CN111988490B - Tetrolet-DCT-based medical image robust watermarking method - Google Patents
Tetrolet-DCT-based medical image robust watermarking method Download PDFInfo
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- H04N1/32144—Display, printing, storage or transmission of additional information, e.g. ID code, date and time or title embedded in the image data, i.e. enclosed or integrated in the image, e.g. watermark, super-imposed logo or stamp
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- H04N1/32144—Display, printing, storage or transmission of additional information, e.g. ID code, date and time or title embedded in the image data, i.e. enclosed or integrated in the image, e.g. watermark, super-imposed logo or stamp
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Abstract
The application discloses a medical image robust watermarking method based on tetrol-DCT, comprising the following steps: extracting features of the original medical image through Tetrolet-DCT transformation and generating a visual feature sequence by utilizing a hash function; performing chaotic scrambling encryption on the original watermark to obtain chaotic scrambling watermark, embedding watermark information into the original medical image, and simultaneously obtaining and storing a binary logic key sequence; in the same way, the feature extraction is carried out on the medical image to be detected through the Tetrolet-DCT transformation, and a visual feature sequence is generated; extracting an encrypted watermark according to the visual characteristic sequence and the binary logic key sequence, and decrypting to obtain a restored watermark; and carrying out normalized correlation coefficient calculation on the original watermark and the restored watermark, and determining ownership and watermark information of the medical image to be detected. The zero watermark embedded in the method has invisibility and robustness, and can protect the privacy information of patients and the data security of medical images.
Description
Technical Field
The invention relates to the field of multimedia signal processing, in particular to a medical image robust watermarking method based on Tetrolet-DCT.
Background
With the development of modern technology and information, humans have entered the information age. Medical field is no exception, and medical image is with the help of computer for medical image uses and study in a larger scope, and simultaneously, medical image is patient privacy in fact, so probably suffers the tampering in the network transmission process, problem such as misappropriation.
Since the transmission of a large number of medical images over a network presents security problems and medical data is not allowed to be modified, it is necessary to provide effective protection for the medical images. However, the traditional digital watermark embedding method is only used for protecting copyright of digital media, cannot protect the medical image, is easy to cause defects on original image data modification, has less research on the digital watermark algorithm of the medical image at present, and has less research results on the zero watermark algorithm of the medical data resisting geometric attacks.
Therefore, how to embed a digital robust watermark in medical data and not allow to modify the content of the medical data is a technical problem to be solved by those skilled in the art.
Disclosure of Invention
Therefore, the invention aims to provide a medical image robust watermarking method based on Tetrolet-DCT, and the embedded watermark has better robustness and invisibility in geometric attack resistance and conventional attack resistance, and can simultaneously protect privacy information of patients and data security of medical images. The specific scheme is as follows:
a tetrol-DCT based medical image robust watermarking method, comprising:
extracting features of an original medical image through Tetrolet-DCT (discrete cosine transform) to obtain a coefficient matrix of the original medical image, and generating a visual feature sequence of the original medical image by utilizing Hash function operation;
performing chaotic scrambling encryption on an original watermark to obtain an encrypted chaotic scrambling watermark, embedding watermark information into the original medical image according to the obtained chaotic scrambling watermark and a generated visual characteristic sequence of the original medical image, and simultaneously obtaining a binary logic key sequence and storing the binary logic key sequence in a third party;
extracting features of a medical image to be detected through Tetrolet-DCT (discrete cosine transform) to obtain a coefficient matrix of the medical image to be detected, and generating a visual feature sequence of the medical image to be detected by utilizing Hash function operation;
extracting an encrypted watermark according to the generated visual characteristic sequence of the medical image to be detected and the binary logic key sequence stored in a third party, and decrypting the extracted encrypted watermark to obtain a restored watermark;
and carrying out normalized correlation coefficient calculation on the original watermark and the restored watermark, and determining ownership of the medical image to be detected and embedded watermark information.
Preferably, in the foregoing robust watermarking method for medical images based on tetrol-DCT according to the embodiment of the present invention, feature extraction is performed on an original medical image by tetrol-DCT transformation, so as to obtain a coefficient matrix of the original medical image, which specifically includes:
decomposing an original medical image through Tetrolet transformation to obtain a Tetrolet coefficient of the original medical image after secondary decomposition;
DCT transformation is carried out on the tetrol coefficient of the original medical image, and a coefficient matrix of the original medical image is obtained.
Preferably, in the foregoing robust watermarking method for medical images based on tetrelet-DCT according to the embodiment of the present invention, the generating the visual feature sequence of the original medical image by using hash function operation specifically includes:
selecting a matrix of 4*8 at a coefficient matrix low frequency of the original medical image to form a new matrix;
and generating a visual characteristic sequence of the 32-bit original medical image by utilizing hash function operation.
Preferably, in the method for robust watermarking of medical images based on tetrelet-DCT provided by the embodiment of the present invention, the chaotic scrambling encryption is performed on the original watermark, so as to obtain an encrypted chaotic scrambling watermark, which specifically includes:
generating a chaotic sequence through a Logistic Map;
sorting the generated chaotic sequence according to the order from small to large;
and scrambling the position space of the original watermark pixels according to the position change before and after each value in the chaotic sequence to obtain the encrypted chaotic scrambling watermark.
