CN108876692A - Watermark embedding method and detection method based on extremely humorous-Fourier's square statistical modeling - Google Patents
Watermark embedding method and detection method based on extremely humorous-Fourier's square statistical modeling Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T1/00—General purpose image data processing
- G06T1/0021—Image watermarking
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- G—PHYSICS
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2201/00—General purpose image data processing
- G06T2201/005—Image watermarking
Abstract
The invention discloses a kind of watermark embedding methods and detection method based on extremely humorous-Fourier's square statistical modeling, belong to watermark insertion and detection field, technical problems to be solved are existing watermark insertion and detection method exist can not resist geometric attacks, watermark capacity is small and some algorithm is non-Blind Detect Algorithm；The embedding grammar includes the following steps：Construct original picture block；Construct extremely humorous-Fourier's square of original picture block；Watermark is embedded in original picture block, calculates extremely humorous-Fourier's square square value after the insertion watermark of each original picture block；Original picture block is reconstructed based on square value；Building insertion watermarking images.The method of detecting watermarks includes：Construct original image to be detected block；Extremely humorous-Fourier's square reconstruct is carried out to original image to be detected block；The corresponding watermark information of amplitude of extremely humorous-Fourier's square square value of each original image to be detected block is detected based on maximal possibility estimation.It is big that the water mark method can resist geometric attack, watermark capacity.
Description
Technical field
The present invention relates to watermark insertion and detection fields, specifically a kind of to be based on extremely humorous-Fourier's square statistical modeling
Watermark embedding method and method of detecting watermarks.
Background technique
Mainly there are two steps for digital figure watermark algorithm：Watermark insertion and watermark detection.Watermark detection is for judging figure
It whether there is watermark as in, be broadly divided into two class method of the detection based on correlation and the detection based on statistics.Based on correlation
Detection method be judged based on the linear dependence between the watermark of extraction and original watermark signal watermark whether there is in
In image.Since this method is very simple, it is often used in watermark detection scheme.But signal detection theory show when
When watermark carrier Gaussian distributed, the detection method based on correlation is only optimal, and the spatial domain of image and transform domain
Gaussian Profile is disobeyed, i.e. the detection method based on correlation is not optimal.Detection method based on statistics can solve
The above problem, since the watermark information of insertion only changes the redundancy section of host image, this variation will not reduce picture quality,
But the statistical property of host image can be changed, so can use statistical property effectively detects watermark information.Based on statistics
Accuracy of the method dependent on carrier data distribution modeling, model is more accurate, and the result of detection is more credible, such method can divide
For two classes, i.e. additive insertion method and multiplying property embedding grammar.
Since multiplying property watermarking algorithm is to rely on picture material, so than additive algorithms with stronger robustness and not
Sentience, therefore the insertion of the multiplying property based on statistics causes and widely pays close attention to and propose many algorithms.However it existing is based on
The multiplying property embedded mobile GIS majority of statistics can not extract specific information, therefore the copyright ownership etc. that can not effectively solve image is asked
Topic.Even if the rare algorithm that can extract watermark, there is also following disadvantages：Geometric attack cannot be resisted, watermark capacity is smaller,
And some algorithm is non-Blind Detect Algorithm.
Summary of the invention
Technical assignment of the invention is against the above deficiency, to provide a kind of water based on extremely humorous-Fourier's square statistical modeling
Embedding grammar and method of detecting watermarks are printed, it can not resist geometric attacks, watermark to solve existing watermark insertion and detection method presence
The problem of capacity is small and some algorithm is non-Blind Detect Algorithm.
