CN104008521B - LSB based on gray level co-occurrence matrixes statistical nature replaces steganalysis method - Google Patents

Abstract

The invention discloses a kind of LSB based on gray level co-occurrence matrixes statistical nature replaces steganalysis method, decompose including image bit-plane, calculate gray level co-occurrence matrixes, the selection and extraction of feature, classifying step, comprise the concrete steps that, gray level image is decomposed into into 8 bit planes first, the difference matrix between minimum bit plane and remaining seven bit plane is calculated respectively, then calculate difference matrix and matrix, generate the gray level co-occurrence matrixes with matrix, by the characteristic for researching and analysing co-occurrence matrix, statistical significance feature is extracted therefrom, using support vector machine as grader distinguishing carrier image and hidden close image.The inventive method, intrinsic dimensionality are few, efficiently avoid " dimension disaster "；Accuracy of detection is high, and algorithm has stability；There is robustness to the image procossing of such as JPEG compression, medium filtering, these content retentivities operation of addition noise；And there is gratifying generalization ability, computation complexity is low.

Description

LSB based on gray level co-occurrence matrixes statistical nature replaces steganalysis method

Technical field

The invention belongs to field of information security technology, is related to a kind of LSB based on gray level co-occurrence matrixes statistical nature and replaces Steganalysis method.

Background technology

Used as an important branch of information security, Information hiding is had become the one of the secret transmission message of overt channel Important means is planted, also becomes one of important research content of information security field.Difference with cryptographic technique is, its handle Specific secret information is hidden in information disclosed in certain (carrier signal), will not both change the audio visual effect of carrier signal Really, will not also change the form and size of bearer documents, the performance of outward appearance be still the content of carrier signal (public information) and Feature, therefore third party will not perceive the presence for secret information, it is achieved thereby that covert communications.

With the Internet widely use and Information Hiding Techniques it is day by day ripe, have a large amount of steganography method quilts in recent years Propose, secret protection can be provided the user, it is also possible to ensure politics, military, economic dispatch important information in common transmission process In safety and reliability, but while also becoming offender carries out the instrument and means of unlawful activities, to the network information Safety generates serious threat.Summer in 2010, the U.S. there occurs sensational " Russian case of espionage ", have 10 E Luo This special agent is arrested, and FBI is exactly to make use of the knowledge of some image procossings to be disclosed on the Internet from them to be passed Be found that important information in some normal pictures broadcast, these information alerts one conclave of Russia's secret service plan and Various details, later by a series of entrapment, they are arrested by FBI, and current Russian case of espionage is to be demonstrate,proved first Actual gains picture steganography is engaged in the case [1] of espionage.

Used as the technology for attacking Steganography, steganalysis [2] are to detect whether with the presence of steganography information, estimate steganography letter For the purpose of embedding information in the carrier is hidden in the length of breath and extraction steganography information, destruction.It has important application demand, In recent years, just becoming one new study hotspot of information security field.Existing steganalysis method, according to ultimate principle not It is same to be divided three classes [3]：Organoleptic method, based on identification characteristics analytic process and be based on statistics characteristic analysis method.Statistical nature Analytic process can be divided into two classes according to the difference of detection target：Specific steganalysis method and general blind Detecting method.Least significant bit It is the steganography method with image as carrier for commonly using that (LeastSignificant Bit, LSB) replaces steganography, in recent years, Detection LSB replaces the method for steganography and causes extensive concern [4,5,6].

As gray level co-occurrence matrixes can effectively describe the calculation of correlation that joint probability occurs between specific pixel pair, because This can be used to describe image latent writing before and after statistical nature change.According to this thought, Abolghasemi et al. [7] Propose a kind of method that utilization gray level co-occurrence matrixes carry out steganalysis, Kekre et al. and propose improved method [8], text Chapter [9] using the co-occurrence matrix structural features of horizontal and vertical difference image with support vector machine as grader to distinguish is The no presence for having steganography information.New result of study [10] proposes a kind of new steganalysis method, with image symbiosis square The difference of battle array carrys out structural classification feature, and carrier image and hidden close image are distinguished using neutral net.