Preferably, in the method for robust watermarking of a medical image based on tetrol-DCT provided by the embodiment of the present invention, watermark information is embedded into the original medical image according to the obtained chaotic scrambling watermark and the generated visual feature sequence of the original medical image, and the method specifically includes:
and performing exclusive OR operation on the generated visual characteristic sequence and the obtained chaotic scrambling watermark bit by bit so as to embed watermark information into the original medical image.
Preferably, in the method for robust watermarking of medical images based on tetrol-DCT provided by the embodiment of the present invention, feature extraction is performed on medical images to be measured through tetrol-DCT transformation, so as to obtain coefficient matrices of the medical images to be measured, which specifically includes:
decomposing the medical image to be detected through Tetrolet transformation to obtain a Tetrolet coefficient of the medical image to be detected after secondary decomposition;
DCT transformation is carried out on the tetrol coefficient of the medical image to be detected, and a coefficient matrix of the medical image to be detected is obtained.
Preferably, in the method for robust watermarking of medical images based on tetrelet-DCT provided by the embodiment of the present invention, the generating the visual feature sequence of the medical image to be measured by using hash function operation specifically includes:
selecting a matrix of 4*8 at a coefficient matrix low frequency of the medical image to be detected to form a new matrix;
and generating a visual characteristic sequence of the medical image to be detected with 32 bits by utilizing Hash function operation.
Preferably, in the method for robust watermarking of medical images based on tetrol-DCT provided by the embodiment of the present invention, the extracting the encrypted watermark according to the generated visual feature sequence of the medical image to be detected and the binary logic key sequence stored in the third party specifically includes:
and performing exclusive OR operation on the generated visual characteristic sequence of the medical image to be detected and the binary logic key sequence stored in the third party to extract the encrypted watermark.
Preferably, in the method for robust watermarking of medical images based on tetrol-DCT provided by the embodiment of the present invention, decrypting the extracted encrypted watermark to obtain a restored watermark, including:
generating the chaotic sequence through a Logistic Map;
sorting the generated chaotic sequence according to the order from small to large;
and restoring the position space of the original watermark pixels according to the position change before and after each value in the chaotic sequence to obtain a restored watermark.
From the above technical solution, the medical image robust watermarking method based on tetrol-DCT provided by the invention includes: extracting features of the original medical image through Tetrolet-DCT transformation to obtain a coefficient matrix of the original medical image, and generating a visual feature sequence of the original medical image by utilizing Hash function operation; performing chaotic scrambling encryption on an original watermark to obtain an encrypted chaotic scrambling watermark, embedding watermark information into the original medical image according to the obtained chaotic scrambling watermark and a generated visual characteristic sequence of the original medical image, and simultaneously obtaining a binary logic key sequence and storing the binary logic key sequence in a third party; extracting features of the medical image to be detected through Tetrolet-DCT transformation to obtain a coefficient matrix of the medical image to be detected, and generating a visual feature sequence of the medical image to be detected by utilizing Hash function operation; extracting an encrypted watermark according to the generated visual characteristic sequence of the medical image to be detected and a binary logic key sequence stored in a third party, and decrypting the extracted encrypted watermark to obtain a restored watermark; and carrying out normalized correlation coefficient calculation on the original watermark and the restored watermark, and determining ownership of the medical image to be detected and embedded watermark information.
The medical image robust watermarking method based on the tetrol-DCT comprises five parts of feature vector extraction, watermark encryption, watermark embedding, watermark extraction and watermark decryption based on the tetrol-DCT, the feature vector, cryptography, hash function and zero watermark embedding technology obtained through the tetrol-DCT are combined, personal information of a patient can be hidden in the medical image by utilizing the characteristics of invisibility, robustness and the like of the zero watermark, so that the safe transmission of the personal information of the patient on the Internet is ensured, the zero watermark can avoid tampered medical data, the defect that the traditional digital watermarking method cannot protect the medical image and cause defects to original image data modification is effectively overcome, the anti-geometric attack and conventional attack functions of the zero watermark of the embedded medical image can be simultaneously protected, the privacy information of the patient and the data security of the medical image can be simultaneously protected, and the practical and standardized of the third party network technology can be adapted by utilizing the concept of the third party.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the related art, the drawings that are required to be used in the embodiments or the related technical descriptions will be briefly described, and it is apparent that the drawings in the following description are only embodiments of the present invention, and other drawings may be obtained according to the provided drawings without inventive effort for those skilled in the art.
