CN105160618A - Image watermark detection system - Google Patents

Abstract

The invention discloses an image watermark detection system. The system comprises an extraction module (21), a calculation module (22), a correction module (23) and a judgment module (24), and is characterized in that the extraction module (21) extracts watermark signals contained in an image to be detected to act as first watermark signals; the calculation module (22) acquires a maximum coordinate point with the correlation value being the maximum; the correction module (23) restores the image to be detected according to coordinate values of the maximum coordinate point so as to acquire a corrected image; and the judgment module (24) judges whether the maximum correlation value is greater than a preset threshold or not based on a maximum correlation value in values of correlation between areas, which are as large as an image formed by a reference watermark template, in the corrected image and the reference watermark template, and judges whether the corrected image contains reference watermark signals or not according to a judgment result. The image digital watermark detection system disclosed by the invention can resist complex geometric attacks within a reasonable range, such as rotation, cutting and zooming, and is high in safety.

Description

A kind of image watermark detection system

The application is the applying date is on August 26th, 2011, the application number divisional application that to be 201110248307.X and invention and created name be " a kind of image watermark detection method and system thereof ".

Technical field

The present invention relates to a kind of digital image watermarking, particularly relate to a kind of image watermark detection system.

Background technology

Universal along with internet, applications such as the development of digital photographic technology and ecommerce, digital publishing, news portal and social network sites, digital picture has become a part for people's daily life.A base attribute of digital picture is very easy to copy, and therefore brings serious digital publishing rights piracy and threaten.

A kind of technical method of protection digital picture copyright adopts digital copyright management (DigitalRightsManagement is called for short DRM) system.DRM principle of work is that digital picture supplier utilizes double secret key picture to be encrypted protection, sets up the propagation of authorization center control figure picture simultaneously.This means that DRM protects digital picture copyright to need to carry out in the environment of a relative closure, if there is the malicious user in DRM system to propagate into outside system by digital picture, DRM system is just helpless.Visible, DRM technology is difficult to be applied in the open environment of society.

The another kind of effective method of protection digital picture copyright adopts digital image watermarking; copyright owner first by copyright owner's information insertion in picture; third party can be submitted to after finding infringement to detect copyright owner's information of comprising of infringement picture, thus reach the effect of fright copyright infringement.

In digital image watermarking embody rule, when detecting certain digital picture and whether comprising the partial content of watermark information or watermark information, be generally difficult to obtain original image corresponding to this picture.This just requires that adopted digital image watermarking can carry out blind Detecting, even if also can detect when not using original image information.

The ultimate challenge that image digital watermark blind Detecting faces is generally geometric attack, namely rotates the picture comprising watermark, convergent-divergent, the operation such as shearing cannot detect to the copyright watermark information making picture comprise.For geometric attack, the countermeasure of image watermark detection algorithm is generally the template watermark first detecting that image-carrier comprises, by the geometric attack parameter that template watermark information determination image may suffer, then recover the watermark information not being subject to the original image carrier of geometric attack to carry out next step again and detect.

Current existing blind Detecting Arithmetic on Digital Watermarking of Image cannot effectively solve geometry shearing attack mostly, especially when shearing attack and rotation, convergent-divergent attack in conjunction with.

Summary of the invention

Technical matters to be solved by this invention needs to provide a kind of opposing geometric attack image watermark detection system.In order to solve the problems of the technologies described above, the invention provides a kind of image watermark detection system.This image watermark detection system, comprising: extraction module (21), and it extracts the watermark signal comprised in image to be detected, as the first watermark signal; Computing module (22), it obtains the maximum maximum coordinates point of correlation; Correction module (23), it is treated detected image carry out recovering to obtain correcting image according to the coordinate figure of described maximum coordinates point; And judge module (24), it is based on the maximum related value in each correlation between the region consistent with the picture shape size that reference watermark template is formed and reference watermark template each in described correcting image, judge whether described maximum related value is greater than predetermined threshold value, and judge whether comprise reference watermark signal in this correcting image according to judged result;

Described computing module (22) obtains the maximum maximum coordinates point of correlation by processing as follows: carry out autocorrelation function calculating to described first watermark signal, to generate autocorrelation function image; Be mapped in log-polar system respectively with reference to layout information and described autocorrelation function image, obtain the maximum coordinates point making the two linear correlation values maximum, wherein, map respectively according to Cartesian coordinates mooring points P (x, y) of following expression to described reference layout information and described autocorrelation function image:

$\left\{\begin{array}{c}r=M_c\·\log \left(\sqrt{x^2+y^2}\right)\\ \mathrm{\θ}=\arctan (u/x)\end{array}\right.$

In formula, (r, θ) represents footpath, pole and the polar angle of log-polar respectively, M _cfor constant;

Wherein, described reference layout information is the information with setting rule being attached to image in the watermark signal telescopiny of image.

