the first k projection coefficients with larger amplitude and energy and record the index position To form a row sequence and column sequence; again, the selected projection coefficients, row sequence and column sequence are embedded into the corresponding public image small blocks through the steganography method of small adjustments to the public image and large-capacity embedding to form a public channel The image is transmitted; finally, the transformation parameters extracted from the image are publicly transmitted through the channel and combined with the key to reconstruct the secret image. Compared with the existing method, the present invention can realize the reconstruction of the secret map with different precision and strictly depends on the user key, thus possessing higher security.

Description

technical field [0001] The invention belongs to the intersecting field of image information security and digital image signal processing, and relates to a secret map camouflage restoration method, in particular to a secret map camouflage restoration method combined with a secret key and a random orthogonal tensor base. Background technique [0002] In recent years, with the development of computer and network technology, more and more digital images are spread in public channels. Unrestricted access to and use of secret images not only involves personal privacy, media credibility, and government integrity, but can also lead to social unrest and induce military conflicts. [0003] How to effectively protect secret images transmitted in public channels has become a hot research topic at present. Aiming at this problem, people have proposed a variety of image protection methods, such as digital image encryption technology that converts plaintext images into ciphertext and digi...

AI Technical Summary

Problems solved by technology

[0010] However, whether it is ① standard Tangram algorithm, or ② block average 5-tuple matching Tangram method and the previously given ③ digital image camouflage method based on improved Tangram algorithm and 2-dimensional dual-scale rectangular mapping, ④ digital image camouflage based on block sequence and reconstruction method, ⑤ digital audio camouflage and reconstruction method based on segmented sequence, and ⑥ audio information hiding method combined with dictionary and sorting linear fitting. The transformation models based on these methods are all affine transformation models. For image , the affine transformation model only has the mean block and the difference block, and for audio, the affine transformation model only has the mean vector and the difference vector, and the mean block and the difference block as well as the mean vector and the difference vector do not satisfy the basic orthogonal relationship, resulting in affine transformation The transformation accuracy of the model is generally low, and it cannot effectively guarantee the fitting accuracy of the secret image audio to the public image audio, so that effective channel deception cannot be carried out, and at the same time, it cannot effectively guarantee the fitting accuracy of the public image audio to the secret image audio. Therefore, it is impossible to guarantee the accurate reconstruction of secret image audio and obtain high-precision secret image audio

On the other hand, if the difference block and the mean value block or the mean value vector and the difference vector of the affine model tend to be consistent, it corresponds to a constant value block or a constant value sequence processing. At this time, random disturbance must be added to improve the secret image audio and Publicly discloses the matching performance of small blocks and segments of image and audio division, otherwise it will not be restored, thus further limiting the transformation accuracy of the radial transformation model

For the Tangram algorithm based on triangulation (7), it can only approximate the reconstruction of the triangulation area of ​​the secret image, and its practical application value is still small

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