CN113012020B - Image watermarking method, system and electronic equipment - Google Patents
Image watermarking method, system and electronic equipment Download PDFInfo
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Abstract
The invention relates to an image watermarking method, an image watermarking system and electronic equipment, which are characterized in that on one hand, no modification of any content is caused to a host image, the integrity of the host image is effectively protected, and on the other hand, a first characteristic image and a second characteristic image are constructed by combining the advantages of a two-dimensional empirical mode decomposition BEMD and a Singular Value Decomposition (SVD) algorithm so as to be further used for copyright authentication and tamper detection. A large number of experimental results and comparison with the existing watermark algorithm prove that the method has good performance on tamper detection, and also has good performance on resisting various attacks, especially the attacks such as shearing attack, gaussian noise, median filtering, image enhancement and the like, and the robustness is improved.
Description
Technical Field
The present invention relates to the field of image watermarking technologies, and in particular, to an image watermarking method, system and electronic device.
Background
Images often store private information of individuals, such as: digital medical images store private information of patients and play an important role in the diagnosis of disease. Therefore, it is very important to protect private information of patients. Digital medical images may be copied and tampered with by unauthorized persons during the transmission and the like, greatly compromising the medical value of the medical image and infringing the personal interests of the patient. In order to better protect ownership of a patient's medical image from human use and abuse, watermarking algorithms directed to digital medical images have been widely used.
The copyright protection provided by conventional watermarking algorithms is generally achieved by embedding the watermark image into the host image, which may cause various problems due to the specificity of the digital medical image, in particular:
1) The insertion of watermark information, i.e. watermark images, in a host image, i.e. a digital medical image, inevitably requires modification of the host image, i.e. modification of the digital medical image, which may cause misdiagnosis by the physician, as any modification of the content may mask the actual condition of the patient;
2) The direct insertion of watermark information, i.e. watermark images, into a host image, i.e. a digital medical image, may lead to the difficulty of the conventional watermarking algorithm in balancing robustness and imperceptibility.
Disclosure of Invention
The invention aims to solve the technical problem of providing an image watermarking method, an image watermarking system and electronic equipment aiming at the defects of the prior art.
The technical scheme of the image watermarking method is as follows:
decomposing a host image by using a two-dimensional empirical mode decomposition algorithm to obtain a first intrinsic mode function and a residual quantity corresponding to the host image, decomposing the first intrinsic mode function corresponding to the host image by using a singular value decomposition algorithm with a preset window to obtain a plurality of first feature matrixes, and decomposing the residual quantity corresponding to the host image by using a singular value decomposition algorithm with a preset window to obtain a plurality of second feature matrixes;
acquiring elements corresponding to the same preset position from each first feature matrix and binarizing the elements to obtain a third feature matrix, and acquiring elements corresponding to the same preset position from each second feature matrix and binarizing the elements to obtain a fourth feature matrix;
arnold scrambling is carried out on the watermark image corresponding to the host image, and repetition is carried out, so that a fifth feature matrix with the same size as the third feature matrix is obtained;
performing exclusive-or operation on the fifth feature matrix and the third feature matrix to obtain the first feature image, and performing exclusive-or operation on the fifth feature matrix and the fourth feature matrix to obtain the second feature image so as to perform copyright authentication and/or tampering detection according to the first feature image and the second feature image.
The image watermarking method has the following beneficial effects:
on the one hand, the host image is not modified in any content, the integrity of the host image is effectively protected, and on the other hand, the advantages of the BEMD and the SVD are combined, so that the good characteristics of the BEMD are fully exerted, and the host image is adaptively decomposed into a limited number of inherent modal functions and margins. Secondly, a singular value decomposition algorithm is respectively used from a first inherent mode function and the allowance corresponding to the host image, and binarization operation is carried out to obtain a third feature matrix and a fourth feature matrix; the watermark image is then transformed using Arnold to enhance the security of the watermark, the first and second feature images are constructed by an exclusive OR operation (XOR), which may be securely stored in a copyright authentication database for further use in copyright authentication and tamper detection. A large number of experimental results and comparison with the existing watermark algorithm prove that the method has good performance on tamper detection, and also has good performance on resisting various attacks, especially the attacks such as shearing attack, gaussian noise, median filtering, image enhancement and the like, and the robustness is improved.
The technical scheme of the image watermarking system is as follows:
the system comprises a decomposition module, a binarization module, a scrambling and repeating module and an exclusive or operation module;
the decomposition module is used for: decomposing a host image by using a two-dimensional empirical mode decomposition algorithm to obtain a first intrinsic mode function and a residual quantity corresponding to the host image, decomposing the first intrinsic mode function corresponding to the host image by using a singular value decomposition algorithm with a preset window to obtain a plurality of first feature matrixes, and decomposing the residual quantity corresponding to the host image by using a singular value decomposition algorithm with a preset window to obtain a plurality of second feature matrixes;
the binarization module is used for: acquiring elements corresponding to the same preset position from each first feature matrix and binarizing the elements to obtain a third feature matrix, and acquiring elements corresponding to the same preset position from each second feature matrix and binarizing the elements to obtain a fourth feature matrix;
the scrambling repetition module is used for: arnold scrambling is carried out on the watermark image corresponding to the host image, and repetition is carried out, so that a fifth feature matrix with the same size as the third feature matrix is obtained;
the exclusive-or operation module is used for: performing exclusive-or operation on the fifth feature matrix and the third feature matrix to obtain the first feature image, and performing exclusive-or operation on the fifth feature matrix and the fourth feature matrix to obtain the second feature image so as to perform copyright authentication and/or tampering detection according to the first feature image and the second feature image.
