Watermark processing method and device for digital audio, equipment and storage medium

Abstract

The invention discloses a digital audio watermark processing method and device, equipment and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: receiving a watermark adding instruction, and carrying out Fourier transform on original host voice to obtain an audio signal; identifying a watermark information adding bit in the audio signal through the audio watermark model, and embedding a watermark message in the watermark information adding bit; the interfering audio signal and the audio signal carrying the watermark message are combined. Receiving the message extraction signal, and separating an interference audio signal in the combined audio signal; a watermark information adding bit in the audio signal is identified, and a watermark message is extracted from the watermark information adding bit. The invention also relates to the technical field of block chains, and the host voice is stored on a block chain network. According to the method, the watermark information adding position is automatically identified through the audio watermark model, so that the watermark information is added and extracted, the dependence on expert knowledge is remarkably reduced, and the watermark processing speed and efficiency are improved.

Description

Technical Field

[0001] The present application belongs to the field of artificial intelligence technology, and specifically relates to a watermark processing method, device, equipment and storage medium for digital audio. Background Art

[0002] With the popularization and application of Internet technology, various audio data are constantly circulating on the Internet. The copyright protection of audio data is becoming increasingly serious. Piracy and infringement are frequent, causing huge losses to creators. Therefore, strengthening the copyright protection of audio data and cracking down on infringements have become urgent issues to be resolved.

[0003] In view of the above problems, the use of digital watermarking technology is an effective solution, which can be used for copyright and authenticity authentication. For example, in the insurance sales scenario, digital watermarking technology can be applied to the copyright protection of audio materials such as insurance contracts and product descriptions, ensuring the authenticity and integrity of information in the sales process, preventing tampering or theft, and thus safeguarding the legitimate rights and interests of insurance companies. At present, digital audio watermarking implementations usually design digital watermark embedding and extraction methods based on expert knowledge and experience, but this method is highly dependent on expert knowledge and is greatly affected by expert knowledge reserves. In addition, the processing speed of this method is slow and the efficiency is low. Summary of the invention

[0004] The purpose of the embodiments of the present application is to propose a digital audio watermark processing method, device, computer equipment and storage medium to solve the technical problems of existing digital audio watermark processing methods, such as high dependence on expert knowledge, great influence of expert knowledge reserves, slow processing speed and low efficiency.

[0005] In order to solve the above technical problems, the present application provides a digital audio watermark processing method, which adopts the following technical solution:

[0006] A digital audio watermark processing method, comprising:

[0007] Receive a watermark adding instruction, obtain the original host voice, and perform Fourier transform on the original host voice to obtain the original host audio signal;

[0008] The watermark information adding bit is identified in the original host audio signal through the pre-trained audio watermark model, and the watermark message is embedded in the watermark information adding bit to obtain an audio signal carrying the watermark message;

[0009] Acquire an interference audio signal, combine the interference audio signal with an audio signal carrying a watermark message to obtain a combined audio signal, and output the combined audio signal;

[0010] Receive a message extraction signal, obtain a combined audio signal, separate an interfering audio signal from the combined audio signal, and obtain an audio signal carrying a watermark message;

[0011] The watermark information added bits in the audio signal carrying the watermark message are identified, and the watermark message is extracted from the watermark information added bits.

[0012] Furthermore, the watermark information adding bit includes a first adding bit and a second adding bit, the watermark message includes a watermark message body and an audio synchronization code matching the watermark message body, the watermark information adding bit is identified in the original host audio signal by a pre-trained audio watermark model, and the watermark message is embedded in the watermark information adding bit to obtain the audio signal carrying the watermark message, specifically including:

[0013] Dividing the original host audio signal to obtain a number of audio signal segments;

[0014] Counting the frequency range of each audio signal segment, and determining the insensitive audio signal segment based on the frequency range of each audio signal segment;

[0015] The watermark information adding bit is embedded in the non-sensitive audio signal segment through the audio watermark model;

[0016] embedding a watermark message body in the first adding position;

[0017] An audio synchronization code is embedded in the second adding position to generate an audio signal carrying a watermark.

