JP4360341B2 - Digital watermark detection apparatus, method, and program - Google Patents

Description

  The present invention relates to a digital watermark detection apparatus, method, and program, and in particular, a modification expressed by primary transformation such as enlargement / reduction, rotation, change in aspect ratio, affine transformation, etc., for a signal with a digital watermark embedded therein. The present invention relates to a digital watermark detection apparatus, method, and program for facilitating detection of a digital watermark even when added.

  Conventionally, by embedding digital watermarks into digital content, copyright information protection of digital content is performed, copyright information related to digital content is referred to, or analog media such as printed matter as digital content is used as an advertisement. On the other hand, services such as photographing with a digital camera or the like and reading information related to advertisement by reading a digital watermark have been realized.

  For example, when embedding a digital watermark, based on the embedding target component position information, spectrum spreading is performed to change the real and imaginary components of the complex matrix independently, and a watermark pattern is generated independently of the input image, and the actual image When the embedded image is generated by adding the pattern and the digital watermark is detected, the detection target sequence is generated based on the detection target component position information, the offset information is extracted, and the detection target sequence is corrected. There is a digital watermark method that performs spectral despreading later and detects a digital watermark embedded in the cut out pixel block (see, for example, Patent Document 1).

  Further, in the digital watermark, it is necessary that the digital signal representing the digital content is resistant to various modifications received after the digital watermark is embedded, that is, the digital watermark can be detected after being subjected to various modifications.

  For example, there is a method of estimating the degree of alteration applied to a digital signal by embedding a signal for geometric correction separately from the digital watermark information and detecting this (for example, , See Patent Document 2). However, in such a method, a signal for geometric correction is embedded in the digital signal in addition to the information of the digital watermark, which leads to deterioration of the quality of the digital signal embedded with the digital watermark and an increase in processing time of the digital watermark. There was a possibility.

  On the other hand, as a method of detecting a digital watermark from a digital signal in which a digital watermark is embedded (hereinafter referred to as a digital watermark embedded signal), a pattern having a common property is embedded as a digital watermark at a plurality of positions in the signal in advance. In addition, by obtaining the autocorrelation function of the signal embedded with the digital watermark at the time of detecting the digital watermark, it is expressed by the primary conversion such as enlargement / reduction, rotation, aspect ratio change, affine transformation added to the signal embedded with the digital watermark. There has been proposed a method of detecting a digital watermark that estimates the degree of modification to be performed, corrects the digital watermark-embedded signal that has been modified according to the estimated amount of modification, and facilitates detection of the digital watermark. It was.

  For example, a marker image having a pair of identical features is embedded in the original image to generate an image, and from the position of the peak point appearing in the autocorrelation function of the image rotated or enlarged / reduced to the marked image, There is a method for calculating the degree of rotation or enlargement / reduction applied to a signal embedded with a digital watermark (see, for example, Patent Document 3). Also, in this method, in order to find the peak point, the autocorrelation function value is filtered using a high-frequency pass filter (HPF) (hereinafter referred to as conventional method 3).

In addition, for example, a periodic pattern having repetition is used as the embedding pattern itself having information to be embedded as a digital watermark, and the enlargement / reduction ratio is calculated by observing the change of the period from the autocorrelation function at the time of detection. There is a method of performing digital watermark detection processing based on the enlarged / reduced rate (for example, see Patent Document 4).
Japanese Patent Application Laid-Open No. 2003-219148 “Digital watermark embedding method, digital watermark detection method, digital watermark embedding device, digital watermark detection device, storage medium storing a digital watermark embedding program, and storage medium storing a digital watermark detection program” Japanese Patent Application Laid-Open No. 11-355547 “Geometric Transformation Specific System” Japanese Patent Laid-Open No. 2001-209806 “Method and Computer Program for Detecting Image Rotation or Enlargement / Reduction” Japanese Patent Application Laid-Open No. 2002-111994 “Digital watermarking method and system having scaling resistance”

  However, in the method of performing processing using a fixed filter such as HPF in order to find the peak point of the autocorrelation function value as in the conventional method 3, for example, in the following situation, the autocorrelation function value If the peak point is buried by the autocorrelation function value that appears due to the autocorrelation of the original signal, the position of the peak point cannot be detected correctly, and as a result, it is added to the digital watermark embedded signal. There is a problem that parameters for primary conversion cannot be calculated correctly.

In the original signal, for example,
-When a signal with strong autocorrelation such as a repetitive pattern or straight line in the image signal is included;
-If the autocorrelation characteristics of the embedded pattern have deteriorated due to compression coding, noise, etc. added to the digital watermark embedded signal on the communication path,
In addition, when a complex filtering process is performed on the autocorrelation function value and a peak point is to be searched, if orthogonal transform such as discrete Fourier transform (DFT) or discrete cosine transform (DCT) is frequently used, There is a problem that the amount of processing required for detection becomes enormous and processing takes time.

  The present invention has been made in view of the above points. An electronic watermark detection apparatus and method capable of easily searching for a peak point appearing in an autocorrelation function value with high accuracy and facilitating detection of an electronic watermark, and The purpose is to provide a program.

  FIG. 1 is a principle configuration diagram of the present invention.

The present invention (claim 1) is previously for the two-dimensional image signal repeatedly a predetermined pattern, the predetermined pattern is embedded as an electronic watermark so that it is not perceived by the human perception, enlarged or reduced as modified, rotary A digital watermark detection apparatus for estimating a primary conversion parameter representing the primary conversion applied to the digital signal when a digital signal subjected to any primary conversion of aspect ratio change is input ,
An autocorrelation function value array generating means for calculating an autocorrelation function value of the input digital signal subjected to modification and storing the autocorrelation function value in a two-dimensional autocorrelation function value array corresponding to the image signal;
By filtering the autocorrelation sequences using fixed signal filter having a shape imitating the shape of the spatial domain of the impulse response of the filter having the same frequency distribution and the frequency distribution of the predetermined pattern, within the autocorrelation function sequence Fixed filter search means 502 for searching for the top N peak points in descending order of the result of filtering by a fixed signal filter ,
90-degree pair search means for generating N vectors from the origin to each peak point for the top N peak points , finding a pair of vectors orthogonal to each other from the N vectors, and using them as basis vector candidates 503,
Using the detailed filter having the shape in the spatial domain of the impulse response of the filter having the same frequency component as the frequency distribution of the predetermined pattern, the two peak points used for generating the basis vector candidates are used as the pair candidates. An autocorrelation function value array within a predetermined range is filtered in the vicinity, and the basis value candidates generated from M pairs of candidates in descending order of the evaluation value are estimated base values using the filtered result value as an evaluation value. Detailed filter search means 504 to select as a vector ;
A conversion parameter calculation means 505 for obtaining a vector to a peak point appearing in the autocorrelation function value array when no alteration is made from the origin as a base vector and a parameter for converting the base vector into an estimated base vector as an estimated primary conversion parameter And having.
In the present invention (Claim 2), a predetermined pattern is repeated in advance for a two-dimensional image signal, and the predetermined pattern is embedded as a digital watermark so as not to be perceived by human perception. A digital watermark detection apparatus for estimating a primary conversion parameter representing the primary conversion applied to the digital signal when a digital signal subjected to any primary conversion of aspect ratio change is input,
An autocorrelation function value array generating means for calculating an autocorrelation function value of the input digital signal subjected to modification and storing the autocorrelation function value in a two-dimensional autocorrelation function value array corresponding to the image signal;
By filtering the autocorrelation array with a fixed signal filter having a shape imitating the shape of the impulse response in the spatial domain of a filter having the same frequency distribution as the frequency distribution of the predetermined pattern, Fixed filter search means for searching for the top N peak points in descending order of the result of filtering by a fixed signal filter,
90-degree pair search means for generating N vectors from the origin to each peak point for the top N peak points, finding a pair of vectors orthogonal to each other from the N vectors, and using them as basis vector candidates When,
Using the detailed filter having the shape in the spatial domain of the impulse response of the filter having the same frequency component as the frequency distribution of the predetermined pattern, the two peak points used for generating the basis vector candidates are used as the pair candidates. An autocorrelation function value array within a predetermined range is filtered in the vicinity, and the basis value candidates generated from M pairs of candidates in descending order of the evaluation value are estimated base values using the filtered result value as an evaluation value. A detailed filter search means to select as a vector;
A conversion parameter calculating means for obtaining a vector to a peak point appearing in the autocorrelation function value array when no alteration is made from the origin as a basis vector, and a parameter for converting the basis vector into an estimated basis vector as an estimated primary transformation parameter; ,
The digital signal that has been modified is geometrically corrected by applying an inverse transformation to the primary conversion parameter, and the geometrically corrected digital signal is translated by a predetermined amount of movement within a predetermined region. A parallel movement amount estimation means that attempts to detect a digital watermark from a digital signal and sets a movement amount when the digital watermark is detected as a parallel movement amount added to the digital signal;
Have

The digital watermark detection apparatus of the present invention (Claim 3 ) is the 90-degree pair search means of Claim 1 or 2 ,
Near each of the top N peak points rotated 90 degrees counterclockwise,
A peak point is searched for within a predetermined range determined by the assumed size of the modification, and when a peak point exists within the range, the two peak points are used as a pair candidate and a vector pair from the origin to the pair candidate is used as a basis. Let it be a vector candidate .