FIG. 1 is a flowchart of a medical image robust watermarking method based on Tetrolet-DCT provided by an embodiment of the invention;
fig. 2 is a schematic diagram of a tetrol et transformation structure according to an embodiment of the present invention;
FIG. 3 is a raw medical image provided by an embodiment of the present invention;
FIG. 4 is an original watermark image provided by an embodiment of the present invention;
fig. 5 is an encrypted watermark image provided in an embodiment of the present invention;
FIG. 6 is a watermark extracted without interference according to an embodiment of the present invention;
FIG. 7 is a medical image of the Gaussian noise disturbance intensity of 3% provided by an embodiment of the invention;
FIG. 8 is a watermark extracted when the Gaussian noise interference strength is 3% according to an embodiment of the present invention;
FIG. 9 is a medical image with 40% compression quality JPEG compression according to an embodiment of the present invention;
FIG. 10 is a watermark extracted during JPEG compression with a compression quality of 40% according to an embodiment of the present invention;
FIG. 11 is a median filtered medical image with a window size of [3x3] and a number of 10 filters provided in an embodiment of the present invention;
FIG. 12 is a watermark extracted after median filtering for 10 times of filtering, with a window size of [3x3] provided in an embodiment of the present invention;
FIG. 13 is a median filtered medical image with a window size of [5x5] and a number of 10 filters provided in an embodiment of the present invention;
fig. 14 shows a watermark extracted after median filtering for 10 times of filtering, with a window size of [5x5] provided in an embodiment of the present invention;
FIG. 15 is a medical image rotated 10 clockwise provided by an embodiment of the present invention;
fig. 16 is a watermark extracted when rotated 10 ° clockwise provided by an embodiment of the present invention;
FIG. 17 is a medical image rotated 3 clockwise as provided by an embodiment of the present invention;
fig. 18 is a watermark extracted when rotated 3 ° clockwise provided by an embodiment of the present invention;
FIG. 19 is a medical image provided with 0.25 zoom in and out according to an embodiment of the present invention;
FIG. 20 is a watermark extracted at 0.25 times scale provided by an embodiment of the present invention;
FIG. 21 is a medical image provided by an embodiment of the present invention shifted horizontally to the left by 5%;
FIG. 22 is a watermark extracted when the level is shifted 5% to the left provided by an embodiment of the present invention;
FIG. 23 is a medical image with 20% vertical shift provided by an embodiment of the present invention;
fig. 24 is a watermark extracted when 20% is shifted vertically according to an embodiment of the present invention;
FIG. 25 is a view of a medical image cut 10% along the Y-axis provided by an embodiment of the present invention;
FIG. 26 is a watermark extracted when 10% of the watermark is sheared along the Y-axis provided by an embodiment of the present invention;
FIG. 27 is a view of a medical image cut 1% along the X-axis provided by an embodiment of the present invention;
fig. 28 is a watermark extracted when 1% of the watermark is cut along the X-axis provided by an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention provides a medical image robust watermarking method based on tetrol-DCT, which is shown in figure 1 and comprises the following steps:
s101, extracting features of an original medical image through Tetrolet-DCT (discrete cosine transform) to obtain a coefficient matrix of the original medical image, and generating a visual feature sequence of the original medical image by utilizing Hash function operation;
in practical application, before executing step S101, a meaningful binary text image is selected as an original watermark of the embedded medical image, denoted as w= { W (i, j) |w (i, j) =0, 1; i is more than or equal to 1 and less than or equal to M 1 ,1≤j≤M 2 Size of original watermark image M 1 *M 2 ，M 1 And M 2 The sizes of the original watermark images are respectively longer and wider, the watermark is used for protecting personal information of a patient, and the personal information of the patient can be hidden in the medical image of the patient, so that the safe transmission on a network is realized. Meanwhile, selecting a 512 x 512 medical image as an original medical image, and marking the original medical image as I (I, j); w (I, j) and I (I, j) represent pixel gray values of the original watermark and the original medical image, respectively;
s102, performing chaotic scrambling encryption on an original watermark to obtain an encrypted chaotic scrambling watermark, embedding watermark information into the original medical image according to the obtained chaotic scrambling watermark and a generated visual characteristic sequence of the original medical image, and simultaneously obtaining a binary logic key sequence and storing the binary logic key sequence in a third party;
s103, extracting features of the medical image to be detected through Tetrolet-DCT transformation to obtain a coefficient matrix of the medical image to be detected, and generating a visual feature sequence of the medical image to be detected by utilizing Hash function operation;
it can be understood that the medical image to be detected can be considered as a medical image formed by the original medical image possibly subjected to geometric attacks such as Gaussian noise interference, median filtering, compression, rotation, translation and the like or conventional attacks in the network transmission process;
s104, extracting an encrypted watermark according to the generated visual feature sequence of the medical image to be detected and the binary logic key sequence stored in the third party, and decrypting the extracted encrypted watermark to obtain a restored watermark;
s105, carrying out normalized correlation coefficient calculation on the original watermark and the restored watermark, and determining ownership of the medical image to be detected and embedded watermark information.
In the robust watermarking method of the medical image based on the tetrol-DCT provided by the embodiment of the invention, the robust watermarking method comprises five parts of feature vector extraction, watermark encryption, watermark embedding, watermark extraction and watermark decryption based on the tetrol-DCT, the feature vector, cryptography, hash function and zero watermark embedding technology obtained through the tetrol-DCT are combined, the characteristics of invisibility, robustness and the like of the zero watermark are utilized, personal information of a patient can be hidden in the medical image of the patient, so that the safe transmission of the personal information on the Internet is ensured, the tampered medical data can be avoided by the zero watermark, and the related patient information required by remote medical diagnosis is realized, so that the defects that the traditional digital watermarking method cannot protect the medical image and cause defects to original image data modification are effectively overcome, the geometrical attack resistance and the conventional attack resistance functions of the zero watermark of the embedded medical image can be simultaneously protected, the privacy information of the patient and the data security of the medical image can be realized, and the practical attack and standardization of the current network technology can be adapted by utilizing the concept of a third party.
