CN103971321B - Method and system for steganalysis of JPEG compatibility - Google Patents
Method and system for steganalysis of JPEG compatibility Download PDFInfo
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Abstract
The invention discloses a method for steganalysis of JPEG compatibility. The method comprises the steps that the observation insertion rate r' of each JPEG steganography image JS in a steganography image training base is calculated; according to the observation insertion rate r' and the real insertion rate r of each JPEG steganography image in the steganography image training base, a relation model between the observation insertion rate r' and the real insertion rate r is established; the observation insertion rate of the steganography image to be detected is calculated and the detected insertion rate of the steganography image to be detected is calculated according to the relation model between the observation insertion rate and the real insertion rate. The invention further discloses a system for steganalysis of JPEG compatibility. The similarity of two images is measured through a self-defined similarity function in the JPEG quantization table extraction process, compression distortion and insertion distortion are differentiated on the basis of the pixel variation amplitude characteristic in the insertion estimation process, and estimation of the insertion rate of the non-self-adaptation steganography algorithm and the insertion rate of the self-adaptation steganography algorithm is accurate.
Description
Technical field
The invention belongs to multi-media safety technical field, more particularly, to a kind of compatible steganalysis methods of JPEG
With system.
Background technology
With cyber-net continuous popularization and develop rapidly, the Internet becomes indispensable one in people's life
Part, at the same time, the Internet also becomes the important channel of people's transmission information, the letter that daily on the internet face is transmitted
Breath occupies most of flow of the Internet.Information of these transmission are related to national security, army secret, trade secret and individual
How people's privacy etc., protect these confidential information not stolen by the illegal molecule such as terrorist and distort even destruction, during to information
The information safety protection in generation brings greatly challenge.The Internet has become countries in the world and has obtained economic, military and scientific and technological information
Important battlefield.Information Hiding Techniques have obtained huge development and attention as a kind of emerging technology of information security field.
It has been recognized that protection information can be completed safely by cryptographic technique, cryptographic technique is the letter that will be transmitted
Breath (plaintext) forms unrecognizable mess code form (ciphertext) by AES, and the file after the encryption for being becomes indigestion
(failing to understand).From for the angle of attacker, if seeming rambling ciphertext in the face of a pile, from being in a certain respect to tell him
Secret communication is carried out, and can more cause his vigilance, if attacker cuts off communication port and causes communication failure, password
Safety be constantly on the hazard.
Steganography (Digital Steganography) is an important branch of Information hiding.It is mainly sharp
With all kinds of Digital Medias (including picture, video, the audio frequency, text etc.) redundancy of itself, and the psychology and physiology of human perception
Characteristic, the classified information after encryption is embedded in disclosed Digital Media and is transmitted, to reach the mesh of secret transmission message
's.Steganography has been the important means for becoming human information safe transmission, while also bringing threat：It both can be with
For transmission military affairs, information, national security information of safety etc. in open internet, it is also possible to by terrorist and hostile
Force is utilized, and endangers country and public safety.In succession terroristic organization of the report headed by this Laden can for each medium of the U.S.
The steganography software based on image can be adopted to issue action order, the base terroristic organization headed by this Laden adopts and is based on image
Steganography software issue action instruction.After 9.11 events occur, including《The New York Times》With《Washington Post》Interior
Duo Jia U.S. newpapers and periodicals are reported on famous shopping website e-Bay and are found that the concealed information stayed suspected of terrorist.Although
These reports are not confirmed, but they demonstrate the behavior that steganography be used to constituting a threat to social safety
Probability.
Therefore, attack of the research to Steganography, it is national and public for safeguarding to detect and find hiding information
Safety is most important.Under this situation, steganalysis (Steganalysis) technology is arisen at the historic moment, and its objective is that detection is suspicious
The presence of information, estimates message embedded quantity, then extracts, destroys or further reduce secret information.Steganalysis into
For an important research direction in Information hiding field：Research steganalysis not only have critically important using value,
With very important academic significance.On the one hand the illegal application of Steganography can be prevented, prevents lawless person from utilizing Steganography
Criminal activity is carried out, national security is endangered.On the other hand it can also promote the raising of steganographic algorithm safety, promote steganography to calculate
Method it is practical.