Technical assignment of the invention is realized in the following manner：
Watermark embedding method based on extremely humorous-Fourier's square statistical modeling, it is characterised in that include the following steps：
S100, building original picture block：Original image is divided into the image block of N number of non-overlap, calculates each image block
Entropy, and choose the image block of L high entropy as original picture block, N is natural number, and N >=L, L are watermark length；
S200, the extremely humorous-Fourier's square for constructing original picture block：Extremely humorous-Fourier's square of each original picture block is calculated,
Choose square value Pn,mFor extremely humorous-Fourier's square square value of corresponding original picture block；
S300, watermark is embedded in original picture block：Each original image is embedded a watermark into based on multiplying property watermark embedding method
The amplitude of extremely humorous-Fourier's square square value of block, and calculate extremely humorous-Fourier's square square after the insertion watermark of each original picture block
Value, and it is denoted as square value P'n,m；
S400, reconstructed image block：Based on square value Pn,mReconstruct original picture block, obtain origin pole it is humorous-Fourier's square reconstruct image
As block, and it is based on square value P'n,mReconstruct original picture block, obtain insertion watermark it is extremely humorous-Fourier's square reconstructed image block；
S500, building insertion watermarking images：Based on original picture block, origin pole it is humorous-Fourier's square reconstructed image block and
Be embedded in watermark it is extremely humorous-Fourier's square reconstructed image block, original picture block after insertion watermark is calculated, and by original graph after insertion watermark
As block replacement original picture block, the image of insertion watermark is obtained；
Wherein, n is the order of original picture block, and n >=0, m are multiplicity, and | m | >=0.
Original image is divided into the image block of N number of non-overlap in watermark embedding method step S100, and is chosen L high
The image block of entropy is original picture block, and the high entropy block of image has concentrated a large amount of information, and human eye is insensitive to its, this algorithm will
Watermark is embedded into the image block of high entropy the invisibility that can enhance watermark；Each original picture block is calculated by step S200
Extremely humorous-Fourier's square, choose square value Pn,mFor extremely humorous-Fourier's square square value of corresponding original picture block；It is based in step S300
Multiplying property embedding grammar embeds a watermark into the amplitude of extremely humorous-Fourier's square square value of each original picture block, obtains each original graph
It is embedded in after watermark the extremely amplitude of humorous-Fourier's square square value as block, and square value P' is obtained based on amplitude and corresponding phasen,m,
Relative to additive algorithms, multiplying property algorithm has stronger robustness and not sentience.
Further, in step S100, the image block for choosing L high entropy is as the step of original picture block：
Descending arrangement is carried out to above-mentioned image block according to entropy, L image block is original picture block before choosing；Alternatively, according to
Ascending order arrangement is carried out to above-mentioned image block according to entropy, L image block is original picture block after selection.
Further, the pole of each original picture block is embedded a watermark into step S300 based on multiplying property watermark embedding method
The amplitude of humorous-Fourier's square square value, the embedding formula used for：
xiFor the amplitude of extremely humorous Fourier's square square value of original picture block,For insertion watermark after original picture block it is extremely humorous
The amplitude of Fourier's square square square value, i=0,1,2 ... L, f1(x) and f0It (x) is watermark intensity function of embedding, f1(x) and
f0(x) expression formula is：
a1、a2、b1And b2It is watermark embedding parameter.
Further, square value P' is obtained in step S300n,mAfterwards, square value P' is modifiedn,mAbout multiplicity m=0 symmetric position
Square value P'n,-m, square value P' after modificationn,-mIt can be with square value P'n,mIt is adapted.
Further, in step S300, watermark is the binary sequence generated by pseudo-random sequence generator, watermark information table
It is shown as：
W={ wi, 0≤i < L }
Wherein, wiFor the value of the i-th bit of watermark.
Further, in step S500, based on original picture block, origin pole it is humorous-Fourier's square reconstructed image block and embedding
Enter watermark it is extremely humorous-Fourier's square reconstructed image block, calculate original picture block after insertion watermark, calculation formula is：
Fw=Fo-Fr+Fr'
Wherein, FoFor original picture block, FrFor origin pole it is humorous-Fourier's square reconstructed image block, Fr'For insertion watermark it is extremely humorous-
Fourier's square reconstructed image block, FwFor original picture block after insertion watermark.