In a specific implementation, in the method for robust watermarking of a medical image based on tetrol-DCT provided in the embodiment of the present invention, step S101 performs feature extraction on an original medical image through tetrol-DCT transformation to obtain a coefficient matrix of the original medical image, which may specifically include: firstly, decomposing an original medical image I (I, J) through Tetrolet transformation to obtain a high-pass coefficient and a low-pass coefficient, and decomposing the low-pass coefficient at the next stage to finally obtain a unit array b0 of the Tetrolet coefficient (namely 2 (J+1)) of the original medical image I (I, J) after the second-stage decomposition; then, DCT-transforming the tetrelet coefficients (selected b0{1,1 }) of the original medical image I (I, j) to obtain a coefficient matrix D (I, j) of the original medical image I (I, j):
D(i,j)＝DCT2(b0{1,1}(i,j))
it should be noted that, the construction of Tetrolet transformation is similar to wedglet transformation, haar function is applied to the edge part, multi-scale sparse transformation can be performed on the basis of better describing the image set characteristics, and the transformed image coefficients are concentrated. The method is initially applied to a polygonal split plate in a jigsaw game, and Krommweh refers to the multi-scale multi-directional transform as a tetrol transform. Firstly, dividing an image into N multiplied by N (N is a natural number) sub-blocks, and defining different types of jointed boards with the size of N multiplied by N (N is a natural number) in each sub-block according to the geometric structure of the image to realize multi-scale division of the image.
Let an image F (i, j) of size n x n i＝0,j＝0 The main steps of the Tetrolet transformation and decomposition (the jointed board dimension is 4) are as follows:
step one, F (i, j) i＝0,j＝0 Decomposing into 4×4 sub-blocks; thereby obtaining a 2×2 low-pass portion and a 12×1 high-pass portion;
step two, high-pass coefficient H of the image i,j Reserving and not processing;
step three, low-pass coefficient L of image i,j Different jointed boards are defined according to image set characteristics to be segmented, and Haar wavelet transformation is applied to obtain a high-frequency coefficient H 'on the basis of the segmentation' i,j And a low pass coefficient L' i,j Selecting small tetrelet coefficients to perform better sparse representation on the image;
step four, rearranging the high frequency coefficient H' i,j And a low frequency coefficient L' i,j Continuing to decompose the lower layer;
step five, repeating the operations from step two to step four on the low-pass coefficient obtained after each decomposition until the requirement of sparse representation of the image is met;
step six, after the operations from the step one to the step five, obtaining a decomposed image sequence
As shown in fig. 2, tetrol is adaptive based on geometric characteristics in a block image, so that tetrol transformation can better maintain information such as image edges and directional textures, and is very effective in image compression, denoising and nonlinear approximation.
In addition, the two-dimensional Discrete Cosine Transform (DCT) is formulated as follows:
wherein F (x, y) is the pixel value of the point (x, y), F (u, v) is the 2D-DCT transform coefficient of F (x, y), and the size of the image is m×n.
Further, in a specific implementation, in the method for robust watermarking of medical images based on tetrol-DCT provided in the embodiment of the present invention, step S101 generates a visual feature sequence of an original medical image by using a hash function operation, which may specifically include: firstly, selecting a matrix of 4*8 at low frequency of a coefficient matrix D (I, j) of an original medical image I (I, j) to form a new matrix A (I, j); then, a visual feature sequence V (I, j) of the 32-bit original medical image I (I, j) is generated using a hash function operation.
It should be noted that, the main reason why most medical image watermarking algorithms have poor resistance to geometric attacks is as follows: the digital watermark is embedded in the pixel or transformation coefficient, and the slight geometric transformation of the medical image often causes a large change in the pixel value or transformation coefficient value, so that the embedded watermark is easily attacked. Through experimental data, the invention combines the tetrol transformation and the DCT transformation of the medical image, and can find a proper feature vector. When a medical image is subjected to conventional geometric transformations, the magnitude of the DCT low-IF coefficient values may vary somewhat, but their coefficient signs remain substantially unchanged. Experimental data after selecting some conventional attacks and geometric attacks are shown in table one:
table-image full-image tetrol-DCT conversion low intermediate frequency part coefficient and variation value after different attacks
tetrelet-DCT coefficient units 1.0e+04, the correlation coefficient takes the 16bit comparison result.
Column 1 of table one shows the type of medical image that is attacked. The coefficient units 1.0e+04 in columns 4 to 7, the correlation coefficient taking 16bit comparison result is D (1, 1) -D (1, 4) taken in the tetrol-DCT coefficient matrix, for a total of 4 low intermediate frequency coefficients, for a total of 4x2=8 low intermediate frequency coefficients. For conventional attacks, the sign of these low intermediate frequency coefficient values remains substantially unchanged, approximately equal to the medical image values; for geometrical attacks, part of the coefficients change greatly, but it can be found that when the medical image is subjected to geometrical attacks, the magnitudes of part of the tetrelet-DCT low intermediate frequency coefficients change, but the signs of the medical image are not changed basically. The invention uses positive tetrelet-DCT coefficient as 1 (coefficient with zero value), and negative coefficient as 0, then for medical image, the corresponding coefficient sign sequence of D (1, 1) -D (1, 4) coefficient in tetrelet-DCT coefficient matrix is: "1100 0010", see Table 8, which shows that the symbol sequence and the original medical image can remain similar regardless of conventional or geometric attacks, the normalized correlation coefficient with the original medical image is large (see column 9), and 4 DCT coefficient symbols are taken here for convenience.
