CN112800395B - Copyright authentication and verification method for multiple images based on zero watermark technology - Google Patents
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Abstract
The invention discloses a method for authenticating the copyright of a plurality of images based on a zero watermark technology, which comprises the following steps: processing a plurality of images to be protected to fuse the images into one image; determining an effective area of the fusion image, and extracting features in the effective area to obtain a feature image; and respectively scrambling and encrypting the characteristic image and the identification image, and carrying out XOR operation on the scrambled characteristic image and the scrambled identification image to obtain a zero watermark image. A copyright verification method for a plurality of images based on a zero watermark technology comprises the following steps: and performing exclusive OR operation on the scrambled characteristic image and the zero watermark image to obtain a scrambled identification image, then performing scrambling and decryption to obtain an identification image, and verifying and comparing the identification image with the original identification image. The method can protect the copyright of a plurality of images more efficiently and safely, effectively reduce the time and the storage cost in the copyright protection process, and has higher practical value.
Description
Technical Field
The invention belongs to the technical field of digital multimedia anti-counterfeiting and information security protection, and particularly relates to a method for authenticating and verifying copyright of a plurality of images based on a zero watermark technology.
Background
With the rapid development of the internet, computer and mobile communication technologies, people can upload or download multimedia information (images, videos, texts, audio, etc.) more conveniently and quickly. However, the rapidly developed technology brings convenience to people, and also brings information security problems such as easy copying and stealing of multimedia information to people. In recent years, the problem of security protection of multimedia information is solved to a certain extent by the rapid development of digital watermarking technology, the traditional embedded watermarking technology is the most widely applied image copyright protection technology in recent years, but as the watermark information is embedded into an original image, the data of the image is necessarily modified, so that the quality of the image is influenced.
At present, most of zero watermark algorithms perform copyright protection on a single image, and when large-scale image sets are subjected to copyright protection, repeated operation not only consumes a large amount of time, but also copyright storage evidence occupies a large amount of storage space. Therefore, how to reduce the time and storage cost in copyright protection will become a research focus in the future. In 2016, shore macros et al propose a robust zero-watermark scheme based on orthogonal fourier mellin moment and chaotic mapping, which can realize copyright protection for two images at the same time. Firstly, two images are respectively used as a real part and an imaginary part of a complex number, so that a 'two-channel structure' is combined into a 'single-channel structure', then a characteristic invariant obtained by orthogonal Fourier mellin moments is calculated, and a binary characteristic image is constructed by utilizing the characteristic invariant. And finally, scrambling the watermark image and the characteristic image together by using chaotic mapping to generate a verification image. However, this method maps images to complex fields, so that only two images can be processed simultaneously, and the algorithm may be difficult to cope with malicious attacks such as translation, mirror image, and joint attack. In 2019, summer, autumn and the like propose a scheme for protecting the copyright of three CT images based on quaternion polar harmonic Fourier moments, the scheme is that the three CT images are firstly mapped into three imaginary parts of pure quaternion, so that a three-channel structure is combined into a single-channel structure, the quaternion polar harmonic Fourier moments are calculated, then binary feature images are constructed by using the amplitude of the quaternion polar harmonic Fourier moments, and finally the feature images are chaotic scrambled and are subjected to exclusive or operation with watermark images to generate key images.
In summary, although the current zero-watermarking algorithms for multiple images can map multiple images to a complex field, thereby implementing copyright protection for two or three images at the same time, these algorithms lack flexibility, that is, a reasonable image copyright protection scheme and the number of images to be protected at one time cannot be flexibly determined according to the number of large-scale image sets, and copyright protection cannot be performed on all images in the large-scale image sets.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a method for authenticating and verifying the copyright of a plurality of images based on a zero watermark technology, in order to overcome the defects in the prior art.
In order to realize the technical purpose, the following technical scheme is adopted:
a copyright authentication method for a plurality of images based on a zero watermark technology comprises the following steps:
step S1: processing a plurality of images to be protected to fuse the images into a fused image;
step S2: determining an effective area of the fused image, and extracting features in the effective area to obtain a feature image;
and step S3: and respectively scrambling and encrypting the characteristic image and the identification image, and carrying out XOR operation on the scrambled characteristic image and the scrambled identification image to obtain a zero watermark image.
In order to optimize the technical scheme, the specific measures adopted further comprise:
further, step S1 specifically includes:
s11: carrying out X-axis direction shearing normalization, Y-axis direction shearing normalization, scaling normalization and translation normalization on the multiple images by using an image normalization method to obtain corresponding standard images;
s12: fusing a plurality of standard images into a fused image by utilizing a gray-scale weighted average image fusion method, wherein the fusion formula is as follows:
Im F ＝α 1 *Im 1 +α 2 *Im 2 +…+α i *Im i
α 1 ＝α 2 ＝…＝α i ，α 1 +α 2 +…+α i ＝1，i∈N +
wherein: im is 1 ,Im 2 ,...,Im i Representing a plurality of images to be fused, im F Representing the image obtained by fusing a plurality of images, and i represents the number of the plurality of images to be fused.
Further, step S2 specifically includes:
s21: extracting an effective area with a fixed size according to the centroid of the fused image;
s22: performing lifting wavelet transform on the effective region to obtain a low-frequency component matrix of image signals in the effective region, decomposing the low-frequency component matrix into a plurality of sub-matrix blocks A with fixed sizes, performing QR decomposition on each sub-matrix block A to obtain an orthogonal matrix Q and an upper triangular matrix R,
the low-frequency component matrix A is decomposed by the following formula: a = QR
Wherein: q is an orthogonal matrix, and R is an upper triangular matrix;
s23: and calculating the mean value of matrix elements consisting of 2-norm of the first row vector of the upper triangular matrix R, and if the element value is greater than or equal to the mean value, taking 1, otherwise, taking 0, thereby obtaining the binary characteristic image.
Further, the scrambling procedure in step S3 is: scrambling and encrypting and decrypting the characteristic image and the identification image by utilizing Cat mapping according to the key K,
the encryption formula is:
wherein: (x) n ,y n ) Coordinates representing pixels of the original gray-scale image, (x) n+1 ,y n+1 ) Representing transformed pixel coordinates, a and b being scrambling parameters, n representing the current transformationThe number of times mod () is the modulo operation and N is the order of the image. The scrambling parameter and the number of transformations constitute a key K.
A copyright verification method of a plurality of images based on a zero watermark technology comprises the steps of scrambling and encrypting a characteristic image by utilizing Cat mapping according to a secret key K to obtain a scrambled characteristic image, carrying out anti-exclusive OR operation on the scrambled characteristic image and the zero watermark image to obtain a scrambled identification image, then scrambling and decrypting according to the secret key K to obtain an identification image, carrying out similarity comparison on the identification image and an original identification image to obtain a similarity coefficient, and when the similarity coefficient is larger than a set threshold value, considering that copyright verification is correct, otherwise, failing;
the scrambled identification image decryption formula is as follows:
the invention has the beneficial effects that:
the invention relates to a method for authenticating and verifying the copyright of a plurality of images based on a zero watermark technology, which can fuse a plurality of images into one image by using a gray-scale weighted average image fusion method, so that the time and the storage cost brought by repeated operation on a single image in copyright protection can be effectively reduced by only carrying out operations such as feature extraction on one fused image, and meanwhile, a reasonable protection scheme and the number of images protected at one time are determined according to the number of images in a large-scale image set, so that the protection method has certain flexibility, thereby realizing the copyright protection on all the images in the image set, and the effective effect is obvious.
The method and the device have the advantages that multiple images are converted into standard images by using an image normalization technology, and the LWT-QR decomposition is used for feature extraction, compared with a zero watermark algorithm of a single image, the method and the device have higher robustness in coping with image attack, meanwhile, the method and the device for feature extraction by using a multiple-image fusion method and the LWT-QR decomposition can effectively save the operation time of the algorithm, protect the copyrights of the multiple images more efficiently and safely, effectively reduce the time and the storage cost in the copyright protection process, and have higher practical value.
Drawings
Fig. 1 is a flowchart of a copyright authentication method of the present invention.
Fig. 2 is a schematic flow chart of the copyright authentication algorithm of the present invention.
Fig. 3 is a flow chart of the copyright verification algorithm of the present invention.
Fig. 4 is a graph comparing robustness of different aspects of the invention.
FIG. 5 is a comparison graph of run times for different aspects of the present invention.
Fig. 6 is a graph comparing the robustness of different aspects of the invention.
Detailed Description
Embodiments of the present invention are described in further detail below with reference to the accompanying drawings.
Firstly, respectively normalizing a plurality of images into corresponding standard images, fusing the plurality of standard images into one image by using a gray-scale weighted average image fusion method, then performing feature extraction on an effective region of the fused image by using LWT-QR decomposition, scrambling the feature images and the identification images by using Cat mapping, and performing XOR operation on the scrambled feature images and the identification images to obtain a key-zero watermark image for copyright authentication and verification. Therefore, the problems of time and storage cost caused by repeated operation of a single image zero-watermarking algorithm during large-scale image set copyright protection are solved, and the problems that the conventional multi-image zero-watermarking algorithm is lack of flexibility and cannot perform copyright protection on all images in an image set are solved.
The method comprises two stages of copyright authentication and copyright verification, which are respectively explained with reference to fig. 1 and fig. 2.
As shown in fig. 1, the present invention is a method for authenticating copyright of multiple images based on zero watermark technology, comprising the following steps:
step S1: and carrying out image processing on the plurality of images to be protected, and fusing the images into one image.
The step S1 specifically comprises the following steps:
s11: and carrying out X-axis direction shearing normalization, Y-axis direction shearing normalization, scaling normalization and translation normalization on the multiple images by using an image normalization method to obtain corresponding standard images.
S12: fusing a plurality of standard images into a fused image by utilizing a gray-scale weighted average image fusion method, wherein the fusion formula is as follows:
Im F ＝α 1 *Im 1 +α 2 *Im 2 +…+α i *Im i
α 1 ＝α 2 ＝…＝α i ，α 1 +α 2 +…+α i ＝1，i∈N +
wherein: im (c) 1 ,Im 2 ,...,Im i Representing a plurality of images to be fused, im F Representing the image obtained by fusing a plurality of images, and i represents the number of the plurality of images to be fused.
Step S2: determining an effective area of the fused image, and extracting features in the effective area to obtain a feature image;
the step S2 specifically comprises the following steps:
s21: and extracting an effective area with a fixed size according to the centroid of the fused image.
S22: lifting wavelet transformation is carried out on the effective area to obtain a low-frequency component matrix of an image signal in the effective area, the low-frequency component matrix is decomposed into a plurality of sub-matrix blocks A with fixed sizes, and QR decomposition is carried out on each sub-matrix block A to obtain an orthogonal matrix Q and an upper triangular matrix R.
The low-frequency component matrix A is decomposed by the following formula: a = QR
Wherein: q is an orthogonal matrix and R is an upper triangular matrix.
S23: and calculating the mean value of matrix elements formed by 2-norm of the first row vector of the upper triangular matrix R, if the element value is larger than or equal to the mean value, taking 1, otherwise, taking 0, thereby obtaining the binary characteristic image.
And step S3: and respectively scrambling and encrypting the characteristic image and the identification image, and carrying out XOR operation on the scrambled characteristic image and the scrambled identification image to obtain a zero watermark image.
The scrambling encryption process comprises the following steps: and scrambling and encrypting the characteristic image and the identification image by utilizing Cat mapping according to the key K.
The encryption formula is:
wherein: (x) n ,y n ) Coordinates representing pixels of the original grayscale image, (x) n+1 ,y n+1 ) Representing the transformed pixel coordinates, a and b are scrambling parameters, N represents the number of current transformations, mod () is the modulo operation, and N is the order of the image. The scrambling parameter and the number of times of transformation constitute a key K.
Example one
Firstly, four different original images with the size of M multiplied by M are respectively normalized so as to be converted into standard images, and then the four standard images are fused by utilizing a gray-scale weighted average image fusion method so as to obtain a fused image containing information of each image. Then extracting an effective region with a fixed size of NxN according to the non-deformation center of the fused image, then performing l-level lifting wavelet transform on the effective region to obtain a low-frequency component matrix, and dividing the low-frequency component matrix into sub-matrix blocks A with a size of nxn, wherein the total number of the sub-matrix blocks A is
The ith sub-block is marked as A i I =1, 2.., K., order @>
Then for each sub-matrix block A i Performing QR decomposition and calculating each sub-matrix block A i The 2-norm of the first row vector of the R matrix of (a) constitutes a matrix B of size k x k. And then, calculating the average value ave of the elements in the matrix B, taking ave as a basis for generating the characteristic image, and if the value of the elements in the matrix B is greater than or equal to the average value ave, taking 1, and if the value of the elements in the matrix B is less than the average value ave, taking 0, so as to generate the corresponding characteristic image F.
And finally, scrambling the characteristic image F and the identification image W by utilizing Cat mapping, wherein scrambling parameters are used as a key K for copyright verification and are stored by a copyright owner, and then carrying out XOR operation on the scrambled characteristic image F 'and the scrambled identification image W', wherein the formula of the XOR operation is as follows:
and meanwhile, the zero watermark image omega is stored to a third-party trusted authority to be used as a basis for copyright verification.
A copyright verification method for a plurality of images based on a zero watermark technology specifically comprises the following steps: according to a secret key K, scrambling and encrypting the characteristic image by utilizing Cat mapping to obtain a scrambled characteristic image, carrying out anti-exclusive OR operation on the scrambled characteristic image and a zero watermark image to obtain a scrambled identification image, then carrying out scrambling and decrypting according to the secret key K to obtain an identification image, carrying out similarity comparison on the identification image and an original identification image to obtain a similarity coefficient, when the similarity coefficient is greater than a set threshold value, determining that the copyright verification is correct, otherwise, failing;
the scrambled identification image decryption formula is as follows:
example two
In the process of copyright verification, a copyright owner needs to provide a secret key K and an identification image to prove the ownership of the image. As can be seen from fig. 1 and 2, the feature extraction step for verification is the same as the authentication part, and therefore, the description is omitted here, and only different parts are specifically described below.
Scrambling the generated characteristic image F by utilizing Cat mapping according to the key K, and carrying out exclusive OR operation on the scrambled characteristic image F 'and a zero watermark image omega stored by a trusted third party organization to obtain a scrambled identification image W', wherein the exclusive OR operation formula is as follows:
and finally, carrying out reverse scrambling on the scrambled identification image W' by using the key K, and recovering the identification image W. And comparing the similarity of the recovered identification image with the identification image stored by the copyright owner to obtain a similarity coefficient, if the similarity coefficient is greater than a set threshold value, the copyright verification is considered, otherwise, the copyright verification fails.
Fig. 4 and 6 show the robustness of the proposed scheme and compare it with the three schemes. The scheme is a copyright protection scheme based on discrete cosine transform, normalization processing is adopted in watermark embedding and extracting processes, and certain malicious signal processing and geometric attack can be resisted; and the second scheme utilizes an image normalization technology to map the image to a geometric invariant space. Then, an important region is extracted from the normalized image. Finally, carrying out exclusive OR operation by using the low-frequency discrete cosine transform coefficient of the important area and the watermark image so as to construct copyright information; and the third scheme is a strong robust zero-watermark algorithm based on non-subsampled Contourlet transform and image normalization. The scheme converts an original image into a standard image by utilizing an invariant moment based image normalization technology. The active area is then subjected to a block-wise non-subsampled Contourlet transform. And carrying out block singular value decomposition on the low-frequency coefficient sub-band obtained by transformation, and generating a zero watermark according to the parity of the highest bit of the maximum singular value of each block. Compared with the other three schemes, the method of the scheme has higher robustness in coping with image attacks, as shown in fig. 4 and 6. Meanwhile, the feature extraction by adopting a multi-image fusion method and LWT-QR decomposition can effectively save the operation time of the algorithm, as shown in FIG. 5, thereby having lower time cost. From the above chart, the method proposed by the present scheme is an optimal scheme.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above-mentioned embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may be made by those skilled in the art without departing from the principle of the invention.
Claims (2)
1. A copyright authentication method for a plurality of images based on a zero watermark technology is characterized by comprising the following steps:
step S1: processing a plurality of images to be protected to fuse the images into a fused image; the method specifically comprises the following steps:
s11: carrying out X-axis direction shearing normalization, Y-axis direction shearing normalization, scaling normalization and translation normalization on the multiple images by using an image normalization method to obtain corresponding standard images;
s12: fusing a plurality of standard images into a fused image by utilizing a gray-scale weighted average image fusion method, wherein the fusion formula is as follows:
Im F ＝α 1 *Im 1 +α 2 *Im 2 +…+α i *Im i
α 1 ＝α 2 ＝…＝α i ，α 1 +α 2 +…+α i ＝1，i∈N +
wherein: im (c) 1 ,Im 2 ,...,Im i Representing a plurality of images to be fused, im F Representing an image obtained by fusing a plurality of images, wherein i represents the number of the images to be fused;
step S2: determining an effective area of the fused image, and extracting features in the effective area to obtain a feature image; the method specifically comprises the following steps:
s21: extracting an effective area with a fixed size according to the centroid of the fused image;
s22: performing lifting wavelet transformation on the effective area to obtain a low-frequency component matrix of an image signal in the effective area, decomposing the low-frequency component matrix into a plurality of sub-matrix blocks A with fixed sizes, and performing QR decomposition on each sub-matrix block A to obtain an orthogonal matrix Q and an upper triangular matrix R;
the low-frequency component matrix A is decomposed by the following formula: a = QR
Wherein: the size of the orthogonal matrix Q is m multiplied by m, and the size of the upper triangular matrix R is m multiplied by n;
s23: calculating the mean value of matrix elements consisting of 2-norm of the first row vector of the upper triangular matrix R, if the element value is more than or equal to the mean value, taking 1, otherwise, taking 0, thereby obtaining a binary characteristic image;
and step S3: respectively scrambling and encrypting the characteristic image and the identification image, and carrying out XOR operation on the scrambled characteristic image and the scrambled identification image to obtain a zero watermark image, wherein the scrambling and encrypting process specifically comprises the following steps:
scrambling and encrypting the characteristic image and the identification image by utilizing Cat mapping according to the key K,
the encryption formula is:
wherein: (x) n ,y n ) Coordinates representing pixels of the original grayscale image, (x) n+1 ,y n+1 ) Representing the transformed pixel coordinates, a and b are scrambling parameters, N represents the current number of transformations, mod () is a modulo operation, N is the order of the image, and the scrambling parameters and the number of transformations constitute a key K.
2. A copyright verification method for a plurality of images based on a zero watermark technology is characterized in that: according to a secret key K, scrambling and encrypting the characteristic image by utilizing Cat mapping to obtain a scrambled characteristic image, carrying out anti-exclusive OR operation on the scrambled characteristic image and a zero watermark image to obtain a scrambled identification image, then carrying out scrambling and decrypting according to the secret key K to obtain an identification image, carrying out similarity comparison on the identification image and an original identification image to obtain a similarity coefficient, when the similarity coefficient is greater than a set threshold value, determining that the copyright verification is correct, otherwise, failing;
the zero watermark image is obtained according to a plurality of image copyright authentication methods based on the zero watermark technology in claim 1;
the scrambled identification image decryption formula is as follows:
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| CN113658030A (en) * | 2021-08-18 | 2021-11-16 | 辽宁工程技术大学 | Low false alarm zero watermark algorithm based on regional XOR |
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