Based on the method for detecting watermarks of extremely humorous-Fourier's square, watermark inspection can be carried out to image to be detected of insertion watermark
It surveys, described image to be detected is embedding by the multiplying property watermark described in any of the above embodiments based on extremely humorous-Fourier's square statistical modeling
Enter that method insertion watermark obtains containing watermarking images, watermark detection includes the following steps：
L100, the original image to be detected block of building：Image to be detected is divided into the image block of N number of non-overlap, is calculated every
The entropy of a image block, and the image block of L high entropy is chosen as original image to be detected block；
Extremely humorous-Fourier's square of L200, the original image to be detected block of building：Calculate the pole of each original image to be detected block
Humorous-Fourier's square chooses square value Pn,mFor extremely humorous-Fourier's square square value of the original image to be detected block of correspondence, calculate each original
The amplitude of extremely humorous-Fourier's square square value of image to be detected block；
L300, detected based on maximal possibility estimation each original image to be detected block extremely humorous-Fourier's square square value width
It is worth corresponding watermark information.
The watermark detection can be regarded as detecting known signal in noise circumstance, pole in the watermark detection of this algorithm
The amplitude of humorous-Fourier's square square value is considered as noise circumstance, and watermark is signal to be detected.
Further, in step L100, the step of image block of L high entropy is as original image to be detected block is chosen
For：Descending arrangement is carried out to above-mentioned image block according to entropy, L image block is original image to be detected block before choosing；Alternatively, according to
Ascending order arrangement is carried out to above-mentioned image block according to entropy, L image block is original image to be detected block after selection.
Further, in step L 300, based on maximal possibility estimation detect each original image to be detected block it is extremely humorous-
The corresponding watermark information of amplitude of Fourier's square square value, includes the following steps：
L310, maximum likelihood detector is constructed based on Weibull parameter Estimation, maximum likelihood detector is：
Wherein, yiFor the amplitude of extremely humorous-Fourier's square square value of i-th of original image to be detected block, i=1,
2 ... L,
α is the scale parameter of Weibull distribution, and β is the form parameter of Weibull distribution, g1It (y) is watermark embedment strength
Function g1(x) inverse function, g0It (y) is watermark intensity function of embedding f1(x) inverse function, g1(y) and g0(y) expression formula is：
Lambertw () is lambert's W function；
L320, above-mentioned maximum likelihood detector is parsed based on lambert's W function, above-mentioned maximum likelihood detector simplifies
For：
Wherein, TiFor the threshold value for detecting watermark, TiExpression formula is：
Ti=ln (g1(yi)/g0(yi))=ln ((lambertw ((a1·yi)/b1)/a1)/(lambertw((a2·yi)/
b2)/a2))。
By the simplified expression formula of above-mentioned maximum likelihood detector it is found that each image to be detected block extremely humorous-Fu
In leaf square square value amplitude in estimate scale parameter, extremely humorous-Fourier's square square value of each image to be detected block can be acquired
The watermark information being embedded in amplitude.
Of the invention being had based on the extremely watermark embedding method of humorous-Fourier's square statistical modeling and method of detecting watermarks is following
Advantage：Image moment is applied in the watermarking algorithm based on statistical model, there is very strong not sentience, and can effectively support
Resist geometric attacks and normal image processing attack, robustness are better than the existing watermarking algorithm based on statistical model, and watermark capacity
It is far longer than the existing watermarking algorithm based on statistical model.
Detailed description of the invention
The following further describes the present invention with reference to the drawings.
Attached drawing 1 is flow diagram of the embodiment 1 based on the extremely watermark embedding method of humorous-Fourier's square statistical modeling；
Attached drawing 2 is flow diagram of the embodiment 2 based on the extremely method of detecting watermarks of humorous-Fourier's square statistical modeling.
Specific embodiment
It is embedding to the watermark of the invention based on extremely humorous-Fourier's square statistical modeling referring to Figure of description and specific embodiment
Enter method and method of detecting watermarks is described in detail below.