According to human visual characteristics (HVS), the low-intermediate frequency signal has a large visual impact on humans, representing the main features of medical images. The visual feature vector of the selected medical image is a sign of low intermediate frequency coefficients, the number of the low intermediate frequency coefficients is selected to be related to the size of the original medical image subjected to the full-image tetrelet-DCT conversion and the correlation between the medical images, and the smaller the L value is, the higher the correlation is. In the latter test, the length of L was chosen to be 32.
In a specific implementation, in the robust watermarking method for medical images based on Tetrolet-DCT provided by the embodiment of the present invention, step S102 performs chaotic scrambling encryption on an original watermark to obtain an encrypted chaotic scrambling watermark, which may specifically include: first, according to the initial value x 0 Generating a chaotic sequence X (j) through a Logistic Map; wherein, the initial value of the chaos coefficient is set to 0.2, the growth parameter is 4, and the iteration number is 32; then, sorting the generated chaotic sequence X (j) according to the order from small to large; scrambling the position space of the pixels of the original watermark W (i, j) according to the position change before and after each value sequence in the chaotic sequence X (j) to obtainThe encrypted chaos scrambling watermark BW (i, j).
It should be noted that, the watermark is scrambled and encrypted by utilizing the property of the Logistic Map, where the Logistic Map is one of the best known chaotic mappings, and is a simple dynamic nonlinear regression with chaotic behavior, and its mathematical definition can be expressed as follows:
X K+1 ＝μ·X K ·(1-X K )
wherein X is K Belonging to (0, 1), 0<u is less than or equal to 4; experiments show that when 3.5699456<And when u is less than or equal to 4, the Logistic mapping enters a chaotic state, and the Logistic chaotic sequence can be used as an ideal key sequence.
Further, in a specific implementation, in the robust watermarking method for medical images based on tetrol-DCT provided by the embodiment of the present invention, step S102 embeds watermark information into an original medical image according to the obtained chaotic scrambling watermark and a generated visual feature sequence of the original medical image, and simultaneously obtains a binary logic key sequence and stores the binary logic key sequence in a third party, which specifically may include: performing exclusive OR operation on the generated visual characteristic sequence V (i, j) and the obtained chaotic scrambling watermark BW (i, j) bit by bit so as to embed watermark information into an original medical image, and simultaneously acquiring a binary logic Key sequence Key (i, j):
the Key (i, j) is stored for use by a third party in later watermark extraction. By applying Key (i, j) as a secret Key to a third party, ownership and use rights of an original medical image can be obtained, so that the purpose of protecting the medical image is achieved.
In a specific implementation, in the method for robust watermarking of a medical image based on tetrol-DCT provided in the embodiment of the present invention, step S103 performs feature extraction on the medical image to be measured through tetrol-DCT transformation to obtain a coefficient matrix of the medical image to be measured, which may specifically include: firstly, decomposing a medical image I '(I, j) to be detected through Tetrolet transformation to obtain Tetrolet coefficients (such as a cell array b1 (2 multiplied by 3 cells)) of the medical image I' (I, j) to be detected after secondary decomposition; DCT transformation is carried out on the tetrelet coefficient (b 1{1,1 }) of the medical image I ' (I, j) to be detected, so as to obtain a coefficient matrix D ' (I, j) of the medical image I ' (I, j) to be detected:
D′(i,j)＝DCT2(b1{1,1}(i,j))
further, in a specific implementation, in the method for robust watermarking of medical images based on tetrol-DCT provided in the embodiment of the present invention, step S103 generates a visual feature sequence of a medical image to be detected by using a hash function operation, which may specifically include: selecting a matrix of 4*8 at a low frequency of a coefficient matrix D ' (I, j) of the medical image I ' (I, j) to be detected to form a new matrix A ' (I, j); and generating a visual characteristic sequence V '(I, j) of the 32-bit medical image I' (I, j) to be detected by utilizing Hash function operation.
In a specific implementation, in the method for robust watermarking of medical images based on tetrol-DCT provided by the embodiment of the present invention, step S104 extracts an encrypted watermark according to a generated visual feature sequence of a medical image to be detected and a binary logic key sequence stored in a third party, which may specifically include: performing exclusive or operation on the generated visual characteristic sequence V ' (I, j) of the medical image I ' (I, j) to be detected and a binary logic Key sequence Key (I, j) stored in a third party to extract an encrypted watermark BW ' (I, j):
the algorithm only needs a Key Key (i, j) when extracting the watermark, does not need the participation of an original image, and is a zero watermark extraction algorithm.
In a specific implementation, in the method for robust watermarking of medical images based on tetrol-DCT provided by the embodiment of the present invention, step S104 decrypts the extracted encrypted watermark to obtain a restored watermark, which may specifically include: similar to the watermark encryption method, the same chaotic sequence X (j) is generated through a Logistic Map; sorting the generated chaotic sequence X (j) according to the order from small to large; and restoring the position space of the original watermark pixels according to the position change before and after each value sequence in the chaotic sequence X (j) to obtain a restored watermark W' (i, j).