The digital watermark detection apparatus of the present invention (Claim 4 ) is a detailed fixed signal filter according to Claim 1 or 2 ,
A rectangular filter having a large value at the center of the rectangular region and a small value around one pixel away from the center is used.

The digital watermark detection apparatus of the present invention (Claim 5) is the detailed filter search means according to claim 1 or 2 ,
A predetermined pattern has a frequency distribution represented by a rectangular area, and a filter having a shape based on a sinc function is used as a detailed filter.

Further, the present invention (Claim 6), in the digital watermark detection apparatus as claimed in any one of claims 1, 2, or 5, in the filtered pair candidate Detailed filter search there pairs Dependent point peak point searching means for searching for a pair candidate represented by a linear combination of candidates is provided.

  Further, according to the present invention (Claim 7), in the digital watermark detection apparatus according to Claim 6, the evaluation value of the corresponding pair candidate is evaluated to be lower than the evaluation value of the pair candidate serving as the basis of the linear combination. Dependent peak point removal means for increasing or decreasing the evaluation value of either one of the pair candidate evaluation values, the pair candidate evaluation that is the basis of the linear combination, or both of the evaluation values so as to be a value.

According to the present invention (Claim 8), a predetermined pattern is composed of sub-information to be embedded in a digital signal which is a two-dimensional image signal, and the sub-information embedded in the digital signal which is a two-dimensional image signal is A digital watermark detection apparatus according to claim 1 for detecting, wherein
Conversion correction means for geometrically correcting the modified digital signal by performing inverse conversion on the primary conversion parameter ;
Embedded information detecting means for detecting sub-information by detecting a predetermined pattern from the geometrically corrected digital signal;
It has further.

  FIG. 2 is a diagram for explaining the principle of the present invention.

The present invention (claim 9) in advance for the two-dimensional image signal repeatedly a predetermined pattern, the predetermined pattern is embedded as an electronic watermark so that it is not perceived by the human perception, enlarged or reduced as modified, rotary A digital watermark detection method in a digital watermark detection apparatus for estimating a primary conversion parameter representing the primary conversion applied to the digital signal when a digital signal subjected to any primary conversion for changing the aspect ratio is input Because
An autocorrelation function value array generation step of calculating an autocorrelation function value of the input digital signal subjected to modification and storing the autocorrelation function value in a two-dimensional autocorrelation function value array corresponding to the image signal;
By filtering the autocorrelation array using a fixed signal filter having a shape imitating the shape of the impulse response in the spatial domain of a filter having the same frequency distribution as the frequency distribution of the predetermined pattern, the autocorrelation function value array A fixed filter search step for searching for the top N peak points in descending order of the value of the result of filtering by the fixed signal filter appearing in the medium;
Relative top N peak point, from the origin to each peak point to generate N vectors, find a pair of vectors which are orthogonal to each other from among the N vectors, 90 pairs search step to a base vector candidates When,
Using a detailed filter with a shape in the spatial domain of the impulse response of the filter having the same frequency distribution as the frequency distribution of the predetermined pattern, the two peak points used for generating the basis vector candidates are used as pair candidates, and the vicinity of the pair candidate A detailed filter search step of filtering an autocorrelation function value array within a predetermined range and selecting a basis vector candidate generated by M pairs of candidates in descending order as a result of filtering, as an estimated basis vector ;
A conversion parameter calculation step for obtaining a vector to a peak point appearing in the autocorrelation function value array when no alteration is made from the origin as a basis vector, and a parameter for converting the basis vector to an estimated basis vector as an estimated primary transformation parameter ; , it carried out.
The present invention (Claim 10) repeats a predetermined pattern in advance for a two-dimensional image signal, the predetermined pattern is embedded as a digital watermark so as not to be perceived by human perception, and is expanded or reduced or rotated as a modification. A digital watermark detection method in a digital watermark detection apparatus for estimating a primary conversion parameter representing the primary conversion applied to the digital signal when a digital signal subjected to any primary conversion for changing the aspect ratio is input Because
An autocorrelation function value array generation step of calculating an autocorrelation function value of the input digital signal subjected to modification and storing the autocorrelation function value in a two-dimensional autocorrelation function value array corresponding to the image signal;
By filtering the autocorrelation array using a fixed signal filter having a shape imitating the shape of the impulse response in the spatial domain of a filter having the same frequency distribution as the frequency distribution of the predetermined pattern, the autocorrelation function value array A fixed filter search step for searching for the top N peak points in descending order of the value of the result of filtering by the fixed signal filter appearing in the medium;
90-degree pair search step for generating N vectors from the origin to each peak point for the top N peak points, finding pairs of vectors orthogonal to each other from the N vectors, and using them as basis vector candidates When,
Using a detailed filter with a shape in the spatial domain of the impulse response of the filter having the same frequency distribution as the frequency distribution of the predetermined pattern, the two peak points used for generating the basis vector candidates are used as pair candidates, and the vicinity of the pair candidate A detailed filter search step of filtering an autocorrelation function value array within a predetermined range and selecting a basis vector candidate generated by M pairs of candidates in descending order as a result of filtering, as an estimated basis vector;
A conversion parameter calculation step for obtaining a vector to a peak point appearing in the autocorrelation function value array when no alteration is made from the origin as a basis vector, and a parameter for converting the basis vector to an estimated basis vector as an estimated primary transformation parameter; ,
The digital signal that has been modified is geometrically corrected by applying an inverse transformation to the primary conversion parameter, and the geometrically corrected digital signal is translated by a predetermined amount of movement within a predetermined region. A parallel movement amount estimation step that attempts to detect a digital watermark from a digital signal and sets the movement amount when the digital watermark can be detected as a parallel movement amount added to the digital signal;
I do.

The digital watermark detection method of the present invention (invention 11 ) is the 90-degree pair search step of claim 9 or 10 ,
In the 90 degree pair search step,
Near each of the top N peak points rotated 90 degrees counterclockwise,
A peak point is searched for within a predetermined range determined by the assumed size of the modification, and when a peak point exists within the range, a pair of vectors from the origin to the pair candidate is determined using the two peak points as a pair candidate. Let it be a basis vector candidate .

In the digital watermark detection method of the present invention (claim 12 ), in the detailed filter search step of claim 9 or 10 , as a fixed signal filter,
A rectangular filter having a large value at the center of the rectangular region and a small value around one pixel away from the center is used.

Further, the present invention (Claim 13), in the detailed filter search step according to claim 9 also 10,
A predetermined pattern has a frequency distribution represented by a rectangular area, and a filter having a shape based on a sinc function is used as a detailed filter.

Further, the present invention (Claim 14), in the claims 9 and 10, or the digital watermark detection method as claimed in any one of 13, filtered by advanced filters search step the pair candidates, performing subordinate peak point search steps for searching for a pair candidate represented by a linear combination of a pair candidate.

The present invention (Claim 15) is the digital watermark detection method of Claim 14,
The evaluation value of the candidate for the pair and the basis of the linear combination are set so that the evaluation value of the candidate for the corresponding pair becomes an evaluation value evaluated lower than the evaluation value of the pair candidate as the basis of the linear combination. A dependent peak point removing step of increasing or decreasing the evaluation value of either or both of the pair candidate evaluations is performed.

Further, the present invention (Claim 16), be composed of sub information to be embedded into a digital signal is an image signal of a predetermined pattern is two-dimensional, is embedded into the digital signal is a two-dimensional image signal sub A digital watermark detection method in a digital watermark detection apparatus according to claim 1 for detecting information, comprising:
A conversion correction step for geometrically correcting the modified digital signal by performing an inverse conversion on the primary conversion parameter ;
And an embedded information detecting step of detecting sub-information by detecting a predetermined pattern from the geometrically corrected digital signal.

The present invention (claim 17) in advance for the two-dimensional image signal repeatedly a predetermined pattern, the predetermined pattern is embedded as an electronic watermark so that it is not perceived by the human perception, enlarged or reduced as modified, rotary A digital watermark detection program for estimating a primary conversion parameter representing the primary conversion applied to the digital signal when a digital signal subjected to any primary conversion of aspect ratio change is input ,
A electronic watermark detection program for causing a computer to function as each means of the digital watermark detection apparatus according to any one of claims 1 to 8.

According to the digital watermark detection apparatus of claims 1 and 2 and the digital watermark detection method of claims 9 and 10 , there is a high possibility that the target peak point is obtained by the fixed filter search means and the 90-degree pair search means. The number of points that apply a detailed filter that requires processing time is reduced while leaving a point, and a high-accuracy autocorrelation peak point can be searched by the detailed filter search means, and the amount of modification can be estimated at high speed and with high accuracy. Watermark detection can be facilitated.

Further, according to the electronic watermark detecting device according to item 3, and digital watermark detection according to the method, the autocorrelation peak point also include aspect ratio changing before and after rotation as a modification that can be added to the signal of claim 11 of the present invention And the amount of modification can be estimated, and detection of digital watermarks can be facilitated.

According to the digital watermark detection apparatus of claim 4 and the digital watermark detection method of claim 12 of the present invention, it is possible to perform a detailed filter search requiring a processing time by only a correlation operation in a limited range of the spatial domain. It is possible to perform detailed filter search processing at high speed without performing discrete Fourier transform for each filter processing, and to perform autocorrelation peak point search and estimation of the amount of modification at high speed. Detection can be facilitated.