Most camera exports the photo of jpeg format, and picture major part on internet is also JPEG lattice
Formula.Although also there is many steganographic algorithms such as Jsteg, Outguess, Model-based steganography, JPHide,
And F5, there is provided directly carry out steganography on the picture of jpeg format, but embedding capacity well below it is vacant carry out it is embedded
Capacity.The steganography instrument major part provided on network is for vacant embedded, and the steganographic algorithm for being used is not accounted for
The problem of JPEG compatibility.So can have many consumers to select to carry out vacant being embedded on the picture of jpeg decompression contracting.
Existing program accuracy rate in terms of quantization table is extracted is not high, in terms of embedded rate estimation, some steganalysis methods
The steganographic algorithm of non-self-adapting is confined to, the accuracy rate extracted to adaptive steganographic algorithm is not high.
The content of the invention
For the disadvantages described above or Improvement requirement of prior art, the invention provides a kind of JPEG compatible steganalysis side
Method and system, it is intended that solving to quantify table and the inaccurate technical problem of embedded rate estimation present in existing method.
For achieving the above object, according to one aspect of the present invention, there is provided a kind of JPEG compatible steganalysis method,
Comprise the following steps：
(1) the embedded rate r ＇ of observation of each JPEG hidden image JS in hidden image training storehouse is calculated；
(2) the embedded rate r ＇ of the observation of each JPEG hidden image JS in storehouse is trained according to hidden image and its is truly embedded in rate
R, the relational model set up between the embedded rate of observation and true embedded rate；
(3) the embedded rate of observation of hidden image to be detected is calculated, and according between the embedded rate of observation and true embedded rate
Relational model calculates the embedded rate of detection of hidden image to be detected.
Preferably, embedded rate r' of observation that JPEG hidden image JS are calculated in the step (1) is specifically included：
(1.1) its quantization table QF is obtained according to JPEG steganography picture JS；
(1.2) JPEG compression again is carried out to steganography picture JS with the quantization table QF obtained in step (1.1) step, is obtained
The carrier picture JS ＇ for estimating, calculate the difference image D of JS and JS ＇；
(1.3) in the difference image obtained in (1.2), the pixel according to caused by embedded change and recompression distortion becomes
Change amplitude is different, distinguishes embedded change and compression artefacts, obtains the embedded rate r ＇ of observation.
Preferably, obtain its quantization table QF according to JPEG steganography picture JS in the step (1.1) to specifically include：
(1.1.1) to JPEG steganography picture JS, JPEG compression is carried out with all of quantization table QF, wherein quantifying corresponding to table
Quality factor be 1 to 95, obtain 95 compression after picture JSi(i=1,2 ..., 95)；
(1.1.2) similarity function Sim is utilizedi(JS,JSi) calculate JS and JSi(i=1,2 ..., 95) between it is similar
Degree：
Simi(JS,JSi)=card (E)/m × n
Wherein E=(x, y) | | JS (x, y)-JSi(x, y) |=0,1≤x≤m, 1≤y≤n }, card () represents limited
The element number of set, the number of pixels that card (E) does not change after recompressing, m, n is respectively picture length and cross direction
On number of pixels；
(1.1.3) curve of similarity function is drawn, it is described hidden to take the quantization table corresponding to first peak value of curve
Write the quantization table QF corresponding to picture JS.
Preferably, the step (1.3) specifically includes：
In the difference image D that step (1.2) is obtained, using the embedded rate r ＇ of following formula calculating observation：
R'=card (A)/m × n
Wherein A=(x, y) | and | D (x, y) |=1,1≤x≤m, 1≤y≤n }, card () represents the element of finite aggregate
Number, card (A) is the number of pixels that pixel value is 1 in difference image, m, n be respectively picture length and cross direction on pixel
Number.
Preferably, the step (2) specifically includes：
Using libsvm, the embedded rate r ＇ of observation of each JPEG hidden image JS in storehouse and its true is trained to hidden image
Embedded rate r is trained, and obtains the relational model observed between embedded rate and true embedded rate.
