CN110728614B - Grey wolf optimization algorithm and full three-tree structure wavelet domain color multi-watermarking method - Google Patents
Grey wolf optimization algorithm and full three-tree structure wavelet domain color multi-watermarking method Download PDFInfo
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Abstract
The invention belongs to the field of digital image watermarking technology and copyright protection, and provides a wavelet domain color multi-watermarking technology which can adjust the invisibility, robustness and capacity balance of watermarks and can also effectively resist various known attacks such as noise attack, shearing attack, rotation attack and the like. Therefore, in the invention, three color watermark images are firstly encrypted into a gray image by a three-tree encryption technology in the watermark embedding process by using a gray wolf optimization algorithm and a full three-tree structure wavelet domain color multi-watermark method; then, wavelet transformation is carried out on the color host image, the selected frequency part is transformed into a YCbCr color space, singular value decomposition is carried out, and a plurality of encrypted color watermarks are embedded into the singular value part of the carrier image; and finally, combining the singular value part containing the secret image with the feature vector, and then performing inverse wavelet transformation to obtain the carrier image containing the secret image. The invention is mainly applied to the occasion of embedding the image watermark.
Description
Technical Field
The invention belongs to the fields of digital image watermarking technology and copyright protection, and relates to a wavelet domain color multi-watermarking technology based on a gray wolf optimization algorithm and a complete three-tree structure.
Background
With the continuous development of computer technology, digital products (such as images, audio, video, etc.) bring convenience to people, and meanwhile, the digital products also face the problems of illegal copying, spreading, etc. How to protect these digital products from illegal acquisition or copying is a research topic that is in need of solution in the field of network security today. In order to solve the above-described problems, digital watermarking techniques and encryption techniques are proposed successively.
The main principle of digital watermarking technology is to embed watermark with copyright information into digital multimedia without affecting the original value of the digital multimedia, and can effectively avoid the perception of human audio-visual system and not be perceived by the outside. In order to improve the performance of the watermark, many schemes have been proposed to accommodate the contradiction between robustness and invisibility of the watermark. The digital watermark based on DCT (discrete cosine transform), DWT (discrete wavelet transform), FT (Fourier transform) and the like, which are originally proposed, can lead a watermark image to have good invisibility by adjusting the embedding strength, but the invisibility of the embedded watermark image and the robustness of a hiding method to various geometric attacks or image processing attacks are difficult to balance in the operation. Based on this, digital image encryption is then introduced on the basis of the watermark, and the robustness of the watermark is improved by the encryption.
The main problem of the digital watermark at the present stage is how to balance the constraint relation among the invisibility, the robustness and the capacity of the watermark. In order to enhance the invisibility of the watermark, the watermark is typically invisibly embedded in the host in combination with the visual characteristics of the human eye. The robustness of the watermark is improved by encrypting or scrambling the watermark. In terms of capacity, there are binary watermarks, gray-scale watermarks, color watermarks, 3D watermarks, and the like. In the watermark embedding process, the embedding factor determines the invisibility, robustness and capacity of the watermark to a large extent. Whereas the embedding factor in the usual watermarking technique is determined by artificial experiments, there are contingencies and uncertainties. The different image information additionally determines that the embedding strength is dynamic accordingly. Therefore, how to find the best embedding factor to balance the main performance of the watermark, so as to achieve a good watermark effect, is an important research direction in the current digital watermarking technology.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a wavelet domain color multi-watermark technology based on a gray-wolf optimization algorithm and a complete three-tree structure, and the digital watermark technology is mainly aimed at determining dynamic embedding factors corresponding to different watermark images in the watermark embedding process, and provides a optimizing operation for the watermark embedding process based on the gray-wolf optimization algorithm (GWO), and the encryption processing is carried out on a plurality of color watermark images by introducing the three-tree encryption technology, so that the robustness of the watermark is improved. The proposed trigeminal-gray-wolf optimized (Tree-GWO) watermarking scheme can adjust the balance between invisibility, robustness and capacity of the watermark. In addition, the method can also effectively resist various attacks such as known noise attack, shearing attack, rotation attack and the like. Therefore, the technical scheme adopted by the invention is that a gray wolf optimization algorithm and a full three-tree structure wavelet domain color multi-watermark method are adopted, and three color watermark images are firstly encrypted into a gray image through a three-tree encryption technology in the watermark embedding process; then, wavelet transformation is carried out on the color host image, the selected frequency part is transformed into a YCbCr color space, singular value decomposition is carried out, and a plurality of encrypted color watermarks are embedded into the singular value part of the carrier image; and finally, combining the singular value part containing the secret image with the feature vector, and then performing inverse wavelet transformation to obtain the carrier image containing the secret image.