By the research to present a large amount of steganalysis algorithms, it is found that many existing algorithms have some and negative ask Topic, such as relatively low accuracy of detection, although some algorithms are with higher accuracy of detection, as the characteristic vector of high dimension makes Its detection efficiency is very low, causes " dimension disaster ".

The content of the invention

It is an object of the invention to provide a kind of LSB based on gray level co-occurrence matrixes statistical nature replaces steganalysis method, The dimension of characteristic vector is the method reduced, " dimension disaster " present in prior art is solved and accuracy of detection is low asks Topic.

The technical solution adopted in the present invention is that a kind of LSB based on gray level co-occurrence matrixes statistical nature replaces steganography point Analysis method, implements according to following steps：

Step 1, decomposition image bit-plane

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

Wherein, I (x, y) represent image I in (x, y) place pixel value, B_iRepresent i-th bit plane, B_i(x, y) is represented Bit plane B_iIn (x, y) place pixel value, i=1,2 ..., 8, s, t=256,

Step 2, calculating gray level co-occurrence matrixes

2.1) calculate difference matrix D_k：

Wherein, s, t=256, k=1,2 ..., 7；

2.2) calculate difference matrix and matrix：

Wherein, s, t=256, d₁₁,...,d_st∈{0,1,2,3,4,5,6, 7}；

2.3) calculate M_DCo-occurrence matrix

Mathematically, the co-occurrence matrix being defined on the matrix I of a n × m, by arranging a group of side-play amount (Δ x, Δ y) It is parameterized as follows：

Wherein, the formula of and if only if for δ=1 bracket behind is set up,

M is calculated below_DCo-occurrence matrix G, obtain the matrix of 8 × 8：

Step 3, extraction feature

According to the dependency between image neighbor and the distribution character with matrix, in G, the distribution pattern of element is to become In integrated distribution, the value of zone line is bigger than the value of marginal area, then the value of zone line is regarded as the main letter of image Breath；

Step 4, classification

If secret information is embedded into the minimum bit plane of image, the dependency between bit plane will change, this Change will be shown on co-occurrence matrix G, while can prove whether hide secret information in image as evidence.

The invention has the beneficial effects as follows：

1) method of the present invention make use of the dependency between image bit-plane, from being total to for the difference matrix of different bit planes Feature is extracted in raw matrix, the dimension of characteristic vector is reduced, be efficiently avoid " dimension disaster ", and reached expected effect Really.

2) algorithm has higher verification and measurement ratio, and stability preferably, is robust to JPEG compression, addition noise, filtering, With preferable generalization ability, computation complexity is low.

3) method of the present invention can be used to detect that LSB replaces steganography that the effect spread that has to protecting internet information is tieed up Shield the Internet public order has effective meaning.

Description of the drawings

Fig. 1 is that LSB of the present invention based on co-occurrence matrix replaces steganalysis method flow chart；

Fig. 2 is the distributed mode of the assembly average that using the inventive method 1600 carrier images are generated with co-occurrence matrix G Formula；

Fig. 3 is the distributed mode of the assembly average that co-occurrence matrix G is generated using the inventive method close image hidden to 1600 Formula；

Fig. 4 is the false positive rate comparison diagram using the inventive method；

Fig. 5 is the false negative rate comparison diagram using the inventive method.

Specific embodiment

The present invention is described in detail with reference to the accompanying drawings and detailed description.