Specifically, the foregoing detailed description of each step can be simply understood as: firstly, chaotic scrambling encryption is carried out on a watermark in a frequency domain by utilizing the property of a Logistic Map; then extracting a feature vector through Tetrolet-DCT transformation on the medical image to embed the watermark, correlating the feature vector with the binary watermark to obtain a binary logic sequence, and storing the binary sequence in a third party; extracting the characteristic vector of the medical image to be detected by Tetrolet-DCT transformation, and extracting the watermark by correlating the characteristic vector with a binary sequence stored in a third party.
Step S105 may then be performed to determine ownership of the medical image and embedded watermark information by calculating the normalized correlation coefficients NC of W (i, j) and W' (i, j).
It should be noted that, the number similarity between the embedded original watermark and the extracted restored watermark is measured by using a Normalized Cross-correlation (NC) method, which is defined as:
wherein W (i, j) represents the feature vector of the original watermark image, and the length of the feature vector is 32 bits; w' (i, j) represents a feature vector of the restored watermark image, which is also 32 bits. The normalized correlation coefficient is a method for measuring the similarity of two images, and the similarity of the images can be estimated more accurately by data observability through solving the normalized correlation coefficient.
In addition, it should be noted that the distortion degree of the picture can be expressed by peak signal to noise ratio (PSNR) according to the present invention, when the PSNR value is larger, the distortion degree of the picture is smaller.
The peak signal to noise ratio is formulated as follows:
where the pixel value of each point of the image is I (I, j), the average pixel value of the image is I' (I, j), and for convenience of operation, the digital image is usually represented by a pixel matrix, i.e., m=n. Peak signal-to-noise ratio is an engineering term that represents the ratio of the maximum possible power of a signal to the destructive noise power affecting his presentation accuracy, and is generally used as an objective evaluation criterion for medical image quality.
The invention is further described below with reference to the accompanying drawings: as shown in fig. 3, the subject of the experimental test is a 512 x 512 head medical image, denoted by I (I, j), where 1.ltoreq.i, j.ltoreq.512. Selecting a meaningful binary image as an original watermark, and marking as: w= { W (i, j) |w (i, j) =0, 1; 1.ltoreq.i.ltoreq.M1, 1.ltoreq.j.ltoreq.M2, as shown in FIG. 4, where the watermark has a size of 32.times.32.
The original image is first processed by tetrelet transformation, DCT transformation is performed on the tetrelet coefficient after the second-level decomposition, and 32 coefficients, namely a 4*8 module, are taken in consideration of robustness and capacity of embedding watermark at one time. The initial value of the chaos coefficient is set to 0.2, the increment parameter is 4, and the iteration number is 32. The original watermark W (i, j) is then chaotic scrambling encrypted, and fig. 5 shows the encrypted chaotic scrambling watermark. After W' (i, j) is detected by the watermark algorithm, whether watermark embedding exists or not is judged by calculating a normalized correlation coefficient NC, and when the value of the normalized correlation coefficient NC is closer to 1, the similarity is higher, so that the robustness of the algorithm is judged. The degree of distortion of a picture expressed by PSNR is smaller as the PSNR value is larger.
Fig. 6 shows the watermark extracted without interference, and it can be seen that nc=1.00, the watermark can be extracted accurately.
The conventional attack resistance and the geometric attack resistance of the digital watermarking method are judged through specific examples.
First, adding gaussian noise: gaussian noise is added to the watermark using the imnoise () function.
And the second table is experimental data of watermark anti-Gaussian noise interference. It can be seen from table two that when the gaussian noise intensity is up to 15%, the PSNR of the image after attack is reduced to 10.61dB, and the watermark extracted at this time has a correlation coefficient nc=0.93, so that the watermark can still be extracted more accurately, and the overall data is above 1.00. This illustrates that gaussian noise can be resisted with the invention. FIG. 7 shows a medical image at a Gaussian noise intensity of 3%; fig. 8 shows the watermark extracted at a gaussian noise intensity of 3%, nc=1.00.
anti-Gaussian noise interference data of surface two watermarks
| Noise intensity (%) | 1 | 3 | 5 | 10 | 15 | 20 |
| PSNR(dB) | 20.46 | 16.24 | 14.30 | 11.86 | 10.61 | 9.79 |
| NC | 1.00 | 1.00 | 0.94 | 0.90 | 0.93 | 0.89 |
Second, JPEG compression processing
JPEG compression is carried out on the head medical image by taking the image compression quality percentage as a parameter; and the third table is the experimental data of the watermark for resisting JPEG compression. When the compression quality is 1%, the image quality is low, and the watermark can still be extracted, nc=1.00. FIG. 9 shows a medical image with a compression quality of 40%; fig. 10 shows a watermark extracted with a compression quality of 40%, nc=1.00, which can be accurately extracted.
Table three watermark JPEG compression resistant experimental data
| Compression mass (%) | 1 | 5 | 10 | 20 | 40 | 60 | 80 |
| PSNR(dB) | 26.28 | 28.44 | 31.29 | 33.81 | 35.46 | 36.42 | 37.82 |
| NC | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
Third, median filtering
Table four is the median filtering resistance of the watermark of the medical image, and as seen from table four, when the median filtering parameter is [3x3], and the number of filtering repetitions is 5, the existence of the watermark can still be measured, nc=1.00. FIG. 11 shows a medical image with median filter parameters [3x3], filter repetition number 10, the image having been blurred; fig. 12 shows a watermark extracted at a median filter parameter of [3x3] and a filter repetition number of 10, nc=1.00, and the watermark can be extracted. FIG. 13 shows a medical image with a median filter parameter of [5x5] and a filter repetition number of 10; fig. 14 shows a watermark extracted at a median filter parameter of [5x5] and a filter repetition number of 10, nc=1.00, and the watermark can be extracted.
