CN112132731B - DWT-SVD domain self-adaptive robust watermark extraction method adopting preset PSNR - Google Patents
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Abstract
The invention belongs to the technical field of image information processing, and particularly relates to a DWT-SVD domain self-adaptive robust watermarking algorithm adopting preset PSNR. The method utilizes the stability of the discrete wavelet transformation on the attack resistance and singular value decomposition, embeds the watermark by modifying the size relation between the first column elements of the left singular matrix of the image, and modifies the right singular matrix to realize the quality compensation. The method is different from other algorithms which use fixed embedding parameters or repeatedly experiment to obtain the embedding parameters, the embedding strength of the watermark depends on the embedding parameters, the method not only establishes the association among the embedding parameters, the carrier image and the watermark information, but also does not need to sacrifice the time complexity of the algorithm, and pixel overflow and correction after the embedding are finished enhance the reliability of the algorithm.
Description
Technical Field
The invention belongs to the technical field of image information processing, and particularly relates to a DWT-SVD domain self-adaptive robust watermarking algorithm adopting preset PSNR.
Background
The network opening brings convenience to the multimedia circulation, and simultaneously, the intellectual property protection of various content information faces the unprecedented challenges. Among various electronic information protection means, electronic watermarks having characteristics such as concealment and security have attracted attention from students. Electronic watermarking refers to the embedding of electronic information called watermark in carrier information such as images, video, audio and the like, and the accuracy of watermark extraction by an extracting party indicates the ownership, integrity and the like of the carrier.
Traditional image watermarking is divided into two classes according to functions: the robust watermark and the fragile watermark are used for protecting the copyright of the image and the integrity of the content of the image respectively. The robust watermark is a watermark embedded by modifying the carrier content, and identifiable watermark information can be extracted after the image is attacked by various attacks. Embedding modifications to the carrier information necessarily causes degradation of image quality. Therefore, there are three main measures of the performance of the robust watermark: capacity, robustness, visibility. These three criteria cancel each other out and when the watermark capacity is determined, the watermarking method requires sacrificing the image quality after watermark embedding in order to pursue robustness, and vice versa. The information load of the carrier, the quality of the watermark image and the integrity of the watermark extracted after being attacked are optimized as much as possible, and the method is designed by a robust watermarking method.
The watermark embedding process can be classified into spatial domain watermark and frequency domain watermark according to the embedded domain. Spatial watermark directly modifies spatial pixel embedding watermark, and although the computational complexity is low, the image processing has larger interference to pixels, resulting in reduced robustness. The frequency domain-based algorithm has stronger robustness and image quality by utilizing the advantages of the energy aggregation capability and the multiple resolution capability of the frequency domain conversion and embodying the time domain or frequency domain characteristics of the image, so that the frequency domain-based algorithm is widely adopted in the watermarking algorithm. In the frequency domain algorithm, DCT, DWT, RDWT, frFT is a common conversion mode, and various conversions enable the algorithm to integrate the advantages of different conversions, so that the aim of improving the performance of the algorithm is finally achieved, and the method becomes a research hot spot of a watermark algorithm in recent years.
The robust watermark design is also important as a parameter selection means, except for the design of the embedding and extraction process. More and more algorithms use artificial intelligence techniques such as evolutionary algorithms, neural networks, etc. to select parameters. Compared with the traditional watermark algorithm using fixed parameters, the method has the advantages that the parameters are selected by utilizing the artificial intelligence technology, the connection among the parameters, the watermark and the carrier can be established, for example, the Particle Swarm Optimization (PSO), the Firefly Algorithm (FA) and the artificial bee colony optimization (ABC) are used for selecting the embedding parameters, and the watermark image quality and the robust embedding strength can be balanced, however, the technologies cannot ensure that the watermark image achieves the preset quality.
Disclosure of Invention
Aiming at the defects and problems of the current robust watermarking algorithm based on SVD, the invention provides a DWT-SVD domain adaptive robust watermarking algorithm which does not depend on experimental feedback results and can ensure the quality of watermark images and adopts preset PSNR.
