CN108010532B - Digital watermark detection method based on multivariate generalized Gaussian distribution - Google Patents

Digital watermark detection method based on multivariate generalized Gaussian distribution Download PDF

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CN108010532B
CN108010532B CN201711363551.4A CN201711363551A CN108010532B CN 108010532 B CN108010532 B CN 108010532B CN 201711363551 A CN201711363551 A CN 201711363551A CN 108010532 B CN108010532 B CN 108010532B
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watermark
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CN108010532A (en
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牛盼盼
李丽
王向阳
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Liaoning Normal University
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Abstract

The invention discloses a digital audio watermark detection method based on multivariate generalized Gaussian distribution, which comprises the steps of firstly carrying out segmentation processing on original audio, constructing an SWT (single-tone transform) important coefficient section with higher local energy according to a short-time energy calculation method, modifying the coefficient of the SWT important coefficient section in a multiplicative embedding mode, and carrying out inverse SWT (single-tone transform) conversion on a sub-band of the modified coefficient and other sub-bands to obtain digital audio containing watermarks; then, carrying out statistical modeling on the SWT coefficient by using the correlation existing between the SWT high-frequency sub-bands and using multivariate generalized Gaussian distribution, and carrying out parameter estimation by combining an MLE (maximum likelihood estimation) method and a Newton-Raphson iteration method; and finally, constructing a maximum likelihood detector by using a log-likelihood ratio method to extract watermark information.

Description

Digital watermark detection method based on multivariate generalized Gaussian distribution
Technical Field
The invention belongs to the technical field of copyright protection of digital audio, relates to a digital audio watermarking method based on a statistical model, and particularly relates to a digital watermark detection method based on multivariate generalized Gaussian distribution.
Background
With the rapid development of multimedia technology and the popularization of internet technology, the way for people to acquire various digital multimedia resources becomes more and more convenient, so that the behaviors of illegally copying, tampering and spreading the multimedia resources become more and more rampant, and therefore, how to safely and effectively store and transmit multimedia information becomes the focus of attention of people. The digital audio watermarking technology is produced as an important branch in the field of information security, provides an effective means for solving the problem of audio information intellectual property protection, and has great research and application values in the fields of digital audio content authentication and copyright protection.
The digital audio watermarking technology is characterized in that watermarking information is embedded into audio by using a data embedding strategy, so that the watermarking information is hidden in original audio data and cannot be perceived, the integrity of the audio is kept, legal protection for an audio property owner is realized, and the ownership of an audio work can be proved by using the watermarking technology even if the digital audio is attacked or decrypted. In recent years, the digital audio watermarking technology has been developed dramatically, and many excellent audio watermarking algorithms appear successively, which have great significance in both theoretical research and practical application, but still have many shortages to be solved, wherein the balance problem between robustness and imperceptibility is still a difficult problem in the field.
The transform domain audio watermarking method based on the statistical model can simultaneously optimize the imperceptibility and the robustness, the watermark can be embedded into the transform domain coefficient to improve the robustness of the watermark, and the multiplicative embedding algorithm can enable the embedding strength of the watermark to change in proportion with the strength of a carrier signal, so that the balance problem between the robustness and the imperceptibility can be effectively solved by combining the multiplicative embedding method in the transform domain, and the transform domain audio watermarking method is widely concerned by researchers. Watermark detection methods based on transform domain statistical modeling are proposed in succession, and mainly comprise two types: one method is to detect whether the watermark exists only at the detection end, and the other method is to extract specific watermark information at the detection end, so that the second method has practical value obviously. However, the statistical model-based watermarking method still has the following disadvantages: firstly, the distributions selected by the current statistical modeling are unit distributions, and only intra-scale correlation is utilized without fully considering inter-scale correlation; secondly, when the transform domain coefficient is modeled, the selected distribution model is not deeply analyzed, and whether the established model is optimal to the selected transform domain is not fully proved; third, most digital watermarking methods based on statistical modeling are image watermarking directions, and there is not much attention and research in audio watermarking.
Disclosure of Invention
The invention provides a digital watermark detection method based on multivariate generalized Gaussian distribution, aiming at solving the technical problems in the prior art.