The image watermarking system has the following beneficial effects:
on the one hand, the host image is not modified in any content, the integrity of the host image is effectively protected, and on the other hand, the advantages of the BEMD and the SVD are combined, so that the good characteristics of the BEMD are fully exerted, and the host image is adaptively decomposed into a limited number of inherent modal functions and margins. Secondly, a singular value decomposition algorithm is respectively used from a first inherent mode function and the allowance corresponding to the host image, and binarization operation is carried out to obtain a third feature matrix and a fourth feature matrix; the watermark image is then transformed using Arnold to enhance the security of the watermark, the first and second feature images are constructed by an exclusive OR operation (XOR), which may be securely stored in a copyright authentication database for further use in copyright authentication and tamper detection. A large number of experimental results and comparison with the existing watermark algorithm prove that the method has good performance on tamper detection, and also has good performance on resisting various attacks, especially the attacks such as shearing attack, gaussian noise, median filtering, image enhancement and the like, and the robustness is improved.
The technical scheme of the electronic equipment is as follows:
comprising a memory, a processor and a program stored on said memory and running on said processor, said processor implementing the steps of an image watermarking method according to any of the preceding claims when said program is executed.
Drawings
Fig. 1 is a schematic flow chart of an image watermarking method according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of acquiring a watermark image to be detected;
FIG. 3 is a schematic representation of a host image;
FIG. 4 is a schematic diagram of a watermark image;
FIG. 5 is a graph showing the results of a median filter attack;
FIG. 6 is a graph showing the results of a salt and pepper noise attack, a speckle noise attack, and a Gaussian noise attack;
FIG. 7 is one of the results of comparison with the literature;
FIG. 8 is a second comparison result with the literature;
fig. 9 is a schematic structural diagram of an image watermarking system according to an embodiment of the present invention;
Detailed Description
As shown in fig. 1, an image watermarking method according to an embodiment of the present invention includes the following steps:
s1, for a host image, sequentially decomposing by using a two-dimensional empirical mode decomposition algorithm and a singular value decomposition algorithm, and specifically:
decomposing a host image by using a two-dimensional empirical mode decomposition algorithm to obtain a first intrinsic mode function and a residual quantity corresponding to the host image, decomposing the first intrinsic mode function corresponding to the host image by using a singular value decomposition algorithm with a preset window to obtain a plurality of first feature matrixes, and decomposing the residual quantity corresponding to the host image by using a singular value decomposition algorithm with a preset window to obtain a plurality of second feature matrixes;
s2, acquiring elements corresponding to the same preset position and binarizing the elements, and specifically:
acquiring elements corresponding to the same preset position from each first feature matrix and binarizing the elements to obtain a third feature matrix, and acquiring elements corresponding to the same preset position from each second feature matrix and binarizing the elements to obtain a fourth feature matrix;
s3, scrambling and repeating the watermark image, specifically: arnold scrambling is carried out on the watermark image corresponding to the host image, and repetition is carried out, so that a fifth feature matrix with the same size as the third feature matrix is obtained;
s4, performing exclusive OR operation to obtain a first characteristic image and a second characteristic image, and specifically:
performing exclusive-or operation on the fifth feature matrix and the third feature matrix to obtain the first feature image, and performing exclusive-or operation on the fifth feature matrix and the fourth feature matrix to obtain the second feature image so as to perform copyright authentication and/or tampering detection according to the first feature image and the second feature image.
On the one hand, the host image is not modified in any content, the integrity of the host image is effectively protected, and on the other hand, the advantages of the BEMD and the SVD are combined, so that the good characteristics of the BEMD are fully exerted, and the host image is adaptively decomposed into a limited number of inherent modal functions and margins. Secondly, a singular value decomposition algorithm is respectively used from a first inherent mode function and the allowance corresponding to the host image, and binarization operation is carried out to obtain a third feature matrix and a fourth feature matrix; the watermark image is then transformed using Arnold to enhance the security of the watermark, the first and second feature images are constructed by an exclusive OR operation (XOR), which may be securely stored in a copyright authentication database for further use in copyright authentication and tamper detection. A large number of experimental results and comparison with the existing watermark algorithm prove that the method has good performance on tamper detection, and also has good performance on resisting various attacks, especially the attacks such as shearing attack, gaussian noise, median filtering, image enhancement and the like, and the robustness is improved.