Table four watermark median filtering resisting experimental data
Fourth, rotation conversion
Table five is watermark anti-rotation attack experimental data. From table five it can be seen that nc=0.73, the watermark can still be extracted when the image is rotated 20 ° clockwise. Fig. 15 shows a medical image rotated 10 ° in time; fig. 16 shows that watermark extracted by rotating 10 ° in time, nc=0.81, can be extracted accurately. Fig. 17 shows a medical image rotated by 3 ° in time; fig. 18 shows that watermark extracted by rotating 3 ° in time, nc=0.81, can be extracted accurately.
Anti-rotation attack experimental data of five-watermark table
| Degree of rotation ° | 2 | 4 | 6 | 8 | 10 | 20 |
| PSNR(dB) | 22.36 | 19.02 | 17.24 | 16.21 | 15.60 | 14.68 |
| NC | 0.81 | 0.81 | 0.81 | 0.81 | 0.81 | 0.73 |
Fifth, scaling transform
Table six is experimental data of watermark anti-scaling attack of medical image, and it can be seen from table six that when the scaling factor is as small as 0.25, the correlation coefficient nc=0.95 can extract the watermark. FIG. 19 shows a scaled medical image (scale factor of 0.25); fig. 20 shows the watermark extracted after a scaling attack, nc=0.95, and the extracted watermark can be accurately obtained.
Table six watermark anti-scaling attack experimental data
| Scaling factor | 0.25 | 0.5 | 0.75 | 1 | 1.5 | 2.0 | 2.5 |
| NC | 0.95 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
Sixth, translation transformation
Table seven is watermark anti-translation transformation experimental data. When the image data moves vertically by 20% from the seventh table, NC values are higher than 0.65, so that the watermark can be extracted accurately, and the watermark method has strong translation transformation resistance. FIG. 21 shows the image after a 5% horizontal left shift of the medical image; fig. 22 shows the watermark extracted after shifting horizontally by 5%, and the watermark can be accurately extracted, nc=0.92. FIG. 23 shows the image after the medical image has been shifted down vertically by 20%; fig. 24 shows that the watermark extracted after being shifted down by 20% vertically can be extracted accurately, nc=0.65.
Table seven watermark anti-translation transformation experimental data
| Downshifting distance (%) | 2 | 4 | 6 | 8 | 12 | 15 | 20 |
| PSNR(dB) | 16.76 | 15.28 | 15.03 | 14.93 | 14.60 | 14.28 | 13.78 |
| NC | 1 | 1 | 1 | 0.94 | 0.87 | 0.76 | 0.65 |
Seventh, shearing attack
Table eight is watermark anti-shearing attack experimental data, and it can be seen from Table eight that when medical images are sheared along coordinate axis Y, and shearing amount is 20%, NC value is greater than 0.5, watermark can still be extracted, and the watermark algorithm has strong anti-shearing attack capability. FIG. 25 shows the medical image after 10% clipping along the Y-axis; fig. 26 shows that the watermark extracted after cutting 10% along the Y-axis can be accurately extracted, nc=0.82. FIG. 27 shows the medical image after 1% clipping along the X-axis; fig. 28 shows that the watermark extracted after 1% of clipping along the X-axis can be accurately extracted, nc=0.92.
Table eight watermark anti Y axis direction shearing attack experimental data
| Y-direction shear (%) | 2 | 6 | 9 | 15 | 18 | 20 |
| NC | 0.95 | 0.83 | 0.83 | 0.66 | 0.60 | 0.53 |
From the description, the PHTs-DCT-based medical image digital watermarking technology has better robustness, and can still accurately extract the watermark aiming at conventional attacks such as Gaussian noise, JPEG compression processing, median filtering processing and geometric attacks such as rotation transformation, scaling transformation, translation transformation, shearing attack and the like, and has stronger capability of resisting the conventional attacks and the geometric attacks.
