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Steganalysis method based on lsb substitution matrix

A gray-level co-occurrence matrix and statistical feature technology, applied in the field of information security, can solve the problems of dimensionality disaster, low detection accuracy, and dimensionality reduction, and achieve high detection rate, low computational complexity, and dimensionality reduction effects

Active Publication Date: 2017-04-05
深兰人工智能应用研究院(山东)有限公司
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Problems solved by technology

[0007] The purpose of the present invention is to provide a LSB replacement steganalysis method based on the statistical characteristics of the gray level co-occurrence matrix, which reduces the dimension of the feature vector and solves the "dimension disaster" and low detection accuracy existing in the prior art And other issues

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  • Steganalysis method based on lsb substitution matrix
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  • Steganalysis method based on lsb substitution matrix

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Embodiment Construction

[0039] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0040] The present invention is based on the LSB replacement steganalysis method of the gray level co-occurrence matrix statistical feature, see figure 1 , including image bit plane decomposition, calculation of gray level co-occurrence matrix, feature selection and extraction, and classification steps, the specific description is as follows:

[0041] Step 1. Decompose the image bit plane

[0042] For the image I whose gray level is 0-255, the decomposition formula of its image bit plane is as follows:

[0043]

[0044]

[0045] Among them, I(x, y) represents the pixel value at (x, y) in the image I, B i Denotes the ith bit-plane, B i (x,y) represents the bit plane B i The pixel value at (x,y), i=1,2,...,8, s,t=256,

[0046] Step 2. Calculate the gray level co-occurrence matrix

[0047] 2.1) Calculate the difference matrix D k ...

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Abstract

The invention discloses an LSB replacement steganalysis method based on grey co-occurrence matrix statistic features. The method includes the steps of image bit plane decomposition, grey co-occurrence matrix calculation, feature selection and extraction and classification. Firstly, a grey image is decomposed into eight bit planes, differential matrixes between the lowest bit plane and other seven bit planes are respectively calculated, then a sum matrix of the differential matrixes is calculated, a grey co-occurrence matrix of the sum matrix is generated, the obvious features are extracted and calculated by researching and analyzing the characteristics of the co-occurrence matrix, and a support vector machine is used as a classifier to distinguish carrier images and hidden images. According to the method, the number of feature dimensions is small, curse of dimensionality is effectively avoided, detection precision is high, an algorithm is stable, robustness of image processing of retaining operations of JPEG compression, median filtering, noise addition and the like is achieved, satisfying generalization ability is achieved, and calculation complexity is low.

Description

technical field [0001] The invention belongs to the technical field of information security, and relates to an LSB replacement steganalysis method based on the statistical characteristics of a gray level co-occurrence matrix. Background technique [0002] As an important branch of information security, information hiding has become an important means of secretly transmitting messages in open channels, and has also become one of the important research contents in the field of information security. The difference from cryptographic technology is that it hides specific secret information in a certain public information (carrier signal), which will neither change the audio-visual effect of the carrier signal nor change the format and size of the carrier file. The performance of the appearance is still the content and characteristics of the carrier signal (public information), so the third party will not be aware of the existence of secret information, thereby realizing covert co...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T1/00
Inventor 王晓峰魏程程韩萧周晓瑞曾能亮
Owner 深兰人工智能应用研究院(山东)有限公司
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