CN116485623B - Multispectral image gray feature watermarking method based on sixteen-element rapid accurate moment - Google Patents

Multispectral image gray feature watermarking method based on sixteen-element rapid accurate moment Download PDF

Info

Publication number
CN116485623B
CN116485623B CN202310735591.6A CN202310735591A CN116485623B CN 116485623 B CN116485623 B CN 116485623B CN 202310735591 A CN202310735591 A CN 202310735591A CN 116485623 B CN116485623 B CN 116485623B
Authority
CN
China
Prior art keywords
gray
sixteen
image
watermark
multispectral image
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202310735591.6A
Other languages
Chinese (zh)
Other versions
CN116485623A (en
Inventor
王春鹏
李光泽
魏子麒
马宾
王玉立
夏之秋
李琦
李健
咸永锦
王晓雨
韩冰
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Qilu University of Technology
Original Assignee
Qilu University of Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Qilu University of Technology filed Critical Qilu University of Technology
Priority to CN202310735591.6A priority Critical patent/CN116485623B/en
Publication of CN116485623A publication Critical patent/CN116485623A/en
Application granted granted Critical
Publication of CN116485623B publication Critical patent/CN116485623B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T1/00—General purpose image data processing
    • G06T1/0021—Image watermarking
    • G06T1/005—Robust watermarking, e.g. average attack or collusion attack resistant
    • G06T1/0064—Geometric transfor invariant watermarking, e.g. affine transform invariant
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T1/00—General purpose image data processing
    • G06T1/0021—Image watermarking
    • G06T1/005—Robust watermarking, e.g. average attack or collusion attack resistant
    • G06T1/0078—Robust watermarking, e.g. average attack or collusion attack resistant using multiple thresholds
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2201/00—General purpose image data processing
    • G06T2201/005—Image watermarking
    • G06T2201/0064—Image watermarking for copy protection or copy management, e.g. CGMS, copy only once, one-time copy
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2201/00—General purpose image data processing
    • G06T2201/005—Image watermarking
    • G06T2201/0065—Extraction of an embedded watermark; Reliable detection
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
    • Y02A40/10—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in agriculture

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Editing Of Facsimile Originals (AREA)
  • Image Processing (AREA)

Abstract

The invention discloses a multispectral image gray feature watermarking method based on sixteen-element rapid and accurate moments, which relates to the field of image processing and is characterized by comprising the following steps: s1: gray characteristic watermark construction; constructing a gray feature watermark by calculating FASRHFMs of an original multispectral image; s11: and rapidly and accurately calculating FASRHFMs of the original multispectral image, and calculating FASRHFMs with the maximum moment of the original multispectral image in the inscribed circle by a rapid and accurate calculation method. The technical problem to be solved by the invention is to provide a multispectral image gray feature watermarking method based on sixteen-element rapid accurate moment, which is convenient for multispectral image gray feature watermarking verification.