LSB of the present invention based on gray level co-occurrence matrixes statistical nature replaces steganalysis method, referring to Fig. 1, including image Bit Plane Decomposition, calculating gray level co-occurrence matrixes, the selection of feature and extraction, classifying step, are described in detail below：

Step 1, decomposition image bit-plane

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

Wherein, I (x, y) represent image I in (x, y) place pixel value, B_iRepresent i-th bit plane, B_i(x, y) is represented Bit plane B_iIn (x, y) place pixel value, i=1,2 ..., 8, s, t=256,

Step 2, calculating gray level co-occurrence matrixes

2.1) calculate difference matrix D_k：

Wherein, s, t=256, k=1,2 ..., 7；

2.2) calculate difference matrix and matrix：

Wherein, s, t=256, d₁₁,...,d_st∈{0,1,2,3,4,5,6, 7}；

2.3) calculate M_DCo-occurrence matrix

Mathematically, the co-occurrence matrix being defined on the matrix I of a n × m, by arranging a group of side-play amount (Δ x, Δ y) It is parameterized as follows：

Wherein, the formula of and if only if for δ=1 bracket behind is set up.

In the method for the invention, the parameter of co-occurrence matrix selects as follows：The offset parameter Δ x of co-occurrence matrix is set to 1, Δ y is set to 0, that is to say, that only consider the adjacent element of horizontal direction, the co-occurrence matrix for being generated when co-occurrence matrix is calculated It is unsymmetrical matrix,

M is calculated below_DCo-occurrence matrix G, obtain the matrix of 8 × 8：

Step 3, extraction feature

According to the dependency between image neighbor and the distribution character with matrix, in G, the distribution pattern of element is to become In integrated distribution, that is to say, that the value of zone line is bigger than the value of marginal area, then regard the value of zone line as image Main information, before and after steganography image essence occur minor variations, the main information of image also can be changed therewith, with this As statistical significance feature.That is, after secret information is embedded into minimum bit plane, minimum bit plane and remaining seven Dependency between bit plane changes, and this change is described by the value of the zone line of G, therefore, select the centre of G 16 elements in region are used as characteristic vector：

F={ g₃₃,g₃₄,g₃₅,g₃₆,g₄₃,g₄₄,g₄₅,g₄₆,g₅₃,g₅₄,g₅₅,g₅₆,g₆₃,g₆₄,g₆₅,g₆₆,

In order to illustrate that characteristic vector f is sensitive to steganography, tested in experiment respectively 1600 secondary carrier images and 1600 secondary hidden close images, Fig. 2 and Fig. 3 show the distribution of the assembly average of the gray level co-occurrence matrixes G for generating in aforementioned manners Pattern.

Fig. 2 represents dividing for the assembly average of the gray level co-occurrence matrixes G for being generated to 1600 secondary carrier images in aforementioned manners Cloth pattern, Fig. 3 represent the distribution of the assembly average of the gray level co-occurrence matrixes G for being generated to 1600 secondary hidden close images in aforementioned manners Pattern.As can be seen that the assembly average of carrier image and the gray level co-occurrence matrixes G corresponding to hidden close image from two figures Distribution pattern all shows as the significant characteristic of zone line, and before and after data are embedded, the value of zone line element can occur Change, and the amount of this change is different, this different patterns of change can be used for distinguishing whether have steganography information Exist.

Step 4, classification

If secret information is embedded into the minimum bit plane of image, the dependency between bit plane will change, this Change will be shown on co-occurrence matrix G, while can prove whether hide secret information in image as evidence.

Characteristic vector f is defined as characteristic of division, using LS-SVM as grader distinguishing carrier image and hidden close Image, here select RBF RBF as kernel function.

The simulation experiment result of the inventive method

Image used in experiment is from Colombia's image library [12] and UCID image libraries [13], the image of these tests Different shading values, texture and details are contained, and the hidden close image of different embedded rates are generated to assess this using these images Bright method, the foundation of sample set are as follows：

First, 1600 test images (size is 256 × 256) are converted to the gray level image of bitmap format, generate and carry Body image pattern collection, is defined as I_C；Secondly, the minimum bit plane in carrier image is embedded in secret information and generates hidden close image, embedding The size for entering rate is 12%, 25% and 50% respectively, obtains three hidden Mi Tuxiangyangbenji, is defined as I_S-1, I_S-2And I_S-3。

1) test of accuracy of detection

A) false positive rate and false negative rate

For the accuracy of detection of quantitative analyses the inventive method, false positive rate and false negative rate are defined as follows：

Here, | | | | represent the operation of the radix of set of computations.