It is another aspect of this invention to provide that a kind of compatible steganalysis systems of JPEG are additionally provided, including：Observation is embedded
Rate computing module, embedded rate relational model set up module and the embedded rate computing module of detection, wherein：
The embedded rate computing module of observation, trains the observation of each JPEG hidden image JS in storehouse embedding for calculating hidden image
Enter rate r ＇；
Embedded rate relational model sets up module, for training the sight of each JPEG hidden image JS in storehouse according to hidden image
Embedded rate r ＇ and its truly embedded rate r are surveyed, the relational model set up between the embedded rate of observation and true embedded rate；
The embedded rate computing module of detection, the embedded rate of the observation for calculating hidden image to be detected, and it is embedded according to observation
Relational model between rate and true embedded rate calculates the embedded rate of detection of hidden image to be detected.
Preferably, the embedded rate computing module of the observation specifically includes quantization table acquisition module, difference image computing module
And the embedded rate calculating sub module of observation, wherein：
The quantization table acquisition module, for obtaining its quantization table QF according to JPEG steganography picture JS；
The difference image computing module, for the quantization table QF that obtained with the quantization table acquisition module to steganography picture
JS carries out JPEG compression again, obtains the carrier picture JS ＇ for estimating, and calculates the difference image D of JS and JS ＇；
The observation is embedded in rate calculating sub module, for the difference image to the acquisition, presses according to embedded change and again
Pixel amplitude of variation caused by contracting distortion is different, distinguishes embedded change and compression artefacts, obtains the embedded rate r ＇ of observation.
Preferably, the quantization table acquisition module specifically includes JPEG compression module, similarity calculation module and quantization
Table acquisition submodule, wherein：
The JPEG compression module, for JPEG steganography picture JS, with all of quantization table QF JPEG compression being carried out, its
Quality factor corresponding to middle quantization table is 1 to 95, obtains the picture JS after 95 compressionsi(i=1,2 ..., 95)；
The similarity calculation module, for using similarity function Simi(JS,JSi) calculate JS and JSi(i=1,
2 ..., 95) between similarity：
Simi(JS,JSi)=card (E)/m × n
Wherein E=(x, y) | | JS (x, y)-JSi(x, y) |=0,1≤x≤m, 1≤y≤n }, card () represents limited
The element number of set, the number of pixels that card (E) does not change after recompressing, m, n is respectively picture length and cross direction
On number of pixels；
The quantization table acquisition submodule, for drawing the curve of similarity function, the first peak value institute for taking curve is right
The quantization table answered is the quantization table QF corresponding to the steganography picture JS.
Preferably, the embedded rate calculating sub module of the observation specifically for：
According to the difference image D that step (1.2) is obtained, using the embedded rate r ＇ of following formula calculating observation：
R'=card (A)/m × n
Wherein A=(x, y) | and | D (x, y) |=1,1≤x≤m, 1≤y≤n }, card () represents the element of finite aggregate
Number, card (A) is the number of pixels that pixel value is 1 in difference image, m, n be respectively picture length and cross direction on pixel
Number.
Preferably, the embedded rate relational model set up module specifically for：
Using libsvm, the embedded rate r ＇ of observation of each JPEG hidden image JS in storehouse and its true is trained to hidden image
Embedded rate r is trained, and obtains the relational model observed between embedded rate and true embedded rate.
In general, by the contemplated above technical scheme of the present invention compared with prior art, can obtain down and show
Beneficial effect：
(1) present invention quantifies table extraction accuracy rate height：As a result of new similarity function, and using similarity letter
Quantization table corresponding to first peak value of number curve solves what original method quantization table was extracted by mistake as the quantization table for extracting
Shortcoming.Therefore, the extraction accuracy rate of the present invention is high compared with existing scheme.Experimental Comparison is carried out on matlab, the present invention
Quantization table extract accuracy rate major part be higher than original method.
(2) the embedded rate of the present invention estimates that accuracy rate is high：Distortion and recompression distortion are embedded in due to having distinguished difference image,
Recompression distortion is removed in difference image, embedded distortion is only considered, embedded rate is more accurately have estimated.Enter on matlab
Row Experimental comparison, the embedded rate of the present invention estimates more accurate.