According to one embodiment of present invention, wherein, the mapping result of described computing module (22) to described reference layout information and described autocorrelation function image carries out matched filtering to obtain the maximum coordinates point making the two linear correlation values maximum.

According to one embodiment of present invention, wherein, described correction module (23) obtains the geometric attack parameter characterizing the geometric attack that described image to be detected is subject to according to the coordinate figure of described maximum coordinates point, utilize described geometric attack parameter to treat detected image and carry out recovering to obtain correcting image.

According to one embodiment of present invention, described extraction module (21) is by the gray-level histogram equalization of described image to be detected, obtain equalization image, based on described first watermark signal comprised in image to be detected described in described equalization image zooming-out.

According to one embodiment of present invention, described extraction module (21) obtains high-frequency part in described image to be detected, as the first watermark signal by carrying out predictive filtering to described equalization image.

According to one embodiment of present invention, described extraction module (21) carries out predictive filtering by Gauss's Hi-pass filter or butterworth high pass filter.

According to one embodiment of present invention, if described judge module (24) judges that described correcting image contains reference watermark signal, then obtain the watermark signal in correcting image and decoding output is carried out to it.

According to one embodiment of present invention, the mapping result of described computing module (22) to described reference layout information and described autocorrelation function image carries out matched filtering to obtain the maximum coordinates point making the linear correlation values of the two maximum.

According to one embodiment of present invention, described correction module (23) obtains the geometric attack parameter for characterizing the geometric attack that described image to be detected is subject to according to the coordinate figure of described maximum coordinates point, utilizes described geometric attack parameter to treat detected image and carries out recovering to obtain correcting image.

According to one embodiment of present invention, described judge module (24) calculates maximum related value by following expression:

$\underset{p(x,y)}{\max }{W}_r(x,y){*}_m{B}_c(x,y)$

Wherein, W _r(x, y) represents reference watermark template, is made up of, W L modulator block _r(x, y)=(w _r[1] ..., w _r[i] ..., w _r[L]), w _r[i] represents i-th modulator block; B _c(x, y) represents central area, corresponding to reference watermark template, is also made up of a corresponding L element, B _c(x, y)=(B [1] ..., B [i] ..., B [L]), wherein B [i] is the region corresponding to i-th modulator block of central area; P (x, y) represents the point of correcting image; * _mrepresent a kind of modification of convolution operation, * convolution operation is represented here.

In brief, for this deficiency of existing blind Detecting image algorithm, the invention provides a kind of image watermark detection system.This system calculates the geometric attack parameter of the geometric attack such as rotation, convergent-divergent that image to be detected stands according to the reference layout information that image to be detected embeds in advance.Then, image to be detected recovers by the geometric attack parameter according to geometric attacks such as rotation, convergent-divergents, recover correcting image, correcting image and reference watermark template are carried out relevant matches by this system, determine whether image to be detected comprises reference watermark template, if comprised, decode comprised watermark signal.

Compared with prior art, the present invention has the following advantages: image digital watermark detection system of the present invention can resist rotation in zone of reasonableness, shearing and convergent-divergent compound geometric attack; In the present invention, the reference watermark template of image digital watermark detection system can be used as key and uses, even if assailant is aware of detection algorithm and also cannot detects or erase watermark when not knowing key; In the present invention, image digital watermark detection algorithm calculated amount the best part is convolutional calculation, and convolutional calculation can realize by the Fast Fourier Transform (FFT) of fast operation, and this just makes this algorithm can obtain computing velocity faster.

Other features and advantages of the present invention will be set forth in the following description, and, partly become apparent from instructions, or understand by implementing the present invention.Object of the present invention and other advantages realize by structure specifically noted in instructions, claims and accompanying drawing and obtain.

Although the present invention will be described in conjunction with some exemplary enforcements and using method hereinafter, it will be appreciated by those skilled in the art that, for not being intended to, the present invention will be limited to these embodiments.Otherwise, be intended to cover all substitutes be included in spirit of the present invention and scope that appending claims defines, correction and equivalent.