Description

Multispectral image gray feature watermarking method based on sixteen-element rapid accurate moment
Technical Field
The invention relates to the field of image processing, in particular to a multispectral image gray feature watermarking method based on sixteen-element rapid and accurate moments.
Background
With the continuous development of the internet and the popularization of open sharing of data, protecting the security of digital resources in storage and transmission has presented an increasingly important meaning. However, copyright infringement problems become more and more serious in this process. At present, encryption and signature technologies are widely used for protecting digital image copyright, but the problems of illegal copying, tampering, re-transmission and the like of the decrypted image cannot be solved. The digital watermarking technology provides a new thought for the problem of image copyright protection and becomes a hot spot field of current research.
The digital watermarking technology is to hide watermark information into image content to be protected, and the existing image characteristic watermarks are mostly used for binarizing image characteristics, so that the characteristic watermarks among different images are less in distinction, and the protection of image copyright is not facilitated. Currently, digital watermarking has been studied in the form of gray-scale images, color images, stereoscopic images, and the like. However, there is no mature scheme for researching the multi-spectrum image watermarking technology. And the method has the defects of slow time and low precision when the moment is used for calculating the moment values of the original multispectral image and the multispectral image to be verified. Multispectral images are images made up of a plurality of bands and widths of spectrum, the image information captured by each band providing detailed information about specific attributes and features of the subject, these images typically being acquired using special sensors or cameras, these devices being capable of recording spectra in many bands simultaneously, and are widely used in aeronautical surveying, satellite telemetry and meteorology. The research of the related field of multispectral images is very extensive, but no effective copyright protection strategy exists.
The existing image characteristic watermarks are used for binarizing image characteristics, so that the characteristic watermarks among different images are small in distinction, and the copyright protection of the images is not facilitated. Currently, digital watermarking has been studied in the form of gray-scale images, color images, stereoscopic images, and the like. However, there is no mature scheme for researching the multi-spectrum image watermarking technology. When the moment is used for calculating the moment value of the acquired original multispectral image and the multispectral image to be verified, the defects of slow time and low precision exist.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a multispectral image gray feature watermarking method based on sixteen-element rapid accurate moment, which is convenient for multispectral image gray feature watermarking verification.
The invention adopts the following technical scheme to realize the aim of the invention:
the multispectral image gray feature watermarking method based on sixteen-element rapid accurate moment is characterized by comprising the following steps of:
s1: gray characteristic watermark construction; constructing a gray feature watermark by calculating FASRHFMs of an original multispectral image;
s11: calculating fashfms of the original multispectral image by calculating the original multispectral image within the inscribed circleIs +.>Fashfms of (a) to obtain->Moment values;
s12: amplitude sequence construction, the S11 is obtainedThe moment value copy expansion can obtain the moment value sequence +.>；
S13: mapping gray scale; mapping the amplitude ratio of the original multispectral image space to the range of 0-255 to finish mapping from large space to small space;
s14: encrypting the gray characteristic watermark; mapping by using a Henon algorithm to generate a chaotic sequence;
s2: verifying gray characteristic watermark;
s21: calculating FASRHFMs of the multispectral image to be verified, and calculating the multispectral image to be verified in the inscribed circleIs +.>Fashfms of (a) to obtain->Moment values;
s22: constructing an amplitude sequence; obtained by the step S21The moment value copy expansion can obtain the moment value sequence +.>；
S23: mapping gray scale; mapping the amplitude ratio of the multispectral image space to be verified to be within the range of 0-255 to obtain a P multiplied by Q gray feature watermark of the multispectral image to be verified;
s24: decrypting the original gray feature watermark; decrypting the original gray feature watermark by using a key generated by a Henon algorithm;
s25: verifying the gray characteristic watermark, namely using a positive code rate to measure the consistency of the gray characteristic watermark to be verified and the original gray characteristic watermark, and verifying all image copyrights;
sixteen-element fast and accurate circular harmonic-Fourier moments, namely FASRHFMs, can be constructed based on sixteen-element theory and RFMs and eight-path symmetrical fast calculation method and Gaussian integral accurate calculation method, because the multiplication of sixteen elements does not meet the exchange law, butAnd->Sixteen elements are adopted, so the FASRHFMs are defined in two ways:
（9）
（10）
wherein:radial basis functions for FARFMs;
is the order;
is the degree of repetition;
radius of polar coordinates of pixel points in the inscribed circle;
is the angle of the polar coordinates of the pixel points in the inscribed circle;
is a unit of pure sixteen-element number, and can be expressed as: />（11）
Is an imaginary unit;
the same group of imagesRight fashfms and left fashfms of (c) may be derived from each other, and the relationship may be expressed as:
（12）
because ofIs a pure sixteen-element number matrix, then +.>Therefore:
（13）
by usingAnd->The formula for reconstructing an image can be expressed as:
（14）
（15）；
according to the relationship of eight symmetrical points on the image, the radii of the eight points are the same, and the angles have a corresponding relationship, so that the basis function value of any point in the calculated unit circle has a basis function value of the corresponding point, and the integral area based on the algorithm is changed into one eighth of the integral area of the traditional algorithm.
As a further limitation of the present solution, the multispectral image is generated into a polar coordinate imageThe circular harmonic-fourier moment, RHFMs, is:
（1）
wherein:RFMs;
is the imaginary part of the calculation;
（2）
at->Within-range orthogonality, the orthogonality relationship of which can be expressed as:
（3）
wherein:is a kronecker function;
the basis functions of RHFMs are defined as:
（4）
from the angular Fourier factorNature of conjugation and radial basis function->The basis functions are orthogonal within a unit circle, and the orthogonality relationship can be expressed as:
（5）
wherein:is->Conjugation of->，/>，/>Is a normalization factor;
is a kronecker symbol;
since the basis functions of RFMs have orthogonality, the original imageReconstruction can be performed using RHFMs,the image reconstruction function of (2) can be expressed as:
（6）
the complex number can be extended to sixteen dimensions, called sixteen, consisting of one real part and fifteen imaginary parts:
（7）
wherein:is the real part;
a multispectral image containing 5 viewing angles can be represented as a set of pure sixteen elements, specifically expressed as follows:
（8）
wherein:is->Polar form of (c);
、/>、…、/>、/>、/>representing multispectral images +.>Red, green, and blue components of 5 views of (c).
As a further limitation of the present solution, the original multispectral image。
As a further limitation to the present technical solution, the specific flow of S13 is as follows:
s131: firstly, setting two thresholds of amplitude, wherein the maximum value of the amplitude isMinimum value +.>Dividing the difference between the amplitude output by the original multispectral image and the minimum amplitude by the threshold value difference;
s132: dividing the threshold value difference by using the difference between the amplitude value output by the original multispectral image and the minimum amplitude value;
s133: multiplying 255 by the obtained result to complete gray mapping and generate a P multiplied by Q gray characteristic watermark;
the gray mapping formula is as follows:
（16）
wherein:representing the pixel gray value of the small space as output;
input pixel gray values representing the original multispectral image space;
is a function mapping formula, will ∈>Is rounded to the nearest integer position.
As a further limitation of the present technical solution, the specific process of S23 is the same as S13.
As a further limitation to the present technical solution, the Henon algorithm mapping definition is as follows:
（17）
wherein:and->Is two real parameters;
and->Represents->State variables at each time.
As a further limitation to the technical scheme, during verification, converting the gray values of the original gray characteristic watermark and the gray characteristic watermark to be verified into binary values, and performing pixel-by-pixel bit comparison to complete verification, wherein the calculation formula of the positive code rate is as follows:
（18）
wherein:the correct bit number in the detected gray characteristic watermark;
p×q is the size of the gray feature watermark;
the larger the value of BCR is between 0 and 1, the more similar the detected gray feature watermark is to the original gray feature watermark, and the better the robustness of the algorithm is.
Compared with the prior art, the invention has the advantages and positive effects that: 1. the sixteen-element fast and accurate moment theory is applied to the gray feature watermark of the multispectral image. The principle of gray characteristic watermarking technology is that amplitude characteristic information constructed by utilizing a moment is mapped to gray space instead of binarizing image characteristics, and gray characteristic watermarking distinguishing degree is larger, so that copyright protection is facilitated. The sixteen-element number moment is combined with a rapid and accurate calculation method to form a sixteen-element number rapid and accurate moment, the sixteen-element number rapid and accurate moment has high-efficiency calculation, extremely high accuracy, strong stability and excellent image reconstruction capability, is excellent image characteristics, ensures the relevance of a constructed gray characteristic watermark and an original multispectral image, can effectively resist geometric attacks such as rotation, scaling, shearing, aspect ratio change and the like, various noise attacks and filtering attacks, and has good robustness. After the image is attacked, the consistency between the gray characteristic watermark of the original multispectral image and the gray characteristic watermark of the multispectral image to be verified can be verified by calculating the sixteen-element rapid and accurate moment of the multispectral image to be verified, so that copyright protection is finished. 