Taking a grayscale image with a host image H of m×n pixels and a binary image with a watermark image W of p×q pixels as an example, specific examples are:
s10, decomposing by using a two-dimensional empirical mode decomposition algorithm: specifically:
decomposing the host image H by using a two-dimensional empirical mode decomposition algorithm BEMD (Bi-dimensional Empirical Mode Decomposition, abbreviated BEMD) to obtain a plurality of intrinsic mode functions, setting a termination condition of the decomposition, such as a termination threshold of a margin, when the residual res obtained by decomposing the host image H reaches the termination threshold of the margin, stopping the decomposition, or directly setting the total number of the obtained intrinsic mode functions as the termination condition, for exampleIf 5 natural mode functions are obtained, stopping decomposition at this timeWhere k represents the total number of the obtained intrinsic mode functions, IMF i Representing an ith natural mode function corresponding to the host image H, wherein i is a positive integer, thereby obtaining a first natural mode function corresponding to the host image H and a residual res, and then:
s11, decomposing by utilizing a singular value decomposition algorithm, and specifically:
setting a preset window of a singular value decomposition algorithm as [ L, L ], wherein L represents the number of pixels, decomposing a first natural mode function corresponding to a host image H by using the singular value decomposition algorithm with the preset window to obtain a plurality of first feature matrixes, and decomposing the residual corresponding to the host image by using the singular value decomposition algorithm with the preset window to obtain a plurality of second feature matrixes;
s12, obtaining a third feature matrixAnd fourth feature matrix->Specifically:
1) The elements corresponding to the same preset position are obtained from each first feature matrix, and the preset position is generally set as the position of the first element in the upper left corner, for example, the matrix is:the position of the element 1 is the position of the first element in the upper left corner, so that a first intermediate feature matrix I is obtained, and the size of the first intermediate feature matrix I is m/L multiplied by n/L;
each element in the first intermediate feature matrix I is binarized respectively, specifically: comparing each element in the first intermediate feature matrix I with the average value of all the elements in the first intermediate feature matrix I, respectively, to be larger than the average valueThe element is denoted as 1, and the element smaller than the average value is denoted as 0; thereby obtaining a third feature matrix
2) Obtaining elements corresponding to the same preset position from each second feature matrix respectively, thereby obtaining a second intermediate feature matrix R, wherein the size of the second intermediate feature matrix R is m/L multiplied by n/L;
each element in the second intermediate feature matrix R is binarized, specifically: comparing each element in the second intermediate feature matrix R with the average value of all elements in the second intermediate feature matrix R, respectively, marking the element larger than the average value as 1, and marking the element smaller than the average value as 0; thereby obtaining a fourth feature matrix
S13, processing the watermark image, specifically:
firstly, performing Amold scrambling on a watermark image W to obtain a scrambled image W ', and repeating the scrambled image W' for m/(Lp) x n/(Lq) times to obtain a third feature matrixA fifth feature matrix WA of uniform size, wherein the third feature matrix +.>And fourth feature matrix->Size uniformity, also described herein as: obtain a fourth feature matrix->A fifth feature matrix WA of uniform size;
s14, performing exclusive OR operation to obtain a first characteristic image F' I And a second characteristic image F' R Specifically:
combining the fifth feature matrix WA with the third feature matrixPerforming exclusive OR operation, i.e. comparing the fifth feature matrix WA with the third feature matrix +.>If the values of the same-position elements in the image are equal, the result of the exclusive-or operation is 0, and if not, the result of the exclusive-or operation is 1, thereby obtaining a first characteristic image F' I The method comprises the steps of carrying out a first treatment on the surface of the Similarly, and the fifth feature matrix WA is combined with the fourth feature matrix +.>Performing exclusive or operation to obtain a second characteristic image F' R First characteristic image F' I And the second characteristic image F' R Securely stored in a copyright authentication database for use in accordance with said first feature image F' I And the second characteristic image F' R Copyright authentication and/or tamper detection are performed.
It will be appreciated that S10-S14 correspond to the watermark embedding process, except that in this process, no modification of the host image H is made, which may be considered as a zero watermark algorithm, effectively protecting the integrity of the host image H, then:
1) As shown in fig. 2, the process of detecting whether the host image to be detected is tampered with includes S5-S9, specifically:
s5, for a host image to be detected, sequentially decomposing by using a two-dimensional empirical mode decomposition algorithm and a singular value decomposition algorithm, and specifically:
decomposing the host image H' to be detected corresponding to the host image H by utilizing a two-dimensional empirical mode decomposition algorithm to obtain a plurality of natural mode functions at the momentres′Representing the corresponding allowance of the host image H 'to be detected, IMF' 1 Representing the ith natural mode function corresponding to the host image H 'to be detected, wherein i is a positive integer, so as to obtain a first natural mode function IMF' corresponding to the host image H 'to be detected' 1 And a margin res',
and utilize a window having a predetermined window, i.e. [ L, L ]]The singular value decomposition algorithm of (2) corresponds to a first intrinsic mode function IMF 'corresponding to the host image to be detected' 1 Decomposing to obtain multiple sixth feature matrices, and using a window with preset values]Decomposing the residual res' corresponding to the host image to be detected by a singular value decomposition algorithm to obtain a plurality of seventh feature matrixes;
it can be understood that the host image H' to be detected corresponds to the host image H, so that various parameters of the pre-stored host image H are used to verify whether the host image to be detected is tampered with, and the various parameters include: the termination conditions for decomposing the host image H' to be detected by using the two-dimensional empirical mode decomposition algorithm are the same, and the preset windows in the singular value decomposition algorithm are the same.