  Further, according to the digital watermark detection apparatus of claim 5 and the digital watermark detection method of claim 13 of the present invention, it is possible to easily and quickly configure a spatial domain filter in the detailed filter search means, Since the search for the autocorrelation peak point and the estimation of the amount of modification can be realized at high speed, the detection of the digital watermark can be facilitated.

  According to the digital watermark detection apparatus of claims 6 and 7 and the digital watermark detection method of claims 14 and 15 of the present invention, it is possible to suppress erroneous selection of dependent peak point candidates that can be represented by linear combination of basis vectors. In addition, since the search for the autocorrelation peak point and the estimation of the amount of modification can be performed with high accuracy, the detection of the digital watermark can be facilitated.

  According to the digital watermark detection apparatus of claim 8 and the digital watermark detection method of claim 16 of the present invention, the embedding pattern itself represents the sub-information to be embedded, so that the amount of modification is estimated and the signal is corrected. Therefore, it is not necessary to embed a special signal, and signal deterioration due to embedding of a digital watermark can be reduced. Further, when the degree of signal degradation is set to the same level, the strength of the digital watermark signal can be increased, the resistance of the digital watermark to noise and modification can be increased, and the detection of the digital watermark can be facilitated.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings.

  First, “signals” and “communication model for detecting digital watermark embedding” used in the following description will be described.

  In the present invention, information to be embedded and detected as a digital watermark is referred to as a “signal”. In the following embodiments, processing for mainly two-dimensional image signals will be described as an example, but video and audio signals may be used. It goes without saying that the same method can be applied to any n-dimensional signal.

  Next, a communication model for digital watermark embedding detection will be described.

  FIG. 3 shows a communication model for digital watermark embedding detection according to an embodiment of the present invention.

  Embedded information 102 is embedded in the pre-embedding signal 101 given to the system using the digital watermark embedding apparatus 200. The embedded information 102 may be any information as long as it is detected and used later. For example, the identifier of the content, the copyright information, the URL, the identifier of the user who purchased the content, the name, the address, the content It may be an identifier of a transaction that sold the product, an identifier of a device such as a digital camera that generated the content, information on the device, a flag for controlling whether or not the content can be copied, a signal for detecting alteration of the content, and the like.

  In a system that aims at obtaining the size of the signal modification 107 described later by digital watermark detection, the embedded information 102 may be a predetermined fixed value. When the embedded information 102 is a fixed value, a value stored in advance in the digital watermark embedding apparatus 200 may be used without being explicitly input to the digital watermark embedding apparatus 200. Needless to say.

  The embedded signal 104 output from the digital watermark embedding apparatus 200 is added with noise such as additive noise 106 and signal modification 107 through the communication path 105.

  Here, the additive noise 106 is, for example, noise that is added and subtracted with respect to values taken by elements divided in terms of time, space, frequency, etc., such as pixel values and DCT coefficients in an image signal. The component may be, for example, noise added to a signal or encoding noise accompanying encoding of a signal such as MPEG or JPEG.

  The signal modification 107 is modification that changes the temporal, spatial, and frequency positions of the signal, such as the shape of the subject in the image signal, and includes, for example, signal translation, enlargement / reduction, Modifications such as rotation, aspect ratio change, primary transformation such as affine transformation, and partial cropping may be used.

  The additive noise 106 and the signal modification 107 may or may not be intended by a person. The order in which the additive noise 106 and the signal modification 107 are added does not matter.

  Further, a change with respect to a signal other than the above may be given on the communication path 105.

  The embedded signal 108 modified through the communication path 105 is input to the digital watermark detection apparatus 400, and the same detection information 110 as the embedded information 102 is output.

[First Embodiment]
An example of a digital watermark embedding method detected by the present invention will be shown below.

  FIG. 4 shows the configuration of the digital watermark embedding apparatus according to the first embodiment of the present invention.

  The digital watermark embedding device 200 includes a watermark pattern generation unit 201 and a watermark pattern addition unit 202.

  The digital watermark embedding process is performed according to the following procedure.

  1) The watermark pattern generation unit 201 generates a watermark pattern 203 to be embedded as a digital watermark into a signal based on the embedded information 102 input to the digital watermark embedding device 200.

  As a method of generating the watermark pattern 203, for example, a discrete inverse Fourier transform is applied to a watermark coefficient matrix that has been changed based on the embedding information 102 as described in the first embodiment of Patent Document 1 described above. It may be a signal pattern obtained by applying. An example of a watermark pattern generated when a two-dimensional image signal is targeted is shown as a watermark pattern 301 in FIG. Note that the method of generating the watermark pattern 203 is not limited to this, and is configured so that the presence of the watermark pattern 203 is less likely to be perceived by human perception in the embedded signal 104 obtained as a result of adding a watermark pattern described later. Any pattern may be used as long as it is performed. For example, a pattern configured based on another existing digital watermark embedding method may be used.

  2) In the watermark pattern addition unit 202, the embedded signal 104 is obtained by repeatedly arranging and adding the watermark pattern 203 generated by the watermark pattern generation unit 201 to the pre-embedding signal 101 input to the digital watermark embedding device 200. . For example, when a two-dimensional image signal is targeted, the watermark pattern 301 is repeatedly added to the pre-embedding signal 101 that is the input image signal in a tiled manner as in the example shown in FIG.

  As a method of adding the watermark pattern 203, for example, as described in the first embodiment of Patent Document 1 described above, a pattern is emphasized and added as necessary according to a given intensity parameter. May be. Further, according to the characteristics of the signal of the part to which the pre-embedding signal 101 is added, for example, the pattern strength is reduced at a part where the added pattern is conspicuous, and the pattern strength is increased at a part where the pattern is not conspicuous. The watermark pattern may be added by changing the degree of pattern emphasis.

  Next, digital watermark detection processing in the present embodiment will be described.

  FIG. 6 shows the configuration of the digital watermark detection apparatus in the first embodiment of the present invention.

  The digital watermark detection apparatus 400 converts the digital watermark embedding signal obtained by using the above-described digital watermark embedding apparatus 200 into a primary conversion such as translation, enlargement / reduction, rotation, aspect ratio change, affine transformation, partial cut, non- This is a device for detecting an embedded digital watermark from a signal subjected to processing such as lossless compression.

  The digital watermark detection apparatus 400 shown in the figure includes a primary conversion parameter estimation unit 401, a geometric transformation correction unit 402, a parallel movement amount estimation unit 403, and an embedded information detection unit 404.

  The digital watermark detection process is performed through the following steps.

  FIG. 7 is a flowchart of digital watermark detection processing according to the first embodiment of the present invention.

Step 101) Primary transformation parameter estimation:
The primary conversion parameter estimation unit 401 calculates an autocorrelation value 405 of the input signal, searches for a point where the autocorrelation value takes a peak, estimates a primary conversion parameter given to the signal, and estimates an estimated primary A conversion parameter 406 is obtained.

Step 102) Geometric correction by linear transformation:
In the geometric transformation correction unit 402, the input signal 405 is subjected to inverse transformation with respect to the primary transformation parameter 406 estimated in step 101 to geometrically correct the signal.

Step 103) Parallel displacement estimation:
The parallel movement amount estimation unit 403 estimates the parallel movement amount given to the signal by performing a full search of the parallel movement amount with respect to the geometric transformation corrected signal 407 corrected in step 102, and the estimated parallel movement amount. 408 is obtained.

Step 104) Detection of embedded information:
The embedded information detection unit 404 detects the remaining embedded information from the geometric transformation corrected signal 407 corrected in step 102 based on the estimated parallel movement amount 408 estimated in step 103, and obtains watermark detection information 409.

  The detection of embedding information, for example, generates a geometric transformation detection target coefficient matrix as described in Patent Document 1 described above, and is used in digital watermark embedding corresponding to the detection target coefficient matrix and symbols of the embedding information. It may be made by obtaining a correlation value with the symbol series.

  The primary conversion parameter estimation process will be described in detail.

  FIG. 8 shows the configuration of the primary conversion parameter estimation unit in the first embodiment of the present invention.

  The primary transformation parameter estimation unit 401 shown in the figure includes an autocorrelation function value array generation unit 501, a fixed filter search unit 502, a 90-degree pair search unit 503, a detailed signal filter search unit 504, and a conversion parameter estimation unit 505. The

  An example of the procedure for estimating the primary conversion parameter by the primary conversion parameter estimation unit 401 is shown below.

  FIG. 9 is a flowchart of the primary conversion parameter estimation procedure according to the first embodiment of the present invention.

  Step 201) First, the autocorrelation function value array 508 of the input signal 508 is calculated using the autocorrelation function value array generation unit 501 with respect to the input signal 506 inputted.

  Here, the autocorrelation function value array is obtained by calculating an autocorrelation function of an input signal given as n-dimensional discrete data and storing the obtained n-dimensional data in the array. An example of the autocorrelation function value array 508 is shown in FIG. An autocorrelation function value array obtained for a two-dimensional image signal (FIG. 10 (a)) that has been rotated, enlarged, or reduced as a modification, and the value of the autocorrelation function value expressed as the luminance of the image Is FIG. 10 (b). Here, it can be seen from the periodicity of the pattern previously embedded in the input signal that lattice-like peak points appear in the autocorrelation function value array.