2. And the gray characteristic watermark technology is used for the multispectral image, the amplitude characteristic information after scrambling and encryption is mapped to the gray space, the binarization of the image characteristic after calculating the moment value is not carried out, and the formed gray characteristic watermark has larger distinguishing degree, thereby being beneficial to copyright protection. The gray characteristic watermark algorithm mainly comprises two processes, namely the construction of gray characteristic watermark and verification of gray characteristic watermark, when the matrix values of the original multispectral image and the multispectral image to be verified are calculated by utilizing sixteen-element matrix, the defects of slow time and low precision exist, and the speed and precision in calculating the matrix values are greatly improved by utilizing an eight-path symmetrical rapid calculation method and a Gaussian integral precision calculation method. The sixteen-element fast and accurate matrix is called by combining sixteen-element fast and accurate matrix, has high-efficiency calculation, extremely high precision, strong stability and excellent image reconstruction capability, is excellent image characteristics, ensures the relevance of constructed gray characteristic watermarks and original multispectral images, can effectively resist geometric attacks such as rotation, scaling, shearing, aspect ratio change and the like, various noise attacks and filtering attacks, and has good robustness.
Drawings
Fig. 1 is a flowchart of the gray feature watermarking of a multispectral image according to the present invention.
Detailed Description
One embodiment of the present invention will be described in detail below with reference to the attached drawings, but it should be understood that the scope of the present invention is not limited by the embodiment.
Generating a polar image from a multispectral imageThe circular harmonic-fourier moment, RHFMs, is:
（1）
wherein:RFMs;
is the order;
is the degree of repetition;
is a radial basis function;
radius of polar coordinates of pixel points in the inscribed circle;
is the angle of the polar coordinates of the pixel points in the inscribed circle;
is the imaginary part of the calculation;
（2）
at->Within-range orthogonality, the orthogonality relationship of which can be expressed as:
（3）
wherein:is in the form of gramLuo Nake function;
the basis functions of RHFMs are defined as:
（4）
from the angular Fourier factorThe nature of the conjugation and the orthogonality of the radial basis functions can be deduced, the basis functionsAre orthogonal within a unit circle, and the orthogonality relationship can be expressed as:
（5）
wherein:is->Conjugation of->，/>，/>Is a normalization factor;
is a kronecker symbol;
since the basis functions of RFMs have orthogonality, the original imageReconstruction can be performed using RHFMs,the image reconstruction function of (2) can be expressed as:
（6）
the complex number can be extended to sixteen dimensions, called sixteen, consisting of one real part and fifteen imaginary parts:
（7）
wherein:is the real part; />Is an imaginary unit;
a multispectral image containing 5 viewing angles can be represented as a set of pure sixteen elements, specifically expressed as follows:
（8）
wherein:is->Polar form of (c);
、/>、…、/>、/>、/>representing multispectral images +.>Red, green, and blue components of 5 views of (c).
Sixteen-element fast and accurate circular harmonic-Fourier moments, namely FASRHFMs, can be constructed based on sixteen-element theory and RFMs and eight-path symmetrical fast calculation method and Gaussian integral accurate calculation method, because the multiplication of sixteen elements does not meet the exchange law, butAnd->Sixteen elements are adopted, so the FASRHFMs are defined in two ways:
（9）
（10）
wherein:radial basis functions for FARFMs;
is a unit of pure sixteen-element number, and can be expressed as:
（11）
the same group of imagesRight fashfms and left fashfms of (c) may be derived from each other, and the relationship may be expressed as:
（12）
because ofIs a pure sixteen-element number matrix, then +.>Therefore:
（13）
by usingAnd->The formula for reconstructing an image can be expressed as:
（14）
（15）
original multispectral image。