The invention solves the technical problems by adopting the scheme that: a DWT-SVD domain adaptive robust watermarking algorithm employing a preset PSNR, the algorithm comprising the steps of:
step one, selecting an embedded block: let the carrier image be A epsilon R M×N M, N is even, the watermark w= { W to be embedded r R is equal to or more than 1 and equal to or less than m, m is the length of the watermark; dividing the carrier image into non-overlapping blocks, calculating entropy values of the blocks, calculating weighted average sum of the entropy values of the blocks and adjacent blocks, sequencing the entropy values, and selecting the blocks with the same number as watermark bits as embedded blocks.
Step two, determining an adaptive quantization step length: dividing the embedded block into w r =1 andinsert block when and when w r =0 and->The sequence numbers of the two embedded blocks are respectively recorded as a sequence S 1 And S is equal to 2 Low frequency domain based on carrier image DWTCalculating S by the difference between the singular value of each block and the second and third elements of the first column of left singular vectors 1 And S is equal to 2 Square error Ls of image pixels before and after embedding sub-sequence watermark 1 And Ls 2 According to the carrier image, watermark and preset PSNR value, by the formula +.>And calculating to obtain the self-adaptive quantization step length t.
Embedding a watermark: performing primary haar wavelet transformation on the carrier image, dividing a low-frequency sub-band into mutually non-overlapping blocks, performing SVD (singular value decomposition) on the embedded blocks, and modulating difference quantization indexes between a first column, a second column and a third column of left singular vectors to embed watermarks;
step four, constructing a watermark image: performing reverse SVD decomposition and reverse Harr wavelet conversion on the modified embedded block to obtain a watermark image;
step five, extracting the watermark: SVD decomposition is carried out on the low-frequency sub-bands of the watermark image which can be modified, and the size relation of the first column, the second column and the third column of the singular vectors is compared to extract the watermark.
The DWT-SVD domain adaptive robust watermarking algorithm adopting the preset PSNR comprises the following steps:
(1) Dividing the carrier image into non-overlapping tiles, each tile containing 8 x 8 pixels, and marking the set of tiles asTo round the symbol upwards, the number of tiles is +.>
(2) Sequentially calculating the blocks l i,j Entropy value of (2)And->The visual entropy and the edge entropy of the block are respectively represented, and the weighted average sum of the block and the adjacent block is calculated:
wherein max and min respectively represent the maximum value and the minimum value of the variable;
(3) Will E i,j Sorting from small to large, selecting the first m blocks as embedded blocks, and recording sequence number sets of the embedded blocks as sequencesS will be the side information of the extraction process.
The DWT-SVD domain adaptive robust watermarking algorithm adopting the preset PSNR comprises the following calculation process of t in the step two:
for embedded blocksWatermark embedding is marked as +.> And->The SVD decomposed form of (c) can be expressed as:
modified embedded blocks fall into two categories:
1) When w is r =1 andan embedded block;
2) When w is r =0 andthe embedded block.
The serial numbers of the two types of embedded blocks are respectively recorded as a sequence S in the embodiment 1 And S is equal to 2 ，S 1 And S is equal to 2 Are subsequences of S and do not cross each other. Corresponding to the first type of embedded blockDifferently, it is specifically defined as:
the difference between the embedded blocks before and after embedding is:
after knowing the modification amplitude of the embedded block element, further establishing a corresponding relation between the embedded block difference value and the square error of the LL subband coefficient after primary Haer wavelet transformation, and calculating the square error of the LL subband coefficient:
the square error between the LL subband coefficient of the haar wavelet transformation and the image pixel is known to be satisfied
The calculation formula for PSNR is known as:
wherein MAX A Is the maximum of the matrix a elements.
Is available in the form of
Namely:i.e. < ->And according to the determined image, watermark and preset PSNR value, calculating by a formula to obtain the self-adaptive quantization step length t.