Those of skill would further appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, in computer software, or in a combination of the two, and that the elements and steps of the examples have been generally described in terms of function in the foregoing description to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software modules may be disposed in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The medical image robust watermarking method based on Tetrolet-DCT provided by the embodiment of the invention comprises the following steps: extracting features of the original medical image through Tetrolet-DCT transformation to obtain a coefficient matrix of the original medical image, and generating a visual feature sequence of the original medical image by utilizing Hash function operation; performing chaotic scrambling encryption on an original watermark to obtain an encrypted chaotic scrambling watermark, embedding watermark information into the original medical image according to the obtained chaotic scrambling watermark and a generated visual characteristic sequence of the original medical image, and simultaneously obtaining a binary logic key sequence and storing the binary logic key sequence in a third party; extracting features of the medical image to be detected through Tetrolet-DCT transformation to obtain a coefficient matrix of the medical image to be detected, and generating a visual feature sequence of the medical image to be detected by utilizing Hash function operation; extracting an encrypted watermark according to the generated visual characteristic sequence of the medical image to be detected and a binary logic key sequence stored in a third party, and decrypting the extracted encrypted watermark to obtain a restored watermark; and carrying out normalized correlation coefficient calculation on the original watermark and the restored watermark, and determining ownership of the medical image to be detected and embedded watermark information. According to the medical image robust watermarking method based on the tetrol-DCT, the feature vector, the cryptography, the hash function and the zero watermark embedding technology obtained through the tetrol-DCT are combined, the characteristics of invisibility, robustness and the like of the zero watermark are utilized, personal information of a patient can be hidden in the medical image of the patient so as to ensure safe transmission of the personal information on the Internet, and the zero watermark can avoid tampered medical data, so that relevant patient information required by remote medical diagnosis is realized, the defect that the traditional digital watermarking method cannot protect the medical image and cause defects to original image data is effectively overcome, the geometrical attack resistance and the conventional attack function of the zero watermark of the embedded medical image can be realized, the privacy information of the patient and the data security of the medical image can be simultaneously protected, and the third party concept is utilized, so that the practical and standardized network technology is adapted.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The robust watermarking method for medical images based on tetrol-DCT provided by the invention is described in detail above, and specific examples are applied to illustrate the principle and the implementation of the invention, and the description of the above examples is only used for helping to understand the method and the core idea of the invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.
Claims (7)
1. A tetrol-DCT based medical image robust watermarking method, comprising:
decomposing an original medical image through Tetrolet transformation to obtain a high-pass coefficient and a low-pass coefficient, and decomposing the low-pass coefficient at the next stage to obtain a Tetrolet coefficient of the original medical image after the second-stage decomposition;
DCT transformation is carried out on the Tetrolet coefficient of the original medical image to obtain a coefficient matrix of the original medical image, and a visual feature sequence of the original medical image is generated by utilizing Hash function operation;
performing chaotic scrambling encryption on an original watermark to obtain an encrypted chaotic scrambling watermark, embedding watermark information into the original medical image according to the obtained chaotic scrambling watermark and a generated visual characteristic sequence of the original medical image, and simultaneously obtaining a binary logic key sequence and storing the binary logic key sequence in a third party;
decomposing the medical image to be detected through Tetrolet transformation to obtain a high-pass coefficient and a low-pass coefficient, and decomposing the low-pass coefficient at the next stage to obtain a Tetrolet coefficient of the medical image to be detected after the second-stage decomposition;
DCT transformation is carried out on the tetrol coefficient of the medical image to be detected, a coefficient matrix of the medical image to be detected is obtained, and a visual feature sequence of the medical image to be detected is generated by utilizing Hash function operation;
extracting an encrypted watermark according to the generated visual characteristic sequence of the medical image to be detected and the binary logic key sequence stored in a third party, and decrypting the extracted encrypted watermark to obtain a restored watermark;
and carrying out normalized correlation coefficient calculation on the original watermark and the restored watermark, and determining ownership of the medical image to be detected and embedded watermark information.
2. The tetrol-DCT based medical image robust watermarking method according to claim 1, characterized in that the visual feature sequence of the original medical image is generated by means of a hash function operation, in particular comprising:
selecting a matrix of 4*8 at a coefficient matrix low frequency of the original medical image to form a new matrix;
and generating a visual characteristic sequence of the 32-bit original medical image by utilizing hash function operation.
3. The tetrol-DCT-based medical image robust watermarking method according to claim 2, characterized in that the original watermark is chaotically scrambled and encrypted to obtain an encrypted chaos scrambled watermark, and specifically comprising:
generating a chaotic sequence through a Logistic Map;
sorting the generated chaotic sequence according to the order from small to large;
and scrambling the position space of the original watermark pixels according to the position change before and after each value in the chaotic sequence to obtain the encrypted chaotic scrambling watermark.
4. A tetrol-DCT-based medical image robust watermarking method according to claim 3, characterized in that watermark information is embedded into the original medical image according to the obtained chaotic scrambling watermark and the generated visual feature sequence of the original medical image, in particular comprising:
and performing exclusive OR operation on the generated visual characteristic sequence and the obtained chaotic scrambling watermark bit by bit so as to embed watermark information into the original medical image.
5. The tetrol-DCT-based medical image robust watermarking method according to claim 1, characterized in that the visual feature sequence of the medical image to be detected is generated by using a hash function operation, and specifically comprising:
selecting a matrix of 4*8 at a coefficient matrix low frequency of the medical image to be detected to form a new matrix;
and generating a visual characteristic sequence of the medical image to be detected with 32 bits by utilizing Hash function operation.
6. The tetrol-DCT-based medical image robust watermarking method according to claim 5, characterized in that the extracting the encrypted watermark according to the generated visual feature sequence of the medical image to be detected and the binary logical key sequence stored in the third party specifically comprises:
and performing exclusive OR operation on the generated visual characteristic sequence of the medical image to be detected and the binary logic key sequence stored in the third party to extract the encrypted watermark.
7. A tetrol-DCT-based medical image robust watermarking method according to claim 3, characterized in that decrypting the extracted encrypted watermark results in a restored watermark, comprising in particular:
generating the chaotic sequence through a Logistic Map;
sorting the generated chaotic sequence according to the order from small to large;
and restoring the position space of the original watermark pixels according to the position change before and after each value in the chaotic sequence to obtain a restored watermark.
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