CN112800395B - A Multi-Image Copyright Authentication and Verification Method Based on Zero Watermark Technology - Google Patents
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Abstract
本发明公开了一种基于零水印技术的多幅图像版权认证方法,包括如下步骤:将多幅待保护的图像进行图像处理,融合成一幅图像;确定该幅融合图像的有效区域,并在有效区域内提取特征,得到特征图像;将特征图像和标识图像分别进行置乱加密,并将置乱后的特征图像和置乱后的标识图像进行异或运算,得到零水印图像。一种基于零水印技术的多幅图像版权验证方法,包括如下步骤:对置乱的特征图像与零水印图像进行反异或运算,得到置乱后的标识图像,然后再进行置乱解密,得到标识图像,将该标识图像与原本的标识图像进行验证对比。本方法更高效、安全的同时保护多幅图像的版权,有效降低版权保护过程中的时间和存储成本,实用价值较大。
The invention discloses a multiple image copyright authentication method based on zero watermark technology, comprising the following steps: performing image processing on multiple images to be protected, and fusing them into one image; determining the effective area of the fused image, and Features are extracted in the region to obtain a feature image; the feature image and the logo image are respectively scrambled and encrypted, and the scrambled feature image and the scrambled logo image are XORed to obtain a zero-watermark image. A method for verifying the copyright of multiple images based on zero-watermark technology, comprising the following steps: performing an anti-XOR operation on a scrambled feature image and a zero-watermark image to obtain a scrambled logo image, and then performing scrambled decryption to obtain An identification image is verified and compared with the original identification image. The method protects the copyrights of multiple images more efficiently and safely at the same time, effectively reduces the time and storage costs in the copyright protection process, and has great practical value.
Description
技术领域Technical Field
本发明属于数字多媒体防伪和信息安全保护技术领域,具体涉及一种基于零水印技术的多幅图像版权认证和验证方法。The invention belongs to the technical field of digital multimedia anti-counterfeiting and information security protection, and specifically relates to a multi-image copyright authentication and verification method based on zero watermark technology.
背景技术Background Art
随着互联网、计算机和移动通讯技术的迅速发展,人们能够更方便和快捷地上传或下载多媒体信息(图像、视频、文本和音频等)。但是,快速发展的科技在带给人们便利的同时,也给人们带来了多媒体信息易复制、易盗用等信息安全问题。这几年来,数字水印技术的快速发展在一定程度上解决了多媒体信息的安全保护问题,传统的嵌入式水印技术是近几年应用最为广泛的图像版权保护技术,但是,随着水印信息嵌入到原始图像中,必定会修改图像的数据,从而影响图像的质量。With the rapid development of the Internet, computers and mobile communication technologies, people can upload or download multimedia information (images, videos, texts and audio, etc.) more conveniently and quickly. However, while the rapid development of technology brings convenience to people, it also brings information security issues such as easy duplication and theft of multimedia information. In recent years, the rapid development of digital watermarking technology has solved the problem of multimedia information security protection to a certain extent. Traditional embedded watermarking technology is the most widely used image copyright protection technology in recent years. However, as the watermark information is embedded in the original image, the image data will inevitably be modified, thereby affecting the image quality.
目前,多数零水印算法都是针对单幅图像进行版权保护,在对大规模图像集进行版权保护时,重复的操作不仅会耗费大量时间,而且版权存证还会占用大量存储空间。因此,如何降低版权保护中的时间和存储成本将会成为日后的研究热点。2016年,邵珠宏等人提出了一种基于正交傅立叶梅林矩和混沌映射的鲁棒零水印方案,可以实现同时对两幅图像的版权保护。首先,将两幅图像分别作为复数的实部和虚部,从而将“两通道结构”合并成“单通道结构”,然后计算由正交傅立叶梅林矩得到的特征不变量,并利用其构造二值特征图像。最后,将水印图像与特征图像一起用混沌映射进行置乱,从而生成验证图像,此方案具有一定的鲁棒性和安全性。但是,这种方法是将图像映射到复数域,因此只能同时处理两张图像,而且该算法有可能难以应对平移、镜像和联合攻击等恶意攻击。2019年,夏之秋等人提出了一种基于四元数极谐波傅里叶矩的同时对三幅CT图像进行版权保护的方案,该方案首先将三幅CT图像映射为纯四元数的三个虚部,从而将“三通道结构”合并成“单通道结构”,再计算它的四元数极谐波傅里叶矩,然后利用四元数极谐波傅里叶矩的振幅来构造二值特征图像,最后对特征图像进行混沌置乱,并将其与水印图像进行异或运算,生成关键图像。At present, most zero-watermark algorithms are aimed at copyright protection of a single image. When copyright protection is performed on a large-scale image set, repeated operations will not only consume a lot of time, but also occupy a lot of storage space for copyright evidence. Therefore, how to reduce the time and storage costs in copyright protection will become a research hotspot in the future. In 2016, Shao Zhuhong et al. proposed a robust zero-watermark scheme based on orthogonal Fourier Mellin moments and chaotic mapping, which can realize copyright protection of two images at the same time. First, the two images are respectively regarded as the real and imaginary parts of complex numbers, so that the "two-channel structure" is merged into a "single-channel structure", and then the characteristic invariants obtained by the orthogonal Fourier Mellin moments are calculated, and the binary feature image is constructed using it. Finally, the watermark image and the feature image are scrambled together using chaotic mapping to generate a verification image. This scheme has certain robustness and security. However, this method maps the image to the complex domain, so it can only process two images at the same time, and the algorithm may be difficult to deal with malicious attacks such as translation, mirroring and joint attacks. In 2019, Xia Zhiqiu et al. proposed a scheme for copyright protection of three CT images simultaneously based on the quaternion pole harmonic Fourier moment. The scheme first maps the three CT images into three imaginary parts of pure quaternions, thereby merging the "three-channel structure" into a "single-channel structure", and then calculates its quaternion pole harmonic Fourier moment. Then, the amplitude of the quaternion pole harmonic Fourier moment is used to construct a binary feature image. Finally, the feature image is chaotically scrambled and XORed with the watermark image to generate a key image.
综上,虽然目前的多幅图像零水印算法可以将多幅图像映射到复数域,从而实现同时对两幅或三幅图像的版权保护,但是这些算法缺乏灵活性,即不能够根据大规模图像集的数量,灵活地确定合理的图像版权保护方案和一次保护的图像数量,并且不能对大规模图像集中的所有图像进行版权保护。In summary, although the current multi-image zero-watermark algorithms can map multiple images to the complex domain, thereby achieving copyright protection for two or three images at the same time, these algorithms lack flexibility, that is, they cannot flexibly determine a reasonable image copyright protection scheme and the number of images to be protected at one time according to the number of large-scale image sets, and cannot provide copyright protection for all images in a large-scale image set.
发明内容Summary of the invention
本发明所要解决的技术问题是针对上述现有技术的不足,提供一种基于零水印技术的多幅图像版权认证和验证方法。The technical problem to be solved by the present invention is to provide a copyright authentication and verification method for multiple images based on zero watermark technology in view of the deficiencies of the above-mentioned prior art.
为实现上述技术目的,采用以下技术方案:In order to achieve the above technical objectives, the following technical solutions are adopted:
一种基于零水印技术的多幅图像版权认证方法,包括如下步骤:A method for copyright authentication of multiple images based on zero watermark technology comprises the following steps:
步骤S1:将多幅待保护的图像进行图像处理,融合成一幅融合图像;Step S1: performing image processing on a plurality of images to be protected and fusing them into a fused image;
步骤S2:确定该幅融合图像的有效区域,并在有效区域内提取特征,得到特征图像;Step S2: determining the effective area of the fused image, and extracting features in the effective area to obtain a feature image;
步骤S3:将特征图像和标识图像分别进行置乱加密,并将置乱后的特征图像和置乱后的标识图像进行异或运算,得到零水印图像。Step S3: The feature image and the identification image are scrambled and encrypted respectively, and the scrambled feature image and the scrambled identification image are XOR-ed to obtain a zero-watermark image.
为优化上述技术方案,采取的具体措施还包括:To optimize the above technical solutions, the specific measures taken also include:
进一步地,步骤S1具体为:Furthermore, step S1 is specifically as follows:
S11:将多幅图像利用图像归一化方法,进行X轴方向剪切归一化、Y轴方向剪切归一化、缩放归一化和平移归一化,得到相应的标准图像;S11: performing shear normalization in the X-axis direction, shear normalization in the Y-axis direction, scaling normalization, and translation normalization on the multiple images using an image normalization method to obtain corresponding standard images;
S12:将多幅标准图像利用灰度加权平均图像融合法融合成一幅融合图像,其融合公式为:S12: Multiple standard images are fused into a fused image using the grayscale weighted average image fusion method. The fusion formula is:
ImF=α1*Im1+α2*Im2+…+αi*Imi Im F =α 1 *Im 1 +α 2 *Im 2 +…+α i *Im i
α1=α2=…=αi,α1+α2+…+αi=1,i∈N+ α 1 =α 2 =…=α i , α 1 +α 2 +…+α i =1, i∈N +
其中:Im1,Im2,...,Imi表示待融合的多幅图像,ImF代表多幅图像融合后得到的图像,i表示待融合的多幅图像的数量。Wherein: Im 1 , Im 2 , ..., Im i represent multiple images to be fused, Im F represents an image obtained after the multiple images are fused, and i represents the number of the multiple images to be fused.
进一步地,步骤S2具体为:Furthermore, step S2 is specifically as follows:
S21:按照融合图像的形心提取固定大小的有效区域;S21: extracting a valid area of a fixed size according to the centroid of the fused image;
S22:对有效区域进行提升小波变换,得到有效区域内图像信号的低频分量矩阵,将低频分量矩阵分解成多个固定大小的子矩阵块A,再对每个子矩阵块A进行QR分解,得到一个正交矩阵Q和一个上三角形矩阵R,S22: Perform lifting wavelet transform on the effective area to obtain a low-frequency component matrix of the image signal in the effective area, decompose the low-frequency component matrix into multiple sub-matrix blocks A of fixed size, and then perform QR decomposition on each sub-matrix block A to obtain an orthogonal matrix Q and an upper triangular matrix R.
低频分量矩阵A分解公式为:A=QRThe decomposition formula of the low-frequency component matrix A is: A = QR
其中:Q为正交矩阵,R为上三角形矩阵;Where: Q is an orthogonal matrix, R is an upper triangular matrix;
S23:计算上三角形矩阵R的第一行向量的2-范数组成的矩阵元素的均值,若元素值大于等于均值,取1,否则,取0,从而得到二值化的特征图像。S23: Calculate the mean of the matrix elements composed of the 2-norm of the first row vector of the upper triangular matrix R. If the element value is greater than or equal to the mean, take 1, otherwise take 0, so as to obtain a binary feature image.
进一步地,步骤S3中置乱加密过程为:根据密钥K,利用Cat映射对特征图像和标识图像进行置乱加密与解密,Furthermore, the scrambling encryption process in step S3 is: according to the key K, the feature image and the identification image are scrambled and encrypted and decrypted using Cat mapping.
加密公式为: The encryption formula is:
其中:(xn,yn)表示原灰度图像像素的坐标,(xn+1,yn+1)表示变换后的像素坐标,a和b为置乱参数,n表示当前变换的次数,mod()为模运算,N为图像的阶数。置乱参数和变换次数组成密钥K。Where: ( xn , yn ) represents the coordinates of the original grayscale image pixel, ( xn+1 , yn +1 ) represents the pixel coordinates after transformation, a and b are scrambling parameters, n represents the number of current transformations, mod() is the modulus operation, and N is the order of the image. The scrambling parameters and the number of transformations constitute the key K.
一种基于零水印技术的多幅图像版权验证方法,根据密钥K,利用Cat映射对特征图像进行置乱加密得到置乱的特征图像,将置乱的特征图像与零水印图像进行反异或运算,得到置乱后的标识图像,然后再根据密钥K进行置乱解密,得到标识图像,将该标识图像与原始标识图像进行相似性对比,得到相似性系数,当该相似性系数大于设定的阈值,即认为版权验证正确,反之则失败;A multi-image copyright verification method based on zero watermark technology, according to the key K, Cat mapping is used to scramble and encrypt the feature image to obtain a scrambled feature image, the scrambled feature image is de-XORed with the zero watermark image to obtain a scrambled identification image, and then the scrambled decryption is performed according to the key K to obtain the identification image, and the identification image is compared with the original identification image to obtain a similarity coefficient. When the similarity coefficient is greater than a set threshold, it is considered that the copyright verification is correct, otherwise it fails;
所述置乱后的标识图像解密公式为:The decryption formula of the scrambled logo image is:
本发明的有益效果:Beneficial effects of the present invention:
本发明一种基于零水印技术的多幅图像版权认证和验证方法,使用灰度加权平均图像融合方法可以将多幅图像融合为一幅图像,因此只需对一幅融合图像进行特征提取等操作,从而有效降低在版权保护中对单幅图像进行重复操作所带来的时间和存储成本,同时,根据大规模图像集中的图像数量,确定合理的保护方案和一次保护的图像数量,使得保护方法具有一定的灵活性,从而实现对图像集中所有图像的版权保护,这种有效的效果是显而易见的。The present invention discloses a method for copyright authentication and verification of multiple images based on zero watermark technology. The method can fuse multiple images into one image by using a grayscale weighted average image fusion method. Therefore, only one fused image needs to be subjected to feature extraction and other operations, thereby effectively reducing the time and storage cost caused by repeated operations on a single image in copyright protection. At the same time, according to the number of images in a large-scale image collection, a reasonable protection scheme and the number of images to be protected at one time are determined, so that the protection method has a certain flexibility, thereby realizing copyright protection of all images in the image collection. This effective effect is obvious.
其次,本发明利用图像归一化技术将多幅图像转换为标准图像,并利用LWT-QR分解来进行特征提取,相比于单幅图像的零水印算法,本发明在应对图像攻击上具有更高的鲁棒性,同时,采用多幅图像融合方法和LWT-QR分解来进行特征提取能够有效节省算法运行时间,且可以更高效、安全的同时保护多幅图像的版权,有效降低版权保护过程中的时间和存储成本,实用价值较大。Secondly, the present invention uses image normalization technology to convert multiple images into standard images, and uses LWT-QR decomposition to perform feature extraction. Compared with the zero watermark algorithm of a single image, the present invention has higher robustness in dealing with image attacks. At the same time, the use of multiple image fusion method and LWT-QR decomposition for feature extraction can effectively save algorithm running time, and can protect the copyright of multiple images more efficiently and safely, effectively reducing the time and storage costs in the copyright protection process, and has great practical value.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1是本发明的版权认证方法流程图。FIG1 is a flow chart of the copyright authentication method of the present invention.
图2是本发明的版权认证算法流程示意图。FIG. 2 is a schematic diagram of the copyright authentication algorithm flow of the present invention.
图3是本发明的版权验证算法流程示意图。FIG3 is a schematic diagram of the copyright verification algorithm flow of the present invention.
图4是本发明的不同方案鲁棒性比较图。FIG. 4 is a diagram comparing the robustness of different solutions of the present invention.
图5是本发明的不同方案运行时间对比图。FIG5 is a comparison chart of the running time of different schemes of the present invention.
图6是本发明的不同方案鲁棒性比较折线图。FIG. 6 is a line graph comparing the robustness of different solutions of the present invention.
具体实施方式DETAILED DESCRIPTION
以下结合附图对本发明的实施例作进一步详细描述。The embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings.
本方法首先将多幅图像分别归一化为相应的标准图像,再利用灰度加权平均图像融合方法将多幅标准图像融合为一幅图像,然后利用LWT-QR分解对融合图像的有效区域进行特征提取,最后利用Cat映射将特征图像和标识图像进行置乱,并将置乱后的特征图像与标识图像进行异或操作,便可得到版权认证与验证的关键——零水印图像。从而解决大规模图像集版权保护时,单幅图像零水印算法重复操作所带来的时间和存储成本问题,同时解决了现有多幅图像零水印算法缺乏灵活性,无法对图像集中所有图像进行版权保护的问题。This method first normalizes multiple images to corresponding standard images, then fuses multiple standard images into one image using the grayscale weighted average image fusion method, then uses LWT-QR decomposition to extract features from the effective area of the fused image, and finally uses Cat mapping to scramble the feature image and the identification image, and performs an XOR operation on the scrambled feature image and the identification image to obtain the key to copyright authentication and verification - the zero-watermark image. This solves the time and storage cost problems caused by repeated operations of the zero-watermark algorithm for a single image when protecting the copyright of a large-scale image set, and also solves the problem that the existing zero-watermark algorithm for multiple images lacks flexibility and cannot protect the copyright of all images in the image set.
该方法包括版权认证和版权验证两个阶段,结合图1、图2分别对两个阶段进行说明。The method includes two stages: copyright authentication and copyright verification. The two stages are respectively described in conjunction with FIG1 and FIG2 .
如图1所示,本发明为一种基于零水印技术的多幅图像版权认证方法,包括如下步骤:As shown in FIG1 , the present invention is a method for copyright authentication of multiple images based on zero watermark technology, comprising the following steps:
步骤S1:将多幅待保护的图像进行图像处理,融合成一幅图像。Step S1: Process multiple images to be protected and fuse them into one image.
步骤S1具体为:Step S1 is specifically as follows:
S11:将多幅图像利用图像归一化方法,进行X轴方向剪切归一化、Y轴方向剪切归一化、缩放归一化和平移归一化,得到相应的标准图像。S11: performing shear normalization in the X-axis direction, shear normalization in the Y-axis direction, scaling normalization, and translation normalization on the multiple images using an image normalization method to obtain corresponding standard images.
S12:将多幅标准图像利用灰度加权平均图像融合法融合成一幅融合图像,其融合公式为:S12: Multiple standard images are fused into a fused image using the grayscale weighted average image fusion method. The fusion formula is:
ImF=α1*Im1+α2*Im2+…+αi*Imi Im F =α 1 *Im 1 +α 2 *Im 2 +…+α i *Im i
α1=α2=…=αi,α1+α2+…+αi=1,i∈N+ α 1 =α 2 =…=α i , α 1 +α 2 +…+α i =1, i∈N +
其中:Im1,Im2,...,Imi表示待融合的多幅图像,ImF代表多幅图像融合后得到的图像,i表示待融合的多幅图像的数量。Wherein: Im 1 , Im 2 , ..., Im i represent multiple images to be fused, Im F represents an image obtained after the multiple images are fused, and i represents the number of the multiple images to be fused.
步骤S2:确定该幅融合图像的有效区域,并在有效区域内提取特征,得到特征图像;Step S2: determining the effective area of the fused image, and extracting features in the effective area to obtain a feature image;
步骤S2具体为:Step S2 is specifically as follows:
S21:按照融合图像的形心提取固定大小的有效区域。S21: Extracting a valid area of a fixed size according to the centroid of the fused image.
S22:对有效区域进行提升小波变换,得到有效区域内图像信号的低频分量矩阵,将低频分量矩阵分解成多个固定大小的子矩阵块A,再对每个子矩阵块A进行QR分解,得到一个正交矩阵Q和一个上三角形矩阵R。S22: Perform lifting wavelet transform on the effective area to obtain a low-frequency component matrix of the image signal in the effective area, decompose the low-frequency component matrix into multiple sub-matrix blocks A of fixed size, and then perform QR decomposition on each sub-matrix block A to obtain an orthogonal matrix Q and an upper triangular matrix R.
低频分量矩阵A分解公式为:A=QRThe decomposition formula of the low-frequency component matrix A is: A = QR
其中:Q为正交矩阵,R为上三角形矩阵。Among them: Q is an orthogonal matrix, and R is an upper triangular matrix.
S23:计算上三角形矩阵R的第一行向量的2-范数组成的矩阵元素的均值,若元素值大于等于均值,取1,否则,取0,从而得到二值化的特征图像。S23: Calculate the mean of the matrix elements composed of the 2-norm of the first row vector of the upper triangular matrix R. If the element value is greater than or equal to the mean, take 1, otherwise take 0, so as to obtain a binary feature image.
步骤S3:将特征图像和标识图像分别进行置乱加密,并将置乱后的特征图像和置乱后的标识图像进行异或运算,得到零水印图像。Step S3: The feature image and the identification image are scrambled and encrypted respectively, and the scrambled feature image and the scrambled identification image are XOR-ed to obtain a zero-watermark image.
置乱加密过程为:根据密钥K,利用Cat映射对特征图像和标识图像进行置乱加密。The scrambling encryption process is: according to the key K, the feature image and the identification image are scrambled and encrypted using Cat mapping.
加密公式为: The encryption formula is:
其中:(xn,yn)表示原灰度图像像素的坐标,(xn+1,yn+1)表示变换后的像素坐标,a和b为置乱参数,n表示当前变换的次数,mod()为模运算,N为图像的阶数。置乱参数和变换次数组成密钥K。Where: ( xn , yn ) represents the coordinates of the original grayscale image pixel, ( xn+1 , yn +1 ) represents the pixel coordinates after transformation, a and b are scrambling parameters, n represents the number of current transformations, mod() is the modulus operation, and N is the order of the image. The scrambling parameters and the number of transformations constitute the key K.
实施例一
首先对M×M大小的四幅不同的原始图像分别进行归一化处理,从而转换为标准图像,紧接着利用灰度加权平均图像融合方法对四幅标准图像进行融合,从而得到包含每幅图像信息的融合图像。再根据融合图像的不变形心提取固定大小为N×N的有效区域,随后在有效区域上执行l级提升小波变换,得到低频分量矩阵,并将其划分为大小为n×n的子矩阵块A,子矩阵块A的总数为第i个子块记为Ai,i=1,2,...,K,令 First, four different original images of size M×M are normalized respectively to convert them into standard images. Then, the grayscale weighted average image fusion method is used to fuse the four standard images to obtain a fused image containing the information of each image. Then, a fixed-size N×N effective area is extracted based on the indeformed center of the fused image. Then, a level l lifting wavelet transform is performed on the effective area to obtain a low-frequency component matrix, which is divided into sub-matrix blocks A of size n×n. The total number of sub-matrix blocks A is The i-th sub-block is denoted as A i , i=1,2,...,K, let
然后对每个子矩阵块Ai进行QR分解,计算每个子矩阵块Ai的R矩阵的第一行向量的2-范数,组成大小为k×k的矩阵B。随后,计算矩阵B中元素的均值ave,并将ave作为生成特征图像的依据,如果矩阵B中元素的值大于等于均值ave,取1,矩阵B中元素的值小于均值ave,取0,于是便生成对应的特征图像F。Then, QR decomposition is performed on each sub-matrix block Ai , and the 2-norm of the first row vector of the R matrix of each sub-matrix block Ai is calculated to form a matrix B of size k×k. Subsequently, the mean ave of the elements in the matrix B is calculated, and ave is used as the basis for generating the feature image. If the value of the element in the matrix B is greater than or equal to the mean ave, it is taken as 1, and the value of the element in the matrix B is less than the mean ave, it is taken as 0, and the corresponding feature image F is generated.
最后利用Cat映射对特征图像F和标识图像W进行置乱,其中置乱参数作为版权验证时的密钥K由版权所有者保存,随后将置乱的特征图像F'与置乱的标识图像W'进行异或运算,异或运算的公式为:从而得到零水印图像ω,同时,将零水印图像ω保存至第三方可信机构,作为版权验证的依据。Finally, the Cat mapping is used to scramble the feature image F and the identification image W, where the scrambling parameter is saved by the copyright owner as the key K for copyright verification. Then, the scrambled feature image F' and the scrambled identification image W' are XORed. The formula for the XOR operation is: Thus, a zero-watermark image ω is obtained. At the same time, the zero-watermark image ω is saved to a third-party trusted organization as a basis for copyright verification.
一种基于零水印技术的多幅图像版权验证方法,具体为:根据密钥K,利用Cat映射对特征图像进行置乱加密得到置乱的特征图像,将置乱的特征图像与零水印图像进行反异或运算,得到置乱后的标识图像,然后再根据密钥K进行置乱解密,得到标识图像,将该标识图像与原始标识图像进行相似性对比,得到相似性系数,当该相似性系数大于设定的阈值,即认为版权验证正确,反之则失败;A copyright verification method for multiple images based on zero watermark technology, specifically: according to a key K, a feature image is encrypted by using Cat mapping to obtain a scrambled feature image, the scrambled feature image is de-XORed with a zero watermark image to obtain a scrambled identification image, and then the scrambled decryption is performed according to the key K to obtain an identification image, and the identification image is compared with the original identification image to obtain a similarity coefficient. When the similarity coefficient is greater than a set threshold, it is considered that the copyright verification is correct, otherwise it fails.
所述置乱后的标识图像解密公式为:The decryption formula of the scrambled logo image is:
实施例二
在版权验证的过程中,版权所有人需提供密钥K和标识图像来证明自己对图像的所有权。由图1和图2可知,验证的特征提取步骤与认证部分相同,因此就不在此赘述,只具体介绍下不同的部分。In the copyright verification process, the copyright owner needs to provide the key K and the identification image to prove his ownership of the image. As shown in Figures 1 and 2, the feature extraction steps of verification are the same as those of authentication, so we will not go into details here, and only introduce the different parts in detail.
根据密钥K,利用Cat映射对生成的特征图像F进行置乱,并将置乱的特征图像F'与可信第三方机构保存的零水印图像ω进行反异或运算,从而得到置乱的标识图像W',反异或运算的公式为: According to the key K, the generated feature image F is scrambled using Cat mapping, and the scrambled feature image F' is subjected to an anti-XOR operation with the zero watermark image ω stored by a trusted third-party organization to obtain the scrambled identification image W'. The anti-XOR operation formula is:
最后,利用密钥K对置乱的标识图像W'进行反置乱,恢复出标识图像W。并将恢复出的标识图像与版权所有者保存的标识图像进行相似性比较,得到相似性系数,若大于设定的阈值,即认为版权验证,反之,则失败。Finally, the scrambled logo image W' is descrambled using the key K to restore the logo image W. The restored logo image is compared with the logo image saved by the copyright owner to obtain a similarity coefficient. If it is greater than the set threshold, it is considered copyright verification, otherwise it fails.
图4和图6展示了所提方案的鲁棒性,并与三种方案进行了鲁棒性比较。其中方案一是一种基于离散余弦变换的版权保护方案,该方案在水印嵌入和提取过程中均采用了归一化处理,能够抵抗一定的恶意信号处理和几何攻击;方案二利用图像归一化技术将图像映射到几何不变空间。然后,从归一化图像中提取重要区域。最后,利用重要区域低频离散余弦变换系数和水印图像进行异或操作,从而构造版权信息;方案三是一种基于非下采样Contourlet变换和图像归一化的强鲁棒零水印算法。该方案利用基于不变矩的图像归一化技术,将原始图像转换为标准图像。然后对有效区域进行分块非下采样Contourlet变换。对变换得到的低频系数子带进行块奇异值分解,根据每个块的最大奇异值的最高位的奇偶性生成零水印。相比于其他三种方案,本方案的方法在应对图像攻击上具有更高的鲁棒性,如图4、图6所示。同时,采用多幅图像融合方法和LWT-QR分解来进行特征提取能够有效节省算法运行时间,如图5所示,因此具有较低的时间成本。由以上图表可知,本方案所提出的方法是一种最优的方案。Figures 4 and 6 show the robustness of the proposed scheme and compare the robustness with the three schemes.
以上仅是本发明的优选实施方式,本发明的保护范围并不仅局限于上述实施例,凡属于本发明思路下的技术方案均属于本发明的保护范围。应当指出,对于本技术领域的普通技术人员来说,在不脱离本发明原理前提下的若干改进和润饰,应视为本发明的保护范围。The above are only preferred embodiments of the present invention. The protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should be regarded as the protection scope of the present invention.
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