The technical solution of the invention is as follows: a digital watermark detection method based on multivariate generalized Gaussian distribution comprises watermark embedding and watermark extraction, and is characterized in that:
appointing:
Figure 835554DEST_PATH_IMAGE001
representing a host audio signal;
Figure 384478DEST_PATH_IMAGE002
which represents the length of the watermark or watermarks,
Figure 65602DEST_PATH_IMAGE003
；
Figure 547530DEST_PATH_IMAGE004
representing the length of each segment as
Figure 353943DEST_PATH_IMAGE005
The audio frequency of (a) is selected,
Figure 912533DEST_PATH_IMAGE006
represents the high-frequency sub-band of the second scale,
Figure 321648DEST_PATH_IMAGE007
representing a third scale high frequency sub-band;
Figure 999361DEST_PATH_IMAGE008
represents the window length;
Figure 445517DEST_PATH_IMAGE009
represents the original position of the significant SWT coefficients;
Figure 175225DEST_PATH_IMAGE010
represents
Figure 263398DEST_PATH_IMAGE006
A segment of the importance coefficient of the image,
Figure 126443DEST_PATH_IMAGE011
represents
Figure 412675DEST_PATH_IMAGE007
A significant coefficient segment;
Figure 172556DEST_PATH_IMAGE012
represents a scattering matrix;
Figure 64420DEST_PATH_IMAGE013
representing a shape parameter;
Figure 247883DEST_PATH_IMAGE014
representing a scale parameter;
Figure 35842DEST_PATH_IMAGE015
a representative dimension;
the watermark embedding is carried out according to the following steps:
a. initial setting
Obtaining a host audio signal
Figure 412246DEST_PATH_IMAGE001
And initializing the setting;
b. watermark embedding
b.1 pairs
Figure 779905DEST_PATH_IMAGE001
Carrying out segmentation treatment, wherein the length of each segment is as follows:
Figure 880192DEST_PATH_IMAGE016
；
b.2 pairs
Figure 245576DEST_PATH_IMAGE004
Carrying out three-stage SWT conversion to obtain
Figure 284509DEST_PATH_IMAGE006
And
Figure 924700DEST_PATH_IMAGE007
calculating
Figure 692542DEST_PATH_IMAGE006
The short-time energy of each SWT coefficient is arranged in descending order according to the size:
Figure 90812DEST_PATH_IMAGE017
，
wherein the content of the first and second substances,
Figure 173300DEST_PATH_IMAGE018
；
b.3 filtering out coefficients near the end of the audio and coefficients that are too close to each other, before selection
Figure 145411DEST_PATH_IMAGE019
An important SWT coefficient is used as a watermark embedding initial position, and after recording and selection
Figure 285536DEST_PATH_IMAGE020
A coefficient of
Figure 538530DEST_PATH_IMAGE021
Will be
Figure 45997DEST_PATH_IMAGE006
The location of the embedded watermark is also used as
Figure 228323DEST_PATH_IMAGE007
The watermark embedding location of (a);
b.4, two multiplicative embedding strength functions are constructed for embedding watermark bits of 1 or 0:
Figure 773355DEST_PATH_IMAGE022
，
Figure 244919DEST_PATH_IMAGE023
；
b.5 modifications
Figure 971173DEST_PATH_IMAGE021
And
Figure 138281DEST_PATH_IMAGE024
obtaining the corresponding coefficient
Figure 49737DEST_PATH_IMAGE025
And
Figure 879152DEST_PATH_IMAGE026
：
Figure 79321DEST_PATH_IMAGE027
；
b.6 will
Figure 324488DEST_PATH_IMAGE025
And
Figure 680996DEST_PATH_IMAGE026
combining other sub-bands to perform inverse SWT to obtain digital audio containing watermarks;
the watermark extraction is carried out according to the following steps:
c. coefficient multivariate generalized Gaussian distribution statistical modeling
c.1, determining an important SWT coefficient according to the record, and determining a SWT coefficient section of the watermark to be extracted;
c.2 estimating the noise standard deviation of SWT sub-band by using median method
Figure 353417DEST_PATH_IMAGE028
：
Figure 165515DEST_PATH_IMAGE029
；
c.3 obtaining the results by numerical methods
Figure 276690DEST_PATH_IMAGE030
Is inverse function of
Figure 366000DEST_PATH_IMAGE031
；
c.4 respectively calculating the probability of the multivariate generalized Gaussian distribution under the two assumptions of embedding watermark '1' and embedding watermark '0' in each coefficient section
Figure 212252DEST_PATH_IMAGE032
And
Figure 511647DEST_PATH_IMAGE033
：
Figure 98617DEST_PATH_IMAGE034
，
Figure 432646DEST_PATH_IMAGE035