The DWT-SVD domain adaptive robust watermarking algorithm adopting the preset PSNR comprises the following steps:
(1) Performing one-level haar wavelet transform on the carrier image, dividing LL sub-band into 4×4 blocks which are not overlapped with each other to obtainIndividual blocks, wherein the set of embedded blocks is denoted +.>
(2) For embedded blocksSVD decomposition is carried out, left singular vectors are +.>Is marked as +.>Wherein the difference between the second and the third element is denoted +.>
(3) For embedded blocksDifference of->Quantization to embed watermark w r Second, thirdThe corresponding modification modes of the individual elements are as follows:
if w r ＝1
if w r ＝0
wherein t is the adaptive quantization step size of the quantization index modulation strategy.
The step five of the DWT-SVD domain adaptive robust watermarking algorithm adopting the preset PSNR comprises the following steps:
(1) Performing primary Harr wavelet conversion on the watermark image, blocking the LL sub-band, and determining the blocks for extracting the watermark as extraction blocks according to the sequence number set S of the embedded block;
(2) SVD decomposition is carried out on each extraction block, and left singular vectors U are recorded * Is the first column vector of (1)The difference between the second element and the third element is +.>Wherein s is r ∈S；
(3) Extracting 1-bit watermark from each extraction block in turn by the following methodAnd finally synthesizing the complete watermark.
The invention has the beneficial effects that: the invention obtains the relation between PSNR value and quantization step length and image characteristic, under the condition of determining carrier image and watermark, calculates according to the preset PSNR value to obtain the optimal quantization step length, clarifies the corresponding relation between image quality and quantization step length, can obtain the quantization step length ensuring the image quality to reach the preset value without repeated embedding and extraction process, DWT conversion enhances the common image processing capability of watermark algorithm such as noise resistance, compression and the like, and SVD decomposition increases the capability of resisting geometric attack; the stability of the singular vector element relationship is utilized in the proposed algorithm, so that the robustness is further enhanced, the actual watermark image can be ensured to reach the preset image quality, and the time complexity of the algorithm is not required to be sacrificed; the pixel overflow and correction after the embedding is finished also enhances the reliability of the algorithm, and the invisibility and the robustness of the algorithm are superior to those of other similar algorithms, so that the algorithm has practical value in application occasions such as copyright protection and the like.
Drawings
Fig. 1 is a flowchart of a watermarking algorithm based on an adaptive quantization step size.
FIG. 2 is a flow chart of the embedding process of the present invention.
Fig. 3 is a listing of images and watermarks in accordance with the present invention.
Fig. 4 is a corresponding relationship between a parameter t mean value of a test image and a preset PSNR mean value, an actual PSNR mean value.
Fig. 5 shows the test image after watermark embedding and their embedding parameters t and the actual PSNR on the premise that the preset PSNR is 40 db.
Detailed Description
The invention will be further described with reference to the drawings and examples.
Example 1: the embodiment provides a DWT-SVD domain adaptive robust watermarking algorithm adopting preset PSNR, which mainly comprises the following contents, and the flow is shown in figure 1.
Step one, setting a carrier image as a 512×512 gray level image Lena, and obtaining a binary image with a watermark of 32×32 to be embedded, which is denoted as w= { W r R is equal to or more than 1 and equal to or less than 1024. The specific embedding steps are as follows:
step1: dividing the carrier image into non-overlapping tiles, each tile containing 8 x 8 pixels; to collect blocksDenoted as l= { L i,j| I1 is less than or equal to i and less than or equal to 64, j is less than or equal to 1 and less than or equal to 64, and the number of the blocks is 4096.
Step2: sequentially calculating the blocks l i,j Entropy value of (2)And->The visual entropy and the edge entropy of the block are respectively represented; calculating a weighted average sum of entropy values of the blocks and the adjacent blocks:
wherein max and min represent the maximum and minimum values of the variable, respectively.