Description of the drawings
Fig. 1 is the flow chart that the present invention is applied to the compatible steganalysis methods of JPEG.
Specific embodiment
In order that the objects, technical solutions and advantages of the present invention become more apparent, it is right below in conjunction with drawings and Examples
The present invention is further elaborated.It should be appreciated that specific embodiment described herein is only to explain the present invention, and
It is not used in the restriction present invention.As long as additionally, technical characteristic involved in invention described below each embodiment
Not constituting conflict each other just can be mutually combined.
Present invention is generally directed to JPEG compatible steganalysis, have detected whether that secret disappears on the picture of jpeg decompression contracting
Cease the length of embedded and embedded message.Hereinafter first just the technical term of the present invention is explained and illustrated：
Embedded distortion：Due to the distortion being embedded in picture caused by classified information
Recompression distortion：Due to the distortion caused by JPEG compression
The whole processing procedure of the present invention can be completed on matlab.
As shown in figure 1, the present invention is applied to the compatible steganalysis methods of JPEG comprising the following steps：
(1) the embedded rate r ＇ of observation of each JPEG hidden image JS in hidden image training storehouse is calculated；Including following sub-step
Suddenly：
(1.1) its quantization table QF is obtained according to JPEG steganography picture JS；Including following sub-step：
(1.1.1) to the JPEG steganography pictures (JPEGStego Image are abbreviated as JS) for giving, with all of quantization table
(Quantization Table are abbreviated as QF) carries out JPEG compression, wherein it is 1 to 95 (matter to quantify the quality factor corresponding to table
The amount factor is higher, and the picture quality that JPEG compression is obtained is higher), obtain the picture JS after 95 compressionsi(i=1,2 ..., 95)；
(1.1.2) similarity function Sim is utilizedi(JS,JSi) calculate JS and JSi(i=1,2 ..., 95) between it is similar
Degree：
Define a JS and JSi(i=1,2 ..., 95) between similarity function (different from existing method)
Simi(JS,JSi)=card (E)/m × n
Wherein E=(x, y) | | JS (x, y)-JSi(x, y) |=0,1≤x≤m, 1≤y≤n }, card () represents limited
The element number of set, the number of pixels that card (E) does not change after recompressing, m, n is respectively picture length and cross direction
On number of pixels；
(1.1.3) extracted using the method for curve and quantify table：Draw the curve of similarity function, according to the shape of curve come
Judge used quantization table QF, it is the quantization table QF for extracting to take the quantization table corresponding to first peak value of curve.
(1.2) JPEG compression again is carried out to steganography picture JS with the quantization table QF obtained in step (1.1) step, is obtained
The carrier picture JS ＇ for estimating, calculate the difference image D of JS and JS ＇；
(1.3) in the difference image obtained in (1.2), the pixel according to caused by embedded change and recompression distortion becomes
Change amplitude is different, distinguishes embedded change and compression artefacts, obtains the embedded rate r ＇ of observation.Specially：
In the difference image obtained in step (1.2), existing embedded change also has recompression in step (1.2) to be made
Into distortion, need to distinguish both distortions, only extract embedded distortion, be more accurately embedded in rate and estimate to obtain.Due to being empty
± the 1 of domain is embedded in, and after JPEG compression, the pixel amplitude of variation of embedded change is generally 1, but recompression distortion is then showed
Unlike this, amplitude of variation is from 1 to higher, this feature not only to adaptive steganographic algorithm effectively, and to non-adaptive
The steganographic algorithm answered is highly effective.Different with the pixel amplitude of variation caused by recompression distortion using embedded change, this is special
Levy to distinguish compression artefacts and embedded change to obtain the embedded rate r ＇ of observation.
According to the difference image D that step (1.2) is obtained, using the embedded rate r ＇ of following formula calculating observation：
R'=card (A)/m × n
Wherein A=(x, y) | and | D (x, y) |=1,1≤x≤m, 1≤y≤n }, card () represents the element of finite aggregate
Number, card (A) is the number of pixels that pixel value is 1 in difference image, m, n be respectively picture length and cross direction on pixel
Number.