[0018] Furthermore, before the step of identifying the watermark information adding bit in the original host audio signal by using the pre-trained audio watermark model and embedding the watermark message in the watermark information adding bit to obtain the audio signal carrying the watermark message, the method further includes:

[0019] Acquire historical host voice, and perform Fourier transform on the historical host voice to obtain historical host audio signal;

[0020] Dividing the historical host audio signal to obtain a number of historical audio signal segments;

[0021] Counting the frequency range of each historical audio signal segment, and determining the historical non-sensitive audio signal segment based on the frequency range of each historical audio signal segment;

[0022] Embedding a historical watermark information adding bit in a historical non-sensitive audio signal segment, and marking the historical watermark information adding bit;

[0023] The marked historical host audio signal is input into a preset initial audio watermark model for model training to obtain a trained audio watermark model.

[0024] Furthermore, the initial audio watermark model includes a message embedding network, a message extraction network and an attack simulation layer. The steps of inputting the marked historical host audio signal into the preset initial audio watermark model for model training to obtain the trained audio watermark model specifically include:

[0025] Adding a historical watermark message to the historical watermark information in the historical host audio signal through a message embedding network to obtain an audio signal carrying the historical watermark message, wherein the historical watermark message includes a historical watermark message body and a historical audio synchronization code;

[0026] Generate a historical interference audio signal using the attack simulation layer, and add the historical interference audio signal to the audio signal carrying the historical watermark message to generate a historical combined audio signal;

[0027] The historical combined audio signal is separated and the message is extracted through the message extraction network to obtain the host audio restoration signal and the restoration watermark message;

[0028] The model prediction error is calculated according to the host audio restoration signal and the restoration watermark message, and the initial audio watermark model is adjusted based on the model prediction error to obtain a trained audio watermark model.

[0029] Further, the step of calculating the model prediction error according to the host audio restoration signal and the restoration watermark message, and adjusting the initial audio watermark model based on the model prediction error to obtain the trained audio watermark model specifically includes:

[0030] Calculate the error between the historical host audio signal and the host audio restoration signal based on a preset first loss function to obtain a first prediction error;

[0031] Calculate the error between the historical watermark message and the restored watermark message based on a preset second loss function to obtain a second prediction error;

[0032] The initial audio watermark model is iterated based on the first prediction error and the second prediction error until the model is fitted, thereby obtaining a trained audio watermark model.

[0033] Furthermore, the expression of the first loss function is as follows:

[0034] L integrity =||(X syn ,X msg ),Ext(Emb(X host ,X syn ,X msg ))||2

[0035] Where, L integrity is the first loss function, Xhost , X syn , X msg They represent the historical host audio signal, the historical audio synchronization code, and the historical watermark message body respectively. Emb and Ext represent the message embedding operation and the message extraction operation respectively.

[0036] Furthermore, the expression of the second loss function is as follows:

[0037] L imperceptibility =||X host ,Emb(X host ,X syn ,X msg )||2

[0038] Where, L imperceptibility is the second loss function, X host , X syn , X msg They represent the historical host audio signal, historical audio synchronization code, and historical watermark message body respectively, and Emb represents the message embedding operation.

[0039] In order to solve the above technical problems, the embodiment of the present application further provides a digital audio watermark processing device, which adopts the following technical solution:

[0040] A digital audio watermark processing device, comprising:

[0041] The audio conversion module is used to receive the watermark adding instruction, obtain the original host voice, and perform Fourier transform on the original host voice to obtain the original host audio signal;

[0042] A watermark adding module is used to identify the watermark information adding bit in the original host audio signal through the pre-trained audio watermark model, and embed the watermark message in the watermark information adding bit to obtain an audio signal carrying the watermark message;

[0043] The simulation attack module is used to obtain an interference audio signal, combine the interference audio signal with an audio signal carrying a watermark message to obtain a combined audio signal, and output the combined audio signal;

[0044] An audio separation module is used to receive a message extraction signal, obtain a combined audio signal, separate an interfering audio signal from the combined audio signal, and obtain an audio signal carrying a watermark message;

[0045] The watermark extraction module is used to identify the watermark information added bits in the audio signal carrying the watermark message, and extract the watermark message from the watermark information added bits.

[0046] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0047] A computer device comprises a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the digital audio watermark processing method as described in any one of the above items are implemented.