The specific steps are refined as follows:
encryption of the watermark image: first, three color watermark images f i Dividing the data into three channels R, G, B, i=1, 2 and 3, and then encrypting based on a trigeminal encryption technology, wherein the encryption result is Cen; then carrying out wavelet transformation on the encryption result Cen, carrying out singular value decomposition on the obtained intermediate frequency part LHCen, and carrying out variational image decomposition on the obtained singular value part SCen to obtain a texture part Cen_u and a detail part Cen_v of the encryption image;
embedding the watermark: the color host image IH is first wavelet transformed and the intermediate frequency portion LHr, LHg, LHb of the resulting RGB color space is converted to YCbCr color space by RGB2YCbCr: LHY, LHCb, LHCr; singular value decomposition is performed on a blue chrominance component LHCb of the vertical component and a red chrominance component LHCr of the vertical component; then, embedding Cen_u and Cen_v in the step 1 into a singular value component SCb of LHCb and a singular value component SCr of LHCr respectively; then obtaining a host image IW embedded with the watermark through inverse singular value transformation and inverse wavelet transformation;
the watermark extraction process comprises the following steps: the watermark embedded image IW is first subjected to wavelet decomposition, and the intermediate frequency part LHEr, LHEg, LHEb of the obtained RGB color space is converted into YCbCr color space by RGB2YCbCr: LHEY, LHECb, LHECr; singular value decomposition is performed on the blue chrominance component LHECb of the vertical component and the red chrominance component LHECr of the vertical component; combining the obtained singular values with the previous eigenvectors to obtain singular values SssCb and SssCr containing watermarks; CEN_u and CEN_v are respectively extracted from SssCb and SssCr, and an extracted encrypted image CEN is finally obtained;
step 4: decryption of watermark images: the extracted encrypted image CEN is decrypted by using the trigeminal technique to obtain a decrypted watermark image F i ；
Step 5: robustness test: and carrying out various attack tests on the host image embedded with the watermark, and evaluating the invisibility and the robustness of the watermark image extracted from the host image which is attacked after the watermark is embedded by calculating a mean square value MSE, a peak signal to noise ratio PSNR and a correlation coefficient CC value.
The watermark embedding process comprises the following specific steps:
step 1: wavelet decomposing the color host image IH:
[LL,LH,HL,HH]＝DWT(IH) (6)
wherein LL, LH, HL, HH is the low frequency, horizontal, vertical, high frequency component after wavelet change of the host image respectively;
step 2: intermediate frequency portion LHr, LHg, LHb of the resulting RGB color space is converted to YCbCr color space by RGB2YCbCr: LHY, LHCb, LHCr:
[LHY,LHCb,LHCr]＝RGB2YCbCr(LHr,LHg,LHb) (7)
step 3: wavelet transform and singular value decomposition are performed on a blue chrominance component LHCb of the vertical component and a red chrominance component LHCr of the vertical component:
[UCb,SCb,VCb]＝SVD(DWT(LHCb)) (8)
[UCr,SCr,VCr]＝SVD(DWT(LHCr)) (9)
wherein UCb, SCb, VCb is left singular value component, characteristic singular value component, right singular value component of LHCb respectively; UCr, SCr, VCr are left singular value component, characteristic singular value component, right singular value component, respectively, of LHCr.
Step 4: embedding cen_u and cen_v in step 1 into the singular value component SCb of LHCb and the singular value component SCr of LHCr, respectively:
SsCb＝SCb+af·Cen_u (10)
SsCr＝SCr+af·Cen_v (11)
wherein af is watermark embedding strength;
step 5: singular value decomposition is performed on singular values SsCb and SsCr embedded with watermarks:
[U1Cb,S1Cb,V1Cb]＝SVD(SsCb) (12)
[U1Cr,S1Cr,V1Cr]＝SVD(SsCr) (13)
step 6: performing inverse transformation on the obtained singular values to obtain an intermediate frequency part containing the watermark:
LH11Cb＝UCb·S1Cb·VCb -1 (14)
LH11Cr＝UCr·S1Cr·VCr -1 (15)
step 7: and performing inverse wavelet transformation on the obtained intermediate frequency part:
LHhCb＝IDWT(LL1Cb,LH11Cb,HL1Cb,HH1Cb) (16)
LHhCr＝IDWT(LL1Cr,LH11Cr,HL1Cr,HH1Cr) (17)
step 8: converting intermediate frequency parts LHhCB and LHhCr of the obtained YCbCr color space into RGB color space through YCbCr2RGB:
[LH1r,LH1g,LH1b]＝YCbCr2RGB(LH1Y,LHhCb,LHhCr) (18)
step 9: performing inverse wavelet transformation on the recombined intermediate frequency part LH1 to obtain a host image embedded with the watermark:
IW＝IDWT(LL,LH1,HL,HH) (19)
the gray wolf optimization algorithm of the watermark embedding factor af has the following optimizing process:
1) Initializing a gray wolf population, and a, A and C; % a, A and C are parameters
2) Calculating fitness function value of each individual wolf, and storing the best individual wolf X of the first three wolves with the best fitness value a Second best individual X of wolves b Third best individual X of wolf d ；
3) T is the current iteration number, max iteration is the maximum iteration number, and when the iteration number t is<When max is equal, for each wolf individual, updating the position of the current wolf individual, updating a, A and C, and calculating the fitness function value of all the wolves; repeating the steps until t is more than or equal to max time, and then X a For finding the optimal solution, i.e.The optimal embedding strength af to be found.