，
Figure 774766DEST_PATH_IMAGE036
，
Figure 702402DEST_PATH_IMAGE037
，
wherein the content of the first and second substances,
Figure 558975DEST_PATH_IMAGE038
，
Figure 278669DEST_PATH_IMAGE039
，
Figure 667056DEST_PATH_IMAGE040
to represent
Figure 347567DEST_PATH_IMAGE041
The coefficients of the noise are not included in the noise,
Figure 604236DEST_PATH_IMAGE042
to represent
Figure 146531DEST_PATH_IMAGE043
A noise-free coefficient;
d. constructing a maximum likelihood detector for watermark extraction
d.1 pair of watermark-containing audios
Figure 830453DEST_PATH_IMAGE044
Performing segmentation processing on
Figure 591736DEST_PATH_IMAGE045
Carrying out three-stage SWT conversion to obtain
Figure 58620DEST_PATH_IMAGE046
And
Figure 97115DEST_PATH_IMAGE047
；
d.2 pairs
Figure 621112DEST_PATH_IMAGE012
And
Figure 604112DEST_PATH_IMAGE013
carrying out initialization processing:
Figure 202584DEST_PATH_IMAGE048
；
d.3 utilization of
Figure 892322DEST_PATH_IMAGE046
And
Figure 918047DEST_PATH_IMAGE047
as a parameter estimation sample, firstly aligning according to the maximum likelihood method
Figure 797797DEST_PATH_IMAGE012
Estimating, and combining with Newton-Raphson method
Figure 199959DEST_PATH_IMAGE013
Estimating, and obtaining the final result according to the estimation
Figure 744204DEST_PATH_IMAGE012
And
Figure 816197DEST_PATH_IMAGE013
to pair
Figure 773788DEST_PATH_IMAGE014
Estimating:
Figure 648816DEST_PATH_IMAGE049
，
Figure 375463DEST_PATH_IMAGE050
，
Figure 415095DEST_PATH_IMAGE051
，
wherein the content of the first and second substances,
Figure 859983DEST_PATH_IMAGE052
，
Figure 276052DEST_PATH_IMAGE053
is a dual gamma function;
d.4, constructing a maximum likelihood detector, and extracting specific watermark information of each watermark coefficient section to be extracted:
Figure 723783DEST_PATH_IMAGE054
；
d.5, obtaining the optimal watermark sequence by using a voting principle.
Firstly, carrying out segmentation processing on original audio, constructing an SWT (single-tone transform) important coefficient section with higher local energy according to a short-time energy calculation method, modifying the coefficient of the SWT important coefficient section in a multiplicative embedding mode, and carrying out inverse SWT (single-tone transform) conversion on a sub-band of the modified coefficient and other sub-bands to obtain digital audio containing watermarks; then, carrying out statistical modeling on the SWT coefficient by using the correlation existing between the SWT high-frequency sub-bands and using multivariate generalized Gaussian distribution, and carrying out parameter estimation by combining an MLE (maximum likelihood estimation) method and a Newton-Raphson iteration method; and finally, constructing a maximum likelihood detector by using a log-likelihood ratio method to extract watermark information. The experimental result shows that the method of the invention utilizes the multivariate generalized Gaussian distribution to construct a more accurate model, thereby effectively improving the detection precision and simultaneously keeping the good balance between the imperceptibility and the robustness.
Compared with the prior art, the invention has the following beneficial effects:
firstly, modeling is carried out on the SWT coefficient by adopting multivariate generalized Gaussian distribution, so that the parent-child correlation between scales and the brother correlation in the scales are fully utilized, and the distribution characteristics of the coefficient can be more accurately fitted;
secondly, parameter estimation is carried out by combining an MLE method and a Newton-Raphson iteration method, and the detection precision of the model is further improved.
Drawings
Wav audio, fig. 1 is a waveform diagram of original audio, watermark-containing audio, and difference audio in wedding.
Fig. 2 is a diagram of a robustness test result of a conventional attack according to an embodiment of the present invention.
Fig. 3 is a diagram of a robustness test result of desynchronization attack according to an embodiment of the present invention.
Fig. 4 is a flowchart of watermark embedding according to an embodiment of the present invention.