Step3: will E i,j Ordering from small to large, selecting the first 32×32 blocks as embedded blocks, and recording sequence number sets of the embedded blocks as a sequence S= { S r |1≤r≤1024，1≤s r And is less than or equal to 4096}, and S is used as side information of the extraction process.
Is provided withAnd LL, LH, HL, HH subband coefficients of the image A after first-level haar wavelet transformation are respectively represented.
The low-frequency subband coefficient after watermarking is embedded isThe original spatial domain pixel is marked as a k,l K is not less than 1 and not more than M, l is not less than 1 and not more than N, and the watermark image pixel is marked as a k,l ' their relationship is shown in FIG. 2.
The square error of the LL subband coefficients with the image pixels satisfies the following equation:
indicating that the square error of the image pixels before and after watermark embedding has an equivalent relationship with the square error of the haar wavelet transform low-frequency subband coefficients.
Step4: performing one-level haar wavelet transformation on the carrier image, dividing the LL sub-band into 4×4 blocks which are not overlapped with each other to obtain 4096 blocks, wherein the embedded block set is recorded as
Step5: for embedded blocksSVD decomposition is carried out, left singular vectors are +.>Is marked as +.>Wherein the difference between the second and the third element is denoted +.>
Step6: for embedded blocksDifference of->Quantization to embed a r-th bit watermark w r The corresponding modifications of the second and third elements are as follows:
if w r ＝1
if w r ＝0
in equations (3) - (6), t is the adaptive quantization step size of the quantization index modulation strategy, and the quantization step size t can be calculated by the equation
The dichotomy is quickly calculated, wherein the quantization step t is specifically selected as follows.
For embedded blocksWatermark embedding is marked as +.>And->The SVD decomposed form of (c) can be expressed as:
modified embedded blocks can be divided into two categories:
1) When w is r =1 andan embedded block;
2) When w is r =0 andthe embedded block.
The serial numbers of the two types of embedded blocks are respectively recorded as a sequence S in the embodiment 1 And S is equal to 2 ，S 1 And S is equal to 2 Are subsequences of S and do not cross each other. Corresponding to the first type of embedded blockDifferently, it is specifically defined as:
as can be seen from equations (7) to (9), the difference between the embedded blocks before and after embedding is:
after knowing the modification amplitude of the embedded block element, a corresponding relation is further established between the embedded block difference value and the square error of the LL subband coefficient after the first-level Haer wavelet transform, namely the square error of the LL subband coefficient is calculated according to the formula (10):
namely:
it is illustrated that there is a relation between the square error of the low frequency subband coefficients and the magnitude of the modification of the embedded block.
The calculation formula for PSNR is known as:
wherein MAX A Is the maximum of the matrix a elements.
Is available in the form of
Namely:
i.e.
It can be known that the left side of the equal sign is related to the maximum singular value of the embedded block, the difference value of the first column singular vector and the two elements, the watermark and the quantization step t, and when the image, the watermark and the PSNR value are determined, the quantization step t satisfying the equation (16) can be rapidly obtained by a dichotomy method. The quantization step length selection strategy combines a PSNR calculation formula and an embedding mode of a watermark algorithm based on singular vector robustness to obtain a corresponding relation between a quantization step length t and preset image quality, so that the quantization step length for ensuring the image quality to reach a preset value can be obtained without repeated embedding and extraction processes.
Step7: and performing reverse SVD decomposition on the modified embedded block.
Step8: repeating Step5 to Step7 for all embedded blocks; and after blocking and merging, replacing the original LL sub-band, and then performing reverse Harr wavelet conversion to obtain a watermark image.
2. Watermark extraction
Step1: firstly, performing primary Harr wavelet conversion on a watermark image, then blocking an LL sub-band, and selecting a blocking for extracting the watermark as an extraction block according to an embedded block sequence number set S;
step2: SVD decomposition is carried out on each extraction block, and left singular vectors U are recorded * Is the first column vector of (1)The difference between the second element and the third element is +.>Wherein s is r ∈S。
Step3: and extracting 1-bit watermark from each extraction block in turn, wherein the extraction method is shown in a formula (18), and finally synthesizing the complete watermark.