When the moment value of the image is calculated, errors exist, the errors have two factors, the image function is discrete, and the traditional calculation method adopts double-integral zero-order approximation, so that numerical integration errors can be generated. Second, in the case where the square domain defining the domain of the image is converted into a unit circle, the image cannot be perfectly mapped into the circular domain, and only those pixels whose centers fall on or within the circle participate in moment calculation, geometric errors may be generated. Therefore, in order to reduce errors, a Gaussian integral accurate calculation method is utilized, 5 known weight parameters and 5 sampling points are adopted to generate 5*5 images, the constraint condition of Gaussian integral ensures that sampling points only located on or in a unit circle participate in moment calculation, so that geometric errors are reduced, and because the constraint condition of Gaussian integral improves zero-order approximation of an inscribed circle and a plurality of sampling points participate in moment calculation, numerical integral errors are also greatly reduced, and meanwhile numerical integral errors and geometric errors are reduced, so that accurate calculation is completed.
In the accurate calculation, the moment values of 5*5 images are required to be calculated, so that the problem of slow speed can occur, the eight-path symmetrical rapid calculation method is utilized, the radius of eight points is the same according to the relation of eight symmetrical points on the images, and the angles have the corresponding relation, so that the basis function value of any point in a unit circle calculated by using a formula can be directly obtained by using the relation, the integral area based on the algorithm is changed into one eighth of the method of directly calculating by using the formula by using the direct algorithm, and the calculation speed is greatly improved. The inventor combines accurate and quick calculation methods, so that the accuracy of the image moment value is ensured, and the speed of calculating the image moment value is improved.
S11: FASRHFMs of original multispectral image are rapidly and accurately calculated, and the original multispectral image in inscribed circle is calculated by a rapid and accurate calculation methodIs +.>Fashfms of (a) to obtain->Moment values;
s12: amplitude sequence construction, the S11 is obtainedThe moment value copy expansion can obtain the moment value sequence +.>；
S13: mapping gray scale; mapping the amplitude ratio of the original multispectral image space to the range of 0-255 to finish mapping from large space to small space;
the specific flow of S13 is as follows:
s131: firstly, setting two thresholds of amplitude, wherein the maximum value of the amplitude isMinimum value +.>Dividing the difference between the amplitude output by the original multispectral image and the minimum amplitude by the threshold value difference;
s132: dividing the threshold value difference by using the difference between the amplitude value output by the original multispectral image and the minimum amplitude value;
s133: multiplying 255 by the obtained result to complete gray mapping and generate a P multiplied by Q gray characteristic watermark;
the gray mapping formula is as follows:（16）
wherein:representing the pixel gray value of the small space as output;
input pixel gray values representing the original multispectral image space;
is a function mapping formula, will ∈>Is rounded to the nearest integer position.
S14: encrypting the gray characteristic watermark; the Henon algorithm map definition is as follows:
（17）
wherein:and->Is two real parameters;
and->Represents->State variables at each time.
The encryption of the gray characteristic watermark is completed by performing cyclic shift, S box and other operations on the sequence to generate a secret key, so that the confidentiality and the security of the gray characteristic watermark can be effectively improved.
S2: verifying gray characteristic watermark;
s21: FASRHFMs of multispectral images to be verified are rapidly and accurately calculated, and the multispectral images to be verified in inscribed circles are calculated through a rapid and accurate calculation methodIs +.>Fashfms of (a) to obtain->Moment values;
s22: constructing an amplitude sequence; obtained by the step S21The moment value copy expansion can obtain the moment value sequence +.>；
S23: mapping gray scale; mapping the amplitude ratio of the multispectral image space to be verified to be within the range of 0-255 to obtain a P multiplied by Q gray feature watermark of the multispectral image to be verified;
s24: decrypting the original gray feature watermark; decrypting the original gray feature watermark by using a key generated by a Henon algorithm;
s25: and verifying the gray characteristic watermark, wherein a positive code rate is used for measuring the consistency of the gray characteristic watermark to be verified and the original gray characteristic watermark, and verifying all the image copyrights.
During verification, converting gray values of an original gray characteristic watermark and a gray characteristic watermark to be verified into binary, and comparing pixel by pixel and bit by bit to finish verification, wherein a calculation formula of a positive code rate is as follows:
（18）
wherein:the correct bit number in the detected gray characteristic watermark;
p×q is the size of the gray feature watermark;
the larger the value of BCR is between 0 and 1, the more similar the detected gray feature watermark is to the original gray feature watermark, and the better the robustness of the algorithm is.
The above disclosure is merely illustrative of specific embodiments of the present invention, but the present invention is not limited thereto, and any variations that can be considered by those skilled in the art should fall within the scope of the present invention.