Fig. 5 is a flowchart of watermark extraction according to an embodiment of the present invention.
Detailed Description
The method mainly comprises three stages: watermark embedding, coefficient multivariate generalized Gaussian distribution statistical modeling and constructing a maximum likelihood detector for watermark extraction.
Appointing:
Figure 58949DEST_PATH_IMAGE001
representing a host audio signal;
Figure 866499DEST_PATH_IMAGE002
which represents the length of the watermark or watermarks,
Figure 414155DEST_PATH_IMAGE003
；
Figure 256340DEST_PATH_IMAGE004
representing the length of each segment as
Figure 231250DEST_PATH_IMAGE005
The audio frequency of (a) is selected,
Figure 54325DEST_PATH_IMAGE006
represents the high-frequency sub-band of the second scale,
Figure 140092DEST_PATH_IMAGE007
representing a third scale high frequency sub-band;
Figure 571205DEST_PATH_IMAGE008
represents the window length;
Figure 389119DEST_PATH_IMAGE009
represents the original position of the significant SWT coefficients;
Figure 30316DEST_PATH_IMAGE010
represents
Figure 922704DEST_PATH_IMAGE006
A segment of the importance coefficient of the image,
Figure 5061DEST_PATH_IMAGE011
represents
Figure 321773DEST_PATH_IMAGE007
A significant coefficient segment;
Figure 122370DEST_PATH_IMAGE012
represents a scattering matrix;
Figure 425306DEST_PATH_IMAGE013
representing a shape parameter;
Figure 952715DEST_PATH_IMAGE014
representing a scale parameter;
Figure 846853DEST_PATH_IMAGE015
a representative dimension;
watermark embedding is shown in fig. 4 and is performed as follows:
a. initial setting
Obtaining a host audio signal
Figure 462642DEST_PATH_IMAGE001
And initializing the setting;
b. watermark embedding
b.1 pairs
Figure 366007DEST_PATH_IMAGE001
Carrying out segmentation treatment, wherein the length of each segment is as follows:
Figure 750852DEST_PATH_IMAGE016
；
b.2 pairs
Figure 299381DEST_PATH_IMAGE004
Carrying out three-stage SWT conversion to obtain
Figure 668046DEST_PATH_IMAGE006
And
Figure 578364DEST_PATH_IMAGE007
calculating
Figure 489819DEST_PATH_IMAGE006
The short-time energy of each SWT coefficient is arranged in descending order according to the size:
Figure 319235DEST_PATH_IMAGE017
，
wherein the content of the first and second substances,
Figure 644037DEST_PATH_IMAGE018
；
b.3 filtering out coefficients near the end of the audio and coefficients that are too close to each other, before selection
Figure 886275DEST_PATH_IMAGE019
An important SWT coefficient is used as a watermark embedding initial position, and after recording and selection
Figure 245712DEST_PATH_IMAGE020
A coefficient of
Figure 996762DEST_PATH_IMAGE021
Will be
Figure 483893DEST_PATH_IMAGE006
The location of the embedded watermark is also used as
Figure 798331DEST_PATH_IMAGE007
The watermark embedding location of (a);
b.4, two multiplicative embedding strength functions are constructed for embedding watermark bits of 1 or 0:
Figure 418800DEST_PATH_IMAGE022
，
Figure 324439DEST_PATH_IMAGE023
；
b.5 modifications
Figure 30358DEST_PATH_IMAGE021
And
Figure 207874DEST_PATH_IMAGE024
obtaining the corresponding coefficient
Figure 682848DEST_PATH_IMAGE025
And
Figure 24968DEST_PATH_IMAGE026
：
Figure 811658DEST_PATH_IMAGE027
；
b.6 will
Figure 202320DEST_PATH_IMAGE025
And
Figure 991853DEST_PATH_IMAGE026
combining other sub-bands to perform inverse SWT to obtain digital audio containing watermarks;
watermark extraction is performed as shown in fig. 5, according to the following steps:
c. coefficient multivariate generalized Gaussian distribution statistical modeling