The objective function corresponding to the embedding factor af is:
wherein N is the total number of attacked types, m is the number of watermarks, CC is a correlation coefficient value, IH is an original color host image, IW is a host image after watermark embedding, F is three original color watermark images, F is three watermark images recovered by extraction, and the objective function value corresponding to the best embedding factor af is found by optimizing through a gray wolf optimization algorithm and should be as close as possible to 4.
The invention has the characteristics and beneficial effects that:
compared with the digital watermarking algorithm, the invention mainly aims at determining dynamic embedding factors corresponding to different watermark images in the watermark embedding process, provides a method for optimizing the watermark embedding process based on a gray wolf optimization algorithm (GWO), and encrypts a plurality of color watermark images before embedding by introducing a three-tree encryption technology, thereby improving the robustness of the watermark. The proposed trigeminal Tree-gray wolf optimization (Tree-GWO) watermarking scheme has the advantages that: (1) On the basis of the prior single binary or gray scale or single color watermarking technology, three watermark images can be simultaneously encrypted into a gray scale encryption result by introducing the trigeminal tree encryption technology, so that the watermark embedding capacity is greatly improved, and various attacks can be effectively resisted by the trigeminal tree encryption technology; (2) For randomness and uncertainty of the existing watermark embedding factors caused by artificial experiment determination, a target function based on watermark embedding and extraction is designed by introducing a gray wolf optimization algorithm, and the watermark embedding factors are optimized by utilizing a GWO optimization algorithm, so that the dynamic characteristics of the embedding factors corresponding to different image characteristics are solved, and the relation of robustness, invisibility and mutual restriction among capacities in the watermark embedding process is effectively balanced; (3) In order to test the wide applicability of the invention, not only various performance tests are carried out on natural color images, but also performance tests are carried out on medical images, which shows that the invention is not only suitable for copyright protection of natural images, but also suitable for copyright protection of medical images; (4) The invention tests color hosts and color watermarks, and is applicable to gray level images and audio watermarks.
Description of the drawings:
fig. 1 is a flow chart of embedding and extracting of the proposed Tree-GWO watermarking technology, in which:
(a) The color multi-watermark embedding and encryption principle schematic diagram provided by the invention;
(b) The color multi-watermark extraction and decryption principle schematic diagram provided by the invention;
FIG. 2 is a schematic view of the convergence history of the gray wolf optimization algorithm to find the optimal embedding factor;
fig. 3 shows four original color watermark images to be embedded and two original color host images:
(a) Is Baboon;
(b) Is the friets;
(c) Are Peppers;
(d) Is Cell;
(e) Is Lena;
FIG. 4 is an encrypted image;
fig. 5 is an extracted and decrypted watermark image:
(a) Decrypting the result Baboon;
(b) Decryption results cruis;
(c) Decryption results Peppers;
fig. 6 is an embedded watermark image that is attacked by gaussian noise with an intensity of 0.2 and the corresponding extracted and decrypted watermark:
(a) A host Cell embedded with watermark and attacked by 0.2 Gaussian noise;
(b) Extracting the decrypted Baboon from fig. 6 (a);
(c) Extracting the decrypted friets from fig. 6 (a);
(d) Extracting the decrypted Peppers from fig. 6 (a);
(e) Watermark embedded host Lena under 0.2 Gaussian noise attack;
(f) Extracting the decrypted Baboon from fig. 6 (e);
(g) Extracting the decrypted friets from fig. 6 (e);
(h) Extracting the decrypted Peppers from fig. 6 (e);
fig. 7 is an embedded watermark image subjected to a 50% intensity shear attack and corresponding extracted and decrypted watermarks:
(a) A host Cell after embedding watermark that is subject to 50% of cut attacks;
(b) Extracting the decrypted Baboon from fig. 7 (a);
(c) Extracting the decrypted friets from fig. 7 (a);
(d) Extracting the decrypted Peppers from fig. 7 (a);
(e) Watermark embedded host Lena under 50% cut attack;
(f) Extracting the decrypted Baboon from fig. 7 (e);
(g) Extracting the decrypted friets from fig. 7 (e);
(h) Extracting the decrypted Peppers from fig. 7 (e);
fig. 8 is an embedded watermark image with a rotation attack of 15 ° and the corresponding extracted and decrypted watermark:
(a) A host Cell embedded with watermark under a rotation attack of 15 degrees;
(b) Extracting the decrypted Baboon from fig. 8 (a);
(c) Extracting the decrypted friets from fig. 8 (a);
(d) Extracting the decrypted Peppers from fig. 8 (a);
(e) A host Lena which is subjected to rotation attack by 15 degrees and is embedded with the watermark;
(f) Extracting the decrypted Baboon from fig. 8 (e);
(g) Extracting the decrypted friets from fig. 8 (e);
(h) The decrypted Peppers are extracted from fig. 8 (e).