Claims (7)

1. The multispectral image gray feature watermarking method based on sixteen-element rapid accurate moment is characterized by comprising the following steps of:
s1: gray characteristic watermark construction; constructing a gray feature watermark by calculating FASRHFMs of an original multispectral image;
s11: calculating FASRHFMs of the original multispectral image by calculating the original multispectral light within the inscribed circleSpectral imageIs +.>Fashfms of (a) to obtain->Moment values;
s12: amplitude sequence construction, the S11 is obtainedThe moment value copy expansion can obtain the moment value sequence +.>；
S13: mapping gray scale; mapping the amplitude ratio of the original multispectral image space to the range of 0-255 to finish mapping from large space to small space;
s14: encrypting the gray characteristic watermark; mapping by using a Henon algorithm to generate a chaotic sequence;
s2: verifying gray characteristic watermark;
s21: calculating FASRHFMs of the multispectral image to be verified, and calculating the multispectral image to be verified in the inscribed circleIs +.>Fashfms of (a) to obtain->Moment values;
s22: constructing an amplitude sequence; obtained by the step S21The length can be obtained by copying and expanding the moment valueMoment sequence of P.times.Q->；
S23: mapping gray scale; mapping the amplitude ratio of the multispectral image space to be verified to be within the range of 0-255 to obtain a P multiplied by Q gray feature watermark of the multispectral image to be verified;
s24: decrypting the original gray feature watermark; decrypting the original gray feature watermark by using a key generated by a Henon algorithm;
s25: verifying the gray characteristic watermark, namely using a positive code rate to measure the consistency of the gray characteristic watermark to be verified and the original gray characteristic watermark, and verifying all image copyrights;
sixteen-element fast and accurate circular harmonic-Fourier moments, namely FASRHFMs, can be constructed based on sixteen-element theory and RFMs and eight-path symmetrical fast calculation method and Gaussian integral accurate calculation method, because the multiplication of sixteen elements does not meet the exchange law, butAnd->Sixteen elements are adopted, so the FASRHFMs are defined in two ways:
（9）
（10）
wherein:radial basis functions for FARFMs;
is the order;
is the degree of repetition;
radius of polar coordinates of pixel points in the inscribed circle;
is the angle of the polar coordinates of the pixel points in the inscribed circle;
is a unit of pure sixteen-element number, and can be expressed as:
（11）
is an imaginary unit;
the same group of imagesRight fashfms and left fashfms of (c) may be derived from each other, and the relationship may be expressed as:
（12）
because ofIs a pure sixteen-element number matrix, then +.>Therefore:
（13）
by usingAnd->The formula for reconstructing an image can be expressed as:
（14）
（15）；
according to the relationship of eight symmetrical points on the image, the radii of the eight points are the same, and the angles have a corresponding relationship, so that the basis function value of any point in the calculated unit circle has a basis function value of the corresponding point, and the integral area based on the algorithm is changed into one eighth of the integral area of the traditional algorithm.
2. The multi-spectral image gray scale characteristic watermarking method based on sixteen-element rapid accurate moment according to claim 1, wherein the method comprises the following steps: generating a polar image from a multispectral imageThe circular harmonic-fourier moment, RHFMs, is:
（1）
wherein:RFMs;
is the imaginary part of the calculation;
（2）
at->Within-range orthogonality, the orthogonality relationship of which can be expressed as:
（3）
wherein:is a kronecker function;
the basis functions of RHFMs are defined as:
（4）
from the angular Fourier factorThe nature of the conjugation and the orthogonality of the radial basis functions can be deduced, the basis functionsAre orthogonal within a unit circle, and the orthogonality relationship can be expressed as:
（5）
wherein:is->Conjugation of->，/>，/>Is a normalization factor;
is a kronecker symbol;
since the basis functions of RFMs have orthogonality, the original imageReconstruction can be performed using RFMs, < - > the->The image reconstruction function of (2) can be expressed as:
（6）
the complex number can be extended to sixteen dimensions, called sixteen, consisting of one real part and fifteen imaginary parts:
（7）
wherein:is the real part;
a multispectral image containing 5 viewing angles can be represented as a set of pure sixteen elements, specifically expressed as follows:
（8）
wherein:in the form of polar coordinates;
、/>、…、/>、/>、/>representing multispectral images +.>Red, green, and blue components of 5 views of (c).
3. The multi-spectral image gray scale characteristic watermarking method based on sixteen-element rapid accurate moment according to claim 2, wherein the method comprises the following steps: original multispectral image 。
4. A multi-spectral image gray scale feature watermarking method based on sixteen-element fast accurate moment according to claim 3, wherein: the specific flow of S13 is as follows:
s131: firstly, setting two thresholds of amplitude, wherein the maximum value of the amplitude isMinimum value +.>Dividing the difference between the amplitude output by the original multispectral image and the minimum amplitude by the threshold value difference;
s132: dividing the threshold value difference by using the difference between the amplitude value output by the original multispectral image and the minimum amplitude value;
s133: multiplying 255 by the obtained result to complete gray mapping and generate a P multiplied by Q gray characteristic watermark;
the gray mapping formula is as follows:（16）
wherein:representing the pixel gray value of the small space as output;
input pixel gray values representing the original multispectral image space;
is a function mapping formula, will ∈>Is rounded to the nearest integer position.
5. The multi-spectral image gray scale characteristic watermarking method based on sixteen-element rapid accurate moment according to claim 4, wherein the method comprises the following steps: the specific flow of S23 is the same as S13.
6. The multi-spectral image gray scale characteristic watermarking method based on sixteen-element rapid accurate moment according to claim 4, wherein the method comprises the following steps: the Henon algorithm map definition is as follows:
（17）
wherein:and->Is two real parameters;
and->Represents->State variables at each time.
7. The multi-spectral image gray scale characteristic watermarking method based on sixteen-element rapid accurate moment according to claim 6, wherein the method comprises the following steps: during verification, converting gray values of an original gray characteristic watermark and a gray characteristic watermark to be verified into binary, and comparing pixel by pixel and bit by bit to finish verification, wherein a calculation formula of a positive code rate is as follows:
（18）
wherein:the correct bit number in the detected gray characteristic watermark;
p×q is the size of the gray feature watermark;
the larger the value of BCR is between 0 and 1, the more similar the detected gray feature watermark is to the original gray feature watermark, and the better the robustness of the algorithm is.
CN202310735591.6A 2023-06-21 2023-06-21 Multispectral image gray feature watermarking method based on sixteen-element rapid accurate moment Active CN116485623B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310735591.6A CN116485623B (en) 2023-06-21 2023-06-21 Multispectral image gray feature watermarking method based on sixteen-element rapid accurate moment