[0048] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0049] A computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the digital audio watermark processing method as described in any one of the above.

[0050] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0051] The present application discloses a watermark processing method, device, equipment and storage medium for digital audio, which belongs to the field of artificial intelligence technology. The present application automatically identifies the watermark information addition position through the audio watermark model, realizes the addition and extraction of watermark information, significantly reduces the dependence on expert knowledge, and improves the speed and efficiency of watermark processing. Compared with the traditional method of manually selecting watermark embedding positions based on expert experience, the pre-trained model can more efficiently and accurately select the appropriate embedding position, thereby improving the processing speed and efficiency. At the same time, the audio signal is converted into a frequency domain signal through Fourier transform, which is conducive to embedding the watermark in the frequency domain, thereby improving the concealment and robustness of the watermark. In addition, the present application also combines the interference audio signal to enhance the anti-attack ability of the watermark, realizes rapid watermark extraction and signal separation, and improves the overall performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0053] Figure 1 An exemplary system architecture diagram is shown in which the present application can be applied;

[0054] Figure 2 A flow chart showing an embodiment of a method for watermarking digital audio according to the present application;

[0055] Figure 3 A schematic structural diagram of an embodiment of a digital audio watermark processing device according to the present application is shown;

[0056] Figure 4 A schematic structural diagram of an embodiment of a computer device according to the present application is shown. DETAILED DESCRIPTION

[0057] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0058] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0059] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0060] like Figure 1 As shown, the system architecture 100 may include a terminal device 101, a network 102 and a server 103. The terminal device 101 may be a laptop 1011, a tablet computer 1012 or a mobile phone 1013. The network 102 is used to provide a medium for a communication link between the terminal device 101 and the server 103. The network 102 may include various connection types, such as wired, wireless communication links or optical fiber cables.

[0061] The user can use the terminal device 101 to interact with the server 103 through the network 102 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0062] The terminal device 101 can be any electronic device with a display screen and supporting web browsing. In addition to a laptop computer 1011, a tablet computer 1012 or a mobile phone 1013, the terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 (Moving Picture Experts Group Audio Layer IV), a laptop computer, a desktop computer, etc.

[0063] The server 103 may be a server that provides various services, such as a background server that provides support for a web page displayed on the terminal device 101 .

[0064] It should be noted that the watermark processing method for digital audio provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the watermark processing device for digital audio is generally arranged in the server / terminal device.

[0065] It should be understood that Figure 1 The numbers of terminal devices, networks and servers in the embodiment are merely illustrative. The above system may have any number of terminal devices, networks and servers according to implementation requirements.

[0066] Continue to refer Figure 2 , shows a flow chart of an embodiment of a method for watermarking of digital audio according to the present application. The method for watermarking of digital audio comprises the following steps:

[0067] S201, receiving a watermark adding instruction, obtaining an original host voice, and performing Fourier transform on the original host voice to obtain an original host audio signal.

[0068] Specifically, the system first obtains the original host voice, which is the audio file to be embedded with the watermark, and can be a voice recording, music, or any other type of audio signal. In order to better process and analyze the audio signal, the system needs to convert the original host voice from a time domain signal to a frequency domain signal, which can be done through Fourier transform (such as Short-Time Fourier Transform, STFT transform). Fourier transform is a mathematical tool used to decompose the audio signal in the time domain into a superposition of a series of frequency components.

[0069] By performing Fourier transform on the original host voice, the audio signal can be represented as various frequency components and their corresponding amplitude and phase. This frequency domain representation can not only more intuitively display the frequency characteristics of the audio signal, but also provide a more flexible operating space for watermark embedding. Embedding the watermark in the frequency domain can improve the concealment of the watermark, making it more difficult to detect and maintaining a high sound quality.

[0070] In addition, the frequency domain representation of the audio signal after Fourier transform enables the system to better achieve robustness and anti-interference capabilities through control of the frequency dimension.

[0071] S202, identifying watermark information adding bits in the original host audio signal through a pre-trained audio watermark model, and embedding a watermark message in the watermark information adding bits to obtain an audio signal carrying the watermark message.