  The calculation method of the autocorrelation function value array 508 is described in, for example, “Keiji Watanabe“ Image Engineering Handbook ”Asakura Shoten (1986) p. 240”. Known procedures such as despread Fourier transform of a power spectrum obtained by discrete Fourier transform of an input signal have been known.

  In this embodiment, the purpose is to detect an autocorrelation peak of a pattern composed of embedded information embedded in a signal. When the array is calculated, the autocorrelation peak for the embedded pattern may not appear sufficiently clearly. Therefore, in order to efficiently detect the target autocorrelation peak, a filter corresponding to the frequency characteristic of the embedding pattern may be applied to the input signal 506 before calculating the autocorrelation function value array. For example, when the embedded information is embedded in the high frequency region of the signal, a high frequency pass filter may be applied and further clipping processing may be performed. For example, a Laplacian filter shown in FIG. 11 may be used as the high-frequency pass filter.

  Step 202) The top N autocorrelation peak points are searched for the autocorrelation function value array 508 obtained in Step 201 using the fixed filter search unit 502.

  The fixed filter search unit 502 uses the autocorrelation function value array 508 obtained in step 201 for the impulse response space of a filter having a frequency distribution similar to the frequency distribution of the pattern formed by the embedded information embedded in the signal. The autocorrelation function value array 508 is filtered using a fixed filter that imitates the shape in the region, and the top N pieces are selected in order from the point where the magnitude of the filtered signal is the strongest in the above array And output as a peak point candidate 509.

  When the pattern constituted by the embedded information has a shape based on the sinc function as shown in an example of the description of the detailed filter search unit 504 described later, the shape based on the sinc function is imitated as a filter shape, You may use the filter of the shape shown in FIG.

  Here, imitation means that the fixed filter is configured so that the rough shape is similar to the ideal filter shape. That is, the ideal filter shape varies depending on the position on the autocorrelation function value array, as performed by the detailed filter search unit 504 described later, but in order to substitute them with a common fixed shape filter, For example, this indicates that a fixed filter having a large center value and a small value around one pixel apart from the ideal sinc function shape shown in FIG.

  How far the peak point appearing in the autocorrelation function value array is from the origin depends on the amount of enlargement / reduction in the modification 107 added to the signal and the size of the embedded pattern (for example, the watermark pattern 301 in FIG. 5). It depends on Sato. For example, in the case of a pattern repeatedly embedded in a two-dimensional image signal, the length of one side of the pattern is L, and the modification 107 added to the signal ranges from 0.5 times reduction to 1.5 times enlargement. If included, it is only necessary to search for a peak point in a range from a distance of 0.5 L to 1.5 L from the origin in the autocorrelation function value array.

  Further, since the autocorrelation function values at the origin symmetry position in the autocorrelation function value array are common from the definition of the autocorrelation function value, it is sufficient to search only two quadrants in the autocorrelation function value array.

  For example, the peak point search range in the fixed filter search unit 502 may be performed only within the frame surrounded by the thick line in FIG.

  The filtering process using the fixed filter can be performed at a higher speed than the process using the detailed filter described later, and it is possible to avoid performing unnecessary arithmetic processing in subsequent processes with a high processing load. It is possible to estimate a primary conversion parameter.

  Step 203) Using the 90-degree pair search unit 503 for the N peak point candidates 509 obtained in Step 202, vectors for each point from the origin are orthogonal to each other, and the lengths of the vectors match. Such a pair of points is searched and selected as a basis vector candidate 510 represented by the basis vector of the primary transformation added to the signal. However, according to the amount of modification given to the assumed signal, the above-described orthogonal relationship and vector length matching may be determined with a margin.

  The concept of the basis vector candidate search is shown in FIG. 14A and 14B, it is assumed that the entire rectangular area represents the autocorrelation function value array 508 and the origin is at the intersection of the arrows. Each point in FIG. 14A is a peak point candidate obtained in step 202. The result of selecting the basis vector candidate 510 from these peak point candidates is shown in FIG. 14B, and here, two sets of basis vector candidates are obtained.

  A specific implementation example of the 90-degree pair search unit 503 will be described later.

  Here, an example is shown in which the pattern constituted by the embedding information is embedded so as to form a square lattice pattern in the signal, but the embedding is performed in a different shape (for example, a rectangle or a parallelogram). In this case, in the above procedure, a point having a predetermined angle determined by the embedding shape instead of the orthogonal relationship and a ratio of a predetermined vector length determined by the embedding shape instead of matching the vector length is selected. It may be.

  Step 204) Using the detailed filter search unit 504, the candidates are narrowed down in more detail for the basis vector candidates 510 obtained in Step 203. Specifically, a filter having a shape in the spatial region of an impulse response having a frequency distribution similar to the frequency distribution of the pattern embedded in the signal in advance for the autocorrelation function value around each point in the basis vector candidate Is used to calculate an evaluation value corresponding to each point. For each base vector candidate, the sum of the above two evaluation values included in the base vector candidate is obtained, and the upper M sets of base vector candidates having a large sum are selected and set as an estimated base vector 511.

  A specific implementation example of the detailed filter search 504 will be described later.

Step 205) Using the conversion parameter estimation unit 505, the modified primary conversion parameter added to the signal is calculated based on the estimated basis vector 511 obtained in Step 204. Specifically, the base vector position (FIG. 15 (a)) assuming that the signal has not been altered is the base vector obtained in step 204 (for example, FIG. 15 (b)), or It is only necessary to obtain a primary transformation parameter that can be transformed into any set of vectors composed of symmetry points with respect to the origin. In this example, the basis vector obtained in step 204 is (x ₁ , y ₁ ), When (x ₂ , y ₂ ), the estimated primary transformation parameter matrix is

There are four types.

  Here, if the embedded information detection unit 404 is configured to be able to detect embedded information even if the signal is a mirror image and a 90-degree rotated signal, only one of the above matrices is estimated and linearly converted. You may make it output as a parameter.

  Here, an example is shown in which the pattern constituted by the embedding information is embedded so as to form a square lattice shape in the signal. However, the embedding information is arranged in different shapes (for example, a rectangle or a parallelogram). In this case, the basis vector position when it is assumed that the signal has not been modified is different from that shown in FIG. It goes without saying that the calculation formula changes to other forms.

  The estimated primary conversion parameter 507 determined above is output as the output of the primary conversion parameter estimation unit 401.

  Next, an implementation example of the 90-degree pair search unit 503 will be described.

  Specifically, the 90-degree pair search unit 503 can be realized by performing the following procedure.

  FIG. 16 is a flowchart of an implementation example of the 90-degree pair search unit in the first embodiment of the present invention.

  Step 1201) A set λ for accumulating basis vector candidates, which is an output value of the 90-degree pair search unit 503, is an empty set, and η is a set equal to a set ξ of peak point candidates 509 obtained by the fixed filter search unit 502 To do.

  Step 1202) One peak point candidate x ″ is arbitrarily extracted from the set η, and x ″ just extracted is removed from η.

  Step 1203) A position z obtained by rotating x ″ by 90 degrees counterclockwise is obtained.

  Step 1204) Let ζ be a set of elements obtained by removing x ″ from ξ.

  Step 1205) One peak point candidate y ″ is arbitrarily extracted from ζ, and y ″ just extracted is removed from ζ.

  Step 1206) It is determined whether or not y ″ exists in the vicinity of z. The criterion for determining whether or not y ″ is near is the primary conversion until a modification such as an aspect ratio change is performed as modification 107 for the signal. The determination criterion is whether or not the parameter exists within a wide area depending on whether the parameter can be estimated.

  Specific criteria for determining whether or not it is close will be described later.

  Step 1207) If y ″ is determined to be in the vicinity of z, a set of (x ″, y ″) is added to λ.

  Step 1208) It is determined whether or not ζ is empty. If it is not empty, steps 1205 to 1208 are repeated until empty.

  If ζ becomes empty, the process proceeds to step 1209.

  Step 1209) It is determined whether or not η is empty. If it is not empty, Steps 1202 to 1209 are repeated until η becomes empty.

  If η becomes empty, the process proceeds to step 1210.

  Step 1210) The basis vector candidates 510 accumulated so far in λ are output and the process ends.

Here, if the fixed filter search unit 502 is configured such that the peak point candidates 509 are limited to the range of y> 0 in the autocorrelation function array 508, η is in the range of x> 0 and y> 0. It is sufficient that the selection is limited to η ← {(x, y) | (x, y) εξ and x> 0 and y> 0} instead of η ← ξ in step 1201.
If it is said, it is efficient. Needless to say, the range to be selected here is limited in accordance with the existence range of the peak point candidate 509 and the rotation direction in step 1203.

  It is assumed that the above sets λ, η, ζ, ξ are stored in storage means (not shown) such as a memory.

  The neighborhood determination method in step 1206 will be described below.

  FIG. 17 shows an example of the neighborhood definition in the 90-degree pair search in the first embodiment of the present invention.