Example 2: in order to verify the effect of the algorithm of the invention, the embodiment compares the method of the invention with three algorithms I, II and III which are based on SVD and have the same watermark capacity from the two aspects of watermark image quality and watermark robustness; the parameters of the four algorithms are detailed in table 1.
Table 1 parameter summary table of four algorithms
The 10 sub 512 x 512 classical test images in fig. 3 are chosen as carrier images, binary images of 32 x 32, 48 x 48, 64 x 64 as watermarks to be embedded. When the experimental result is quantitatively analyzed, PSNR and Bit Error Rate (BER) are respectively adopted as measurement mechanisms of watermark image quality and algorithm robustness,representing a bitwise exclusive or operator.
(1) Invisible feature
Minimizing the impact on the original image is one of the goals pursued by invisible watermarking algorithms. Unlike other algorithms, which balance the relation between the image quality and the robustness through embedding parameters, the watermark algorithm of the invention uses the preset PSNR value as a parameter to reversely calculate the embedding parameters, and the corresponding relation between the parameter t mean value of the test image, the preset PSNR mean value and the actual PSNR mean value is shown in fig. 4.
As can be seen from fig. 4, when the preset PSNR is 30, 35, 40, 45, and 50, the mean value of the embedding parameters of the ten test images and the mean value of the actual PSNR are substantially coincident, which proves that the method can ensure that the quality of the obtained watermark image reaches the preset value.
In general, the difference between the original image and the watermark image is measured in two ways, subjective and objective. On the premise that the preset PSNR is 40db, the test images after watermark embedding and the embedding parameters t and the actual PSNR are shown in figure 5, and on the premise that the preset PSNR is 40db, the six watermark images, the embedding parameters t and the actual PSNR are equal to 40 db.
As can be seen from fig. 5, the algorithm ensures that the PSNR of the watermark image is not lower than 40db, which not only proves the effectiveness of the adaptive parameter selection strategy of the present invention, but also illustrates that the watermark image quality of the algorithm of the present invention can meet the application requirements from both subjective and objective aspects. Meanwhile, the correlation between PSNR and watermark length is shown, and in theory, when the watermark length is not more than 64 multiplied by 64, the watermark image quality of the watermark method can be ensured.
To verify this conclusion, the present example verifies that the watermark was on the Lena image with different sizes, and the results are shown in table 2.
Table 2 different watermark size fixed embedding parameters compared to PSNR of the method of the invention (Lena image)
As can be seen from table 2, when the watermark sizes are 32×32, 32×48, 48×48, and 64×64, respectively, the PSNR of the watermark image can reach a predetermined 40db, which cannot be achieved by using the method of fixing the embedding parameters.
(2) Robustness (robustness)
Robustness is another indicator of the measure of watermarking algorithms. The invention uses BER as an index for measuring the robustness of the watermark, and the smaller the BER value is, the higher the similarity between the extracted watermark and the original watermark is, namely the higher the robustness of the watermark is. To verify the robustness of the algorithm, 13 representative attack modes are selected, wherein the attack modes comprise common image processing means such as compression, filtering, noise and the like, and geometric attack such as scaling and rotation operation, and are specifically shown in table 3.
TABLE 3 robustness of the inventive algorithm to different attacks
When the watermark of 32×32 is embedded in the six-luck test image shown in table 3, it can be seen from the BER results of extracting the watermark after 13 attacks, from the robustness shown by different images, the anti-attack capability shown by the Peppers and Man images when the images are processed by jpeg compression, median filtering and the like with lower quality factors is weaker, which is related to that the algorithm preferentially selects the block with small entropy value as the embedded block and the smooth area of the image itself is relatively less. But in general, the algorithm has certain robustness when facing common attacks, and particularly has excellent robustness for histogram equalization, contrast enhancement, shrinkage and rotation attacks.