Accompanying drawing explanation

Accompanying drawing is used to provide a further understanding of the present invention, and forms a part for instructions, together with embodiments of the present invention for explaining the present invention, is not construed as limiting the invention.In the accompanying drawings:

Fig. 1 is the schematic flow sheet of image watermark detection method according to a first embodiment of the present invention;

Fig. 2 is the structural representation of image watermark detection system according to a second embodiment of the present invention;

Fig. 3 is the example images generated according to the autocorrelation function of the embodiment of the present invention.

Embodiment

Describe embodiments of the present invention in detail below with reference to drawings and Examples, to the present invention, how application technology means solve technical matters whereby, and the implementation procedure reaching technique effect can fully understand and implement according to this.It should be noted that, only otherwise form conflict, each embodiment in the present invention and each feature in each embodiment can be combined with each other, and the technical scheme formed is all within protection scope of the present invention.

In addition, can perform in the computer system of such as one group of computer executable instructions in the step shown in the process flow diagram of accompanying drawing, and, although show logical order in flow charts, but in some cases, can be different from the step shown or described by order execution herein.

first embodiment

Fig. 1 illustrates the schematic flow sheet of image watermark detection method according to a first embodiment of the present invention.Each step of the present embodiment is described below with reference to Fig. 1.

Extract the watermark signal comprised in image to be detected.Image to be detected, in watermark embed process before, embedded in watermark signal.Because image to be detected may receive geometric attack, therefore extracted watermark signal also may be the watermark signal being subject to geometric attack impact.For convenience of explanation, in below by the watermark signal that extracts from image to be detected referred to as the first watermark signal.Detail will be described in following step 110 with step 120.

It should be noted that, in the image to be detected of the present embodiment, addition of reference layout information in advance.Such as, can in the process treating detected image embed watermark signal before, treat detected image and embed synchronization template, wherein, this synchronization template can be the image defining peak value of pulse point that a size is equal with image to be detected.For another example, also can in the process treating detected image embed watermark signal before, first (but invisibly) some peak value of pulse points of layout regularly in the watermark information that will embed, as long as make after the reference layout information of ancillary rules, watermark information region embedded in image to be detected can show the layout information of certain rule.In brief, reference layout information is the information with certain (setting) rule being attached to image in the watermark signal telescopiny of image, and certain rule herein can be the rule preset arbitrarily.

Step 110, by the gray-level histogram equalization of image to be detected, obtains equalization image.

Particularly, first, the half-tone information of described image to be detected is extracted as view data to be detected.If image to be detected is coloured image, be then transformed on YUV color space model, the Y channel information then on extraction model is as view data to be detected; If image to be detected is gray level image, then direct using its half-tone information as view data to be detected.

Particularly, for the input of the color digital picture of the forms such as BMP, JPG, PNG, first color digital picture is decoded, then decoded image is carried out YUV color space conversion.Wherein, in YUV color space model, " Y " represents lightness (Luminance or Luma), namely gray-scale value; And " U " and " V " represents is colourity (Chrominance or Chroma), effect describes colors of image and saturation degree, is used to specify the color of pixel.

Decoded image represented by RGB color space template is carried out color space conversion, namely the information in RGB color space template is transformed on YUV color space, color space conversion is shown below, after carrying out color notation conversion space, extract Y channel information gray-scale value and input as view data to be detected.

Y＝0.299R+0.587G+0.114B

U＝0.492(B-Y)

V＝0.877(R-Y)

Obtain the grey level histogram of gained view data to be detected, then grey level histogram is carried out histogram equalization, to obtain grey level histogram by the image after equalization.

Particularly, the number of times that the gray scale of adding up image to be detected according to the gray-scale value of image to be detected occurs in each gray level, to obtain grey level histogram, then the method for self-adapting histogram equilibrium is adopted to carry out histogram equalization, to make probability density function p (s)=1 of gradation of image distribution after equilibrium treatment, that is the probability that all image gray levels occur is identical.

It should be noted that, the grey level histogram of image is a kind of statistical graph intensity profile of all pixels of image being shown to its frequency of occurrences by the size of gray-scale value, and the general two-dimensional coordinate system that uses represents.Carrying out equalization to grey level histogram is reduce the gray shade scale of image to exchange the expansion of contrast for, by carrying out homogenising correction to histogram, the gray scale spacing of image being increased or uniform gray level distribution, increases contrast, making the details of image become clear.