Embodiment 1：
As shown in Fig. 1, the watermark embedding method of the invention based on extremely humorous-Fourier's square statistical modeling, including it is as follows
Step：
S100, building original picture block：Original image is divided into the image block of N number of non-overlap, calculates each image block
Entropy, and choose the image block of L high entropy as original picture block, N is natural number, and N >=L, L are watermark length；
S200, the extremely humorous-Fourier's square for constructing original picture block：Extremely humorous-Fourier's square of each original picture block is calculated,
Choose square value Pn,mFor extremely humorous-Fourier's square square value of corresponding original picture block；
S300, watermark is embedded in original picture block：Each original image is embedded a watermark into based on multiplying property watermark embedding method
The amplitude of extremely humorous-Fourier's square square value of block, and calculate extremely humorous-Fourier's square square after the insertion watermark of each original picture block
Value, and it is denoted as square value P'n,m；
S400, reconstructed image block：Extremely humorous-Fourier's square square value P based on original picture blockn,mOriginal picture block is reconstructed, is obtained
To origin pole it is humorous-Fourier's square reconstructed image block, and based on insertion watermark it is extremely humorous-Fourier's square square value P'n,mReconstruct original image
Block, obtain insertion watermark it is extremely humorous-Fourier's square reconstructed image block；
S500, building insertion watermarking images：Based on original picture block, origin pole it is humorous-Fourier's square reconstructed image block and
Be embedded in watermark it is extremely humorous-Fourier's square reconstructed image block, original picture block after insertion watermark is calculated, and by original graph after insertion watermark
As block replacement original picture block, the image of insertion watermark is obtained.
Wherein, the watermark being embedded in the present embodiment is the binary sequence generated by pseudo-random sequence generator, watermark information
Expression formula is：
W={ wi, 0≤i < L }
Wherein, wiFor the value of the i-th bit of watermark.
In step S100, descending arrangement is carried out to above-mentioned N number of image block according to the size of entropy, and choose preceding L high entropys
For the image block of value as original picture block, the purpose of the process is to choose the image block of L high entropy as original picture block,
Ascending order arrangement can also be carried out to above-mentioned N number of image block according to the size of entropy in practical application, and after choosing L high entropy figure
As block is as original picture block.
In step S200, extremely humorous-Fourier's square square value P of original picture blockn,mExpression formula be：
Wherein, Pn,mThe order for indicating original picture block Fo is n (n >=0), and multiplicity is in extremely humorous-Fu of m (| m | >=0)
Leaf square,AnIt (r) is the radial basis function of extremely humorous-Fourier's square：
Execute step step S300 before, need to calculate extremely humorous-Fourier's square square value of each original picture block amplitude and
Phase.
In step S300, extremely humorous-Fourier of each original picture block is embedded a watermark into based on multiplying property watermark embedding method
The amplitude of square square value, the embedding formula used for：
For insertion watermark after original picture block extremely humorous-Fourier's square square value amplitude, i=0,1,2 ... L,
f1(x) and f0It (x) is watermark intensity function of embedding, f1(x) and f0(x) expression formula is：
a1、a2、b1And b2It is watermark embedding parameter；f1(x) and f0It (x) is exponential function, exponential function is as a kind of
Nonlinear function has variation range by a relatively large margin in domain, and the spy that the amplitude for meeting the bigger change of square value is bigger
Point, by f1(x) and f0(x) expression formula can be seen that, f1(x) and f0(x) with a1And a2Variation tendency meet the bigger change of square value
The bigger rule of amplitude；
After obtaining the amplitude of extremely humorous-Fourier's square square value of original picture block, to extremely humorous-Fourier's square of original picture block
The amplitude and phase of square value carry out that extremely humorous-Fourier's square square value after the insertion watermark of corresponding original picture block is calculated, and
The square value is denoted as square value P'n,m。
To guarantee that the image with watermark can accurately reconstruct to obtain, when watermark is embedded into Pn,mIn after, need to modify square
Value P'n,mSquare value P' about multiplicity m=0 symmetric positionn,-mSo that square value P'n,-mWith square value P'n,mIt is adapted.
In step S400, it is based on square value Pn,mReconstruct original picture block, obtain origin pole it is humorous-Fourier's square reconstructed image block
Fr, it is based on square value P'n,mReconstruct original picture block, obtain insertion watermark it is extremely humorous-Fourier's square reconstructed image block Fr'。
In step S500 based on original picture block, origin pole it is humorous-Fourier's square reconstructed image block and insertion watermark it is extremely humorous-
Fourier's square reconstructed image block calculates original picture block F after insertion watermarkw, the calculation formula that uses for：Fw=Fo-Fr+Fr'。
In the above method, image moment is applied in the watermarking algorithm based on statistical model, is embedded a watermark into image.