In order to discuss the performance of the inventive method, 30 false negative rates and false positive rate are tested respectively.

In experiment, from I_CIn randomly select 400 carrier images, from I_S-3In randomly select 400 hidden close images as instruction Practice sample, from I_S-3Randomly select 10n hidden close image to be surveyed as test specimens originally in-{ the 400 hidden close images chosen } Examination P_FN, from I_CRandomly select 10n carrier image to be tested as test specimens originally in-{ 400 carrier images chosen } P_FP, n=1 ... here, 30.Experimental result shows in figures 4 and 5.

Fig. 4 and Fig. 5 show and compare the distribution situation of false negative rate and false positive rate respectively, it can be seen that The false negative rate and false positive rate of the inventive method is less than 5%.

From experimental result as can be seen that the testing result of document [7] presents extreme phenomenon, i.e., when training sample is different When, testing result or very high value 99.5% is reached, or be 0, present unstability.In false positive rate and false negative rate In experiment, in order to avoid the extreme phenomenon presented as the verification and measurement ratio that the unstability of algorithm in document [7] causes, 30 experiment institutes The training sample for using is identical, and different from other experiments, the training sample for using is random selection to this point every time 's.

B) comparative experimentss

In order to illustrate that the inventive method has superiority in terms of accuracy of detection than the existing method based on co-occurrence matrix, Experiment is compared, and is tested and true negative rate P has been counted in the case of different embedded rates 12%, 25% and 50%_TN=1-P_FP With True Positive Rate P_TP=1-P_FN, as a result list in table 1.In table 1, P_T=(P_TN+P_TP)/2。

From I_CIn randomly select 400 carrier images, from I_S-1In randomly select 400 hidden close images as training sample, Then from I_C200 carrier images are randomly selected as test sample in-{ 400 carrier images chosen }, using LS- SVM is classified, and obtains false positive rate P_FP.Repeat this process 10 times, the meansigma methodss of statistical experiment result are used as final P_FP, it is shown that its corresponding true negative rate P in table 1_TN=1-P_FP.For the stego image that embedded rate is 25% and 50%, survey Examination process is identical with mentioned above, and all of test result is all displayed in table 1.

From I_CIn randomly select 400 carrier images, from I_S-1In randomly select 400 hidden close images as training sample, Then from I_S-1200 hidden close images are randomly selected in-{ the 400 hidden close images chosen } as test sample, using same Test P_FPThe same method obtains false negative rate P_FN, its corresponding True Positive Rate P displayed in Table 1_TP=1-P_FN。

The comparative result of table 1, accuracy of detection

From table 1 it follows that compare with document [7] with document [9], the true negative rate of the inventive method and True Positive Rate It is gratifying.

2) stability analyses

In order to whether the algorithm for testing the present invention is applied to different sample sets, that is to say, that whether random selection sample set Impact to experimental result is negligible, and defines " stability " this concept.Here stability is referred to for difference Sample set, the verification and measurement ratio of algorithm changes presented stability in the range of very little, and preferable steganalysis algorithm should Should all be stable for different sample sets.In order to prove the stability of inventive algorithm, respectively different embedded rates 12%, 10 experiment tests are carried out under 25% and 50%, wherein the training sample and test sample of experiment is all random selection every time , detailed results are as shown in table 2.

