Lossless data hiding method based on difference image histogram cycle spinning

Abstract

The invention discloses a lossless data hiding method based on difference image histogram cycle spinning. The lossless data hiding method includes steps of encrypting, secret key transmitting and data recovery. During encrypting, an original image is divided into a plurality of sub image blocks at the transmitting end, cycle spinning operation is performed by aid of a sub block difference histogram to achieve step-by-step embedding of secret data, a secret image C' is formed and transmitted to the receiving end. During secret key transmitting, the size of sub image blocks, the length of embedded data and the embedding level L serve as a secret key to be distributed to the receiving end. During data recovery, the receiving end extracts the secret data step by step from the secret image C' in an inverse method of the embedding process, block difference and original image data are recovered, and secret data transmission and lossless recovery based on the image carrier are achieved. The lossless data hiding method overcomes the defect that the capacity of the original histogram spinning algorithm depends on a single peak point, and increases the embedding capacity through a cycle embedding method. Experimental results prove that the lossless data hiding method can well meet requirements for high capacity, low distortion and high efficiency.

Description

technical field [0001] The invention relates to a lossless data hiding method based on the cyclic translation of difference image histograms, and belongs to the field of image lossless data hiding (encryption). Background technique [0002] Lossless image data hiding technology, also known as reversible image data hiding, uses a specific lossless data embedding method to embed covert data in the carrier image to form a covert image, and the covert image can be restored to its original state after the data is extracted. The biggest difference between the lossless data hiding technology and the previous lossy methods is that the data of the carrier itself will not be changed, which greatly expands the application range of data hiding. For example, in some specific medical, legal, military (not related to military secrets, just to illustrate that military images have strict integrity requirements) and art carrier images have extremely high requirements for integrity. Communica...

AI Technical Summary

Problems solved by technology

[0003] The image carrier has high redundancy, which is especially suitable for large-capacity data hiding. The common lossless data hiding methods are divided into two categories. One is the algorithm based on the space domain, which directly modifies the image pixels to complete the data. Embedding, the algorithm embedding capacity is large, the calculation is simple, and it is often used for data hiding of images, but its biggest feature is poor robustness, that is, when the hidden image is tampered to any degree, it will affect the extraction of hidden data and image recovery; the second The class is an algorithm based on the frequency domain. This type of algorithm converts the image pixels into the corresponding transform domain, and then modifies the coefficients of the transform domain to complete the data embedding. The algorithm embedding capacity is small and the calculation is complicated. It is often used for copyright protection of images. , which has the advantage of good robustness and can resist a certain degree of attack

The most commonly used algorithms based on the spatial domain are histogram translation and difference expansion methods. The former is simpler to calculate and has better image quality than the latter, but its disadvantage is that the embedding capacity depends on the individual pixels located at the peak point in the image histogram. number

[0004] In order to increase the embedding capacity of the histogram method, it is proposed to use the characteristics of high correlation and redundancy of adjacent pixels in the image to embed data in the difference histogram, although this method greatly increases the number of individual pixels at the peak point. number, but using a single peak point embedding still loses a lot of pixels that can be used to embed data

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