(2) the embedded rate r ＇ of the observation of each JPEG hidden image JS in storehouse is trained according to hidden image and its is truly embedded in rate
R, the relational model set up between the embedded rate of observation and true embedded rate；Specially：
Through the calculating of step (1), the JPEG steganography pictures of substantial amounts of known embedded rate r in hidden image training storehouse are obtained
The embedded rate r ＇ of observation,
According to embedded rate r' of the substantial amounts of observation of above-mentioned acquisition and known truly embedded rate r, using a libsvm (SVM
Pattern recognition and the software kit for returning) data are trained, obtain a multinomial model, establish the embedded rate r ＇ of observation with
Relational model between true embedded rate r.
(3) the embedded rate of observation of hidden image to be detected is calculated, and according between the embedded rate of observation and true embedded rate
Relational model calculates the embedded rate of detection of hidden image to be detected.
When a JPEG steganography picture to be detected is given, we obtain the steganography picture by above-mentioned steps (1)
The embedded rate r ＇ of observation, then the relational model in step (2), calculates the embedded rate of final detection.
Correspondingly, present invention also offers the steganalysis system for realizing said method, including：The embedded rate meter of observation
Calculate module, embedded rate relational model and set up module and the embedded rate computing module of detection, wherein：
The embedded rate computing module of observation, trains the observation of each JPEG hidden image JS in storehouse embedding for calculating hidden image
Enter rate r ＇；
Embedded rate relational model sets up module, for training the sight of each JPEG hidden image JS in storehouse according to hidden image
Embedded rate r ＇ and its truly embedded rate r are surveyed, the relational model set up between the embedded rate of observation and true embedded rate；
The embedded rate computing module of detection, the embedded rate of the observation for calculating hidden image to be detected, and it is embedded according to observation
Relational model between rate and true embedded rate calculates the embedded rate of detection of hidden image to be detected.
Preferably, the embedded rate computing module of the observation specifically includes quantization table acquisition module, difference image computing module
And the embedded rate calculating sub module of observation, wherein：
The quantization table acquisition module, for obtaining its quantization table QF according to JPEG steganography picture JS；
The difference image computing module, for the quantization table QF that obtained with the quantization table acquisition module to steganography picture
JS carries out JPEG compression again, obtains the carrier picture JS ＇ for estimating, and calculates the difference image D of JS and JS ＇；
The observation is embedded in rate calculating sub module, for the difference image to the acquisition, presses according to embedded change and again
Pixel amplitude of variation caused by contracting distortion is different, distinguishes embedded change and compression artefacts, obtains the embedded rate r ＇ of observation.
Preferably, the quantization table acquisition module specifically includes JPEG compression module, similarity calculation module and quantization
Table acquisition submodule, wherein：
The JPEG compression module, for JPEG steganography picture JS, with all of quantization table QF JPEG compression being carried out, its
Quality factor corresponding to middle quantization table is 1 to 95, obtains the picture JS after 95 compressionsi(i=1,2 ..., 95)；
The similarity calculation module, for using similarity function Simi(JS,JSi) calculate JS and JSi(i=1,
2 ..., 95) between similarity：
Simi(JS,JSi)=card (E)/m × n
Wherein E=(x, y) | | JS (x, y)-JSi(x, y) |=0,1≤x≤m, 1≤y≤n }, card () represents limited
The element number of set, the number of pixels that card (E) does not change after recompressing, m, n is respectively picture length and cross direction
On number of pixels；
The quantization table acquisition submodule, for drawing the curve of similarity function, the first peak value institute for taking curve is right
The quantization table answered is the quantization table QF corresponding to the steganography picture JS.
Preferably, the embedded rate calculating sub module of the observation specifically for：
According to the difference image D that step (1.2) is obtained, using the embedded rate r ＇ of following formula calculating observation：
R'=card (A)/m × n
Wherein A=(x, y) | and | D (x, y) |=1,1≤x≤m, 1≤y≤n }, card () represents the element of finite aggregate
Number, card (A) is the number of pixels that pixel value is 1 in difference image, m, n be respectively picture length and cross direction on pixel
Number.