The present embodiment compares the robustness of the four watermark methods on the premise that their PSNR values are set to about 41 db. Tables 4 and 5 are PSNR values for four watermarking methods obtained herein for images Lena and Peppers, respectively, and watermark ber values after 13 attacks, when the watermark size is 32 x 32.
Table 4 robustness comparison of the inventive algorithm with the same class of algorithms (Lena image)
TABLE 5 robustness comparison of the inventive algorithm with the same class of algorithms (Peppers image)
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In tables 4 and 5, the algorithm of the present invention presets a PSNR of 41db for both test images, but the obtained t values are different (0.041 and 0.045), which illustrates the necessity of adaptive embedding parameters. From the comparison results, although the four algorithms embed watermarks by changing the relation between the two elements, the two comparison elements in the second algorithm are taken from the maximum singular values of the two matrices composed of DCT intermediate frequency coefficients, and are less similar than the first column elements of the block singular matrices, so that the robustness of the first algorithm and the third algorithm is better than that of the second algorithm in the face of most attacks, especially noise attacks, under the same embedding capacity as seen in tables 4 and 5. The first algorithm embeds the watermark in the RIDWT domain, and although the watermark can resist continuous 90-degree rotation and row-column flip attack, the adjacent pixel value similar characteristics of the image are broken due to the pixel position replacement step in the RIDWT conversion, so that the algorithm cannot resist JPEG compression, median filtering, mean filtering, size reduction and Gaussian filtering operations. The comparison shows that algorithm three is deficient: the embedding parameters cannot be dynamically selected according to different carriers, so that the situation of unstable watermark image quality is faced when the algorithm is applied to different images. In addition, the invention provides an algorithm which is superior to the algorithm III and is characterized by optimizing the entropy selection block, which is reflected when facing jpeg compression.
(3) Run time
In order to verify the advantages of the quantization step size selection strategy presented herein in terms of time complexity, the present section uses the ACO-based quantization step size selection strategy and the proposed adaptive quantization step size selection strategy, respectively, in a singular vector robustness based watermarking algorithm and compares their run times. The hardware change used in the experiment is 2.90GHz in main frequency, 8GB in memory and Microsoft Windows 10 flagship edition and MATLAB 2018 in software environment. Table 6 is a comparison of the running time averages taken by the test image Lena and Peppers using two selection strategies, in which ACO-based selection strategies the number of iterations of ACO was set to 50 and the ant colony size to 10; the results are shown in Table 6.
Table 6 run time comparison of two quantization step selection strategies
As can be seen from table 6, the execution time of the two selection strategies is proportional to the watermark size, but the execution time of the adaptive quantization step selection strategy of the present invention is much less than that of the ACO-based selection strategy, which indicates that the selection strategy proposed herein has significant advantages in terms of time complexity.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the particular embodiments disclosed, but on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention.
Claims (4)
1. A DWT-SVD domain self-adaptive robust watermark extraction method adopting preset PSNR is characterized in that: the method comprises the following steps:
step one, selecting an embedded block: let the carrier image be A epsilon R M×N M, N is even, the watermark w= { W to be embedded r R is equal to or more than 1 and equal to or less than m, m is the length of the watermark; dividing the carrier image into non-overlapping blocks, calculating entropy values of the blocks, calculating weighted average sum of the entropy values of the blocks and adjacent blocks, sequencing the entropy values, and selecting the blocks with the same number as watermark bits as embedded blocks;
step two, determining an adaptive quantization step length: dividing the embedded block into w r =1 and d sr Insert block at < t and when w r =0 and d sr Two types of embedded blocks are recorded as sequences S respectively when > -t 1 And S is equal to 2 Calculating S according to the singular value of each block of the low frequency domain of the carrier image DWT and the difference value of the first column, the second column and the third column of the left singular vector 1 And S is equal to 2 Square error Ls of image pixels before and after embedding sub-sequence watermark 1 And Ls 2 According to the carrier image, the watermark and the preset PSNR value, the method passes through the formulaCalculating to obtain an adaptive quantization step length t, wherein the calculation process of t is as follows:
for embedded blocksWatermark embedding is marked as +.>And->The SVD decomposed form of (c) can be expressed as:
modified embedded blocks fall into two categories:
1) When w is r =1 andan embedded block;
2) When w is r =0 andan embedded block;
the serial numbers of the two embedded blocks are respectively recorded as a sequence S 1 And S is equal to 2 ，S 1 And S is equal to 2 Is the subsequence of S and is not crossed with each other, and the first type of embedded block corresponds to the second type of embedded blockDifferently, it is specifically defined as:
the difference between the embedded blocks before and after embedding is:
after knowing the modification amplitude of the embedded block element, further establishing a corresponding relation between the embedded block difference value and the square error of the LL subband coefficient after primary Haer wavelet transformation, and calculating the square error of the LL subband coefficient:
the square error between the LL subband coefficient of the haar wavelet transformation and the image pixel is known to be satisfied
Then:
the calculation formula for PSNR is known as:
wherein MAX A Is the maximum value of the elements in matrix a,
is available in the form of
Namely:i.e. < ->According to the determined image, watermark and preset PSNR value, calculating to obtain a self-adaptive quantization step length t through a formula;
embedding a watermark: performing primary haar wavelet transformation on the carrier image, dividing a low-frequency sub-band into mutually non-overlapping blocks, performing SVD (singular value decomposition) on the embedded blocks, and modulating difference quantization indexes between a first column, a second column and a third column of left singular vectors to embed watermarks;
step four, constructing a watermark image: performing reverse SVD decomposition and reverse Harr wavelet conversion on the modified embedded block to obtain a watermark image;
step five, extracting the watermark: SVD decomposition is carried out on the low-frequency sub-bands of the watermark image which can be modified, and the size relation of the first column, the second column and the third column of the singular vectors is compared to extract the watermark.
2. The DWT-SVD domain adaptive robust watermark extraction method employing a preset PSNR according to claim 1, step one comprising the steps of:
(1) Dividing the carrier image into non-overlapping tiles, each tile containing 8 x 8 pixels, and marking the set of tiles asTo round the symbol upwards, the number of tiles is +.>
(2) Sequentially calculating the blocks l i,j Entropy value of (2)And->The visual entropy and the edge entropy of the block are respectively represented, and the weighted average sum of the block and the adjacent block is calculated:
wherein max and min respectively represent the maximum value and the minimum value of the variable;
(3) Will beSorting from small to large, selecting the first m blocks as embedded blocks, and recording sequence number sets of the embedded blocks as sequencesS will be the side information of the extraction process.
3. The DWT-SVD domain adaptive robust watermark extraction method employing a preset PSNR according to claim 1, wherein: the third step comprises the following steps:
(1) Performing one-level haar wavelet transform on the carrier image, dividing LL sub-band into 4×4 blocks which are not overlapped with each other to obtainIndividual blocks, wherein the set of embedded blocks is denoted +.>
(2) For embedded blocksSVD decomposition is carried out, left singular vectors are +.>Is marked as +.>Wherein the difference between the second and the third element is denoted +.>
(3) For embedded blocksDifference of->Quantization to embed watermark w r The corresponding modification modes of the second and the third elements are as follows:
if w r ＝1
if w r ＝0
wherein t is the adaptive quantization step size of the quantization index modulation strategy.
4. The DWT-SVD domain adaptive robust watermark extraction method employing a preset PSNR according to claim 1, wherein: step five comprises the following steps:
(1) Performing primary Harr wavelet conversion on the watermark image, blocking the LL sub-band, and determining the blocks for extracting the watermark as extraction blocks according to the sequence number set S of the embedded block;
(2) SVD decomposition is carried out on each extraction block, and left singular vectors U are recorded * Is the first column vector of (1)The difference between the second element and the third element is +.>Wherein s is r ∈S；
(3) Extracting 1-bit watermark from each extraction block in turn by the following methodAnd finally synthesizing the complete watermark.
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