  In FIG. 17A, when the modification 1301 that does not include the aspect ratio change is performed as the modification 107 to the signal, the basis vectors represented by the peaks of the autocorrelation function value array 508 are represented by x and y. Further, as a modification 107 to the signal, when a modification 1302 in which an aspect ratio change is further added before and after the modification 1301, the basis vectors represented by the peaks of the autocorrelation function value array 508 are represented by x ″ and y ″. . Further, a position where x ″ is rotated by 90 degrees is set as z.

  Here, since the peak point actually observed as the peak point candidate 509 is x ″ and y ″ when the modification 1302 is added, in step 1206, z and If y ″ is within the range of possible positional relationships, it can be determined that it is in the vicinity.

  In the modification 1302, the aspect ratio changes performed before and after the modification 1301 are respectively represented by A and B below.

Then, y ″ is expressed as follows using z.

When α and β move 1−a ≦ α ≦ 1 + a, 1−b ≦ β ≦ 1 + b (where 0 <a <1, 0 <b <1), z = (x ₀ , y ₀ ) The range in which y ″ = (x ₁ , y ₁ ) moves is a region surrounded by a hyperbola and a straight line in FIG.

  Accordingly, in step 1206, it is sufficient to determine that y ″ is present in the vicinity of y ″, z in the region of FIG. 17B surrounding z.

In order to simplify the determination of the area, another graphic area that roughly includes the area shown in FIG. 17B, for example, a trapezoid area, a sector area, or the like is defined, and the vicinity is determined within that area. It may be. Furthermore, it goes without saying that the processing may be performed by modifying the expression so that the determination can be performed only by integer arithmetic in order to process the operation at high speed. For example, if the determination is made with an area cut out of the fan shape surrounded by the curve in FIG. 17C, the determination is made based on the positive / negative of the following formulas F ₁ , F ₂ , F ₃ , and F _4. Also good.

^{_{F 1 = b m 2 y 0}} x- (b m -b n) 2 x 0 y
^{_{F 2 = b m 2 y 0}} x- (b m + b n) 2 x 0 y
^{_{F 3 = (a m + a}} n) 2 (x 0 2 + y 0 2) -a m 2 (x 2 + y 2)
^{_{F 4 = (a m -a n}} ) 2 (x 0 2 + y 0 2) -a m 2 (x 2 + y 2)
However, α = 1 / α ', 1-a' and ≦ α '≦ 1 + a' , a '= a n / a m, b-b n / b m, a n, a m, b n, b m are It is an integer.

  Next, an implementation example of the detailed filter search unit 504 will be described.

  Since the autocorrelation function value array 503 is represented as an inverse Fourier transform of the power spectrum with respect to the original signal, the frequency distribution of the autocorrelation function value is a watermark pattern embedded in the signal (for example, the watermark pattern 301 in FIG. 5). It is obvious that the frequency band shape is similar to that of. In particular, in the case of a watermark pattern embedded by repeated tiling, a periodic impulse-like peak appears in the autocorrelation function value.

  It is generally known that the Fourier transform of a pattern repeated in the spatial domain has an impulse-like frequency distribution that is sparsely repeated in the frequency domain, and the embedded pattern (d) obtained by repeatedly tiling the embedded pattern in FIG. The frequency distribution is as shown in FIG.

Considering that the observed autocorrelation function value array 508 has undergone modification 107 to the signal, the base vector position (x ₀ , y ₀ ) without modification is moved to (x, y). Considering the modification amount B ^{(x, y)} in the frequency domain corresponding to the modification amount A ^{(x, y)} , the shape obtained by converting the original frequency domain shape (c) with B ^{(x, y)} and converting it to a power spectrum It can be seen that (g) is the frequency distribution of the autocorrelation function value (h). Therefore, a peak as shown in (h) of FIG. 18 appears in the autocorrelation function value array 508 depending on the magnitude of the signal value obtained by filtering the autocorrelation function value array 508 using the bandpass filter having the shape (g). It can be judged whether it is appearing.

  Therefore, for each of the basis vector candidates 510 input to the detailed filter search unit 504, a bandpass filter having a shape (g) in the frequency domain transformed corresponding to the position of the basis vector candidate is configured. After the power spectrum obtained in the calculation process is filtered by the autocorrelation function value array generation unit 501, inverse Fourier transform is performed, and the evaluation value of each base vector candidate 510 is obtained with the signal intensity at the base vector position.

  Next, a specific operation of the parallel movement amount estimation in step 103 of FIG. 7 will be described.

  The parallel movement amount estimation in the parallel movement amount estimation unit 403 can be configured as follows.

  For example, consider the case of watermark embedding for a two-dimensional image signal. Since the watermark pattern is repeatedly embedded in the signal as shown in FIG. 5, if the size of the watermark pattern 301 is n × n, the parallel movement from (0, 0) to (n−1, n−1). The detection of digital watermark is attempted for all of the amounts, and the amount of translation when detected can be set as the amount of translation given as the modification 107.

  Here, the determination as to whether or not the digital watermark has been detected by the trial of the digital watermark may be determined based on whether or not the value indicating the accuracy of the detection result exceeds a predetermined threshold. Further, it may be determined that the detection can be performed with the amount of parallel movement when the value representing the accuracy of the detection result is maximized. For example, when digital watermark detection is performed using a symbol detection unit as described in the second embodiment of Patent Document 1, the correlation between the symbol sequence and the detection target sequence in Patent Document 1 is described above. A value may be used as a value representing the accuracy of the detection result.

  FIG. 19 is a flowchart of the operation of the parallel movement amount estimation unit in the first embodiment of the present invention.

  Steps 1501, 1502, 1503) The variable max and the offset positions x and y for storing the maximum value of the detection result accuracy are initialized to zero. However, if the detection result accuracy can be a negative number, the minimum value that can be obtained the detection result accuracy is set to max.

  Step 1504) Watermark detection is performed when it is assumed that (x, y) translation is performed as the modification 107 for the signal, and a value representing the accuracy of the detection result is substituted into p.

  Step 1505) It is determined whether p is larger than max. If not larger, the process proceeds to Step 1508. If larger, the process proceeds to step 1506.

  Step 1506) p is substituted into max, and the parallel movement amounts x and y at that time are substituted into mx and my, respectively, and stored.

  Step 1507) Further, it is determined whether or not max exceeds a predetermined threshold value, and if it exceeds, the process proceeds to Step 1513. If not, the process proceeds to step 1508.

  Step 1508) Add displacement dx to lateral offset x. When the change movement amount is estimated in pixel units, dx may be 1.

  Step 1509) If x is less than n, return to Step 1504 and repeat Step 1504 to Step 1508 until x reaches n.

  Step 1510) Add dy to the vertical offset y. When estimating the amount of translation in pixel units, dy may be 1.

  Step 1511) If y is less than n, the process returns to Step 1503, and Steps 1503 to 1510 are repeated until y reaches n.

  When y reaches n, the parallel movement amounts mx and my giving the maximum detection result accuracy value in step 1512 are output as the estimated parallel movement amount 408.

  In step 1507, when max exceeds the threshold value, the estimated parallel movement amount may be determined without searching for all the parallel movement amounts by the procedure of steps 1513 to 1521.

  Steps 1513 and 1514) -k is substituted for the displacement amounts xx and yy, respectively.

  Step 1515) Watermark detection is performed when it is assumed that (x + xx, y + yy) has been translated as the modification 107 for the signal, and a value representing the accuracy of the detection result is substituted into p.

  Step 1516) It is determined whether or not p is larger than max. If larger, the process proceeds to step 1517.

  Step 1517) Substituting p for max, and substituting the parallel movement amounts x + xx and y + yy for mx and my, respectively, and storing them.

  Step 1518) The displacement dx is added to the lateral displacement xx. When estimating the amount of translation in pixel units, dx may be 1.

  Step 1519) If xx is less than or equal to k, the process returns to Step 1515, and Steps 1515 to 1518 are repeated until xx reaches k.

  Step 1520) Add dy to the longitudinal displacement yy. When estimating the amount of translation in pixel units, dy may be 1.

  Step 1521) If yy is less than or equal to k, return to Step 1514 and repeat Step 1514 to Step 1520 until yy reaches k.

  When yy reaches k, detection is performed in the vicinity of the offset position (x, y) exceeding the threshold value in steps 1513 to 1521, and the estimated parallel movement is determined with the maximum amount of parallel movement among them. By setting the amount, the estimated parallel movement amount can be determined efficiently.

  Further, as described in Patent Document 1 described above, when the digital watermark information is divided into a plurality of symbols and a signal is embedded, the above-described parallel movement amount search is performed on one of the plurality of symbols. And the resulting parallel movement amount is output as the estimated parallel movement amount 408, and the detection processing of the remaining symbols is performed by the embedded information detection unit 404, thereby efficiently detecting the digital watermark. Can do.

[Second Embodiment]
In the present embodiment, a configuration example for more efficiently processing the detailed filter search unit 504 in the first embodiment will be described.

  The definition of terms and the configuration other than the detailed filter search unit 503 are the same as those in the first embodiment.

  In the realization method of the detailed filter search unit 504 in the first embodiment described above, since it is necessary to perform inverse Fourier transform processing on each of the basis vector candidates, the processing takes a very long time, and the pattern A filter having a repetitive shape of an impulse-like peak with a frequency distribution determined corresponding to repetition has a problem that it is difficult to configure with high accuracy due to a rounding error problem of the discrete Fourier transform.