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310735591.6A CN116485623B (en) 2023-06-21 2023-06-21 Multispectral image gray feature watermarking method based on sixteen-element rapid accurate moment

Publications (2)

Publication Number Publication Date
CN116485623A CN116485623A (en) 2023-07-25
CN116485623B true CN116485623B (en) 2023-09-01

Family

ID=87219921

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310735591.6A Active CN116485623B (en) 2023-06-21 2023-06-21 Multispectral image gray feature watermarking method based on sixteen-element rapid accurate moment

Country Status (1)

Country Link
CN (1) CN116485623B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117541580B (en) * 2024-01-08 2024-03-19 天津市肿瘤医院（天津医科大学肿瘤医院） Thyroid cancer image comparison model establishment method based on deep neural network

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20030073686A (en) * 2002-03-12 2003-09-19 (주)디지탈이노텍 Feature-based robust image watermarking scheme using bispectral invariants
CN104504643A (en) * 2014-12-25 2015-04-08 辽宁师范大学 Robustness digital water mark embedding and detection method based on local content features
CN107067415A (en) * 2017-03-21 2017-08-18 南京航空航天大学 A kind of quick accurate positioning method of target based on images match
WO2019061185A1 (en) * 2017-09-28 2019-04-04 深圳传音通讯有限公司 Method and terminal for digitally signing picture
CN113077376A (en) * 2021-04-21 2021-07-06 齐鲁工业大学 Color image zero-watermarking algorithm based on ternary decimal order extremely complex exponential transformation
CN113409184A (en) * 2021-04-21 2021-09-17 齐鲁工业大学 Color image description method based on sixteen-element polar harmonic-Fourier moment
CN113592698A (en) * 2021-08-16 2021-11-02 齐鲁工业大学 Multi-view color image zero-watermark processing method and system based on sixteen-element moment
CN114742690A (en) * 2022-04-26 2022-07-12 齐鲁工业大学 Multi-view color image watermarking processing method and system based on sixteen-element moment
CN115358906A (en) * 2022-07-11 2022-11-18 湖州师范学院 Color image zero-watermarking method facing color image copyright protection