The comparison of table 2, stability experiment testing result

From Table 2, it can be seen that the inventive method is more stable than document [7] and document [9], the excursion of verification and measurement ratio is less than 10%, rather than as for example high verification and measurement ratio or 0 of document [7] extreme case that occurs, while the verification and measurement ratio excursion of document [9] Less than 27%.Result in table shows that the feature that the inventive method is extracted is stable to classification.

3) robust analysis

This experiment is robust for testing the inventive method to operating the caused distortion that is not intended to by content retentivity, Wherein content retentivity operation includes JPEG compression, adds noise, filtering etc..

The embedded rate of hidden close image that experiment generation training sample is used is 50%.Test sample is respectively from I_CIn it is random Choose 200 carrier images and obtained with filtering by adding noise, JPEG compression.In order to quantitatively analyze inventive algorithm Robustness, defines the concept of " accuracy ", accuracy refer to algorithm will live through content keep the image detection of operation into The probability of non-hidden close image, its formula are as follows：

Here, | | | | represent the operation of the radix of set of computations.

It is shown that the meansigma methodss of 10 experiment accuracy results, the training sample tested every time and test sample in table 3 All it is randomly selected.

Table 3, the accuracy result to the operation of content retentivity

From table 3 it is observed that operating to content retentivity, the method for the present invention presents gratifying detection knot Really, this means that the inventive method is robust to the operation of content retentivity.

4) generalization ability analysis

In general, different image libraries has a great impact to the performance of steganalysis algorithm, in real world, comes It is very big from not homologous image difference, therefore a kind of grader can not possibly be trained suitable for all of image library on the Internet [11].The generalization ability of steganalysis algorithm refers to the usability to different images data base, and high generalization ability is referred to works as Image training sample and test sample from different sources when, the verification and measurement ratio of algorithm changes in the range of very little.

In order to test the generalization ability of the inventive method, in experiment image from UCID and Colombia's image library, 1000 Open UCID images (size is 256 × 256) and 1000 Colombia's images (size is 256 × 256) are converted to bitmap lattice The gray level image of formula, therefrom respectively chooses the embedded secret information of half, and the rate that is embedded in is 50%.The experiment contains four groups of son experiments, Per group of experiment repeats 10 times, randomly chooses sample image every time, and the ratio of wherein training sample and test sample is 4： 1。

Table 4, for the generalization ability analysis result in different images storehouse

Experimentation is as follows：

Training sample and test sample both are from UCID image libraries (UCID → UCID)

, from UCID image libraries, test sample is from Colombia's image library (UCID → Columbia) for training sample

Training sample and test sample both are from Colombia's image library (Columbia → Columbia)

, from Colombia's image library, test sample is from UCID image libraries (Columbia → UCID) for training sample

Testing result is as shown in table 4：

As can be seen from Table 4, when training sample and test sample are from different sources, models of the PTN and PTP in very little Enclose interior change, it means that the inventive method has gratifying generalization ability.

5) computation complexity analysis

Computation complexity refers to extract the time that feature is consumed from training sample and test sample image.In experiment, Training sample is made up of 400 carrier images and 400 hidden close images, and test sample is made up of 200 carrier images 's.

Table 5, computation complexity compares

	The method of the present invention	Method in document [7]	Method in document [9]
				Generate the time (second) that training sample is consumed	22.191	37.138	38.754
Generate the time (second) that test sample is consumed	5.577	8.867	9.637

Experimental result is as shown in table 5, it can be seen that the inventive method is presented relative to document [7] and document [9] High efficiency.

List of references

[1] Lignum Sappan seven. Steganography：Encrypt in figure .http sprouting://www.guokr.com/article/3741/ .2010.

[2] Johnson N F, Jajodia S.Steganalysis of images created using current steganography software[J].In Proceedings of the 2^th International Workshop on Information Hiding, Portland, Oregon, USA, April 15-17,1998,273-289.

[3] button heart Xin. Information hiding and digital watermarking [M]. Beijing：Publishing house of Beijing University of Post ＆ Telecommunication, 2004.