In the drawings, the list of components represented by the various numbers is as follows:
in fig. 1 (a): IH, original color host image; DWT: discrete wavelet transformation; LL: a low frequency component; HL: a horizontal component; LH: a vertical component; HH: a high frequency component; LHr: a red channel of the vertical component; LHg: a green channel of the vertical component; LHb: blue channel of vertical component; RGB2YCbCr: converting the RGB color space into a YCbCr color space; LHY: a luminance component of the vertical component; LHCb: a blue chrominance component of the vertical component; LHCr: a red chrominance component of the vertical component; SVD: singular value decomposition; u: left singular value vector of singular values; s: eigenvalue vectors of singular values; u: right singular value vector of singular values; grey Wolf Optimizer: a gray wolf optimization algorithm; af: watermark embedding strength; cen: the encryption result of the watermark; VID: decomposing the variational image; cen_u: texture portion of the encrypted result of the watermark; cen_v: a detail portion of the encryption result of the watermark; ISVD: inverse singular value decomposition; IDWT: inverse discrete wavelet transform; YCbCr2RGB: the YCbCr color space is converted to RGB color space; IW, embedding watermark into host image.
In fig. 1 (b): LHE: a vertical component of the watermarked host image; cen_u: the texture portion of the extracted encrypted image; cen_v: a detail portion of the extracted encrypted image; CEN: the extracted encrypted image.
Detailed Description
The invention aims to provide a wavelet domain color multi-watermark technology based on a gray-wolf optimization algorithm and a complete trigeminal tree structure, which mainly aims at determining dynamic embedding factors corresponding to different watermark images in a watermark embedding process, and provides a method for optimizing the watermark embedding process based on the gray-wolf optimization algorithm (GWO), and encrypting a plurality of color watermark images by introducing the trigeminal tree encryption technology, so that the robustness of the watermark is improved. The proposed trigeminal-gray-wolf optimized (Tree-GWO) watermarking scheme can adjust the balance between invisibility, robustness and capacity of the watermark. In addition, the method can also effectively resist various attacks such as known noise attack, shearing attack, rotation attack and the like. Therefore, the technical scheme adopted by the invention is that three color watermark images are firstly encrypted into a gray image by a three-tree encryption technology in the watermark embedding process based on a gray wolf optimization algorithm and a wavelet domain color multi-watermark technology with a complete three-tree structure; then, wavelet transformation is carried out on the color host image, the selected frequency part is transformed into a YCbCr color space, singular value decomposition is carried out, and a plurality of encrypted color watermarks are embedded into the singular value part of the carrier image; and finally, combining the singular value part containing the secret image with the feature vector, and then performing inverse wavelet transformation to obtain the carrier image containing the secret image. The best watermark embedding factor was found by a gray wolf optimization algorithm, and furthermore, to test the broad applicability of the present invention, we tested the feasibility of the proposed algorithm in the case of color natural host images and color medical images, respectively.
The specific steps are refined as follows:
step 1: encryption of the watermark image: first, three color watermark images f i (i=1, 2, 3) is divided into respective R, G, B channels, then encryption is carried out based on a trigeminal tree encryption technology, and the encryption result is Cen, so that the robustness and the invisibility of the watermark image are enhanced; then carrying out wavelet transformation on the encryption result Cen, carrying out singular value decomposition on the obtained intermediate frequency part LHCen, and carrying out Variational Image Decomposition (VID) on the obtained singular value part SCen to obtain a texture part Cen_u and a detail part Cen_v of the encryption image;
step 2: embedding the watermark: the color host image IH is first wavelet transformed and the intermediate frequency portion LHr, LHg, LHb of the resulting RGB color space is converted to YCbCr color space by RGB2YCbCr: LHY, LHCb, LHCr; singular Value Decomposition (SVD) is performed on LHCb (blue chrominance component of the vertical component) and LHCr (red chrominance component of the vertical component); then, embedding Cen_u and Cen_v in the step 1 into a singular value component SCb of LHCb and a singular value component SCr of LHCr respectively; then obtaining a host image IW embedded with the watermark through inverse singular value transformation and inverse wavelet transformation;
step 3: the watermark extraction process comprises the following steps: the watermark embedded image IW is first subjected to wavelet decomposition, and the intermediate frequency part LHEr, LHEg, LHEb of the obtained RGB color space is converted into YCbCr color space by RGB2YCbCr: LHEY, LHECb, LHECr; singular Value Decomposition (SVD) is performed on LHECb (blue chrominance component of the vertical component) and LHECr (red chrominance component of the vertical component); combining the obtained singular values with the previous eigenvectors to obtain singular values SssCb and SssCr containing watermarks; CEN_u and CEN_v are respectively extracted from SssCb and SssCr, and an extracted encrypted image CEN is finally obtained;
step 4: decryption of watermark images: the extracted encrypted image CEN is decrypted by using the trigeminal technique to obtain a decrypted watermark image F i ；
Step 5: robustness test: various attack tests are performed on the host image embedded with the watermark, and the invisibility and the robustness of the watermark image extracted from the host image which is attacked after the watermark is embedded are evaluated by calculating MSE (mean square value), PSNR (peak signal to noise ratio) and CC (correlation coefficient) values.