Embodiment 2：
As shown in Fig. 2, pass through the watermark embedding method based on extremely humorous-Fourier's square statistical modeling disclosed in embodiment 1
The image to be detected that can obtain being embedded with watermark, the water of the invention based on extremely humorous-Fourier's square are embedded a watermark into image
Watermark detection can be carried out to above-mentioned image to be detected with watermark by printing detection method, and watermark detection includes the following steps：
S100, the original image to be detected block of building：Image to be detected is divided into the image block of N number of non-overlap, is calculated every
The entropy of a image block carries out descending arrangement to above-mentioned N number of image block according to the size of entropy, and chooses preceding L high entropy
Image block is as original image to be detected block；
Extremely humorous-Fourier's square of S200, the original image to be detected block of building：Calculate the pole of each original image to be detected block
Humorous-Fourier's square chooses square value Pn,mFor extremely humorous-Fourier's square square value of the original image to be detected block of correspondence, calculate each original
The amplitude of extremely humorous-Fourier's square square value of image to be detected block；
S300, detected based on maximal possibility estimation each original image to be detected block extremely humorous-Fourier's square square value width
It is worth corresponding watermark information.
Wherein, in step S100, descending arrangement is carried out to above-mentioned N number of image block according to the size of entropy, and choose first L
For the image block of high entropy as original image to be detected block, the purpose of the process is to choose the image block of L high entropy as former
Beginning image to be detected block can also carry out ascending order arrangement to above-mentioned N number of image block according to the size of entropy in practical applications, and select
Take the image block of rear L high entropy as original image to be detected block.
In step S300, extremely humorous-Fourier's square square of each original image to be detected block is detected based on maximal possibility estimation
The corresponding watermark information of the amplitude of value, includes the following steps：
S310, maximum likelihood detector is constructed based on Weibull parameter Estimation, maximum likelihood detector is：
Wherein, yiFor the amplitude of extremely humorous-Fourier's square square value of i-th of original image to be detected block,
I=1,2 ... L,
α is the scale parameter of Weibull distribution, and β is the form parameter of Weibull distribution, g1It (y) is watermark embedment strength
Function g1(x) inverse function, g0It (y) is watermark intensity function of embedding f1(x) inverse function, g1(y) and g0(y) expression formula is：
Lambertw () is lambert's W function, and above-mentioned formula (4) is represented by：
S320, above-mentioned maximum likelihood detector is parsed based on lambert's W function, by formula (6) to above-mentioned maximum
Likelihood detection device is simplified, and maximum likelihood detector is reduced to：
Wherein, TiFor the threshold value for detecting watermark, TiExpression formula is：
Ti=ln (g1(yi)/g0(yi))=ln ((lambertw ((a1·yi)/b1)/a1)/(lambertw((a2·yi)/
b2)/a2)) (8)
In conjunction with formula (7) and formula (8) it is found that construction maximum likelihood detector after, it is thus only necessary to using it is received it is extremely humorous-
Fourier's square Amplitude Estimation goes out the scale parameter of Weibull distribution.
The technical personnel in the technical field can readily realize the present invention with the above specific embodiments,.But it answers
Work as understanding, the present invention is not limited to above-mentioned specific embodiments.On the basis of the disclosed embodiments, the technical field
Technical staff can arbitrarily combine different technical features, to realize different technical solutions.Except technology described in the specification
Outside feature, it all is technically known to those skilled in the art.