[4] Fridrich J, Goljan M.On Estimation of Secret Message Length in LSB Steganography in Spatial Domain[J].In IS&T/SPIE Electronic Imaging:Security, Steganography, and Watermarking of Multimedia Contents VI, San Jose, California, USA, 19-22January, 2004,23-34.

[5]Ker AD.A general framework for the structural steganalysis of LSB replacement[J].In Proceedings of the 7^thInformation Hiding Workshop, Barcelona, 6-8June, LNCS3727,2005,296-311.

[6]Ker A D.Fourth-order Structural Steganalysis and Analysis of Cover Assumptions[J].In IS&T/SPIE Electronic Imaging:Security, Steganography, and Watermarking of Multimedia Contents VIII, San Jose, USA, 23January, 2006,1-14.

[7] Abolghasemi M, Aghainia H, Faez K, Mehrabi M A.LSB Data Hiding Detection Based on Gray Level Co-Occurrence Matrix(GLCM)[J].International Symposium on Telecommunications, 2008,656-659.

[8] Kekre H B, Athawale AA, Patki S A.Steganalysis of LSB Embedded Images Using Gray Level Co-Occurrence Matrix[J].International Journal of Image Processing, 2011,5 (1):36-45.

[9] Deng Qianlan, Lin Jiajun. the image latent writing parser [J] based on co-occurrence matrix. microcomputer information (observing and controlling Automatization), 2009,25 (12)：6-8.

[10] Ghanbari S, Keshtegary M, Ghanbari N.New Steganalysis Method using GLCM and Neural Network [J] .International Journal of Computer Applications, 2012,42 (7):46-52.

[11] NG W W Y, He Z M, Chan P P K, Yeung D S.Blind Steganalysis with High Generalization Capability for Different Image Databases Using L-GEM[J] .Proceedings of the 2011International Conference on Machine Learning and Cybernetics, Guilin, 10-13July, 2011,1690-1695.

[12] T.T.Ng, S.F.Chang, J.Hsu, M.Pepeljugoski, " Columbia Photographic Images and Photorealistic Computer Graphics Dataset ", Department of Electrical Engineering, Columbia University, Advent Technical Report #205-2004-5, Feb.2005.

[13] UCID, http://www-staff.lboro.ac.uk/～cogs/datasets/UCID/ucid.html.

Claims (4)

1. a kind of LSB based on gray level co-occurrence matrixes statistical nature replaces steganalysis method, it is characterised in that according to following step It is rapid to implement：

Step 1, decomposition image bit-plane

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

$I(x,y)=\underset{i=1}{\overset{8}{\Σ}}{B}_i(x,y)\×2^{i-1},$

$B_i=\left[\begin{array}{cccc}{b}_{11}^i& b_{12}^i& \mathrm{...}& b_{1t}^i\\ b_{21}^i& b_{22}^i& \mathrm{...}& b_{2t}^i\\ \mathrm{...}& \mathrm{...}& \mathrm{...}& \mathrm{...}\\ b_{s1}^i& b_{s2}^i& \mathrm{...}& b_{st}^i\end{array}\right],$

Wherein, I (x, y) represent image I in (x, y) place pixel value, B_iRepresent i-th bit plane, B_i(x, y) represents that position is put down Face B_iIn (x, y) place pixel value, i=1,2 ..., 8, s, t=256,