Step 120, based on the watermark signal in the equalization image zooming-out image to be detected of step 110 gained, referred to as the first watermark signal.

Can by carrying out predictive filtering to extract the first watermark signal to equalization image.Preferably, the present embodiment adopts Gauss's high-pass filtering, the process of Gauss's high-pass filtering can comprise: carry out Gassian low-pass filter to equalization image, calculate the residual values of image after equalization image and Gassian low-pass filter again, to obtain described image high frequency part to be detected, using obtained high-frequency part as the first watermark signal.More preferably, the template of Gassian low-pass filter is as follows:

$\frac{1}{16}\left[\begin{array}{ccc}1& 2& 1\\ 2& 4& 2\\ 1& 2& 1\end{array}\right]$

Particularly, the disposal route of Gassian low-pass filter is multiplied by pixel with coefficients different in template, and from coefficient value, some pixels in template are more even more important than other.Can find out from above formula, represent the wave filter of 3 × 3, the coefficient value being in the pixel pixel more any than other of filter center position is all large.And distance filter center other pixels far away just seem not too important, the pixel more adjacent than orthogonal directions due to diagonal angle item distance center is farther, so four pixels of its important ratio center direct neighbor are low.All coefficients in above-mentioned middle template and should be 16 because the integral number power that it is 2 be convenient to computing machine realize.

In addition, in this step, except adopting Gauss's Hi-pass filter, butterworth high pass filter etc. can also be adopted to carry out predictive filtering, even can also be derived by natural image probability Distribution Model.

Step 130, carries out autocorrelation function calculating to the watermark signal in the image to be detected extracted in step 120, to generate autocorrelation function image, is designated as f (u, v).Formula can be as follows:

$f(u,v)=\underset{x=1}{\overset{M}{\mathrm{\Σ}}}\underset{y=1}{\overset{N}{\mathrm{\Σ}}}I(x,y)I(x-u,y-v)$

Wherein, I represents the image the first watermark signal being carried out to periodically expansion and generation;

X, y represent the coordinate figure of the cross direction of image I and the coordinate figure in high direction respectively;

M, N represent that image I's is wide and high respectively, equal with height with the wide of image to be detected;

U, v represent the coordinate figure of the cross direction of autocorrelation function and the coordinate figure in high direction respectively;

Wherein, x, u=1 ... M; Y, v=1 ... N.

Autocorrelation function f (u, v) changes along with the size variation of u, v, and the value of f (u, v) presents the change of certain periodization.

More specifically, because the watermark signal in image to be detected corresponds to the high-frequency part in image, the sequence that the watermark signal (the first watermark signal) extracted is made up of the random number of normal distribution, zero-mean, and the number of the random number of the first watermark signal is less than the number of image pixel to be detected, therefore, have employed the periodic function the first watermark signal being expanded to (repeat replication) and generation in above formula.

The present embodiment characterizes image to be detected by introducing autocorrelogram picture, both the feature such as distribution characteristics and periodicity of image to be detected can have been reflected better, the impact that this geometric deformation of translation causes can be eliminated again, simultaneously by the rotation of image to be detected and these geometric attack deformation of convergent-divergent rotation being mapped to its autocorrelogram picture one by one and convergent-divergent.

Step 140, autocorrelation function image with reference to layout information and step 130 gained is mapped in log-polar system respectively, then carries out matched filtering to obtain the maximum coordinate points of linear correlation values (being called for short maximum coordinates point) and value Pm (r thereof to the mapping result of the two _p, θ _p).

Particularly, image log polar coordinate transform is that image is converted to log-polar system from cartesian coordinate system.First coordinate transform is carried out with reference to layout information, the image obtained is designated as t (r, θ), then autocorrelation function image is carried out coordinate transform, the image obtained is designated as f (r, θ), then by image t (r, θ) carry out matched filtering with image f (r, θ), that is carry out related operation to obtain the maximum coordinate points of linear correlation values and value Pm (r thereof _p, θ _p).Wherein, can be as follows to the mapping equation of log-polar from a P (x, y):

$\left\{\begin{array}{c}r=M_c\·\log \left(\sqrt{x^2+y^2}\right)\\ \mathrm{\θ}=\arctan (y/x)\end{array}\right.$

In formula, (r, θ) represents footpath, pole and the polar angle of log-polar respectively, M _cfor constant.Can be obtained fom the above equation, a border circular areas is mapped to a rectangular area by this mapping transformation.When cartesian coordinate system hypograph generation convergent-divergent, log-polar will produce translation, and log-polar has good yardstick and rotational invariance.As follows, in formula, s represents zooming parameter.