c.1, determining an important SWT coefficient according to the record, and determining a SWT coefficient section of the watermark to be extracted;
c.2 estimating the noise standard deviation of SWT sub-band by using median method
Figure 176978DEST_PATH_IMAGE028
：
Figure 450965DEST_PATH_IMAGE029
；
c.3 obtaining the results by numerical methods
Figure 707634DEST_PATH_IMAGE030
Is inverse function of
Figure 422780DEST_PATH_IMAGE031
；
c.4 respectively calculating the probability of the multivariate generalized Gaussian distribution under the two assumptions of embedding watermark '1' and embedding watermark '0' in each coefficient section
Figure 106702DEST_PATH_IMAGE032
And
Figure 537159DEST_PATH_IMAGE033
：
Figure 207306DEST_PATH_IMAGE034
，
Figure 636013DEST_PATH_IMAGE035
，
Figure 897361DEST_PATH_IMAGE036
，
Figure 289815DEST_PATH_IMAGE037
，
wherein the content of the first and second substances,
Figure 888287DEST_PATH_IMAGE038
，
Figure 578025DEST_PATH_IMAGE039
，
Figure 603750DEST_PATH_IMAGE040
to represent
Figure 746150DEST_PATH_IMAGE041
The coefficients of the noise are not included in the noise,
Figure 148312DEST_PATH_IMAGE042
to represent
Figure 689627DEST_PATH_IMAGE043
A noise-free coefficient;
d. constructing a maximum likelihood detector for watermark extraction
d.1 pair of watermark-containing audios
Figure 27199DEST_PATH_IMAGE044
Performing segmentation processing on
Figure 656895DEST_PATH_IMAGE045
Carrying out three-stage SWT conversion to obtain
Figure 597169DEST_PATH_IMAGE046
And
Figure 323816DEST_PATH_IMAGE047
；
d.2 pairs
Figure 643675DEST_PATH_IMAGE012
And
Figure 760667DEST_PATH_IMAGE013
carrying out initialization processing:
Figure 114419DEST_PATH_IMAGE048
；
d.3 utilization of
Figure 318742DEST_PATH_IMAGE046
And
Figure 548516DEST_PATH_IMAGE047
as a parameter estimation sample, firstly aligning according to the maximum likelihood method
Figure 510393DEST_PATH_IMAGE012
Estimating, and combining with Newton-Raphson method
Figure 464574DEST_PATH_IMAGE013
Estimating, and obtaining the final result according to the estimation
Figure 165814DEST_PATH_IMAGE012
And
Figure 812827DEST_PATH_IMAGE013
to pair
Figure 957939DEST_PATH_IMAGE014
Estimating:
Figure 43706DEST_PATH_IMAGE049
，
Figure 474819DEST_PATH_IMAGE050
，
Figure 620629DEST_PATH_IMAGE051
，
wherein the content of the first and second substances,
Figure 261826DEST_PATH_IMAGE052
，
Figure 885706DEST_PATH_IMAGE053
is a dual gamma function;
d.4, constructing a maximum likelihood detector, and extracting specific watermark information of each watermark coefficient section to be extracted:
Figure 699553DEST_PATH_IMAGE054
；
d.5, obtaining the optimal watermark sequence by using a voting principle.
Experimental testing and parameter setting:
the experiment was performed on a MATLAB R2011a platform, the audio carrier being a mono digital audio signal with a sampling frequency of 41kHz, a resolution of 16 bits, and a length of 20 seconds.
Wav audio, fig. 1 is a waveform diagram of original audio, watermark-containing audio, and difference audio in wedding.
Fig. 2 is a diagram of a robustness test result of a conventional attack according to an embodiment of the present invention.
Fig. 3 is a diagram of a robustness test result of desynchronization attack according to an embodiment of the present invention.
The experimental result shows that the method of the invention utilizes the multivariate generalized Gaussian distribution to construct a more accurate model, thereby effectively improving the detection precision and simultaneously keeping the good balance between the imperceptibility and the robustness.