[0072] Specifically, the core of this step is to use a pre-trained audio watermark model to automatically identify the most suitable position for embedding watermarks, that is, the watermark information addition position. This audio watermark model is a reversible network-based embedded digital audio watermark implementation model that can embed and extract audio watermark messages in turn. This pre-trained model is usually trained based on a large number of audio samples, and through machine learning technology, it learns how to find those frequency intervals in the audio spectrum that have little impact on sound quality but have high robustness.

[0073] The selection of watermark information adding positions is crucial to the audio quality and the concealment of the watermark. The model will analyze the frequency domain signal after Fourier transformation and select those positions that have the least impact on the original audio characteristics after embedding the watermark to avoid obvious sound quality degradation. At the same time, these positions also need to have anti-interference capabilities to ensure that the watermark information is not easily modified or deleted.

[0074] After determining the added bits, the watermark message is embedded in the audio signal. The watermark message can be any data that needs to be hidden in the audio, such as copyright information, encrypted identification codes, etc. The technology of embedding watermarks usually uses tiny frequency or phase changes, so that the audio after the watermark is embedded is almost indistinguishable from the original audio in appearance, but the watermark information can be accurately extracted. Through this step, the system generates an audio signal carrying the watermark message.

[0075] S203, obtaining an interference audio signal, combining the interference audio signal with the audio signal carrying the watermark message to obtain a combined audio signal, and outputting the combined audio signal.

[0076] Specifically, the purpose of obtaining the interference audio signal is to improve the anti-attack ability and concealment of the watermark. In practical applications, the watermark signal may be subject to various forms of attacks, such as noise addition, compression processing or other types of signal modification, which usually aim to delete or destroy the watermark embedded in the audio, thereby circumventing copyright protection or hiding information.

[0077] Therefore, the introduction of interfering audio signals is a common technical means to improve the robustness of the watermark system. Interfering audio signals can be natural noise, environmental audio clips, randomly generated audio signals, etc., and their purpose is to cover up or confuse the traces of watermark embedding.

[0078] By combining the interference audio signal with the audio signal carrying the watermark message, the system generates a combined audio signal. During the combination process, the interference audio signal and the audio signal carrying the watermark are superimposed according to a certain strategy, which can not only maintain the sound quality of the original audio, but also enhance the hiding effect of the watermark.

[0079] When outputting the combined audio signal, the design of the interference signal should not only ensure the security of the watermark, but also maintain the naturalness of the audio to avoid obvious impact on the auditory experience.

[0080] S204, receiving a message extraction signal, acquiring a combined audio signal, separating an interfering audio signal from the combined audio signal, and obtaining an audio signal carrying a watermark message.

[0081] Specifically, in the process of extracting the watermark message, the system first receives a message extraction signal, which is usually sent by the user or the system to indicate the start of watermark extraction. After the extraction signal is received, the system first needs to obtain the combined audio signal and then separate the interference audio signal, because the watermark information and the interference signal have been mixed together in the previous watermark embedding process.

[0082] In order to accurately extract the watermark, the system effectively separates and removes the interference signal from the combined audio signal through preset separation rules. The separation of interference signals usually relies on the system's analysis of the characteristics of the interference signal. Combined with the specific strategy of adding the interference signal before, the system can reversely process the interference part and retain the original audio signal carrying the watermark. During the separation process, the system must also avoid affecting the integrity of the watermark information to ensure that the watermark message can remain accurate and extractable. The separated audio signal should be the original audio signal carrying the watermark message and no longer contain external interference. Through this step, the system removes the redundant parts of the audio and retains the core watermark carrier.

[0083] S205, identifying the watermark information added bits in the audio signal carrying the watermark message, and extracting the watermark message from the watermark information added bits.

[0084] Specifically, after the system obtains the audio signal carrying the watermark message, the next step is to identify the watermark information added bits in the audio signal. Since the watermark is embedded in a specific frequency domain or time domain position, during the extraction process, the system needs to rely on the same model algorithm to accurately locate the location information of the embedded watermark. This process is usually reversely inferred through the strategy when the watermark was embedded. These added bits usually have specific characteristics, or are automatically identified through a pre-trained model. The identification process needs to consider various deformations of the audio signal during transmission and processing, such as compression, distortion, noise interference, etc., to ensure the robustness of the watermark information. After identifying the watermark information added bits, the system will extract the embedded watermark message from these bits.