Wherein, matched filtering refers to that acquisition makes reference layout information and autocorrelation function image be mapped to maximum coordinates point Pm (r in log-polar system respectively _p, θ _p), make the two linear correlation values maximum, that is g (r, θ)=f (r, θ) ο t (r, θ) reach global peak.Correlation operation can be shown below,

In formula, " ο " expression " correlativity " operates; M, N represent wide and high under log-polar system of autocorrelation function image and reference layout information respectively, equal with height with the wide of image to be detected; F (r, θ), t (r, θ) are autocorrelation function image and the representation of reference layout information under log-polar system respectively.

Step 150, according to the coordinate figure Pm (r of the maximum coordinates point of step 140 gained _p, θ _p), treat detected image and carry out recovery and obtain correcting image.

More specifically, obtain the geometric attack parameter characterizing the geometric attack that described image to be detected is subject to according to the coordinate figure of maximum coordinates point, utilize geometric attack parameter to treat detected image and carry out recovering to obtain correcting image, will be described in more detail below.

According to the coordinate figure Pm (r of maximum coordinates point _p, θ _p), the geometric attack parameter of the geometric attacks such as the rotation that acquisition sign image to be detected stands and/or convergent-divergent.For the situation of carrying out Rotation and Zoom, need to obtain rotation parameter and zooming parameter, according to the character of log-polar coordinate mapping, formula can be expressed as follows:

In formula, s, d represent zooming parameter and rotation parameter respectively;

M _cfor constant;

N represents the height of autocorrelogram picture under log-polar system, equal with the height of image to be detected.

And, utilize gained rotation parameter d and zooming parameter s, treat detected image and carry out recovering to obtain the correcting image without geometric attack, shown in following formula,

$\left(\begin{array}{c}{x}^{\′}\\ y^{\′}\end{array}\right)=\frac{1}{s}\left[\begin{array}{cc}\cos d& \sin d\\ -\sin d& \cos d\end{array}\right]\left(\begin{array}{c}x\\ y\end{array}\right)$

In formula, $\left(\begin{array}{c}x\\ y\end{array}\right)$ Represent image pixel coordinates to be detected;

$\left(\begin{array}{c}{x}^{\′}\\ y^{\′}\end{array}\right)$ Represent correcting image pixel coordinate.

In this enforcement, by comparing with reference to layout information and the autocorrelation function image obtained, to correct the geometric attack suffered by image to be detected.Because reference layout information data can be more much smaller than view data to be detected, so its matching speed is fast, saves the computing time of correlation operation, improve work efficiency.

Step 160, based on maximum related value C in the correlation between the region consistent with reference watermark template image shape size arbitrary in above-mentioned correcting image and reference watermark template image (being called for short reference watermark image) _max, judge whether comprise reference watermark signal in this correcting image.

Such as, first the region consistent with reference watermark template image shape size (abbreviation central area) of centered by the center of correcting image and reference watermark template are contrasted, obtain correlation therebetween, again by region consistent for each shape size on side, central area (such as, the region that central point is left, upper, right or down offset by 1 or some pixels), contrast with reference watermark template, obtain corresponding correlation, the rest may be inferred, thus try to achieve above-mentioned maximum related value.Wherein, in correcting image arbitrary form with reference watermark template picture shape region of the same size, can be calculated as follows with the acquisition of the maximum related value of reference watermark template:

$\underset{p(x,y)}{\max }{W}_r(x,y){*}_m{B}_c(x,y)$

Wherein, W _r(x, y) represents reference watermark template, is made up of, W L modulator block _r(x, y)=(w _r[1] ..., w _r[i] ..., w _r[L]), w _r[i] represents i-th modulator block; B _c(x, y) represents central area (examined fritter), is also made up of a corresponding L element, B corresponding to reference watermark template _c(x, y)=(B [1] ..., B [i] ..., B [L]), wherein B [i] is the region corresponding to i-th modulator block of central area; P (x, y) represents the point (point slided into) of correcting image; * _mrepresent a kind of modification of convolution operation, $W_r{*}_m{B}_c=\underset{i=1}{\overset{L}{\mathrm{\Σ}}}\left|w_r\right[i]*B[i\left]\right|,$ * convolution operation is represented here.