Step 2, calculating gray level co-occurrence matrixes

2.1) calculate difference matrix D_k：

$D_k=B_8-B_k=\left[\begin{array}{cccc}{b}_{11}^8& b_{12}^8& \mathrm{...}& b_{1t}^8\\ b_{21}^8& b_{22}^8& \mathrm{...}& b_{2t}^8\\ \mathrm{...}& \mathrm{...}& \mathrm{...}& \mathrm{...}\\ b_{s1}^8& b_{s2}^8& \mathrm{...}& b_{st}^8\end{array}\right]-\left[\begin{array}{cccc}{b}_{11}^k& b_{12}^k& \mathrm{...}& b_{1t}^k\\ b_{21}^k& b_{22}^k& \mathrm{...}& b_{2t}^k\\ \mathrm{...}& \mathrm{...}& \mathrm{...}& \mathrm{...}\\ b_{s1}^k& b_{s2}^k& \mathrm{...}& b_{st}^k\end{array}\right]=\left[\begin{array}{cccc}{b}_{11}^8-b_{11}^k& b_{12}^8-b_{12}^k& \mathrm{...}& b_{1t}^8-b_{1t}^k\\ b_{21}^8-b_{21}^k& b_{22}^8-b_{22}^k& \mathrm{...}& b_{2t}^8-b_{2t}^k\\ \mathrm{...}& \mathrm{...}& \mathrm{...}& \mathrm{...}\\ b_{s1}^8-b_{s1}^k& b_{s2}^8-b_{s2}^k& \mathrm{...}& b_{st}^8-b_{st}^k\end{array}\right],$

Wherein, s, t=256, k=1,2 ..., 7；

2.2) calculate difference matrix and matrix：

Wherein, s, t=256, d₁₁,...,d_st∈{0,1,2,3,4,5,6,7}；

2.3) calculate M_DCo-occurrence matrix

Mathematically, the co-occurrence matrix being defined on the matrix I of a n × m, by arranging one group of side-play amount, (Δ x, Δ y) are joined Numberization is as follows：

$g(i,j)=\underset{p=1}{\overset{n}{\Σ}}\underset{q=1}{\overset{m}{\Σ}}\mathrm{\δ}\left(I\right(p,q)=i,I(p+\mathrm{\Δ}x,q+\mathrm{\Δ}y)=j),$

Wherein, the formula of and if only if for δ=1 bracket behind is set up,

M is calculated below_DCo-occurrence matrix G, obtain the matrix of 8 × 8：

$G=\left[\begin{array}{cccc}{g}_{11}& g_{12}& \mathrm{...}& g_{18}\\ g_{21}& g_{22}& \mathrm{...}& g_{28}\\ \mathrm{...}& \mathrm{...}& \mathrm{...}& \mathrm{...}\\ g_{81}& g_{82}& \mathrm{...}& g_{88}\end{array}\right];$

Step 3, extraction feature

According to the dependency between image neighbor and the distribution character with matrix, in G, the distribution pattern of element is to tend to collection Middle distribution, the value of zone line is bigger than the value of marginal area, then the value of zone line is regarded as the main information of image；

Step 4, classification

If secret information is embedded into the minimum bit plane of image, the dependency between bit plane will change, this change To show on co-occurrence matrix G, while can prove whether hide secret information in image as evidence.

2. the LSB based on gray level co-occurrence matrixes statistical nature according to claim 1 replaces steganalysis method, its feature It is：Step 2.3) in, the parameter of co-occurrence matrix selects as follows：The offset parameter Δ x of co-occurrence matrix is set to 1, and Δ y is set to 0。

3. the LSB based on gray level co-occurrence matrixes statistical nature according to claim 1 replaces steganalysis method, its feature It is：Phase in step 3, after secret information is embedded into minimum bit plane, between minimum bit plane and remaining seven bit plane Closing property changes, and this change is described by the value of the zone line of G, therefore, select 16 elements of the zone line of G As characteristic vector：

F={ g₃₃,g₃₄,g₃₅,g₃₆,g₄₃,g₄₄,g₄₅,g₄₆,g₅₃,g₅₄,g₅₅,g₅₆,g₆₃,g₆₄,g₆₅,g₆₆}。

4. the LSB based on gray level co-occurrence matrixes statistical nature according to claim 1 replaces steganalysis method, its feature It is：In step 4, defined feature vector f as characteristic of division, using LS-SVM as grader distinguishing carrier image and hidden Close image, here select RBF RBF as kernel function.