  Therefore, the detailed filter search unit 504 may be more efficiently realized by performing filtering on a predetermined range around candidate points in the spatial domain instead of performing filtering in the frequency domain.

  A detailed filter search for a two-dimensional signal will be described below as an example.

  When the embedding pattern has a frequency distribution as shown in FIG. 18A, individual peaks appearing in the autocorrelation function value (FIG. 20A) after only rotation is applied are shown in FIG. It has a frequency distribution like The impulse response of a low-pass filter passing through a rectangular area is the sinc function

Therefore, the shape of the peak in the space region is a sinc function shape as shown in FIG. However, FIG. 20C is expressed in one dimension for simplification.

  Further, the shape of the autocorrelation peak appearing in the autocorrelation function value array 508 depends on how much the watermark pattern embedded in the signal (for example, the watermark pattern 301 in FIG. 5) is enlarged or reduced by the modification 107 to the signal. It becomes gentle and sharp. This is because the frequency band in which the watermark information is embedded is shifted by scaling. This is shown in FIGS. 20 (d) to 20 (f).

  Each peak appearing as a basis vector in the autocorrelation peak (FIG. 20D) after being reduced by 0.5 times and rotated has a frequency distribution as shown in FIG. 20E, and as a result, As shown in FIG. 20 (f), the shape of the peak in the spatial region becomes a shape of a sinc function that is more sensitive than when it is 1.0 times. In addition, the shapes in these two-dimensional spaces are rotated by the same amount as the rotation applied to the signal.

  Therefore, the theoretical peak shape can be expressed by the shape of a sinc function determined for each modification amount corresponding to the position where the autocorrelation peak appears.

  The spatial domain shape for the frequency distribution in FIG. 18A can be easily and quickly obtained using a sinc function without inverse Fourier transform by combining a rectangular domain pass low-pass filter. That is, a filter having a frequency distribution of an embedding pattern (FIG. 18A) is considered as a parallel type and a series type filter of a plurality of rectangular area pass type low pass filters. Further, the impulse response of the parallel type and the series type filter is Therefore, the theoretical peak shape of interest can be obtained only by calculating the sum and product of the sinc function shapes, which are the impulse responses of the respective rectangular area passing low-pass filters. Can be sought.

  Furthermore, the target theoretical peak shape can be obtained by performing coordinate conversion for rotation and enlargement / reduction in the spatial domain according to the rotation amount and enlargement / reduction amount corresponding to the target peak point position.

  Since the range of the sinc function used in the calculation is limited by the target frequency range, the sinc function value can also be expressed in the form of a look-up table, and in this case, higher speed processing is possible.

  Using the theoretical peak shape obtained above, for each point of the basis vector candidate 510, the theoretical spatial shape corresponding to the candidate point position and the autocorrelation function value of a predetermined region around each point By performing the filtering for obtaining the correlation value, it is possible to efficiently obtain the evaluation value only by the correlation calculation in the spatial domain without requiring the inverse Fourier transform for each of the basis vector candidates. For example, the correlation value with the peak shape 1702 obtained from the theoretical frequency distribution determined according to the candidate point position is obtained for the autocorrelation function value array 1701 in FIG. FIG. 21 shows an example of a one-dimensional signal, but it goes without saying that the same applies to an n-dimensional signal.

  Furthermore, since the autocorrelation function value series 508 is expressed as a set of discrete points, the basis vector candidate 510 is also given as a point above the lattice point, but the true basis vector position is at an intermediate position of the lattice point. In some cases, a small amount of deviation is considered for each point of the basis vector candidate 510, and when the amount of deviation is moved, a deviation amount that maximizes the correlation value between the peak shape and the autocorrelation function value array is obtained. A base vector with higher accuracy may be obtained by redefining it as a base vector candidate point. For example, with respect to the autocorrelation function value array 1704 in FIG. 21B, a correlation value with the theoretical peak shape 1705 having a peak at a position slightly shifted leftward from the base vector candidate point is obtained.

  In the case of a two-dimensional signal, for each point of the basis vector candidate 510, for example, 1/10 subpixels of the lattice point interval a are sequentially displaced in the x direction and the y direction, and a peak point exists at each position. Then, the correlation between the theoretical peak shape on the assumption and the observed autocorrelation value is obtained by convolution operation on the lattice points around the original base vector candidate point, and the one with the largest correlation value is determined as the evaluation value. do it. Further, the position of the basis vector candidate may be corrected using the displacement amount at that time.

[Third Embodiment]
In the present embodiment, a configuration example for processing the primary conversion parameter estimation unit 401 in the first and second embodiments described above with higher accuracy will be described below.

  Definitions of terms, configurations other than the primary transformation parameter estimation unit 401, and configurations of parts of the primary transformation parameter estimation unit 401 that are not described below are the same as those in the first and second embodiments.

  In the primary conversion parameter estimation unit 401 of the first and second embodiments described above, the estimated base vector 511 output from the detailed filter search unit 504 erroneously includes a peak point obtained depending on the original base vector. It may be included as a basis vector. Here, the peak point obtained depending on the original basis vector indicates, for example, the peak point (b) represented as a linear combination of the correct basis vectors (a) in FIG. There is a possibility that a high evaluation value can be obtained by the detailed filter search unit even for a basis vector candidate constituted by such points, but a correct estimated primary transformation parameter cannot be obtained from such a basis vector candidate. There is a possibility that the detection of the digital watermark will fail. In the present embodiment, an example of removing such basis vector candidates will be described.

  FIG. 23 illustrates a configuration example of a primary conversion parameter estimation unit that performs dependent peak point removal according to the third embodiment of the present invention.

  The configuration shown in the figure is a configuration in which a dependent peak point removing unit 1912 is added to the primary conversion parameter estimating unit 401 in FIG. 8.

  The dependent peak point removing unit 1912 removes base vector candidates that become dependent peak points from the estimated basis vector 1911 output from the detailed filter searching unit 1904 (same as the detailed filter searching unit 504 in FIG. 8). The remaining one is output as an estimated basis vector 1913 from which the dependent peak points have been removed.

  Further, instead of removing the base vector that becomes the dependent peak point, the evaluation value obtained in the detailed filter search unit 1904 corresponding to the original base vector and the base vector that becomes the dependent peak point are obtained. Based on the obtained evaluation value, each evaluation value may be changed so that the evaluation value of the original base vector is necessarily larger than the evaluation value of the base vector serving as the dependent peak point. By changing the evaluation value in this manner, it is possible to prevent the detection of the watermark from failing by removing the correct base vector from the estimated base vector 1911 due to an incorrect estimated base vector obtained due to noise on the signal. can do.

  The conversion parameter estimation unit 1905 obtains an estimated primary conversion parameter 1907 based on the estimated base vector 1913 from which the dependent peak point output from the dependent peak point removing unit 1912 has been removed.

  FIG. 24 is a flowchart of the operation of the dependent peak point removing unit in the third embodiment of the present invention.

  Step 2001) A set of pairs (x, y) of estimated basis vectors 1911 output from the detailed filter search unit 1904 is denoted by ξ.

  Step 2002) Let η be a set having the same elements as ξ.

  Step 2003) One estimated basis vector pair (x, y) is arbitrarily extracted from η, and (x, y) just extracted is removed from η.

  Step 2004) A set having all elements other than (x, y) in ξ is set as ζ, and a set ω for storing dependent peak points to be searched for is set as an empty set.

  Step 2005) One estimated basis vector pair (x ′, y ′) is arbitrarily extracted from ζ, and the currently extracted (x ′, y ′) is removed from ζ.

  Step 2006) The evaluation value p [(x, y) obtained when the (x ′, y ′) is in the subordinate position of (x, y) and the detailed filter search unit 1904 calculates (x, y). ] Is smaller than the evaluation value p [(x ′, y ′)] for (x ′, y ′).

  If the condition is not satisfied, the process proceeds to step 2008. If the condition is satisfied, the process proceeds to step 2007.

  Step 2007) (x ′, y ′) is added to the set ω in which the dependent peak points are accumulated, and the process proceeds to Step 2008.

  Step 2008) It is determined whether or not ζ is empty, and if not empty, the process returns to Step 2005, and Steps 2005 to 2008 are repeated until empty.

  If ζ becomes empty, the process proceeds to step 2009.

  Step 2009) The evaluation value of each estimated basis vector is changed as follows.

  The evaluation value p [(x, y)] of (x, y) is changed to the maximum evaluation value among the evaluation values of the found dependent peaks (that is, pairs of all estimated basis vectors included in ω).

  The evaluation value p [(x ′, y ′)] of the found dependent peak (that is, all estimated basis vector pairs included in ω) is changed to the original evaluation value before the change of (x, y). To do.

  That is, by changing the evaluation value of the original estimated basis vector to be always larger than the evaluation value of the estimated basis vector that is the dependent peak point, the dependent peak point may be prioritized and detected. There can be no.

  Step 2010) It is determined whether η is an empty set. If it is an empty set, the process proceeds to Step 2003, and Steps 2003 to 2010 are repeated until η is empty.

  The above sets η, ζ, ξ, ω are assumed to be stored in storage means (not shown) such as a memory.