[0085] The technology for extracting watermarks can be based on amplitude changes, phase shifts, or other hidden information embedding technologies at specific frequencies. The extracted watermark message needs to be decoded to finally obtain the embedded original information, such as copyright identification, authentication information, or other embedded secret data. After this step, the complete watermark extraction process is completed. The watermark information can be used to verify the source of the audio, copyright protection, etc., to ensure that the embedded information can be accurately restored without affecting the sound quality.

[0086] In the above embodiments, the present application automatically identifies the watermark information addition position through the audio watermark model, realizes the addition and extraction of watermark information, significantly reduces the dependence on expert knowledge, and improves the speed and efficiency of watermark processing. Compared with the traditional method of manually selecting watermark embedding positions based on expert experience, the pre-trained model can more efficiently and accurately select the appropriate embedding position, thereby improving the processing speed and efficiency. At the same time, the audio signal is converted into a frequency domain signal through Fourier transform, which is conducive to embedding the watermark in the frequency domain and improving the concealment and robustness of the watermark. In addition, the present application also combines the interference audio signal to enhance the anti-attack ability of the watermark, realizes rapid watermark extraction and signal separation, and improves the overall performance of the system.

[0087] Furthermore, the watermark information adding bit includes a first adding bit and a second adding bit, the watermark message includes a watermark message body and an audio synchronization code matching the watermark message body, the watermark information adding bit is identified in the original host audio signal by a pre-trained audio watermark model, and the watermark message is embedded in the watermark information adding bit to obtain the audio signal carrying the watermark message, specifically including:

[0088] Dividing the original host audio signal to obtain a number of audio signal segments;

[0089] Counting the frequency range of each audio signal segment, and determining the insensitive audio signal segment based on the frequency range of each audio signal segment;

[0090] The watermark information adding bit is embedded in the non-sensitive audio signal segment through the audio watermark model;

[0091] embedding a watermark message body in the first adding position;

[0092] An audio synchronization code is embedded in the second adding position to generate an audio signal carrying a watermark.

[0093] In this embodiment, the watermark embedding step first divides the original host audio signal into several audio signal segments, so as to facilitate independent analysis of each part of the audio and make the watermark embedding more accurate. Then, the system counts the frequency range of each audio signal segment, with the purpose of identifying which parts of the audio have less impact on hearing, that is, insensitive audio signal segments. Insensitive segments are usually located in the frequency range that is difficult for the human ear to perceive. By analyzing the frequency characteristics of the audio, the system can automatically select these segments to ensure that the embedding of the watermark will not have a significant impact on the sound quality.

[0094] Afterwards, the audio watermark model calibrates the locations where the watermark information is added in these non-sensitive segments. This method avoids the complexity of manually selecting locations and reduces the reliance on expert knowledge. The watermark information addition bits are divided into the first addition bit and the second addition bit, which are used to embed the watermark message body and the audio synchronization code respectively. The watermark message body contains core hidden information, such as copyright or verification information, while the audio synchronization code ensures that the watermark message can be accurately located and restored during the extraction process. The synchronization code makes watermark extraction more accurate and efficient by marking the time domain or frequency domain features of the audio.

[0095] Through the above steps, the automated watermark embedding process is realized, reducing the dependence on manual and expert knowledge. By embedding the watermark in non-sensitive segments, the fidelity of the sound quality is ensured, while the concealment and anti-attack capabilities of the watermark are improved. The introduction of audio synchronization code enhances the robustness of watermark extraction, so that the watermark information can remain extractable after audio processing.

[0096] Furthermore, before the step of identifying the watermark information adding bit in the original host audio signal by using the pre-trained audio watermark model and embedding the watermark message in the watermark information adding bit to obtain the audio signal carrying the watermark message, the method further includes:

[0097] Acquire historical host voice, and perform Fourier transform on the historical host voice to obtain historical host audio signal;

[0098] Dividing the historical host audio signal to obtain a number of historical audio signal segments;

[0099] Counting the frequency range of each historical audio signal segment, and determining the historical non-sensitive audio signal segment based on the frequency range of each historical audio signal segment;

[0100] Embedding a historical watermark information adding bit in a historical non-sensitive audio signal segment, and marking the historical watermark information adding bit;

[0101] The marked historical host audio signal is input into a preset initial audio watermark model for model training to obtain a trained audio watermark model.