Claims (1)

1. A digital watermark detection method based on multivariate generalized Gaussian distribution comprises watermark embedding and watermark extraction, and is characterized in that:
appointing: a represents a host audio signal; i is1*J1Represents the watermark length, k 1,21*J1(ii) a B represents audio of length L per segment, B2Representing the second-scale high-frequency sub-band, B3Representing a third scale high frequency sub-band; d represents the window length; p (i) represents the original position of the significant SWT coefficients; a. thek＝{x1,x2,...,xDRepresents B2Section of importance, Bk＝{x1,x2,...,xDRepresents B3A significant coefficient segment; m represents a scattering matrix; beta represents a shape parameter; m represents a scale parameter; p represents a dimension;
the watermark embedding is carried out according to the following steps:
a. initial setting
Acquiring a host audio signal A and initializing the host audio signal A;
b. watermark embedding
b.1, carrying out segmentation treatment on the A, wherein the length of each segment is as follows: l ═ I1*J1*100；
b.2 carrying out three-level SWT conversion on B to obtain B2And B3Calculating B2The short-time energy of each SWT coefficient is arranged in descending order according to the size:
Figure FDA0003030475580000011
wherein the content of the first and second substances,
Figure FDA0003030475580000012
wherein n is 1,2, …, D;
b.3 filtering out coefficients near the end of the audio and adjacent coefficients too close, selecting the previous I1*J1An important SWT coefficient is used as a watermark embedding initial position, and D-1 coefficients after the recording and selection are AkA 1 to B2The location of the embedded watermark is also taken as B3The watermark embedding location of (a);
b.4, two multiplicative embedding strength functions are constructed for embedding watermark bits of 1 or 0:
f1(x)＝arctan(x/10+4.5)，f0(x)＝arccot(x/30+2.5)；
b.5 modification AkAnd BkThe middle corresponding coefficient obtains A'kAnd B'k：
Figure FDA0003030475580000013
b.6 mixing A'kAnd B'kCombining other sub-bands to perform inverse SWT to obtain digital audio containing watermarks;
the watermark extraction is carried out according to the following steps:
c. coefficient multivariate generalized Gaussian distribution statistical modeling
c.1, determining an important SWT coefficient according to the record, and determining a SWT coefficient section of the watermark to be extracted;
c.2 estimating SWT sub-bands by using median methodStandard deviation of noise of
Figure FDA0003030475580000021
Figure FDA0003030475580000022
c.3 obtaining f by numerical method1、f0Inverse function g of1、g0；
c.4 respectively calculating the multivariate generalized Gaussian distribution probability H under the two assumptions of embedding watermark '1' and embedding watermark '0' in each coefficient section1And H0：
Figure FDA0003030475580000023
Figure FDA0003030475580000024
Figure FDA0003030475580000025
Figure FDA0003030475580000026
Wherein Q ═ g1(y1i),g1(y2i)]，R＝[g0(y1i),g0(y2i)]，y1iIs represented by B'2Coefficient without noise, y2iIs represented by B'3A noise-free coefficient;
d. constructing a maximum likelihood detector for watermark extraction
d.1, carrying out segmentation processing on the watermark-containing audio A ', and carrying out three-stage SWT conversion on the B ' to obtain B '2And B'3；
d.2, carrying out initialization processing on M and beta:
M(i,j)＝ρ|i-j| i,j∈[0,p-1]；
d.3 utilize B'2And B'3As a parameter estimation sample, estimating M according to a maximum likelihood method, estimating beta by combining a Newton-Raphson method, and estimating M according to the estimated M and beta:
Figure FDA0003030475580000027
Figure FDA0003030475580000028
Figure FDA0003030475580000031
wherein the content of the first and second substances,
Figure FDA0003030475580000032
Ψ (-) is a dual gamma function;
d.4, constructing a maximum likelihood detector, and extracting specific watermark information of each watermark coefficient section to be extracted:
Figure FDA0003030475580000033
d.5, obtaining the optimal watermark sequence by using a voting principle.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2005006326A2 (en) * 2003-07-11 2005-01-20 Koninklijke Philips Electronics N.V. Watermark embedding and detection
CN101039371A (en) * 2006-03-18 2007-09-19 辽宁师范大学 Novel method of digital watermarking for protecting literary property of presswork
CN102074237A (en) * 2010-11-30 2011-05-25 辽宁师范大学 Digital audio watermarking method based on invariant characteristic of histogram
CN102147912A (en) * 2011-03-30 2011-08-10 北京航空航天大学 Adaptive difference expansion-based reversible image watermarking method

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2005006326A2 (en) * 2003-07-11 2005-01-20 Koninklijke Philips Electronics N.V. Watermark embedding and detection
CN101039371A (en) * 2006-03-18 2007-09-19 辽宁师范大学 Novel method of digital watermarking for protecting literary property of presswork
CN102074237A (en) * 2010-11-30 2011-05-25 辽宁师范大学 Digital audio watermarking method based on invariant characteristic of histogram
CN102147912A (en) * 2011-03-30 2011-08-10 北京航空航天大学 Adaptive difference expansion-based reversible image watermarking method

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