S6, acquiring elements and binarizing, specifically:
1) The process of respectively acquiring the elements corresponding to the same preset position from each sixth feature matrix refers to the process of respectively acquiring the elements corresponding to the same preset position from each first feature matrix, and the elements of the first position of the upper left corner are also respectively selected from each sixth feature matrix to obtain a third intermediate feature matrix I'; and binarizing each element in the third intermediate feature matrix I', specifically: comparing each element in the third intermediate feature matrix I 'with the average value of all elements in the third intermediate feature matrix I', respectively, marking the element larger than the average value as 1, and marking the element smaller than the average value as 0; thereby obtaining an eighth feature matrix I';
2) The process of respectively acquiring the elements corresponding to the same preset position from each seventh feature matrix refers to the process of respectively acquiring the elements corresponding to the same preset position from each first feature matrix, and the elements of the first position in the upper left corner are also respectively selected from each seventh feature matrix, so as to obtain a fourth intermediate feature matrix R'; and binarizing each element in the fourth intermediate feature matrix R', specifically: comparing each element in the fourth intermediate feature matrix R 'with the average value of all elements in the fourth intermediate feature matrix R', respectively, marking the element larger than the average value as 1, and marking the element smaller than the average value as 0; thereby obtaining a ninth feature matrix R';
s7, performing exclusive OR operation to obtain a third characteristic image and a fourth characteristic image, and specifically:
a first characteristic image F 'of the host image' I Exclusive or operation is carried out on the eighth characteristic matrix I ", namely the first characteristic image F 'is respectively compared' I Whether the values of the elements at the same position as those in the eighth feature matrix i″ are equal, if equal, the result of the exclusive-or operation is 0, and if unequal, the result of the exclusive-or operation is 1, thereby obtaining a third feature image WF; similarly, and the second characteristic image F 'of the host image' R Performing exclusive OR operation with the ninth feature matrix R 'to obtain a fourth feature image W';
s8, acquiring a watermark image to be detected, and specifically:
dividing the fourth feature image w″ according to the size of the preset window to obtain a plurality of tenth feature matrices, where the total number of the tenth feature matrices is m/(Lp) ×n/(Lq), which can be understood as: the fourth feature image w″ is divided into m/(Lp) ×n/(Lq) blocks, and elements at the same position in each tenth feature matrix are added, specifically: taking the sum of the elements at the first position of the left upper corner in each tenth feature matrix as one element, placing the elements at the first position of the left upper corner in a newly generated fifth intermediate feature matrix, and so on to obtain a fifth intermediate feature matrix, and binarizing each element in the fifth intermediate feature matrix, specifically: comparing each element in the fifth intermediate feature matrix with the average value of all elements in the fifth intermediate feature matrix, respectively, and marking the element larger than the average value as 1 and the element smaller than the average value as 0 to obtain the corresponding host image HImage W' with watermark image W of uniform size r ；
Then, for the image W r Performing Amold inverse transformation to obtain a watermark image W' to be detected;
s9, detecting whether the electronic device is tampered with, specifically:
dividing the third feature image WF according to a preset window size, i.e., [ L, L ], to obtain a plurality of eleventh feature matrices, where the total number of the eleventh feature matrices is m/(Lp) ×n/(Lq), which can be understood as: dividing the third feature image WF into m/(Lp) ×n/(Lq) blocks;
then, NC values between each eleventh feature matrix and the watermark image W ' to be detected are calculated respectively, when NC values smaller than a preset threshold exist, it is determined that the host image H ' to be detected is tampered, and when NC values smaller than the preset threshold do not exist, it is determined that the host image H ' to be detected is not tampered.
Specifically, NC values between each eleventh feature matrix and the watermark image W' to be detected may be calculated according to a first formula, where the first formula is:wherein, K (x, y) is the pixel value of the x-th row and the y-th column in each eleventh feature matrix, and K '(x, y) is the pixel value of the x-th row and the y-th column of the extracted watermark image W' to be detected. The larger the NC value, the higher the similarity between the eleventh feature matrix and the watermark image W' to be detected. When the NC value of the extracted watermark image is greater than 0.9, the quality of the extracted watermark is better, and the calculation process of the NC value of the normalized correlation coefficient is known to those skilled in the art, and will not be described in detail herein, then:
when the NC value smaller than the preset threshold value exists, the host image H 'to be detected is judged to be tampered, and when the NC value smaller than the preset threshold value does not exist, the host image H' to be detected is judged to be not tampered.