[0102] In this embodiment, the audio watermark model needs to be pre-trained before watermark embedding. Specifically, the system first obtains the historical host voice and performs Fourier transform on it to convert the audio signal from the time domain to the frequency domain. Then, the system divides the historical audio signal after the Fourier transform into several historical audio segments. Subsequently, the system counts the frequency range of each historical audio segment, identifies which frequency ranges are not easily perceived by the auditory sense, and determines them as historical non-sensitive audio segments. These segments are considered to have less impact on the embedded watermark and can be automatically identified through statistical analysis.

[0103] Then, in the historical non-sensitive audio segments, the system will embed the historical watermark information addition bits and mark these positions. Through such marking, the system can better understand the characteristics of different audio segments. Next, the system inputs these marked historical audio segments into the initial audio watermark model for training, so that the model can automatically learn how to embed watermarks at different frequencies and positions, thereby forming a fully trained watermark model. Through this step, the model gains the ability to adapt to different types of audio, and can automatically identify which positions are suitable for embedding watermark information, improving the automation and flexibility of watermark embedding.

[0104] Through the above steps, the pre-trained watermark model is trained with historical audio data, so that it can automatically select the watermark embedding position according to the characteristics of the audio signal, reducing manual intervention and improving the robustness and adaptability of the model. By using the frequency analysis of historical non-sensitive fragments, the watermark embedding is more concealed and has minimal impact on the sound quality. At the same time, through training with large-scale data, the model has stronger generalization ability.

[0105] Furthermore, the initial audio watermark model includes a message embedding network, a message extraction network and an attack simulation layer. The steps of inputting the marked historical host audio signal into the preset initial audio watermark model for model training to obtain the trained audio watermark model specifically include:

[0106] Adding a historical watermark message to the historical watermark information in the historical host audio signal through a message embedding network to obtain an audio signal carrying the historical watermark message, wherein the historical watermark message includes a historical watermark message body and a historical audio synchronization code;

[0107] Generate a historical interference audio signal using the attack simulation layer, and add the historical interference audio signal to the audio signal carrying the historical watermark message to generate a historical combined audio signal;

[0108] The historical combined audio signal is separated and the message is extracted through the message extraction network to obtain the host audio restoration signal and the restoration watermark message;

[0109] The model prediction error is calculated according to the host audio restoration signal and the restoration watermark message, and the initial audio watermark model is adjusted based on the model prediction error to obtain a trained audio watermark model.

[0110] The audio watermark model in the embodiment of the present application is a dual-embedded digital audio watermark implementation model based on a reversible network. This model designs a two-stage reversible network to address the problems of existing digital audio watermark processing methods, which are highly dependent on expert knowledge, greatly affected by expert knowledge reserves, slow processing speed, and low efficiency. The synchronization code and watermark message are separated and embedded twice. In the watermark extraction stage, a lightweight synchronization code extraction network is first used to quickly locate the position of the watermark, and then a message extractor with more layers is used to extract the embedded watermark message, thereby improving the watermark positioning speed in the extraction stage. In order to enhance the robustness of the watermark, a watermark removal attack simulation is added between the embedding network and the extraction network during the training process to train the robustness of the digital audio watermark method, so that the audio embedded with the digital watermark can still extract a relatively accurate watermark after various watermark removal attacks.

[0111] The host voice is input into the embedding network after STFT transformation. The embedding network adopts a two-layer design. The first layer embeds the watermark message first, and the second layer embeds the synchronization code with fewer bits at the same position to obtain the audio embedded with the watermark. Various watermark removal attacks are applied to the audio embedded with the watermark, and the attacked audio is input into the extraction network to extract the synchronization code and message. The extraction network is also a two-layer design. First, the watermark positioning code is extracted, and then the output audio is input into the second layer to extract the watermark message.

[0112] In this embodiment, first, the historical watermark message is embedded into the historical watermark information adding bit in the historical host audio signal through the message embedding network. The historical watermark message includes a watermark message body and a historical audio synchronization code. The watermark message body is the main information that needs to be embedded, such as copyright, identity identification, etc., and the historical audio synchronization code ensures that the watermark can be accurately located during the extraction process.