The optimization process of the gray wolf optimization algorithm for the watermark embedding factor af in the step 2 is as follows:
grey Wolf Optimizer (GWO) is a recently developed meta-heuristic search algorithm that simulates the social class and hunting mechanism of the wolf for solving non-convex engineering optimization problems. The method mainly comprises the following steps:
1) Initializing a gray wolf population, and a, A and C; % a, A and C are parameters
2) Calculating the fitness function value of each gray wolf individual, and storing the first three wolves X with the best fitness value a (best individual wolves), X β (second best individual wolf), X δ (third best wolf individuals);
4) T is the current iteration number, max iteration is the maximum iteration number, and when the iteration number t is<At max iteration, for each individual wolf, the current individual position of the wolf is updated, a and C are updated, and the fitness function value of all wolves is calculated. Repeating the steps until t is more than or equal to max time, and then X a For the best found solution, i.e. the best embedding strength af to be found in the present solution.
The objective function corresponding to the embedded factor af in the invention is as follows:
where N is the total number of attacked species, m is the number of watermarks, CC is the correlation coefficient value, IH is the original color host image,
IW is the host image after embedding watermark, F is the three original color watermark images, and F is the three watermark images recovered by extraction. In order to achieve good robustness, invisibility and capacity at the same time, the objective function value corresponding to the optimal embedding factor af found by optimizing through the gray wolf optimization algorithm should be as close to 4 as possible.
In order to overcome the defects of the prior art, the invention mainly aims at determining dynamic embedding factors corresponding to different watermark images in the watermark embedding process, provides a method for optimizing the watermark embedding process based on a gray wolf optimization algorithm (GWO), and encrypts a plurality of color watermark images before embedding by introducing a three-tree encryption technology, thereby improving the robustness of the watermark. The proposed trigeminal Tree-gray wolf optimization (Tree-GWO) watermarking scheme has the advantages that: (1) On the basis of the prior single binary or gray scale or single color watermarking technology, three watermark images can be simultaneously encrypted into a gray scale encryption result by introducing the trigeminal tree encryption technology, so that the watermark embedding capacity is greatly improved, and various attacks can be effectively resisted by the trigeminal tree encryption technology; (2) For randomness and uncertainty of the existing watermark embedding factors caused by artificial experiment determination, a target function based on watermark embedding and extraction is designed by introducing a gray wolf optimization algorithm, and the watermark embedding factors are optimized by utilizing a GWO optimization algorithm, so that the dynamic characteristics of the embedding factors corresponding to different image characteristics are solved, and the relation of robustness, invisibility and mutual restriction among capacities in the watermark embedding process is effectively balanced; (3) In order to test the wide applicability of the invention, not only various performance tests are carried out on natural color images, but also performance tests are carried out on medical images, which shows that the invention is not only suitable for copyright protection of natural images, but also suitable for copyright protection of medical images; (4) The invention tests color hosts and color watermarks, and is applicable to gray level images and audio watermarks.
For clarity of illustration of the objects, technical solutions and advantages of the present invention, the following describes the watermark encryption algorithm and watermark embedding in detail.
(1) The three-tree encryption process of the color watermark image comprises the following steps:
step 1: three color watermark images f i (i=1, 2, 3) is divided into three channels R, G, B, then encryption is performed based on the trigeminal encryption technology, and the encryption result is Cen:
Cen＝Encrypt(f 1 ,f 2 ,f 3 ) (22)
wherein Encrypt (·) is a trigeminal encryption process.
Step 2: wavelet transforming the encrypted result Cen:
[LLCen,LHCen,HLCen,HHCen]＝DWT(Cen) (23)
wherein LLCen, LHCen, HLCen, HHCen are the low frequency, horizontal, vertical, and high frequency components of the encrypted image after wavelet changes, respectively.
Step 3: singular value decomposition is carried out on an intermediate frequency part LHCen obtained by wavelet transformation:
[UCen,SCen,VCen]＝SVD(LHCen) (24)
step 4: VID (variational image decomposition) is carried out on a singular value part SCen obtained by singular value decomposition:
[SCen_u,SCen_v]＝VID(SCen) (25)
(2) Embedding the watermark:
step 1: wavelet decomposing the color host image IH:
[LL,LH,HL,HH]＝DWT(IH) (26)
step 2: intermediate frequency portion LHr, LHg, LHb of the resulting RGB color space is converted to YCbCr color space by RGB2YCbCr: LHY, LHCb, LHCr:
[LHY,LHCb,LHCr]＝RGB2YCbCr(LHr,LHg,LHb) (27)
step 3: wavelet transform and singular value decomposition are performed on LHCb (blue chrominance component of the vertical component) and LHCr (red chrominance component of the vertical component):
[UCb,SCb,VCb]＝SVD(DWT(LHCb)) (28)
[UCr,SCr,VCr]＝SVD(DWT(LHCr)) (29)
wherein UCb, SCb, VCb is left singular value component, characteristic singular value component, right singular value component of LHCb respectively; UCr, SCr, VCr are left singular value component, characteristic singular value component, right singular value component, respectively, of LHCr.
Step 4: embedding cen_u and cen_v in step 1 into the singular value component SCb of LHCb and the singular value component SCr of LHCr, respectively:
SsCb＝SCb+af·Cen_u (30)
SsCr＝SCr+af·Cen_v (31)
wherein af is watermark embedding strength and is obtained by optimizing a gray wolf optimization algorithm.
Step 5: singular value decomposition is performed on singular values SsCb and SsCr embedded with watermarks:
[U1Cb,S1Cb,V1Cb]＝SVD(SsCb) (32)
[U1Cr,S1Cr,V1Cr]＝SVD(SsCr) (33)
step 6: performing inverse transformation on the obtained singular values to obtain an intermediate frequency part containing the watermark:
LH11Cb＝UCb·S1Cb·VCb -1 (34)
LH11Cr＝UCr·S1Cr·VCr -1 (35)
step 7: and performing inverse wavelet transformation on the obtained intermediate frequency part:
LHhCb＝IDWT(LL1Cb,LH11Cb,HL1Cb,HH1Cb) (36)
LHhCr＝IDWT(LL1Cr,LH11Cr,HL1Cr,HH1Cr) (37)
step 8: converting intermediate frequency parts LHhCB and LHhCr of the obtained YCbCr color space into RGB color space through YCbCr2RGB:
[LH1r,LH1g,LH1b]＝YCbCr2RGB(LH1Y,LHhCb,LHhCr) (38)
step 9: performing inverse wavelet transformation on the recombined intermediate frequency part LH1 to obtain a host image embedded with the watermark:
IW＝IDWT(LL,LH1,HL,HH) (39)
(3) The watermark extraction process comprises the following steps:
step 1: the watermark embedded image IW is subjected to wavelet decomposition:
[LLE,LHE,HLE,HHE]＝DWT(IW) (40)
step 2: the intermediate frequency portion LHEr, LHEg, LHEb of the resulting RGB color space is converted to YCbCr color space by RGB2YCbCr: LHEY, LHECb, LHECr:
[LHEY,LHECb,LHECr]＝RGB2YCbCr(LHEr,LHEg,LHEb) (41)
step 3: wavelet transform and singular value decomposition are performed on LHECb (blue chrominance component of the vertical component) and LHECr (red chrominance component of the vertical component):
[U2Cb,S2Cb,V2Cb]＝SVD(DWT(LHECb)) (42)
[U2Cr,S2Cr,V2Cr]＝SVD(DWT(LHECr)) (43)
step 4: combining the obtained singular values with the previous eigenvectors to obtain singular values SssCb and SssCr containing watermarks:
SssCb＝U1Cb·S2Cb·V1Cb -1 (44)
SssCr＝U1Cr·S2Cr·V1Cr -1 (45)
step 5: CEN_u and CEN_v are respectively extracted from SssCb and SssCr, an extracted encrypted image is finally obtained, and then inverse singular value transformation and inverse wavelet transformation are carried out to obtain CEN:
CEN_u＝(SssCb-SCb)/af (46)
CEN_v＝(SssCr-SCr)/af (47)
CEN＝IDWT(ISVD(CEN_u+CEN_v)) (48)
step 6: decryption of watermark images: the extracted encrypted image CEN is decrypted by using the trigeminal technique to obtain a decrypted watermark image F i (i＝1,2,3)：
F 1 ,F 2 ,F 3 ＝Decrypt(CEN) (49)
Step 7: robustness test: various attack tests are performed on the host image embedded with the watermark, and the invisibility and the robustness of the watermark image extracted from the host image which is attacked after the watermark is embedded are evaluated by calculating MSE (mean square value), PSNR (peak signal to noise ratio) and CC (correlation coefficient) values. And calculates the fitness function value of each iteration process.
In order to verify the effectiveness of the method, experimental results are given to the encryption and decryption process of four input color images.
Fig. 1 (a) is a schematic diagram of a color multi-watermark embedding and encryption principle provided by the present invention. The color host image IH is first wavelet transformed and the intermediate frequency portion LHr, LHg, LHb of the resulting RGB color space is converted to YCbCr color space by RGB2YCbCr: LHY, LHCb, LHCr; singular Value Decomposition (SVD) is performed on LHCb (blue chrominance component of the vertical component) and LHCr (red chrominance component of the vertical component); then, embedding Cen_u and Cen_v in the step 1 into a singular value component SCb of LHCb and a singular value component SCr of LHCr respectively; the watermarked host image IW is then obtained by an inverse singular value transformation and an inverse wavelet transformation. Fig. 1 (b) is a watermark extraction process: the watermark embedded image IW is first subjected to wavelet decomposition, and the intermediate frequency part LHEr, LHEg, LHEb of the obtained RGB color space is converted into YCbCr color space by RGB2YCbCr: LHEY, LHECb, LHECr; singular Value Decomposition (SVD) is performed on LHECb (blue chrominance component of the vertical component) and LHECr (red chrominance component of the vertical component); combining the obtained singular values with the previous eigenvectors to obtain singular values SssCb and SssCr containing watermarks; CEN_u and CEN_v are respectively extracted from SssCb and SssCr, and an extracted encrypted image CEN is finally obtained; the extracted encrypted image CEN is decrypted by using the trigeminal technique to obtain a decrypted watermark image F i 。
FIG. 2 is a schematic diagram of the convergence history of the gray wolf optimization algorithm to find the optimal embedding factor. It can be seen that after the 6 th iteration, the objective function starts to converge to a maximum. The optimization efficiency of the gray wolf optimization algorithm is relatively high.
Fig. 3 (a) -3 (c) are three original color watermark images (256×256×3 in size), and fig. 3 (d) and 3 (e) are an original color natural host image Lena (512×512×3 in size) and an original color medical host image Cell (512×512×3 in size), respectively.
As can be seen from fig. 4, the information of the color watermark image is encrypted and encrypted as a gray-scale image of 256×256 in size.
Fig. 5 (a) and 5 (b) are host images Lena and Cell, respectively, after embedding the watermark, and it can be found that they are not distinguishable from the original host image by the naked eye alone. For convenience, the present invention only shows watermark images (as in fig. 5 (c) - (e)) extracted from fig. 5 (a) in this scheme without attack.
In order to verify the robustness of the watermark in the invention, various attack experiments are respectively carried out, and for brevity, only three common attacks are shown in the description.
Fig. 6 (a) shows a watermarked host Lena image that has been attacked by gaussian noise at an intensity of 0.2 and the corresponding extracted and decrypted watermark Baboon, fruits, peppers (as in fig. 6 (b) - (d)). Fig. 6 (e) shows a watermarked host Cell image that has been attacked by gaussian noise at an intensity of 0.2, and the corresponding extracted and decrypted watermark Baboon, fruits, peppers (as in fig. 6 (f) - (h)).
Fig. 7 (a) shows a watermarked host Lena image subjected to a 50% intensity cut attack and the corresponding extracted and decrypted watermark Baboon, fruits, peppers (as in fig. 7 (b) - (d)). Fig. 7 (e) shows a watermarked host Cell image subjected to a 50% cut attack and the corresponding extracted and decrypted watermark Baboon, fruits, peppers (as in fig. 7 (f) - (h)).
Fig. 8 (a) shows the watermarked host Lena image after a rotation attack of 15 ° and the corresponding extracted and decrypted watermark Baboon, fruits, peppers (as in fig. 7 (b) - (d)). Fig. 8 (e) shows the watermarked host Cell image after a rotation attack of 15 ° and the corresponding extracted and decrypted watermark Baboon, fruits, peppers (as in fig. 8 (f) - (h)).
According to the extraction and decryption results under different attacks of fig. 6-8, it can be found that even if the image with the embedded watermark is polluted by a great degree of noise or part of information is missing, the invention can extract and decrypt the identifiable original color image, thereby verifying the feasibility of the system and meeting various requirements in practical application.
While the invention has been described above in connection with the drawings, the invention is not limited to the above-described embodiments, which are intended to be illustrative only and not limiting, and many variations can be made by those of ordinary skill in the art without departing from the spirit of the invention, which fall within the protection of the invention.
Those skilled in the art will appreciate that the drawings are schematic representations of only one preferred embodiment, and that the above-described embodiment numbers are merely for illustration purposes and do not represent advantages or disadvantages of the embodiments.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (5)
1. A gray wolf optimization algorithm and a full three-tree structure wavelet domain color multi-watermark method are characterized in that three color watermark images are firstly encrypted into a gray image through a three-tree encryption technology in the watermark embedding process; then, wavelet transformation is carried out on the color host image, the selected frequency part is transformed into a YCbCr color space, singular value decomposition is carried out, and a plurality of encrypted color watermarks are embedded into the singular value part of the carrier image; and finally, combining the singular value part containing the secret image with the feature vector, and then performing inverse wavelet transformation to obtain the carrier image containing the secret image.
2. The gray wolf optimization algorithm and the full three-tree structure wavelet domain color multi-watermarking method as claimed in claim 1, wherein the specific steps are refined as follows:
encryption of the watermark image: first, three color watermark images f i Dividing the data into three channels R, G, B, i=1, 2 and 3, and then encrypting based on a trigeminal encryption technology, wherein the encryption result is Cen; then carrying out wavelet transformation on the encryption result Cen, carrying out singular value decomposition on the obtained intermediate frequency part LHCen, and carrying out variational image decomposition on the obtained singular value part SCen to obtain a texture part Cen_u and a detail part Cen_v of the encryption image;
embedding the watermark: the color host image IH is first wavelet transformed and the intermediate frequency portion LHr, LHg, LHb of the resulting RGB color space is converted to YCbCr color space by RGB2YCbCr: LHY, LHCb, LHCr; singular value decomposition is performed on a blue chrominance component LHCb of the vertical component and a red chrominance component LHCr of the vertical component; then embedding the Cen_u and Cen_v into a singular value component SCb of LHCb and a singular value component SCr of LHCr respectively; then obtaining a host image IW embedded with the watermark through inverse singular value transformation and inverse wavelet transformation;
the watermark extraction process comprises the following steps: the watermark embedded image IW is first subjected to wavelet decomposition, and the intermediate frequency part LHEr, LHEg, LHEb of the obtained RGB color space is converted into YCbCr color space by RGB2YCbCr: LHEY, LHECb, LHECr; singular value decomposition is performed on the blue chrominance component LHECb of the vertical component and the red chrominance component LHECr of the vertical component; combining the obtained singular values with the previous eigenvectors to obtain singular values SssCb and SssCr containing watermarks; CEN_u and CEN_v are respectively extracted from SssCb and SssCr, and an extracted encrypted image CEN is finally obtained;
decryption of watermark images: the extracted encrypted image CEN is decrypted by using the trigeminal technique to obtain a decrypted watermark image F i 。
3. The gray wolf optimization algorithm and the full trigeminal structure wavelet domain color multi-watermark method of claim 2, further comprising the step of robustness testing: and carrying out various attack tests on the host image embedded with the watermark, and evaluating the invisibility and the robustness of the watermark image extracted from the host image which is attacked after the watermark is embedded by calculating a mean square value MSE, a peak signal to noise ratio PSNR and a correlation coefficient CC value.
4. The gray wolf optimization algorithm and the full three-tree structure wavelet domain color multi-watermarking method according to claim 2, wherein the watermark embedding process comprises the following specific steps:
step 1: wavelet decomposing the color host image IH:
[LL,LH,HL,HH]＝DWT(IH) (6)
wherein LL, LH, HL, HH is the low frequency, horizontal, vertical, high frequency component after wavelet change of the host image respectively;
step 2: intermediate frequency portion LHr, LHg, LHb of the resulting RGB color space is converted to YCbCr color space by RGB2YCbCr: LHY, LHCb, LHCr:
[LHY,LHCb,LHCr]＝RGB2YCbCr(LHr,LHg,LHb) (7)
step 3: wavelet transform and singular value decomposition are performed on a blue chrominance component LHCb of the vertical component and a red chrominance component LHCr of the vertical component:
[UCb,SCb,VCb]＝SVD(DWT(LHCb)) (8)
[UCr,SCr,VCr]＝SVD(DWT(LHCr)) (9)
wherein UCb, SCb, VCb is left singular value component, characteristic singular value component, right singular value component of LHCb respectively; UCr, SCr, VCr are left singular value component, characteristic singular value component, right singular value component of LHCr, respectively;
step 4: embedding cen_u and cen_v in step 1 into the singular value component SCb of LHCb and the singular value component SCr of LHCr, respectively:
SsCb＝SCb+af·Cen_u (10)
SsCr＝SCr+af·Cen_v (11)
wherein af is the optimal embedding factor;
step 5: singular value decomposition is performed on singular values SsCb and SsCr embedded with watermarks:
[U1Cb,S1Cb,V1Cb]＝SVD(SsCb) (12)
[U1Cr,S1Cr,V1Cr]＝SVD(SsCr) (13)
step 6: performing inverse transformation on the obtained singular values to obtain an intermediate frequency part containing the watermark:
LH11Cb＝UCb·S1Cb·VCb -1 (14)
LH11Cr＝UCr·S1Cr·VCr -1 (15)
step 7: and performing inverse wavelet transformation on the obtained intermediate frequency part:
LHhCb＝IDWT(LL1Cb,LH11Cb,HL1Cb,HH1Cb) (16)
LHhCr＝IDWT(LL1Cr,LH11Cr,HL1Cr,HH1Cr) (17)
step 8: converting intermediate frequency parts LHhCB and LHhCr of the obtained YCbCr color space into RGB color space through YCbCr2RGB:
[LH1r,LH1g,LH1b]＝YCbCr2RGB(LH1Y,LHhCb,LHhCr) (18)
step 9: performing inverse wavelet transformation on the recombined intermediate frequency part LH1 to obtain a host image embedded with the watermark:
IW＝IDWT(LL,LH1,HL,HH) (19)。
5. the gray-wolf optimization algorithm and the full trigeminal structure wavelet domain color multi-watermark method according to claim 1, wherein the optimization process of the gray-wolf optimization algorithm of the optimal embedding factor af is as follows:
1) Initializing a gray wolf population, and a, A and C; a. a and C are parameters
2) Calculating fitness function value of each individual wolf, and storing the best individual wolf X of the first three wolves with the best fitness value a Second best individual X of wolves b Third best individual X of wolf d ；
3) T is the current iteration number, max iteration is the maximum iteration number, and when the iteration number t is<When max is equal, for each wolf individual, updating the position of the current wolf individual, updating a, A and C, and calculating the fitness function value of all the wolves; repeating the steps until t is more than or equal to max time, and then X a For the best solution found, i.e. the best embedding factor af to be found;
the objective function corresponding to the optimal embedding factor af is:
wherein N is the total number of attacked types, m is the number of watermarks, CC is a correlation coefficient value, IH is an original color host image, IW is a host image after watermark embedding, F is three original color watermark images, F is three watermark images recovered by extraction, and the objective function value corresponding to the best embedding factor af is found by optimizing through a gray wolf optimization algorithm and should be as close as possible to 4.
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