Preferably, in the above technical solution, the method further includes: when an NC value smaller than a preset threshold exists, dividing the NC value into two types by utilizing a K-means algorithm, acquiring a type with smaller average value of the NC value, and determining the position of the type corresponding to the type in the host image H' to be detected as a tampered position, specifically:
the class with smaller average NC value is marked as the first class, the class with larger average NC value is marked as the second class, then each NC value in the first class can be related to the corresponding eleventh feature matrix, the corresponding eleventh feature matrix is related to the third feature image WF, the third feature image WF is related to the eighth feature matrix I ', and the like, so that the position of each NC value in the first class corresponding to the host image H ' to be detected can be determined, that is, the position of each NC value in the first class corresponding to the host image H ' to be detected can be determined from S9 to S5 in a reverse way, and the subsequent targeted processing is facilitated.
Preferably, in the above technical solution, the method further includes: and obtaining NC values for representing robustness according to the watermark image W' to be detected and the watermark image. The larger the NC value is, the higher the similarity between the watermark image W' to be detected and the watermark image is, which shows that the robustness is strong.
The robustness of the present application against attacks, in particular, is also verified by conducting attack experiments of a number of different parameters: the evaluation criteria were: the similarity measurement of the two images can be carried out, and besides a visual observation-based method, the data can be used for objective evaluation more accurately. The watermark detection results are typically measured by using normalized correlation coefficients (Normalized Correlation Coefficient, NC) for the extracted watermark imageThe degree of similarity to the original embedded watermark image K, NC value calculation formula is not described here in detail,
the main parameters of the invention include the termination condition of BEMD decomposition, the number of decomposition layers and the like, in the BEMD decomposition process of the invention, the termination condition is SD, namely the allowance is smaller than a certain threshold value, the value range of SD is 0.1 and 0.3 in general, the smaller the value of SD is, the more the number of intrinsic mode functions obtained by screening is, but the longer the algorithm is consumed; therefore, in order to balance the algorithm duration with the number of natural mode functions, the termination conditions of the present invention are: SD defaults to 0.2 or the number of the intrinsic mode functions is uniformly valued to 2. In the step of detecting tampering, the preset threshold may be set to 0.98.
The host image mainly used in the experiment of the invention is 8 gray-scale images with the size of 512 multiplied by 512 pixels, which are named as brain, thoracic cavity, cervical vertebra, abdominal cavity, brain kernel, skull, spine and brainstem respectively, as shown in fig. 3, the watermark image used is 4 binary images with the size of 32 multiplied by 32 pixels, which are named as information, elephant, medicine and CAD respectively, as shown in fig. 4, then:
in order to test the robustness of the invention, the section attacks the image containing the embedded watermark before watermark extraction, including filtering attack, noise attack and the like, table 1 shows NC values of different host images under different attacks, and the invention has good resistance under the attack types and corresponding intensities as shown by data in table 1, namely, gaussian filter [3,3], median filter [3,3], wiener filter [3,3], mean filter [3,3], gaussian noise 0.005 and 0.01, sharpening attack 3, histogram equalization 64, JPEG compression 60 and NC values of different host images under scaling attack are all 1;
table 1:
fig. 5 shows the result of a large window median filter attack on a host image, comprising four different windows 7, 9, 11 and 13. In the figure, the host image is subjected to certain smoothing attack, the pixel values tend to be smooth, and the result shows that in four filtering attack experiments on the image, all NC values are more than 0.97 and the highest NC value is 1.000, so that the algorithm has a good effect on resisting the filtering attack. And the extracted watermark image can be clearly distinguished.
Fig. 6 shows the extraction results of salt and pepper noise, speckle noise, and gaussian noise attacks at σ=0.1 and σ=0.15. It can be seen that the watermark image can be completely extracted under the attack of speckle noise, and the corresponding NC values are all 1. Under Gaussian noise and pretzel noise attacks, although a small amount of spots appear on the extracted watermark, the extracted watermark image can be well identified, and the NC value in the watermark image is larger than 0.99. The experimental result shows that the algorithm can resist noise attacks and has good robustness.
FIG. 7 shows the results of comparison of the present application with the literature (Qin F, li J, li H, et al A Robust Zero-Watermarking Algorithm for Medical Images Using Curvelet-Dct and RSA Pseudo-random Sequences [ C ]// International Conference on Artificial Intelligence and Security. Springer, cham, 2020:179-190.) under Gaussian noise attack. Both the inventive algorithm and the algorithm of the comparative document are able to achieve nc=1 when the 1% gaussian noise intensity is low. However, as the intensity increases, the NC value of the reference is continually decreasing, and from 10% intensity, the results of the reference are already lower than the algorithm results of the present invention. When the intensity increased to 75%, the NC value was below 0.5. In contrast, the algorithm of the present invention can still maintain a higher NC value in the following cases. High-intensity gaussian noise attacks have NC values higher than 0.96 even at 75% intensity.