[0113] The message embedding network analyzes the characteristics of the host audio and covertly embeds the watermark message into the frequency domain or time domain of the audio to ensure its concealment and anti-interference. Next, the system uses the attack simulation layer to generate historical interference audio signals. These interference audio signals simulate various attacks that may occur in real scenarios, such as noise, compression, and editing. The interference audio signal is superimposed with the audio signal carrying the historical watermark message to generate a historical combined audio signal in order to test the robustness and anti-attack ability of the watermark. Then, through the message extraction network, the system processes the historical combined audio signal, separates the interference audio signal from the host audio signal, and extracts the embedded watermark message. This step simulates the watermark extraction process in the actual environment. By restoring the host audio signal and restoring the watermark message, the anti-attack effect of the watermark is verified.

[0114] Finally, the system calculates the prediction error of the model based on the host audio restoration signal and the restored watermark message. This error reflects the loss or deviation in the embedding and extraction process. By performing error feedback adjustment on the initial watermark model, the model is gradually optimized to have better robustness and restoration capabilities when facing interference.

[0115] Through the above steps, the attack simulation layer and the message extraction network are combined in the watermark embedding process, which enhances the robustness of the model in complex environments. Through feedback adjustment of the prediction error, the model is continuously optimized during the training process. The final generated audio watermark model can effectively resist various interferences and attacks in practical applications and ensure the accurate extraction of the watermark.

[0116] Further, the steps of calculating the model prediction error according to the host audio restoration signal and the restoration watermark message, and adjusting the initial audio watermark model based on the model prediction error to obtain the trained audio watermark model specifically include:

[0117] Calculate the error between the historical host audio signal and the host audio restoration signal based on a preset first loss function to obtain a first prediction error;

[0118] Calculate the error between the historical watermark message and the restored watermark message based on a preset second loss function to obtain a second prediction error;

[0119] The initial audio watermark model is iterated based on the first prediction error and the second prediction error until the model is fitted, thereby obtaining a trained audio watermark model.

[0120] In this embodiment, first, the system calculates the error between the historical host audio signal and the restored host audio signal based on the preset first loss function to obtain the first prediction error. The purpose of this step is to measure the degree of restoration of the host audio signal after watermark embedding and attack simulation, that is, the integrity and sound quality fidelity of the audio signal. Secondly, the error between the historical watermark message and the restored watermark message is calculated based on the second loss function to obtain the second prediction error. This step evaluates the restoration effect of the watermark message to ensure that the watermark information can still be accurately extracted after interference. Subsequently, the system iteratively optimizes the initial audio watermark model based on the first and second prediction errors, and gradually reduces the error by continuously adjusting the model parameters until the model reaches the optimal fitting state.

[0121] Through the above steps, the audio watermark model is effectively optimized to ensure that watermark embedding has little impact on sound quality and can accurately extract watermark information in complex environments.

[0122] Furthermore, the expression of the first loss function is as follows:

[0123] L integrity =||(X syn ,X msg ),Ext(Emb(X host ,X syn ,X msg ))||2

[0124] Where, L integrity is the first loss function, X host , X syn , X msg They represent the historical host audio signal, the historical audio synchronization code, and the historical watermark message body respectively. Emb and Ext represent the message embedding operation and the message extraction operation respectively.

[0125] In this embodiment, the first loss function L integrity Indicates the watermark integrity loss, requiring the embedded watermark message and the extracted watermark message to be as consistent as possible. Since the output of the extraction network is a number in the range of 0 to 1, the method of setting a threshold and rounding is used in practical application judgment. In the training process, the output is directly used as the extracted watermark information to ensure the stability of the training. The first loss function L is used. integrity Constrain the difference between the two.

[0126] Furthermore, the expression of the second loss function is as follows:

[0127] L imperceptibility =||X host ,Emb(X host ,X syn ,X msg )||2

[0128] Where, L imperceptibility is the second loss function, X host , X syn , X msg They represent the historical host audio signal, historical audio synchronization code, and historical watermark message body respectively, and Emb represents the message embedding operation.