Preferably, the embedded rate relational model set up module specifically for：
Using libsvm, the embedded rate r ＇ of observation of each JPEG hidden image JS in storehouse and its true is trained to hidden image
Embedded rate r is trained, and obtains the relational model observed between embedded rate and true embedded rate.
As it will be easily appreciated by one skilled in the art that the foregoing is only presently preferred embodiments of the present invention, not to
The present invention, all any modification, equivalent and improvement made within the spirit and principles in the present invention etc. are limited, all should be included
Within protection scope of the present invention.
Claims (10)
1. a kind of JPEG compatible steganalysis method, it is characterised in that the method comprising the steps of：
(1) the embedded rate r ＇ of observation of each JPEG hidden image JS in hidden image training storehouse is calculated by following processes：
(1.1) its quantization table QF is obtained according to JPEG steganography picture JS；
(1.2) JPEG compression again is carried out to steganography picture JS with the quantization table QF obtained in step (1.1) step, is estimated
The carrier picture JS ＇ for going out, calculate the difference image D of JS and JS ＇；
(1.3) the pixel change width in the difference image obtained in (1.2), according to caused by embedded change and recompression distortion
Degree is different, distinguishes embedded change and compression artefacts, obtains the embedded rate r ＇ of observation；
(2) the embedded rate r ＇ of the observation of each JPEG hidden image JS in storehouse and its truly embedded rate r are trained according to hidden image, is built
Relational model between the vertical embedded rate of observation and true embedded rate；
(3) the embedded rate of observation of hidden image to be detected is calculated, and according to the relation between the embedded rate of observation and true embedded rate
Model calculates the embedded rate of detection of hidden image to be detected.
2. the method for claim 1, it is characterised in that obtained according to JPEG steganography pictures JS in the step (1.1)
Its quantization table QF is specifically included：
(1.1.1) to JPEG steganography picture JS, JPEG compression is carried out with all of quantization table QF, wherein quantifying the matter corresponding to table
The amount factor is 1 to 95, obtains the picture JS after 95 compressionsi(i=1,2 ..., 95)；
(1.1.2) similarity function Sim is utilizedi(JS,JSi) calculate JS and JSi(i=1,2 ..., 95) between similarity：
Simi(JS,JSi)=card (E)/m × n
Wherein E=(x, y) | | JS (x, y)-JSi(x, y) |=0,1≤x≤m, 1≤y≤n }, card () represents finite aggregate
Element number, the number of pixels that card (E) does not change after recompressing, m, n is respectively on picture length and cross direction
Number of pixels；
(1.1.3) curve of similarity function is drawn, it is the steganography figure to take the quantization table corresponding to first peak value of curve
Quantization table QF corresponding to piece JS.
3. the method for claim 1, it is characterised in that the step (1.3) specifically includes：
According to the difference image D that step (1.2) is obtained, using the embedded rate r ＇ of following formula calculating observation：
Wherein A=(x, y) | and | D (x, y) |=1,1≤x≤m, 1≤y≤n }, card () represents the element of finite aggregate
Number, card (A) is the number of pixels that pixel value is 1 in difference image, m, n be respectively picture length and cross direction on pixel
Number.
4. method as claimed in claim 2, it is characterised in that the step (1.3) specifically includes：
According to the difference image D that step (1.2) is obtained, using the embedded rate r ＇ of following formula calculating observation：
Wherein A=(x, y) | and | D (x, y) |=1,1≤x≤m, 1≤y≤n }, card () represents the element of finite aggregate
Number, card (A) is the number of pixels that pixel value is 1 in difference image, m, n be respectively picture length and cross direction on pixel
Number.
5. the method as described in any one of Claims 1-4, it is characterised in that the step (2) specifically includes：
Using libsvm, the embedded rate r ＇ of observation of each JPEG hidden image JS in storehouse is trained to hidden image and its is truly embedded in
Rate r is trained, and obtains the relational model observed between embedded rate and true embedded rate.