FIG. 8 is a comparison of NC values under shear attack for the present application and literature (Qin F, li J, li H, et al A Robust Zero-Water marking Algorithm for Medical Images Using Curvelet-Dct and RSA Pseudo-random Sequences [ C ]// International Conference on Artificial Intelligence and Security. Springer, cham, 2020:179-190.). The clipping attack mode is to clip the rectangular region from the top left corner of the host image and replace the pixel value of the rectangular region with 255. It is clear that the algorithm of the invention and the algorithm of this document do not differ much under 9% and 15% shear attacks. However, at 20% shear strength, the inventive algorithm is significantly better than this document, achieving NC values of 0.95 with a maximum shear strength of 56%, whereas the comparative algorithm does not exceed 0.5, demonstrating that the inventive algorithm has a strong resistance to gaussian noise attacks and shear attacks.
In the above embodiments, although the steps S1, S2, etc. are numbered, only the specific embodiments are given herein, and those skilled in the art may adjust the execution sequence of the steps S1, S2, etc. according to the actual situation, which is also within the scope of the present invention, and it is understood that some embodiments may include some or all of the above embodiments.
As shown in fig. 9, an image watermarking system 200 according to an embodiment of the present invention includes a decomposition module 210, a binarization module 220, a scrambling repetition module 230, and an exclusive or operation module 240;
the decomposition module 210 is configured to: decomposing a host image by using a two-dimensional empirical mode decomposition algorithm to obtain a first intrinsic mode function and a residual quantity corresponding to the host image, decomposing the first intrinsic mode function corresponding to the host image by using a singular value decomposition algorithm with a preset window to obtain a plurality of first feature matrixes, and decomposing the residual quantity corresponding to the host image by using a singular value decomposition algorithm with a preset window to obtain a plurality of second feature matrixes;
the binarization module 220 is configured to: acquiring elements corresponding to the same preset position from each first feature matrix and binarizing the elements to obtain a third feature matrix, and acquiring elements corresponding to the same preset position from each second feature matrix and binarizing the elements to obtain a fourth feature matrix;
the scrambling repetition module 230 is configured to: arnold scrambling is carried out on the watermark image corresponding to the host image, and repetition is carried out, so that a fifth feature matrix with the same size as the third feature matrix is obtained;
the exclusive-or operation module 240 is configured to: performing exclusive-or operation on the fifth feature matrix and the third feature matrix to obtain the first feature image, and performing exclusive-or operation on the fifth feature matrix and the fourth feature matrix to obtain the second feature image so as to perform copyright authentication and/or tampering detection according to the first feature image and the second feature image.
On the one hand, the host image is not modified in any content, the integrity of the host image is effectively protected, and on the other hand, the advantages of the BEMD and the SVD are combined, so that the good characteristics of the BEMD are fully exerted, and the host image is adaptively decomposed into a limited number of inherent modal functions and margins. Secondly, respectively using singular value decomposition algorithm from the first natural mode function and the residual corresponding to the host image, and performing binarization operation to obtain a third feature matrixAnd a fourth feature matrix; the watermark image is then transformed using Arnold to enhance the security of the watermark, and a first feature image F 'is constructed by an exclusive-OR operation (XOR)' I And the second feature image, the first feature image and the second feature image may be securely stored in a copyright authentication database for further use in copyright authentication and tamper detection. A large number of experimental results and comparison with the existing watermark algorithm prove that the method has good performance on tamper detection, and also has good performance on resisting various attacks, especially the attacks such as shearing attack, gaussian noise, median filtering, image enhancement and the like, and the robustness is improved.
Preferably, in the above technical solution, the device further includes a detection module, and the decomposition module is further configured to: decomposing a host image to be detected corresponding to the host image by utilizing a two-dimensional empirical mode decomposition algorithm to obtain a first intrinsic mode function and a residual quantity corresponding to the host image to be detected, decomposing the first intrinsic mode function corresponding to the host image to be detected by utilizing a singular value decomposition algorithm with a preset window to obtain a plurality of sixth feature matrixes, and decomposing the residual quantity corresponding to the host image to be detected by utilizing the singular value decomposition algorithm with the preset window to obtain a plurality of seventh feature matrixes;
the binarization module 220 is further configured to: acquiring elements corresponding to the same preset position from each sixth feature matrix respectively, binarizing the elements to obtain an eighth feature matrix, acquiring elements corresponding to the same preset position from each seventh feature matrix respectively, binarizing the elements to obtain a ninth feature matrix;
the exclusive-or operation module 240 is further configured to: performing exclusive OR operation on the first characteristic image of the host image and the eighth characteristic matrix to obtain a third characteristic image; performing exclusive OR operation on the second characteristic image of the host image and the ninth characteristic matrix to obtain a fourth characteristic image;
the binarization module 220 is further configured to: dividing the fourth characteristic image according to the size of a preset window to obtain a plurality of tenth characteristic matrixes, adding elements at the same position in each tenth characteristic matrix, binarizing to obtain an image with the same watermark image size as that of the host image, carrying out Arnold inverse transformation to obtain a watermark image to be detected, and dividing the third characteristic image according to the size of the preset window to obtain a plurality of eleventh characteristic matrixes;
the detection module is used for: and respectively calculating NC values between each eleventh feature matrix and the watermark image to be detected, judging that the host image to be detected is tampered when NC values smaller than a preset threshold exist, and judging that the host image to be detected is not tampered when NC values smaller than the preset threshold do not exist.