Claims (9)
1. the watermark embedding method based on extremely humorous-Fourier's square statistical modeling, it is characterised in that include the following steps：
S100, building original picture block：Original image is divided into the image block of N number of non-overlap, calculates the entropy of each image block
Value, and the image block of L high entropy is chosen as original picture block, N is natural number, and N >=L, L are watermark length；
S200, the extremely humorous-Fourier's square for constructing original picture block：Extremely humorous-Fourier's square of each original picture block is calculated, is chosen
Square value Pn,mFor extremely humorous-Fourier's square square value of corresponding original picture block；
S300, watermark is embedded in original picture block：Each original picture block is embedded a watermark into based on multiplying property watermark embedding method
The amplitude of extremely humorous-Fourier's square square value, and extremely humorous-Fourier's square square value after the insertion watermark of each original picture block is calculated, and
It is denoted as square value P'n,m；
S400, reconstructed image block：Based on square value Pn,mReconstruct original picture block, obtain origin pole it is humorous-Fourier's square reconstructed image
Block, and it is based on square value P'n,mReconstruct original picture block, obtain insertion watermark it is extremely humorous-Fourier's square reconstructed image block；
S500, building insertion watermarking images：Based on original picture block, origin pole it is humorous-Fourier's square reconstructed image block and insertion
Watermark is extremely humorous-Fourier's square reconstructed image block, original picture block after insertion watermark is calculated, and by original picture block after insertion watermark
Original picture block is replaced, the image of insertion watermark is obtained；
Wherein, n is the order of original picture block, and n >=0, m are multiplicity, and | m | >=0.
2. the watermark embedding method according to claim 1 based on extremely humorous-Fourier's square statistical modeling, it is characterised in that step
In rapid S100, the image block for choosing L high entropy is as the step of original picture block：
Descending arrangement is carried out to above-mentioned image block according to entropy, L image block is original picture block before choosing；
Alternatively, carrying out ascending order arrangement to above-mentioned image block according to entropy, L image block is original picture block after selection.
3. the watermark embedding method according to claim 1 based on extremely humorous-Fourier's square statistical modeling, it is characterised in that step
The width of extremely humorous-Fourier's square square value of each original picture block is embedded a watermark into rapid S300 based on multiplying property watermark embedding method
Value, the embedding formula used for：
xiFor the amplitude of extremely humorous Fourier's square square value of original picture block,In extremely humorous Fu for original picture block after insertion watermark
The amplitude of leaf square square square value, i=0,1,2 ... L, f1(x) and f0It (x) is watermark intensity function of embedding, f1(x) and f0
(x) expression formula is：
a1、a2、b1And b2It is watermark embedding parameter.
4. the watermark embedding method according to claim 1 or 3 based on extremely humorous-Fourier's square statistical modeling, feature exist
Square value P' is obtained in step S300n,mAfterwards, square value P' is modifiedn,mSquare value P' about multiplicity m=0 symmetric positionn,-m, modification
Square value P' afterwardsn,-mIt can be with square value P'n,mIt is adapted.
5. the watermark embedding method according to claim 1 or 3 based on extremely humorous-Fourier's square statistical modeling, feature exist
In step S300, watermark is the binary sequence generated by pseudo-random sequence generator, and watermark information is expressed as：
W={ wi, 0≤i < L }
Wherein, wiFor the value of the i-th bit of watermark.
6. the watermark embedding method according to claim 1 based on extremely humorous-Fourier's square statistical modeling, it is characterised in that step
In rapid S500, based on original picture block, origin pole it is humorous-Fourier's square reconstructed image block and insertion watermark it is extremely humorous-Fourier's square
Reconstructed image block, calculates original picture block after insertion watermark, and calculation formula is：
Fw=Fo-Fr+Fr'
Wherein, FoFor original picture block, FrFor extremely humorous-Fourier's square reconstructed image block, Fr'For insertion watermark it is extremely humorous-Fourier's square
Reconstructed image block, FwFor original picture block after insertion watermark.