Such as, when maximum related value is greater than a certain predetermined threshold value, be judged as containing reference watermark signal.

In addition, if judge that correcting image contains reference watermark signal based on above-mentioned maximum related value, then obtain the watermark signal of correcting image and decoding output is carried out to it.

Such as, can carry out recovering to obtain the similar mode of correcting image, according to the coordinate figure Pm (r of the maximum coordinates point of step 140 gained with treating detection mode _p, θ _p), recover to obtain the watermarking images after correcting to the first watermark signal image, then decoding is carried out to the watermarking images after correction and export.

For another example, also can from obtaining above-mentioned maximum related value C _maxcorrecting image in region in extract watermark signal and it decoded.

Particularly, the maximum correlation value C of gained is judged _maxwhether meet and be less than predetermined threshold value T, if meet, correcting image, otherwise correcting image comprises reference watermark signal, and carry out decoding output to reference watermark signal if not comprising reference watermark signal.Reference watermark signal by comprising in following steps decoding output calibration image:

1) length inputting the reference watermark template preset is L bit;

2) establish i=1, i is i-th modulator block of reference watermark template;

3) if the normalization linear correlation values C in i-th of reference watermark template region that modulator block is corresponding to central area _ibe greater than T _hthen the corresponding output of watermark information is 1, otherwise exports 0.Wherein, T _hbe the threshold value preset, generally can be set to 0.Here central area refers to the central area of carrying out correlation computing gained maximum related value in correcting image with reference watermark template.Wherein, C _icomputing formula can be as follows:

$C_i=\frac{\underset{k=1}{\overset{n}{\mathrm{\Σ}}}{M}_i\left(k\right)(B_i\left(k\right)-m)}{\sqrt{\underset{k=1}{\overset{n}{\mathrm{\Σ}}}{\left|M_i\left(k\right)\right|}^2}\sqrt{\underset{k=1}{\overset{n}{\mathrm{\Σ}}}{|B_i\left(k\right)-m|}^2}}$

In formula, M _ii-th modulator block of reference watermark template;

B _ibe central area correspond to i-th modulator block region;

N is B _ithe length in region, m represents B _ithe average in region.

4) i is from increasing 1, if i<=L, then returns step 3); Otherwise terminate.Each obtained corresponding positions watermark information is arranged to generate reference watermark signal in order.

It should be noted that, reference watermark signal is set by embody rule, generally can be expressed as the form of white noise (power density spectrum is uniformly distributed) sequence.Reference watermark signal can be binary mode or Gaussian noise form and it is modulated by reference watermark template, and such as noise sequence (as reference watermark template) has modulated reference watermark signal 0100011011.Particularly, i-th modulator block modulation binary message 0 or 1, can be as follows:

$w_m\left[i\right]=\left\{\begin{array}{cc}{w}_r\left[i\right],& \mathrm{bit}=1\\ -w_r\left[i\right],& \mathrm{bit}=0\end{array}\right.$

According to above formula, be binary message bit=1 to embed reference watermark signal, then in fact embed w _r[i]; To embed binary message bit=0, then actual embedding-w _r[i].W _r[i] is generally white noise sequence, w _rthe combination of [i] is namely reference watermark template W _r.

second embodiment

Fig. 2 illustrates the structural representation of image watermark detection system according to a second embodiment of the present invention.The each several part composition of the present embodiment is described below with reference to Fig. 2.

Extraction module (21) with reference to figure 2 the present embodiment performs the step 110 of the first embodiment and the operation of step 120, computing module (22) performs the step 130 of the first embodiment and the operation of step 140, and correction module (23) and judge module (24) perform step 150 and the step 160 of the first embodiment respectively.Launch no longer in detail at this.

Those skilled in the art should be understood that, above-mentioned of the present invention each module or each step can realize with general calculation element, they can concentrate on single calculation element, or be distributed on network that multiple calculation element forms, alternatively, they can realize with the executable program code of calculation element, thus, they can be stored and be performed by calculation element in the storage device, or they are made into each integrated circuit modules respectively, or the multiple module in them or step are made into single integrated circuit module to realize.Like this, the present invention is not restricted to any specific hardware and software combination.