Preferably, in the above technical solution, the detection module is further configured to: when NC values smaller than a preset threshold exist, dividing the NC values into two types by using a K-means algorithm, acquiring the type with smaller average value of the NC values, and determining the position of the type corresponding to the type in the host image H' to be detected as the tampered position.
Preferably, in the above technical solution, the detection module is further configured to: and obtaining an NC value for representing robustness according to the watermark image to be detected and the watermark image.
The above steps for implementing the corresponding functions by using the parameters and the unit modules in the image watermarking system 200 according to the present invention may refer to the parameters and the steps in the above embodiments of an image watermarking method, which are not described herein.
An electronic device according to an embodiment of the present invention includes a memory, a processor, and a program stored in the memory and running on the processor, where the processor implements the steps of an image watermarking method implemented by any one of the above-described methods when the program is executed by the processor.
The electronic device may be a computer, a mobile phone, or the like, and the program is computer software or mobile phone APP, and the parameters and steps in the electronic device of the present invention may refer to the parameters and steps in the embodiment of the image watermarking method, which are not described herein.
Those skilled in the art will appreciate that the present invention may be implemented as a system, method, or computer program product.
Accordingly, the present disclosure may be embodied in the following forms, namely: either entirely hardware, entirely software (including firmware, resident software, micro-code, etc.), or entirely software, or a combination of hardware and software, referred to herein generally as a "circuit," module "or" system. Furthermore, in some embodiments, the invention may also be embodied in the form of a computer program product in one or more computer-readable media, which contain computer-readable program code.
Any combination of one or more computer readable media may be employed. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of the computer-readable storage medium include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (9)
1. An image watermarking method, comprising:
decomposing a host image by using a two-dimensional empirical mode decomposition algorithm to obtain a first intrinsic mode function and a residual quantity corresponding to the host image, decomposing the first intrinsic mode function corresponding to the host image by using a singular value decomposition algorithm with a preset window to obtain a plurality of first feature matrixes, and decomposing the residual quantity corresponding to the host image by using a singular value decomposition algorithm with a preset window to obtain a plurality of second feature matrixes;
acquiring elements corresponding to the same preset position from each first feature matrix and binarizing the elements to obtain a third feature matrix, and acquiring elements corresponding to the same preset position from each second feature matrix and binarizing the elements to obtain a fourth feature matrix;
arnold scrambling is carried out on the watermark image corresponding to the host image, and repetition is carried out, so that a fifth feature matrix with the same size as the third feature matrix is obtained;
performing exclusive-or operation on the fifth feature matrix and the third feature matrix to obtain the first feature image, and performing exclusive-or operation on the fifth feature matrix and the fourth feature matrix to obtain the second feature image so as to perform copyright authentication and/or tampering detection according to the first feature image and the second feature image.
2. An image watermarking method according to claim 1, further comprising:
decomposing a host image to be detected corresponding to the host image by using a two-dimensional empirical mode decomposition algorithm to obtain a first intrinsic mode function and a residual quantity corresponding to the host image to be detected;
decomposing a first natural mode function corresponding to the host image to be detected by utilizing a singular value decomposition algorithm with a preset window to obtain a plurality of sixth feature matrixes, and decomposing the residual quantity corresponding to the host image to be detected by utilizing a singular value decomposition algorithm with a preset window to obtain a plurality of seventh feature matrixes;
respectively acquiring elements corresponding to the same preset position from each sixth feature matrix, and binarizing the elements to obtain an eighth feature matrix;
acquiring elements corresponding to the same preset position from each seventh feature matrix respectively, and binarizing the elements to obtain a ninth feature matrix;
performing exclusive OR operation on the first characteristic image of the host image and the eighth characteristic matrix to obtain a third characteristic image;
performing exclusive OR operation on the second characteristic image of the host image and the ninth characteristic matrix to obtain a fourth characteristic image;
dividing the fourth characteristic image according to the size of a preset window to obtain a plurality of tenth characteristic matrixes, adding elements at the same position in each tenth characteristic matrix, binarizing to obtain an image with the same watermark image size as the host image, and carrying out Arnold inverse transformation to obtain a watermark image to be detected;
dividing the third characteristic image according to the size of a preset window to obtain a plurality of eleventh characteristic matrixes, respectively calculating NC values between each eleventh characteristic matrix and the watermark image to be detected, judging that the host image to be detected is tampered when NC values smaller than a preset threshold value exist, and judging that the host image to be detected is not tampered when NC values smaller than the preset threshold value do not exist.