  Here, in step 2009, an example is shown in which the evaluation value of the original estimated basis vector is exchanged with the evaluation value of the estimated basis vector that is the dependent peak point. However, any other modification method may be used as long as the modification method is such that the evaluation value of the estimated base vector serving as the dependent peak point is always larger than the evaluation value. For example, the evaluation value of the original estimated base vector is assumed to be 1.1 times the maximum evaluation value of the estimated base vector that is the dependent peak point, and the evaluation value of the estimated base vector that is the dependent peak point is It may not be changed.

  Further, as a determination condition in step 2006, the ratio of the evaluation value p [(x ′, y ′)] to (x ′, y ′) and the evaluation value p [(x, y)] to (x, y), or You may add to a condition that a difference is less than a predetermined threshold value. Usually, the dependent peak point and the original peak point are obtained as the same level of peaks in the autocorrelation function value array 1908. Therefore, if the evaluation values are extremely different, the basis vector of (x, y) is There is a possibility that it is an incorrect basis vector obtained by noise. For this reason, by adding the above conditions, if the evaluation value is too far away, the correct basis vector is erroneously removed without performing the dependent peak point removal processing on the corresponding basis vector. Can be prevented.

  Here, the explanation is made on the assumption that the larger the value of the evaluation value p is, the more likely that it is a basis vector, but conversely, the smaller the value of the evaluation value p is, the more likely that the basis vector is. Needless to say, when an evaluation index representing the above is used, the manner of increasing or decreasing the evaluation value is reversed.

  Finally, an example of the subordinate position is shown.

  FIG. 25 shows the subordinate positions in the third embodiment of the present invention. When the basis vector pair (A, B) is limited to the first and second quadrants, a pair that may be erroneously recognized when considering up to a lattice point that is twice the distance is shown in FIGS. k).

  In addition, it is possible to construct the operation of the configuration shown in FIGS. 6, 8, and 23 as a program, install it on a computer used as a digital watermark detection apparatus, execute it, or distribute it via a network. It is.

  It is also possible to store the constructed program in a portable storage medium such as a hard disk device, a flexible disk, or a CD-ROM, and install or distribute the program in a computer.

  The present invention is not limited to the above-described embodiment, and various modifications and applications can be made within the scope of the claims.

  The present invention is applicable to digital watermark detection technology.

It is a principle block diagram of this invention. It is a principle explanatory view of the present invention. It is a model of electronic watermark embedding detection in an embodiment of the present invention. It is an example of a structure of the digital watermark embedding apparatus in the 1st Embodiment of this invention. It is an example of the tiling of the watermark pattern in the 1st Embodiment of this invention. It is a block diagram of the digital watermark detection apparatus in the 1st Embodiment of this invention. It is a flowchart of the digital watermark detection process in the 1st Embodiment of this invention. It is an example of a structure of the primary conversion parameter estimation part in the 1st Embodiment of this invention. It is a flowchart of the estimation procedure of the primary conversion parameter in the 1st Embodiment of this invention. It is an example of the autocorrelation function value arrangement | sequence in the 1st Embodiment of this invention. It is an example of the Laplacian filter in the 1st Embodiment of this invention. It is an example of the shape of the fixed filter in the 1st Embodiment of this invention. It is an example of the search range of the autocorrelation peak point in the 1st Embodiment of this invention. It is an example of a 90 degree pair (basis vector) candidate search in the 1st Embodiment of this invention. It is an example of the conversion parameter calculation from the basis vector in the 1st Embodiment of this invention. It is a flowchart of the 90-degree pair search part implementation example in the 1st Embodiment of this invention. It is an example of the definition of the vicinity in the 90 degree pair search in the 1st Embodiment of this invention. It is explanatory drawing of the frequency distribution of the autocorrelation function value in the 1st Embodiment of this invention. It is a flowchart of operation | movement of the parallel displacement estimation part in the 1st Embodiment of this invention. It is a frequency distribution of the autocorrelation peak point shape in the 2nd Embodiment of this invention. It is a figure for demonstrating evaluation value calculation of the base vector candidate in the sub pixel unit in the 2nd Embodiment of this invention. It is an example of the recognition error of the vector subordinate to the basis vector in the 3rd Embodiment of this invention. It is an example of a structure of the primary transformation parameter estimation part which performs the dependent peak point removal in the 3rd Embodiment of this invention. It is a flowchart of operation | movement of the dependent peak point removal part in the 3rd Embodiment of this invention. It is an example of the subordinate position in the 3rd Embodiment of this invention.

Explanation of symbols

101 Pre-embedding signal 102 Embedding information 104 Embedding signal 105 Communication path 106 Additive noise 107 Modification of signal 108 Modified embedding signal 110 Detection information 200 Digital watermark embedding device 201 Watermark pattern generation unit 202 Watermark pattern addition unit 203 Watermark pattern 301 Watermark pattern 400 Digital watermark detection apparatus 401 Primary transformation parameter estimation unit 402 Geometric transformation correction unit 403 Parallel displacement estimation unit 404 Embedded information detection unit 405 Input signal 406 Estimated primary transformation parameter 407 Geometric transformation corrected signal 408 Estimated translational amount 409 Watermark Detection information 501 Autocorrelation function value array generation unit 502 Fixed filter search means, Fixed filter search section 503 90 degree pair search means, 90 degree pair search section 504 Detailed filter search means, details Filter search unit 505 Conversion parameter calculation means, conversion parameter estimation unit 506 Input signal 507 Estimated primary conversion parameter 508 Autocorrelation function value array 509 Peak point candidate 510 Basis vector candidate 511 Estimated basis vector 1301 Modification 1302 without modification of aspect ratio Modified 1701 with aspect ratio changed before and after Sample 1702 on autocorrelation function value array Peak shape 1703 obtained from theoretical frequency distribution determined according to candidate point position 1703 Theoretical value 1704 corresponding to lattice point position Autocorrelation function value array 1705 Theoretical peak shape 1901 having a peak slightly shifted leftward from the base vector candidate point 1901 Autocorrelation function value array generation unit 1902 Fixed filter search unit 1903 90 degree pair search unit 1904 Detailed filter search unit 1905 Conversion Parameter estimating unit 1906 Input signal 1907 estimation linear transformation parameter 1908 estimated basis vectors of autocorrelation function value sequence 1909 peak point candidate 1910 base vector candidates 1911 estimated basis vectors 1912 subordinate peak point removing section 1913 subordinate peak point has been removed

Claims (17)