6. a kind of JPEG compatible steganalysis system, it is characterised in that include：The embedded rate computing module of observation, embedded rate are closed
It is that model building module and detection are embedded in rate computing module, wherein：
The embedded rate computing module of observation, for calculating hidden image the embedded rate of the observation of each JPEG hidden image JS in storehouse is trained
R ＇；
Embedded rate relational model sets up module, for being trained the observation of each JPEG hidden image JS in storehouse embedding according to hidden image
Enter rate r ＇ and its truly embedded rate r, the relational model set up between the embedded rate of observation and true embedded rate；
The embedded rate computing module of detection, the embedded rate of the observation for calculating hidden image to be detected, and according to the embedded rate of observation with
Relational model between true embedded rate calculates the embedded rate of detection of hidden image to be detected；
The observation is embedded in rate computing module and specifically includes quantization table acquisition module, difference image computing module and observe embedded
Rate calculating sub module, wherein：
The quantization table acquisition module, for obtaining its quantization table QF according to JPEG steganography picture JS；
The difference image computing module, the quantization table QF for being obtained with the quantization table acquisition module enters to steganography picture JS
Row JPEG compression again, obtains the carrier picture JS ＇ for estimating, and calculates the difference image D of JS and JS ＇；
The observation is embedded in rate calculating sub module, for the difference image to the acquisition, is lost according to embedded change and recompression
Very caused pixel amplitude of variation is different, distinguishes embedded change and compression artefacts, obtains the embedded rate r ＇ of observation.
7. system as claimed in claim 6, it is characterised in that the quantization table acquisition module specifically includes JPEG compression mould
Block, similarity calculation module and quantization table acquisition submodule, wherein：
The JPEG compression module, for JPEG steganography picture JS, JPEG compression being carried out with all of quantization table QF, wherein measuring
The quality factor changed corresponding to table is 1 to 95, obtains the picture JS after 95 compressionsi, i=1,2 ..., 95；
The similarity calculation module, for using similarity function Simi(JS,JSi) calculate JS and JSi, i=1,2 ..., 95,
Between similarity：
Simi(JS,JSi)=card (E)/m × n
Wherein E=(x, y) | | JS (x, y)-JSi(x, y) |=0,1≤x≤m, 1≤y≤n }, card () represents finite aggregate
Element number, the number of pixels that card (E) does not change after recompressing, m, n is respectively on picture length and cross direction
Number of pixels；
The quantization table acquisition submodule, for drawing the curve of similarity function, takes corresponding to first peak value of curve
Quantization table is the quantization table QF corresponding to the steganography picture JS.
8. system as claimed in claim 6, it is characterised in that the embedded rate calculating sub module of the observation specifically for：
According to the difference image D that step (1.2) is obtained, using the embedded rate r ＇ of following formula calculating observation：
R'=card (A)/m × n
Wherein A=(x, y) | and | D (x, y) |=1,1≤x≤m, 1≤y≤n }, card () represents the element of finite aggregate
Number, card (A) is the number of pixels that pixel value is 1 in difference image, m, n be respectively picture length and cross direction on pixel
Number.
9. system as claimed in claim 7, it is characterised in that the embedded rate calculating sub module of the observation specifically for：
According to the difference image D that step (1.2) is obtained, using the embedded rate r ＇ of following formula calculating observation：
R'=card (A)/m × n
Wherein A=(x, y) | and | D (x, y) |=1,1≤x≤m, 1≤y≤n }, card () represents the element of finite aggregate
Number, card (A) is the number of pixels that pixel value is 1 in difference image, m, n be respectively picture length and cross direction on pixel
Number.
10. the system as described in any one of claim 6 to 9, it is characterised in that the embedded rate relational model sets up module tool
Body is used for：
Using libsvm, the embedded rate r ＇ of observation of each JPEG hidden image JS in storehouse is trained to hidden image and its is truly embedded in
Rate r is trained, and obtains the relational model observed between embedded rate and true embedded rate.
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| CN201410195302.9A CN103971321B (en) | 2014-05-09 | 2014-05-09 | Method and system for steganalysis of JPEG compatibility |
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