3. An image watermarking method according to claim 2, further comprising: when NC values smaller than a preset threshold exist, dividing the NC values into two types by using a K-means algorithm, acquiring the type with smaller average value of the NC values, and determining the position of the type corresponding to the type in the host image to be detected as the tampered position.
4. An image watermarking method according to claim 2, further comprising: and obtaining an NC value for representing robustness according to the watermark image to be detected and the watermark image.
5. The image watermarking system is characterized by comprising a decomposition module, a binarization module, a scrambling and repeating module and an exclusive or operation module;
the decomposition module is used for: decomposing a host image by using a two-dimensional empirical mode decomposition algorithm to obtain a first intrinsic mode function and a residual quantity corresponding to the host image, decomposing the first intrinsic mode function corresponding to the host image by using a singular value decomposition algorithm with a preset window to obtain a plurality of first feature matrixes, and decomposing the residual quantity corresponding to the host image by using a singular value decomposition algorithm with a preset window to obtain a plurality of second feature matrixes;
the binarization module is used for: acquiring elements corresponding to the same preset position from each first feature matrix and binarizing the elements to obtain a third feature matrix, and acquiring elements corresponding to the same preset position from each second feature matrix and binarizing the elements to obtain a fourth feature matrix;
the scrambling repetition module is used for: arnold scrambling is carried out on the watermark image corresponding to the host image, and repetition is carried out, so that a fifth feature matrix with the same size as the third feature matrix is obtained;
the exclusive-or operation module is used for: performing exclusive-or operation on the fifth feature matrix and the third feature matrix to obtain the first feature image, and performing exclusive-or operation on the fifth feature matrix and the fourth feature matrix to obtain the second feature image so as to perform copyright authentication and/or tampering detection according to the first feature image and the second feature image.
6. An image watermarking system according to claim 5, further comprising a detection module, the decomposition module further adapted to: decomposing a host image to be detected corresponding to the host image by utilizing a two-dimensional empirical mode decomposition algorithm to obtain a first intrinsic mode function and a residual quantity corresponding to the host image to be detected, decomposing the first intrinsic mode function corresponding to the host image to be detected by utilizing a singular value decomposition algorithm with a preset window to obtain a plurality of sixth feature matrixes, and decomposing the residual quantity corresponding to the host image to be detected by utilizing the singular value decomposition algorithm with the preset window to obtain a plurality of seventh feature matrixes;
the binarization module is also used for: acquiring elements corresponding to the same preset position from each sixth feature matrix respectively, binarizing the elements to obtain an eighth feature matrix, acquiring elements corresponding to the same preset position from each seventh feature matrix respectively, binarizing the elements to obtain a ninth feature matrix;
the exclusive-or operation module is further configured to: performing exclusive OR operation on the first characteristic image of the host image and the eighth characteristic matrix to obtain a third characteristic image; performing exclusive OR operation on the second characteristic image of the host image and the ninth characteristic matrix to obtain a fourth characteristic image;
the binarization module is also used for: dividing the fourth characteristic image according to the size of a preset window to obtain a plurality of tenth characteristic matrixes, adding elements at the same position in each tenth characteristic matrix, binarizing to obtain an image with the same watermark image size as that of the host image, carrying out Arnold inverse transformation to obtain a watermark image to be detected, and dividing the third characteristic image according to the size of the preset window to obtain a plurality of eleventh characteristic matrixes;
the detection module is used for: and respectively calculating NC values between each eleventh feature matrix and the watermark image to be detected, judging that the host image to be detected is tampered when NC values smaller than a preset threshold exist, and judging that the host image to be detected is not tampered when NC values smaller than the preset threshold do not exist.
7. An image watermarking system according to claim 6, wherein the detection module is further adapted to: when NC values smaller than a preset threshold exist, dividing the NC values into two types by using a K-means algorithm, acquiring the type with smaller average value of the NC values, and determining the position of the type corresponding to the type in the host image to be detected as the tampered position.
8. An image watermarking system according to claim 7, wherein the detection module is further adapted to: and obtaining an NC value for representing robustness according to the watermark image to be detected and the watermark image.
9. An electronic device comprising a memory, a processor and a program stored on the memory and running on the processor, characterized in that the processor implements the steps of an image watermarking method according to any of claims 1 to 4 when executing the program.
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| CN102129659A (en) * | 2011-03-17 | 2011-07-20 | 浙江工商大学 | Robust zero-watermarking algorithm based on singular value decomposition, Harr wavelet transformation and mean value calculation |
| CN103106636A (en) * | 2013-02-28 | 2013-05-15 | 大连民族学院 | Gray image watermark embedding and extracting method based on singular value decomposition |
| CN110659651A (en) * | 2019-09-05 | 2020-01-07 | 淮阴工学院 | Tampering positioning reversible image watermarking method based on SIFT |
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