Although the embodiment disclosed by the present invention is as above, the embodiment that described content just adopts for the ease of understanding the present invention, and be not used to limit the present invention.Technician in any the technical field of the invention; under the prerequisite not departing from the spirit and scope disclosed by the present invention; any amendment and change can be done what implement in form and in details; but scope of patent protection of the present invention, the scope that still must define with appending claims is as the criterion.

Claims (10)

1. an image watermark detection system, is characterized in that, comprising:

Extraction module (21), it extracts the watermark signal comprised in image to be detected, as the first watermark signal;

Computing module (22), it obtains the maximum maximum coordinates point of correlation;

Correction module (23), it is treated detected image carry out recovering to obtain correcting image according to the coordinate figure of described maximum coordinates point; And

Judge module (24), it is based on the maximum related value in each correlation between the region consistent with the picture shape size that reference watermark template is formed and reference watermark template each in described correcting image, judge whether described maximum related value is greater than predetermined threshold value, and judge whether comprise reference watermark signal in this correcting image according to judged result;

Described computing module (22) obtains the maximum maximum coordinates point of correlation by processing as follows:

Autocorrelation function calculating is carried out to described first watermark signal, to generate autocorrelation function image;

Be mapped in log-polar system respectively with reference to layout information and described autocorrelation function image, obtain the maximum coordinates point making the two linear correlation values maximum, wherein,

Map respectively according to Cartesian coordinates mooring points P (x, y) of following expression to described reference layout information and described autocorrelation function image:

$\left\{\begin{array}{c}r=M_c\&CenterDot;\log \left(\sqrt{x^2+y^2}\right)\\ \mathrm{\&theta;}=\arctan (y/x)\end{array}\right.$

In formula, (r, θ) represents footpath, pole and the polar angle of log-polar respectively, M _cfor constant;

Wherein, described reference layout information is the information with setting rule being attached to image in the watermark signal telescopiny of image.

2. system according to claim 1, is characterized in that,

The mapping result of described computing module (22) to described reference layout information and described autocorrelation function image carries out matched filtering to obtain the maximum coordinates point making the two linear correlation values maximum.

3. system according to claim 1 and 2, is characterized in that,

Described correction module (23) obtains the geometric attack parameter characterizing the geometric attack that described image to be detected is subject to according to the coordinate figure of described maximum coordinates point, utilize described geometric attack parameter to treat detected image and carry out recovering to obtain correcting image.

4. system according to claim 1, is characterized in that,

Described extraction module (21), by the gray-level histogram equalization of described image to be detected, obtains equalization image, based on described first watermark signal comprised in image to be detected described in described equalization image zooming-out.

5. system according to claim 4, is characterized in that,

Described extraction module (21) obtains high-frequency part in described image to be detected, as the first watermark signal by carrying out predictive filtering to described equalization image.

6. system according to claim 5, is characterized in that,

Described extraction module (21) carries out predictive filtering by Gauss's Hi-pass filter or butterworth high pass filter.

7. system according to claim 1, is characterized in that,

If described judge module (24) judges that described correcting image contains reference watermark signal, then obtain the watermark signal in correcting image and decoding output is carried out to it.

8. system according to any one of claim 1 to 7, is characterized in that,

The mapping result of described computing module (22) to described reference layout information and described autocorrelation function image carries out matched filtering to obtain the maximum coordinates point making the linear correlation values of the two maximum.

9. system according to claim 1, is characterized in that,

Described correction module (23) obtains the geometric attack parameter for characterizing the geometric attack that described image to be detected is subject to according to the coordinate figure of described maximum coordinates point, utilizes described geometric attack parameter to treat detected image and carries out recovering to obtain correcting image.

10. system according to claim 1, is characterized in that,

Described judge module (24) calculates maximum related value by following expression:

$\underset{p(x,y)}{\max }{W}_r(x,y){*}_m{B}_c(x,y)$

Wherein, W _r(x, y) represents reference watermark template, is made up of, W L modulator block _r(x, y)=(w _r[1] ..., w _r[i] ..., w _r[L]), w _r[i] represents i-th modulator block; B _c(x, y) represents central area, corresponding to reference watermark template, is also made up of a corresponding L element, B _c(x, y)=(B [1] ..., B [i] ..., B [L]), wherein B [i] is the region corresponding to i-th modulator block of central area; P (x, y) represents the point of correcting image; * _mrepresent a kind of modification of convolution operation, * convolution operation is represented here.