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2010091208A1 (en) * 2009-02-04 2010-08-12 William Charles Fisher Digital watermarking system and method

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20030073686A (en) * 2002-03-12 2003-09-19 (주)디지탈이노텍 Feature-based robust image watermarking scheme using bispectral invariants
CN104504643A (en) * 2014-12-25 2015-04-08 辽宁师范大学 Robustness digital water mark embedding and detection method based on local content features
CN107067415A (en) * 2017-03-21 2017-08-18 南京航空航天大学 A kind of quick accurate positioning method of target based on images match
WO2019061185A1 (en) * 2017-09-28 2019-04-04 深圳传音通讯有限公司 Method and terminal for digitally signing picture
CN113077376A (en) * 2021-04-21 2021-07-06 齐鲁工业大学 Color image zero-watermarking algorithm based on ternary decimal order extremely complex exponential transformation
CN113409184A (en) * 2021-04-21 2021-09-17 齐鲁工业大学 Color image description method based on sixteen-element polar harmonic-Fourier moment
CN113592698A (en) * 2021-08-16 2021-11-02 齐鲁工业大学 Multi-view color image zero-watermark processing method and system based on sixteen-element moment
CN114742690A (en) * 2022-04-26 2022-07-12 齐鲁工业大学 Multi-view color image watermarking processing method and system based on sixteen-element moment
CN115358906A (en) * 2022-07-11 2022-11-18 湖州师范学院 Color image zero-watermarking method facing color image copyright protection

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
多视角彩色图像的十六元数分数阶切比雪夫-傅里叶矩;王春鹏;《计算机学报》;全文 *

Also Published As

Publication number Publication date
CN116485623A (en) 2023-07-25

Similar Documents

Publication Publication Date Title
Yan et al. Quaternion-based image hashing for adaptive tampering localization
Hosny et al. Robust color images watermarking using new fractional-order exponent moments
Wang et al. Reversible fragile watermarking for 2-D vector map authentication with localization
CN104166955B (en) Based on the generation of conformal mapping image Hash and distorted image detection localization method
Peng et al. A semi-fragile watermarking algorithm for authenticating 2D CAD engineering graphics based on log-polar transformation
Wang et al. Reversible fragile watermarking for locating tampered blocks in 2D vector maps
Wang et al. A high capacity reversible data hiding method for 2D vector maps based on virtual coordinates
CN1838172A (en) Method and apparatus for inlaying watermark in digital image and digital camera capable of adding watermark
CN116485623B (en) Multispectral image gray feature watermarking method based on sixteen-element rapid accurate moment
CN110430337B (en) Image disguising and image restoring method, apparatus and storage medium
CN109919824B (en) Color image zero watermarking method based on fast quaternion generalized extremely complex exponential transformation
CN103812638B (en) Method for extracting speed up robust feature (SURF) image features of encryption domain
CN106780287B (en) Lossless watermarking method based on vector geographic line data organization characteristics
Pal et al. A secure reversible color image watermarking scheme based on LBP, lagrange interpolation polynomial and weighted matrix
CN113592698B (en) Sixteen-element moment-based multi-view color image zero watermark processing method and system
CN110211020B (en) Image watermark embedding and extracting method based on SWT-DFT
Hosny et al. Reversible color image watermarking using fractional-order polar harmonic transforms and a chaotic sine map
CN104504645A (en) Method for embedding and detecting robust image watermarks on basis of circular-harmonic-Fourier moments
Yang et al. An efficient and robust zero watermarking algorithm
Kukreja et al. Extended visual cryptography-based copyright protection scheme for multiple images and owners using LBP–SURF descriptors
Kukreja et al. Curvelet transform based robust copyright protection scheme for color images using extended visual cryptography
Saikia et al. Image authentication under geometric attacks via concentric square partition based image hashing
Wang et al. RST invariant fragile watermarking for 2D vector map authentication
Lin et al. Applying projection and B-spline to image authentication and remedy
CN116805069B (en) Track data zero watermark generation method, track data zero watermark detection device and storage medium

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant