Dual-channel-based digital watermark processing method and device and electronic equipment

Abstract

The invention discloses a dual-channel-based digital watermark processing method and device and electronic equipment, and relates to the technical field of digital watermarking. A dual-channel-based digital watermark processing method comprises a watermark embedding process: performing discrete wavelet transform decomposition on a first audio signal to obtain a low-frequency approximate component and a high-frequency detail component; respectively performing singular value decomposition on the low-frequency approximate component and the high-frequency detail component to obtain a high-frequency singular value and a low-frequency singular value; constructing a second channel according to a ratio feature formed by mapping the high-frequency singular value and the low-frequency singular value, and embedding watermark information; performing inversion calculation on a low-frequency singular value of the embedded watermark based on each ratio characteristic component in the second channel of the embedded watermark information, and reconstructing the low-frequency singular value into a low-frequency approximate component of the embedded watermark; and carrying out inverse discrete wavelet transform processing on the watermark-embedded low-frequency approximate component and the watermark-embedded high-frequency detail component to obtain a watermark-embedded second audio signal.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of digital watermarking technology, and in particular to a digital watermarking processing method and device based on a dual-channel and an electronic device. BACKGROUND

[0002] With the iterative upgrade of computer technology and Internet infrastructure, digital media technology has been widely applied and popularized in various fields of life due to its efficient data transmission and storage advantages. In the field of digital audio, the convenience of generation, copying and editing operations of digital audio works enables them to spread rapidly in the network environment. In recent years, with the vigorous development of audio and video streaming media platforms, it has brought new challenges to the distribution and management of massive audio and video data. At the same time, the breakthrough development of machine learning and artificial intelligence technology has promoted the maturity of large models based on human voice audio data sets. Such technology can accurately learn the voice characteristics of a specific individual and generate highly realistic voice simulation digital products accordingly. Under this background, the authenticity verification of digital content is facing severe challenges. Due to the lack of effective traceability means, the public is difficult to quickly and accurately determine the authenticity and original source of digital audio products.

[0003] In the face of such difficulties and challenges, researchers have carried out research on digital watermarking technology. Digital watermarking technology is based on digital signal processing theory, skillfully combines the characteristics of human perception system, and realizes copyright protection and content integrity verification of digital products by embedding specific secret information in the original data, and the process does not affect the use value of the original digital work. Although a large number of algorithms with excellent performance have emerged in the field of digital audio watermarking, most of the existing algorithms lack adaptive adjustment mechanism and universality features, and it is difficult to achieve dynamic balance between watermark robustness and transparency. On the other hand, most algorithms show obvious vulnerability when dealing with digital to analog / analog to digital (DA / AD) conversion attacks. When the audio data undergoes DA / AD conversion, the watermark information is easily lost, resulting in failure of copyright verification and content traceability. SUMMARY

[0004] The purpose of the present application is to provide a digital watermarking processing method and device based on a dual-channel and an electronic device, which solves the problems in the prior art.

[0005] In order to solve the above technical problems, the solution adopted by the present application is as follows:

[0006] A digital watermarking processing method based on a dual-channel includes a watermark embedding process, which is implemented by taking the original time domain space as a first channel and the singular value ratio space as a second channel, and completing watermark embedding in the second channel, specifically as follows:

[0007] Discrete wavelet transform is performed on the first audio signal in the first channel to obtain low-frequency approximate components and high-frequency detail components; then singular value decomposition is performed on the low-frequency approximate components and high-frequency detail components respectively to obtain high-frequency singular values ​​and low-frequency singular values.

[0008] The ratio feature formed by mapping high-frequency singular values ​​and low-frequency singular values ​​is used to construct a second channel, and watermark information is embedded in the second channel.

[0009] Based on the ratio feature components in the second channel with embedded watermark information, the low-frequency singular values ​​of the embedded watermark are inverted and calculated, and then reconstructed into the low-frequency approximate components of the embedded watermark and back mapped to the first channel.

[0010] The low-frequency approximation component and the high-frequency detail component of the embedded watermark are processed by inverse discrete wavelet transform to obtain the second audio signal with the embedded watermark.

[0011] As one specific implementation scheme, the original audio signal to be embedded with the watermark is denoised and normalized to obtain the first audio data.

[0012] As a specific implementation scheme, the first audio data is decomposed using a three-level discrete wavelet transform. The specific implementation process is as follows:

[0013] The first audio data is decomposed into a first-order wavelet using the following formula:

[0014] ;

[0015] ;

[0016] In the formula, This represents the discrete signal after the original audio has been digitized. These are the coefficients of the low-pass filter. These are the coefficients of the high-pass filter. This represents the approximate component of the low-frequency signal at the nth level. Indicates the high-frequency signal at the 1st Level of detail.

[0017] As a specific implementation scheme, a second channel is constructed by the ratio feature formed by high-frequency singular values ​​and low-frequency singular values. The second channel is a ratio space, and the process of embedding watermark information in the ratio space is as follows:

[0018] Step S1: Based on ratio characteristics Construct ratio space :

[0019] ;

[0020] ;

[0021] wherein, is a small positive number, is the i-th singular value in the low-frequency singular value matrix ; is the i-th singular value in the high-frequency singular value matrix ; k represents the first k principal components selected from r singular values.

[0022] Step S2: embedding the binary watermark information into the ratio feature through the quantization index modulation method to construct a ratio space with embedded watermark .

[0023] As a specific implementation, the specific implementation process of step S2 is as follows:

[0024] Let the watermark length be , and the watermark information be: ;

[0025] According to the watermark bit, select the quantizer or , and quantize each ratio to obtain the quantized ratio ;

[0026] ;

[0027] wherein, represents an even quantizer; represents an odd quantizer.

[0028] .

[0029] As a specific implementation, the watermark extraction process is also included, and the implementation of the watermark extraction process is described as follows:

[0030] The second audio data with watermark is processed to obtain a low-frequency approximation component and a high-frequency detail component; the low-frequency approximation component and the high-frequency detail component are subjected to singular value decomposition processing to obtain low-frequency singular values and high-frequency singular values, and the ratio feature is calculated :

[0031] ;

[0032] wherein, represents the low-frequency singular value; represents the high-frequency singular value; represents a small positive number;

[0033] A ratio space is constructed based on the ratio features, and watermark bits are obtained by reverse calculation based on minimum distance decision , thereby obtaining the watermark information :

[0034] ;

[0035] wherein, is a quantizer function.

[0036] A watermark processing device, which implements the watermark processing method, comprises a watermark embedding device, which comprises an audio signal decomposition module, a ratio construction module, a watermark embedding module, and an audio signal synthesis module.

[0037] The audio signal decomposition module is configured to perform denoising and normalization on an original audio signal to obtain a first audio signal, perform discrete wavelet transform decomposition on the first audio signal according to a preset wavelet decomposition level to obtain a high-frequency detail component and a low-frequency approximation component, and perform singular value decomposition on the high-frequency detail component and the low-frequency approximation component to obtain a high-frequency singular value and a low-frequency singular value.

[0038] The ratio space construction module obtains the high-frequency singular value and the low-frequency singular value, forms ratio features based on the high-frequency singular value and the low-frequency singular value, and constructs a ratio space based on a plurality of ratio features.

[0039] The watermark embedding module performs binary processing on watermark information and embeds the watermark information into the ratio space by using a quantization index modulation method.

[0040] The audio signal synthesis module calculates the low-frequency singular value and the low-frequency approximation component in the ratio space in which the watermark is embedded by using each ratio feature component in the ratio space in which the watermark is embedded, and performs inverse wavelet decomposition on the low-frequency approximation component and the high-frequency detail component to obtain a second audio signal in which the watermark is embedded.

[0041] As a specific implementation, the device further comprises a watermark extraction device, which implements the watermark extraction method of claim 6, and the watermark extraction device comprises an audio signal decomposition module, a ratio space construction module, and a watermark extraction module.

[0042] The audio signal decomposition module is configured to perform discrete wavelet transform decomposition on a second audio signal according to a preset wavelet decomposition level to obtain a high-frequency detail component and a low-frequency approximation component, and perform singular value decomposition on the high-frequency detail component and the low-frequency approximation component to obtain a high-frequency singular value and a low-frequency singular value.

[0043] The ratio space construction module obtains the high-frequency singular value and the low-frequency singular value, forms ratio features based on the high-frequency singular value and the low-frequency singular value, and constructs a ratio space based on a plurality of ratio features.

[0044] The watermark extraction module extracts watermark information from the ratio space by using minimum distance decision.

[0045] An electronic device comprises a processor, a storage medium and a bus, the storage medium stores machine readable instructions executable by the processor, when the electronic device is running, the processor communicates with the storage medium through the bus, and the processor executes the machine readable instructions to execute the watermark processing method.

[0046] The technical scheme of the present application has at least the following advantages and beneficial effects:

[0047] The core scheme of the present application is to map the original channel digital audio data to another channel space, modify the corresponding parameters (embed the watermark) and restore to the original channel space through inverse transformation, thereby ensuring the robustness and transparency of the embedded watermark while completing the watermark embedding operation.

[0048] Based on this, the two channels of the present application are the original time domain channel and the ratio feature space channel, first, the original audio data is subjected to transformation processing, and the high-frequency singular value and the low-frequency singular value on the wavelet domain are obtained, which are mapped and constructed into a ratio feature domain, and then the watermark information embedding is completed by modifying the coefficients in the ratio feature space channel; finally, the data in the ratio feature is restored to the original time domain space by means of inverse transformation operation, so as to be applied to subsequent storage, transmission and the like.

[0049] Since the watermark embedding is in the wavelet transform-singular value decomposition-ratio feature multiple conversion deep space, and the space is a stable space constructed by the ratio of high-frequency features and low-frequency features (interference in signal processing process generally affects the absolute value, and has less effect on the ratio of each component in the signal), therefore, the embedded watermark has strong attack resistance and strong interference resistance.

[0050] At the same time, the watermark is embedded in the representative signal component ratio, and the modification is the ratio, which is an abstract quantity directly related to the auditory fog, and the rest of the auditory perception is nonlinearly related, so it provides more space for finding the optimal embedding strength under the constraint of imperceptibility; and the mathematical model can be adjusted more conveniently by using this method, such as dynamically setting the modification range of the ratio when embedding the watermark according to the high-energy segment or the low-energy segment of the audio, so that the dynamic regulation strategy of the watermark embedding strength has very high flexibility. BRIEF DESCRIPTION OF DRAWINGS

[0051] In order to more clearly illustrate the technical scheme of the embodiments of the present application, the drawings needed in the embodiments will be briefly introduced as follows, and it should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope, and other related drawings can also be obtained by those skilled in the art without creative labor on the premise of not paying creative labor.

[0052] Figure 1Flow chart of watermark embedding process in the present application;

[0053] Figure 2 Flow chart of watermark extraction process in the present application;

[0054] Figure 3 Structure schematic diagram of watermark embedding in the present application;

[0055] Figure 4 Structure schematic diagram of watermark extraction in the present application;

[0056] Figure 5 Structure schematic diagram of electronic device in the present application. DETAILED DESCRIPTION

[0057] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. The components of the embodiments of the present application described and shown in the drawings can be arranged and designed in various different configurations.

[0058] Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0059] It should be noted that the relational terms such as "first" and "second" and the like are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a list of elements does not only include those elements, but also includes other elements not explicitly listed, or further includes elements inherent in such a process, method, article or device. Without more limitations, the element defined by the statement "comprises a" does not exclude the presence of additional identical elements in the process, method, article or device that includes the element.

[0060] Firstly, the application scenarios applicable to the present application are introduced. The present application can be applied to the field of digital audio watermark processing technology.

[0061] Based on this, the application provides a double-channel-based digital watermark processing method, and the core scheme is as follows: original audio data is converted from a time domain to a wavelet domain through wavelet decomposition DWT, and is decomposed into a low-frequency approximate component and a high-frequency detail component. The low-frequency approximate component is further decomposed into a singular value matrix through SVD technology, watermark information is embedded in the singular value matrix, and then the low-frequency approximate component and the high-frequency detail component embedded with the watermark are restored into audio data with the watermark. The application adopts a low-frequency embedding strategy, and when lossy compression is performed, the high-frequency detail component is preferentially discarded, so that the low-frequency approximate component is protected and the watermark is protected.

[0062] Please refer to Figure 1 , Figure 1 The application provides a double-channel-based digital watermark processing method, and the core scheme is as follows: original audio data is converted from a time domain to a wavelet domain through wavelet decomposition DWT, and is decomposed into a low-frequency approximate component and a high-frequency detail component. The low-frequency approximate component is further decomposed into a singular value matrix through SVD technology, watermark information is embedded in the singular value matrix, and then the low-frequency approximate component and the high-frequency detail component embedded with the watermark are restored into audio data with the watermark. The application adopts a low-frequency embedding strategy, and when lossy compression is performed, the high-frequency detail component is preferentially discarded, so that the low-frequency approximate component is protected and the watermark is protected. Figure 1 The application provides a double-channel-based digital watermark processing method, and the core scheme is as follows: original audio data is converted from a time domain to a wavelet domain through wavelet decomposition DWT, and is decomposed into a low-frequency approximate component and a high-frequency detail component. The low-frequency approximate component is further decomposed into a singular value matrix through SVD technology, watermark information is embedded in the singular value matrix, and then the low-frequency approximate component and the high-frequency detail component embedded with the watermark are restored into audio data with the watermark. The application adopts a low-frequency embedding strategy, and when lossy compression is performed, the high-frequency detail component is preferentially discarded, so that the low-frequency approximate component is protected and the watermark is protected.

[0063] Specifically, the implementation of the watermark embedding process is as follows:

[0064] Step S100: denoising and normalization processing are performed on input original audio data to obtain first audio data, and signal stability is improved.

[0065] The specific implementation process of step S100 is as follows: an original audio signal is obtained , and is digitized into a discrete signal , then denoising and normalization processing are performed to obtain the first audio data.

[0066] Step S200: preprocessed audio data is subjected to discrete wavelet transform decomposition (Discrete Wavelet Transform, DWT) to obtain a low-frequency approximate component and a high-frequency detail component In this step, Haar is selected as a wavelet mother function.

[0067] Specifically, in step S200, the implementation process of the discrete wavelet transform decomposition is as follows:

[0068] ;

[0069] ;

[0070] ;

[0071] In the formula, This represents the discrete signal of the first digitized audio data; n represents the wavelet decomposition series. ; Indicates the low-frequency signal at the 1st Low-frequency approximation components; Indicates the high-frequency signal at the 1st High-frequency detail components; These represent the coefficients of the low-pass filter. The coefficients are represented by 't'; 't' represents the summation variable, iterating through the previous stage signal. Valid indexes.

[0072] In some feasible embodiments, step S200 performs three-level wavelet decomposition to obtain low-frequency approximate components. and high-frequency detail components .

[0073] Step S300: Approximate low-frequency components and high-frequency detail components Perform Singular Value Decomposition (SVD) on each component to obtain the left singular matrix. Singular value matrix Right singular matrix transpose The details are as follows:

[0074] ;

[0075] ;

[0076] in, Represents the low-frequency approximate component The left singular matrix has column vectors that are eigenvectors; Represents high-frequency detail components The left singular matrix has column vectors that are eigenvectors; It is a right singular matrix The transpose of has a column vector of . Feature vector; Represents a right singular matrix The transpose of has a column vector of . Feature vector; Represents the low-frequency approximate component The singular value matrix, ; Represents high-frequency detail components singular value matrix , is the rank of the matrix.

[0077] Step S400: constructing a ratio feature space from the decomposed low-frequency feature and high-frequency feature and embedding watermark information.

[0078] The specific implementation process of step S400 is as follows:

[0079] Step S410: constructing a ratio feature space based on the ratio feature :

[0080] ;

[0081] ;

[0082] wherein, is a small positive number, is the ith singular value in the singular value matrix ; is the ith singular value in the singular value matrix ; k represents the first k principal components selected from the r singular values.

[0083] In this step, the frequency energy value is converted into the relative intensity relationship between the low-frequency approximate feature and the high-frequency detail feature, a ratio space is constructed, and the watermark information is embedded therein. For audio data, the gain change caused by the digital-to-analog or analog-to-digital conversion of the audio signal will approximately affect the low-frequency energy and the high-frequency energy, so the ratio of the low-frequency feature and the high-frequency feature has stability, and the embedding of the watermark in the stable space constructed by the ratio can resist conversion attacks during digital-to-analog or analog-to-digital conversion, and has strong robustness. Moreover, the watermark is embedded in the mapped space, and has better concealment.

[0084] Step S420: embedding the binary watermark information into the ratio feature through a quantization index modulation method to construct a ratio space embedding watermark .

[0085] wherein, let the watermark length be , and the watermark information is: .

[0086] wherein, according to the watermark bit, a quantizer or is selected, and each ratio is quantized to obtain a quantized ratio ;

[0087] ;

[0088] wherein,​ Indicates an even quantizer; This represents an odd quantizer.

[0089] ;

[0090] Step S500: Calculate the low-frequency singular value of the embedded watermark based on the ratio feature inversion after watermarking, reconstruct the low-frequency approximate component, and process the reconstructed low-frequency approximate component and high-frequency detail component by inverse discrete wavelet transform to obtain the second audio data with the embedded watermark.

[0091] The specific implementation process of this step is as follows:

[0092] Step S510: Calculate the low-frequency singular values ​​of the embedded watermark based on each component in the ratio space after watermark embedding. :

[0093] ;

[0094] Step S520: Low-frequency singular values ​​based on embedded watermark Reconstructing low-frequency approximate components :

[0095] ;

[0096] = ;

[0097] Step S530: Approximate the low-frequency components With high-frequency detail components Inverse wavelet decomposition is performed to obtain a discrete audio signal with an embedded watermark. And based on discrete audio signals The reconstructed second audio data with embedded watermark was obtained. .

[0098] like Figure 2 As shown in the figure, the present invention discloses a digital watermarking processing method based on dual channels, and the watermark extraction process is implemented as follows:

[0099] Step S600: Process the watermarked second audio data DWT processing is performed to obtain low-frequency approximate components. and high-frequency detail components SVD processing is performed on the low-frequency approximation components and the high-frequency detail components to obtain the singular value matrix of the low-frequency approximation components. Singular value matrix of high-frequency detail components ;Calculate the ratio characteristic based on the singular values ​​obtained from the singular value matrix. :

[0100] ;

[0101] wherein, is a singular value matrix of the low frequency approximation component is the i-th singular value in the singular value matrix is a singular value matrix of the high frequency detail component is the i-th singular value in the singular value matrix

[0102] Step S700: constructing a ratio space based on the ratio features, and obtaining the watermark bits by reverse deduction through minimum distance decision , thereby obtaining the watermark information .

[0103] The specific implementation process of this step is as follows:

[0104] Based on each component in the ratio space, it is determined whether it is closer to the even quantizer or the odd quantizer , and only the bit corresponding to the nearest quantizer is selected as the extracted watermark bit :

[0105] ;

[0106] Based on the watermark bits, the watermark information is reconstructed: .

[0107] Based on the same inventive concept, the embodiments of the present application also provide a watermark embedding device corresponding to the watermark embedding method, and a watermark extraction device corresponding to the watermark extraction method. Since the principle of the device in the embodiments of the present application solves the problem, and the watermark embedding method described above is similar, the implementation of the device can be referred to the implementation of the method, and the repeated parts will not be described here.

[0108] Please refer to Figure 3 , Figure 3 is a structural schematic diagram of a watermark embedding device provided by the embodiments of the present application. As Figure 3 indicated, the watermark embedding device comprises an audio signal decomposition module, a ratio space construction module, a watermark embedding module, and an audio signal synthesis module.

[0109] The audio signal decomposition module is configured to perform denoising and normalization processing on an original audio signal to obtain a first audio signal, perform discrete wavelet transform decomposition on the first audio signal according to a preset wavelet decomposition level to obtain a high frequency detail component and a low frequency approximation component, and perform singular value decomposition on the high frequency detail component and the low frequency approximation component to obtain a high frequency singular value and a low frequency singular value.

[0110] The ratio space construction module obtains the high-frequency singular value and the low-frequency singular value, and forms a ratio feature using the high-frequency singular value and the low-frequency singular value, and constructs a plurality of ratio features into a ratio space;

[0111] The watermark embedding module binarizes the watermark information, and embeds the watermark information into the ratio space through a quantization index modulation method;

[0112] The audio signal synthesis module inversely calculates the low-frequency singular value and the low-frequency approximation component embedded with the watermark by using each ratio feature component in the ratio space embedded with the watermark; and obtains the second audio signal embedded with the watermark through inverse wavelet decomposition processing of the low-frequency approximation component and the high-frequency detail component.

[0113] Please refer to Figure 4 , Figure 4 The structure diagram of a watermark extraction device provided by an embodiment of the present application is shown in FIG. 1. Figure 4 As shown in FIG. 1, the watermark extraction device includes an audio signal decomposition module, a ratio space construction module, and a watermark extraction module.

[0114] The audio signal decomposition module decomposes the second audio signal through discrete wavelet transform according to a preset wavelet decomposition level to obtain a high-frequency detail component and a low-frequency approximation component; and obtains a high-frequency singular value and a low-frequency singular value through singular value decomposition of the high-frequency detail component and the low-frequency approximation component.

[0115] The ratio space construction module obtains the high-frequency singular value and the low-frequency singular value, and forms a ratio feature using the high-frequency singular value and the low-frequency singular value, and constructs a plurality of ratio features into a ratio space.

[0116] The watermark extraction module extracts watermark information from the ratio space through minimum distance decision.

[0117] Please refer to Figure 5 , Figure 5 The structure diagram of an electronic device provided by an embodiment of the present application is shown in FIG. 2. Figure 5 As shown in FIG. 2, the electronic device includes a processor, a memory, and a bus.

[0118] The memory stores machine readable instructions executable by the processor, and the processor and the memory communicate through the bus when the electronic device is running. When the machine readable instructions are executed by the processor, the steps of the watermark embedding process and / or the watermark extraction process in the method embodiment shown in the foregoing can be executed. The specific implementation can be referred to the method embodiment, and will not be described here. Figure 1

[0119] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system, device and unit described above can refer to the corresponding process in the foregoing method embodiment, and will not be described here.​

[0120] In several embodiments provided in the present application, it should be understood that the disclosed apparatus and method can also be implemented by other manners. The apparatus embodiments described above are merely illustrative, for example, the flowcharts and block diagrams in the drawings show the possible implementation architecture, function and operation of the apparatus, method and computer program product according to the embodiments of the present application. In this regard, each block in the flowchart or block diagram can represent a module, a program segment or a part of code, which contains one or more executable instructions for implementing the specified logic function. It should also be noted that in some alternative implementation manners, the functions noted in the blocks can also occur in different order from that noted in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and sometimes they can also be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for executing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0121] In addition, the function modules in the embodiments of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0122] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product in essence or the part of the technical solutions that make contributions to the prior art, or part of the technical solutions. The computer software product is stored in a storage medium, including a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the embodiments of the method of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various program code storage media.

[0123] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0124] The present application can have various changes and modifications for those skilled in the art. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A dual channel based digital watermarking method, characterized by, The watermark embedding process comprises the following steps: taking the original time domain space as a first channel, taking a singular value ratio space as a second channel, and embedding the watermark in the second channel. The first audio signal in the first channel is subjected to discrete wavelet transform decomposition to obtain low-frequency approximate components and high-frequency detail components; and the low-frequency approximate components and the high-frequency detail components are subjected to singular value decomposition to obtain high-frequency singular values and low-frequency singular values. The ratio features formed by the high-frequency singular values and the low-frequency singular values are mapped to construct the second channel, and the watermark information is embedded in the second channel. The low-frequency singular values embedded with the watermark are calculated by inverse calculation based on the ratio feature components in the second channel embedded with the watermark information, and the low-frequency approximate components embedded with the watermark are reconstructed to be mapped back to the first channel. The low-frequency approximate components embedded with the watermark and the high-frequency detail components are subjected to inverse discrete wavelet transform to obtain the second audio signal embedded with the watermark.

2. The method of claim 1, wherein, The original audio signal to be embedded with the watermark is subjected to denoising and normalization processing to obtain first audio data.

3. The method of claim 1, wherein the method is characterized by, The first audio data is subjected to three-level discrete wavelet transform decomposition, and the specific implementation process is as follows: The first audio data is subjected to one-level wavelet decomposition according to the following formula: ； ； wherein represents the discrete signal after digitizing the original audio, is the coefficient of the low-pass filter, is the coefficient of the high-pass filter, represents the approximation component of the low frequency signal at the nth stage; represents the detail component of the high frequency signal at the nth stage. stage.

4. The method of claim 1, wherein the method is based on a two-channel digital watermarking process. The ratio features formed by the high-frequency singular values and the low-frequency singular values are mapped to construct the second channel, and the second channel is a ratio space. The process of embedding the watermark information in the ratio space is as follows: Step S1: Based on the ratio feature Constructing the ratio space : ； ； wherein is a small positive number, is the i-th singular value in the low frequency singular value matrix ; and is the i-th singular value in the high frequency singular value matrix ; k denotes the number of principal components selected from the r singular values. Step S2: embed the binary watermark information into the ratio feature through a quantization index modulation method to construct a ratio space with embedded watermark .

5. The method of claim 4, wherein, The specific implementation process of step S2 is as follows: Let the length of the watermark be , and the watermark information be: ; Selecting quantizers according to watermark bits or quantizing each ratio to obtain quantized ratios ; ; wherein represents an even quantizer; represents an odd quantizer. 。 6. The method of claim 4, wherein the method is characterized by, The watermark extraction process also comprises the following steps: Watermarked second audio data The processing obtains a low frequency approximation component and a high frequency detail component; The low-frequency approximate component and the high-frequency detail component are subjected to singular value decomposition processing to obtain low-frequency singular values and high-frequency singular values, and a ratio feature is calculated : ； wherein denotes a low frequency singular value; denotes a high frequency singular value; denotes a small positive number; A ratio space is constructed based on a ratio feature, and watermark bits are obtained by minimum distance decision reverse deduction , thereby obtaining the watermark information : ； In the formula, is a quantizer function.

7. A watermark processing apparatus characterized by comprising: The watermark embedding device comprises an audio signal decomposition module, a ratio space construction module, a watermark embedding module, and an audio signal synthesis module. The audio signal decomposition module is configured to: perform denoising and normalization processing on the original audio signal to obtain a first audio signal; perform discrete wavelet transform decomposition on the first audio signal according to a preset wavelet decomposition level to obtain high-frequency detail components and low-frequency approximate components; and perform singular value decomposition on the high-frequency detail components and the low-frequency approximate components to obtain high-frequency singular values and low-frequency singular values. The ratio space construction module is configured to: obtain the high-frequency singular values and the low-frequency singular values, and form ratio features by using the high-frequency singular values and the low-frequency singular values; and construct a ratio space by using a plurality of ratio features. The watermark embedding module is configured to: perform binary processing on the watermark information, and embed the watermark information into the ratio space by using a quantization index modulation method. The audio signal synthesis module is configured to: calculate the low-frequency singular values embedded with the watermark and the low-frequency approximate components embedded with the watermark by inverse calculation based on the ratio feature components in the ratio space embedded with the watermark; and perform inverse wavelet decomposition processing on the low-frequency approximate components and the high-frequency detail components to obtain a second audio signal embedded with the watermark.

8. The apparatus of claim 7, wherein The watermark extraction device comprises an audio signal decomposition module, a ratio space construction module, and a watermark extraction module. The audio signal decomposition module is configured to: perform discrete wavelet transform decomposition on the second audio signal according to a preset wavelet decomposition level to obtain high-frequency detail components and low-frequency approximate components; and perform singular value decomposition on the high-frequency detail components and the low-frequency approximate components to obtain high-frequency singular values and low-frequency singular values. The ratio space construction module obtains the high-frequency singular value and the low-frequency singular value, and forms a ratio feature using the high-frequency singular value and the low-frequency singular value, and constructs a plurality of ratio features into a ratio space; The watermark extraction module extracts watermark information from the ratio space through minimum distance decision.

9. An electronic device, comprising: The electronic device comprises: A processor, a storage medium and a bus, wherein the storage medium stores machine readable instructions executable by the processor, the processor and the storage medium communicate through the bus when the electronic device is running, and the processor executes the machine readable instructions to execute the watermark processing method in any one of claims 1 to 6.

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