7. the method for detecting watermarks based on extremely humorous-Fourier's square, it is characterised in that can to insertion watermark image to be detected into
Row watermark detection, described image to be detected are to be built by described in any one of claims 1-6 based on extremely humorous-Fourier's square statistics
What the multiplying property watermark embedding method insertion watermark of mould obtained contains watermarking images, and watermark detection includes the following steps：
L100, the original image to be detected block of building：Image to be detected is divided into the image block of N number of non-overlap, calculates each figure
As the entropy of block, and the image block of L high entropy is chosen as original image to be detected block；
Extremely humorous-Fourier's square of L200, the original image to be detected block of building：Calculate each original image to be detected block it is extremely humorous-
Fourier's square chooses square value Pn,mFor extremely humorous-Fourier's square square value of the original image to be detected block of correspondence, calculate it is each it is original to
The amplitude of extremely humorous-Fourier's square square value of detection image block；
L300, detected based on maximal possibility estimation each original image to be detected block extremely humorous-Fourier's square square value amplitude pair
The watermark information answered.
8. the method for detecting watermarks according to claim 7 based on extremely humorous-Fourier's square, it is characterised in that step L100
In, the image block for choosing L high entropy is as the step of original image to be detected block：
Descending arrangement is carried out to above-mentioned image block according to entropy, L image block is original image to be detected block before choosing；
Alternatively, carrying out ascending order arrangement to above-mentioned image block according to entropy, L image block is original image to be detected block after selection.
9. the method for detecting watermarks according to claim 7 based on extremely humorous-Fourier's square, it is characterised in that step L300
In, the corresponding watermark of amplitude of extremely humorous-Fourier's square value of each original image to be detected block is detected based on maximal possibility estimation
Information includes the following steps：
L310, maximum likelihood detector is constructed based on Weibull parameter Estimation, maximum likelihood detector is：
Wherein, yiFor the amplitude of the PHFM value of i-th of original image to be detected block, i=1,2 ... L,
α is the scale parameter of Weibull distribution, and β is the form parameter of Weibull distribution, g1It (y) is watermark intensity function of embedding
g1(x) inverse function, g0It (y) is watermark intensity function of embedding f1(x) inverse function, g1(y) and g0(y) expression formula is：
Lambertw () is lambert's W function；
L320, above-mentioned maximum likelihood detector is parsed based on lambert's W function, above-mentioned maximum likelihood detector is reduced to：
Wherein, TiFor the threshold value for detecting watermark, TiExpression formula is：
Ti=ln (g1(yi)/g0(yi))=ln ((lambertw ((a1·yi)/b1)/a1)/(lambertw((a2·yi)/b2)/
a2))。
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| CN113409184A (en) * | 2021-04-21 | 2021-09-17 | 齐鲁工业大学 | Color image description method based on sixteen-element polar harmonic-Fourier moment |
| CN116193042A (en) * | 2023-02-21 | 2023-05-30 | 齐鲁工业大学(山东省科学院) | Robust reversible information hiding method and system based on polar harmonic Fourier moment |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7817818B2 (en) * | 1999-01-25 | 2010-10-19 | Nippon Telegraph And Telephone Corporation | Digital watermark embedding method, digital watermark embedding apparatus, and storage medium storing a digital watermark embedding program |
| CN106780281A (en) * | 2016-12-22 | 2017-05-31 | 辽宁师范大学 | Digital image watermarking method based on Cauchy's statistical modeling |
-
2018
- 2018-05-30 CN CN201810537788.8A patent/CN108876692A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7817818B2 (en) * | 1999-01-25 | 2010-10-19 | Nippon Telegraph And Telephone Corporation | Digital watermark embedding method, digital watermark embedding apparatus, and storage medium storing a digital watermark embedding program |
| CN106780281A (en) * | 2016-12-22 | 2017-05-31 | 辽宁师范大学 | Digital image watermarking method based on Cauchy's statistical modeling |
Non-Patent Citations (1)
| Title |
|---|
| 王春鹏: "数字图像水印若干关键技术研究", 《万方数据知识服务平台》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113409184A (en) * | 2021-04-21 | 2021-09-17 | 齐鲁工业大学 | Color image description method based on sixteen-element polar harmonic-Fourier moment |
| CN113409184B (en) * | 2021-04-21 | 2022-04-08 | 齐鲁工业大学 | Color image description method based on sixteen-element polar harmonic-Fourier moment |
| CN116193042A (en) * | 2023-02-21 | 2023-05-30 | 齐鲁工业大学(山东省科学院) | Robust reversible information hiding method and system based on polar harmonic Fourier moment |
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