Advance for the two-dimensional image signal repeatedly a predetermined pattern, as an electronic watermark so that it is not perceived by the human perception the predetermined pattern is embedded, enlargement or reduction, rotation, the primary one of aspect ratio change as a modified A digital watermark detection apparatus that estimates a primary conversion parameter representing the primary conversion applied to a digital signal when the converted digital signal is input ,
Autocorrelation function value array generating means for calculating an autocorrelation function value of the inputted digital signal with the modification and storing the autocorrelation function value in a two-dimensional autocorrelation function value array corresponding to the image signal When,
By filtering the autocorrelation sequence using the fixed signal filter having a shape imitating the shape of the spatial domain of the impulse response of the filter having the same frequency distribution and the frequency distribution of the predetermined pattern, the autocorrelation function sequence Fixed filter search means for searching for the top N peak points in descending order of the result of filtering by the fixed signal filter appearing in
For the top N peak points, N vectors are generated from the origin to each peak point, a pair of vectors orthogonal to each other is found out of the N vectors, and a 90-degree pair serving as a basis vector candidate Search means;
The two peak points used for generating the basis vector candidate are paired candidates, and the pair is obtained using a detailed filter having a shape in a spatial region of an impulse response of a filter having the same frequency component as the frequency distribution of the predetermined pattern. An autocorrelation function value array within a predetermined range is filtered in the vicinity of the candidate, and a basis vector candidate generated by M pairs of candidates in descending order of the evaluation value is estimated using the filtered value as an evaluation value. A detailed filter search means for selecting as a basis vector ;
Conversion for obtaining a vector to a peak point appearing in the autocorrelation function value array when the modification is not applied from the origin as a basis vector, and a parameter for converting the basis vector into the estimated basis vector as an estimated primary transformation parameter Parameter calculation means;
A digital watermark detection apparatus comprising: A predetermined pattern is repeated in advance for a two-dimensional image signal, and the predetermined pattern is embedded as a digital watermark so as not to be perceived by human perception. As a modification, any one of enlargement or reduction, rotation, and aspect ratio change is performed. A digital watermark detection apparatus that estimates a primary conversion parameter representing the primary conversion applied to a digital signal when the converted digital signal is input,
Autocorrelation function value array generating means for calculating an autocorrelation function value of the inputted digital signal with the modification and storing the autocorrelation function value in a two-dimensional autocorrelation function value array corresponding to the image signal When,
The autocorrelation array is filtered by using a fixed signal filter having a shape imitating the shape of the impulse response in the spatial domain of the filter having the same frequency distribution as the frequency distribution of the predetermined pattern. Fixed filter search means for searching for the top N peak points in descending order of the result of filtering by the fixed signal filter appearing in
For the top N peak points, N vectors are generated from the origin to each peak point, a pair of vectors orthogonal to each other is found out of the N vectors, and a 90-degree pair serving as a basis vector candidate Search means;
The two peak points used for generating the basis vector candidate are paired candidates, and the pair is obtained using a detailed filter having a shape in a spatial region of an impulse response of a filter having the same frequency component as the frequency distribution of the predetermined pattern. An autocorrelation function value array within a predetermined range is filtered in the vicinity of the candidate, and a basis vector candidate generated by M pairs of candidates in descending order of the evaluation value is estimated using the filtered value as an evaluation value. A detailed filter search means for selecting as a basis vector;
Conversion for obtaining a vector to a peak point appearing in the autocorrelation function value array when the modification is not applied from the origin as a basis vector, and a parameter for converting the basis vector into the estimated basis vector as an estimated primary transformation parameter Parameter calculation means;
The digital signal subjected to the modification is geometrically corrected by performing an inverse transformation on the primary conversion parameter, and the geometrically corrected digital signal is translated by a predetermined amount of movement within a predetermined region, A translation amount estimation means that attempts to detect a digital watermark from a translated digital signal and sets the amount of movement when detected as a translation amount added to the digital signal;
A digital watermark detection apparatus comprising: The 90 degree pair search means
In the vicinity of each of the top N peak points rotated 90 degrees counterclockwise,
A search is made for a peak point within a predetermined range determined by the assumed size of the modification, and when there is a peak point within the range, the two peak points are used as a pair candidate and a vector from the origin to the pair candidate. Use pairs as basis vector candidates
The digital watermark detection apparatus according to claim 1 or 2 . The fixed signal filter is:
3. The digital watermark detection apparatus according to claim 1, wherein the digital watermark detection apparatus uses a filter having a rectangular shape and having a large value at the center of the rectangular region and a small value around one pixel away from the center . The detailed filter search means includes:
The predetermined pattern has a frequency distribution represented by a rectangular region, and a filter having a shape based on a sinc function is used as the detailed filter.
The digital watermark detection apparatus according to claim 1 or 2 . Among the pair candidate filtered by the detailed filter search unit, according to claim 1 having a subordinate peak point searching means for searching for a pair candidate represented by a linear combination of a pair candidate, or, according 5 Digital watermark detection device. Evaluation value of the candidate of the corresponding pair, so that the evaluation value is evaluated rather low Ri by evaluation value pair candidate and the underlying linear combination, evaluation value of the candidate of the pair, the base of the linear coupling one or the digital watermark detection apparatus according to claim 6, further comprising a subordinate peak point removal means to increase or decrease both the evaluation value of the evaluation pair candidate. 2. The method according to claim 1, wherein the predetermined pattern includes sub-information to be embedded in a digital signal that is a two-dimensional image signal, and the sub-information embedded in the digital signal that is the two-dimensional image signal is detected. The digital watermark detection apparatus according to claim 7,
Conversion correction means for geometrically correcting the modified digital signal by performing inverse conversion on the primary conversion parameter ;
Embedded information detecting means for detecting the sub-information by detecting the predetermined pattern from the geometrically corrected digital signal;
The digital watermark detection apparatus further comprising: Advance for the two-dimensional image signal repeatedly a predetermined pattern, as an electronic watermark so that it is not perceived by the human perception the predetermined pattern is embedded, enlargement or reduction, rotation, the primary one of aspect ratio change as a modified A digital watermark detection method in a digital watermark detection apparatus for estimating a primary conversion parameter representing the primary conversion applied to a digital signal when a digital signal subjected to conversion is input ,
An autocorrelation function value array generation step of calculating an autocorrelation function value of the input digital signal subjected to the modification and storing the autocorrelation function value in a two-dimensional autocorrelation function value array corresponding to the image signal When,
The autocorrelation function value is obtained by filtering the autocorrelation array using a fixed signal filter having a shape imitating a shape in a spatial domain of an impulse response of a filter having the same frequency distribution as the frequency distribution of the predetermined pattern. A fixed filter search step for searching for the top N peak points in order from the largest value of the result of filtering by the fixed signal filter appearing in the array;
To the top N peak point, from the origin to each peak point to generate N vectors, find a pair of vectors which are orthogonal to each other from among the N vectors, 90 pairs as a base vector candidates A search step;
The two peak points used for generating the basis vector candidate are paired candidates, and the pair is obtained using a detailed filter having a shape in a spatial region of an impulse response of a filter having the same frequency component as the frequency distribution of the predetermined pattern. An autocorrelation function value array within a predetermined range is filtered in the vicinity of the candidate, and a basis vector candidate generated by M pairs of candidates in descending order of the evaluation value is estimated using the filtered value as an evaluation value. A detailed filter search step to select as a basis vector ;
Conversion for obtaining a vector to a peak point appearing in the autocorrelation function value array when the modification is not applied from the origin as a basis vector, and a parameter for converting the basis vector into the estimated basis vector as an estimated primary transformation parameter A parameter calculation step;
An electronic watermark detection method comprising: A predetermined pattern is repeated in advance for a two-dimensional image signal, and the predetermined pattern is embedded as a digital watermark so as not to be perceived by human perception. As a modification, any one of enlargement or reduction, rotation, and aspect ratio change is performed. A digital watermark detection method in a digital watermark detection apparatus for estimating a primary conversion parameter representing the primary conversion applied to a digital signal when a digital signal subjected to conversion is input,
An autocorrelation function value array generation step of calculating an autocorrelation function value of the input digital signal subjected to the modification and storing the autocorrelation function value in a two-dimensional autocorrelation function value array corresponding to the image signal When,
The autocorrelation function value is obtained by filtering the autocorrelation array using a fixed signal filter having a shape imitating a shape in a spatial domain of an impulse response of a filter having the same frequency distribution as the frequency distribution of the predetermined pattern. A fixed filter search step for searching for the top N peak points in order from the largest value of the result of filtering by the fixed signal filter appearing in the array;
For the top N peak points, N vectors are generated from the origin to each peak point, a pair of vectors orthogonal to each other is found out of the N vectors, and a 90-degree pair serving as a basis vector candidate A search step;
The two peak points used for generating the basis vector candidate are paired candidates, and the pair is obtained using a detailed filter having a shape in a spatial region of an impulse response of a filter having the same frequency component as the frequency distribution of the predetermined pattern. An autocorrelation function value array within a predetermined range is filtered in the vicinity of the candidate, and a basis vector candidate generated by M pairs of candidates in descending order of the evaluation value is estimated using the filtered value as an evaluation value. A detailed filter search step to select as a basis vector;
Conversion for obtaining a vector to a peak point appearing in the autocorrelation function value array when the modification is not applied from the origin as a basis vector, and a parameter for converting the basis vector into the estimated basis vector as an estimated primary transformation parameter A parameter calculation step;
The digital signal subjected to the modification is geometrically corrected by performing an inverse transformation on the primary conversion parameter, and the geometrically corrected digital signal is translated by a predetermined amount of movement within a predetermined region, A translation amount estimation step in which digital watermark detection is attempted from the translated digital signal, and the amount of movement when the digital watermark is detected is defined as a translation amount added to the digital signal;
An electronic watermark detection method comprising: In the 90-degree pair search step,
In the vicinity of each of the top N peak points rotated 90 degrees counterclockwise,
A search is made for a peak point within a predetermined range determined by the assumed size of the modification, and when there is a peak point within the range, the two peak points are used as a pair candidate and a vector from the origin to the pair candidate. Use pairs as basis vector candidates
The digital watermark detection method according to claim 9 or 10 . The fixed signal filter is:
11. The digital watermark detection method according to claim 9, wherein a filter having a rectangular shape is used, the filter having a large value at the center of the rectangular region and a small value around one pixel away from the center . The detailed filter search step includes:
The predetermined pattern has a frequency distribution represented by a rectangular region, and a filter having a shape based on a sinc function is used as the detailed filter.
The digital watermark detection method according to claim 9 or 10 . Among filtered the pair candidates by the detailed filter search step, any one of claims 9, 10 or 13 performs a subordinate peak point search steps for searching for a pair candidate represented by a linear combination of a pair candidate The digital watermark detection method according to the item . Evaluation value of the candidate of the corresponding pair, so that the evaluation value is evaluated rather low Ri by evaluation value pair candidate and the underlying linear combination, evaluation value of the candidate of the pair, the base of the linear coupling Dependent peak point removal step of increasing or decreasing the evaluation value of either or both of the pair candidate evaluations,
15. The digital watermark detection method according to claim 14, wherein: 2. The method according to claim 1, wherein the predetermined pattern includes sub-information to be embedded in a digital signal that is a two-dimensional image signal, and the sub-information embedded in the digital signal that is the two-dimensional image signal is detected. A digital watermark detection method in the digital watermark detection apparatus according to claim 7,
A conversion correction step for geometrically correcting the modified digital signal by performing an inverse conversion on the primary conversion parameter ;
An embedded information detecting step of detecting the sub information by detecting the predetermined pattern from the geometrically corrected digital signal;
An electronic watermark detection method comprising: Advance for the two-dimensional image signal repeatedly a predetermined pattern, as an electronic watermark so that it is not perceived by the human perception the predetermined pattern is embedded, enlargement or reduction, rotation, the primary one of aspect ratio change as a modified A digital watermark detection program for estimating a primary transformation parameter representing the primary transformation applied to the digital signal when the transformed digital signal is input ,
The claimed child watermark detection program electrodeposition for causing a computer to function as each means of the digital watermark detection apparatus according to any one of claim 1 to 8.