[0129] In this embodiment, the second loss function L imperceptibility Indicates the imperceptible loss, requiring the watermarked audio to be as similar as possible to the original host audio, so as to reduce the changes brought by the watermark to the audio. The second loss function L is used imperceptibility The loss bound is the difference between the two.

[0130] Furthermore, the final loss function L of the audio watermark model training is total is the first loss function L integrity and the second loss function L imperceptibility The weighted sum of is expressed as follows:

[0131] L total =a1L integrity +a2L imperceptibility

[0132] Where a1 is the first loss function L integrity The weight of a2 is the second loss function L imperceptibility The weight of .

[0133] In the above embodiments, the present application discloses a watermark processing method for digital audio, which belongs to the field of artificial intelligence technology. The present application automatically identifies the watermark information addition position through the audio watermark model, realizes the addition and extraction of watermark information, significantly reduces the dependence on expert knowledge, and improves the speed and efficiency of watermark processing. Compared with the traditional method of manually selecting watermark embedding positions based on expert experience, the pre-trained model can more efficiently and accurately select the appropriate embedding position, thereby improving the processing speed and efficiency. At the same time, the audio signal is converted into a frequency domain signal through Fourier transform, which is conducive to embedding the watermark in the frequency domain, thereby improving the concealment and robustness of the watermark. In addition, the present application also combines the interference audio signal to enhance the anti-attack ability of the watermark, realizes rapid watermark extraction and signal separation, and improves the overall performance of the system.

[0134] In this embodiment, the watermark processing method for digital audio is executed on an electronic device (eg Figure 1The server shown in the figure) can receive instructions or obtain data through a wired connection or a wireless connection. It should be noted that the above wireless connection method may include but is not limited to 3G / 4G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods currently known or to be developed in the future.

[0135] It should be emphasized that in order to further ensure the privacy and security of the above-mentioned host voice information, the above-mentioned host voice information can also be stored in a node of a blockchain.

[0136] The blockchain referred to in this application is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, platform product service layer, and application service layer.

[0137] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0138] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0139] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0140] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0141] Further references Figure 3 , as a response to the above Figure 2 The present application provides an embodiment of a digital audio watermark processing device. Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0142] like Figure 3 As shown, the digital audio watermark processing device 300 described in this embodiment includes:

[0143] The audio conversion module 301 is used to receive a watermark adding instruction, obtain the original host voice, and perform Fourier transform on the original host voice to obtain the original host audio signal;

[0144] The watermark adding module 302 is used to identify the watermark information adding bit in the original host audio signal through the pre-trained audio watermark model, and embed the watermark message in the watermark information adding bit to obtain the audio signal carrying the watermark message;

[0145] The simulated attack module 303 is used to obtain an interference audio signal, combine the interference audio signal with the audio signal carrying the watermark message to obtain a combined audio signal, and output the combined audio signal;

[0146] The audio separation module 304 is used to receive the message extraction signal, obtain the combined audio signal, separate the interference audio signal from the combined audio signal, and obtain the audio signal carrying the watermark message;

[0147] The watermark extraction module 305 is used to identify the watermark information added bits in the audio signal carrying the watermark message, and extract the watermark message from the watermark information added bits.

[0148] Furthermore, the watermark information adding bit includes a first adding bit and a second adding bit, the watermark message includes a watermark message body and an audio synchronization code matching the watermark message body, and the watermark adding module 302 is specifically used for:

[0149] The original host audio signal is divided to obtain a number of audio signal segments; the frequency range of each audio signal segment is counted, and the non-sensitive audio signal segment is determined based on the frequency range of each audio signal segment; the watermark information addition position is embedded in the non-sensitive audio signal segment through the audio watermark model; the watermark message body is embedded in the first addition position; the audio synchronization code is embedded in the second addition position to generate an audio signal carrying the watermark.

[0150] Furthermore, the digital audio watermark processing device 300 further includes a watermark model training module, which is specifically used for:

[0151] The historical host voice is obtained, and Fourier transform is performed on the historical host voice to obtain a historical host audio signal; the historical host audio signal is divided to obtain a plurality of historical audio signal segments; the frequency range of each historical audio signal segment is counted, and the historical non-sensitive audio signal segment is determined based on the frequency range of each historical audio signal segment; the historical watermark information adding bit is embedded in the historical non-sensitive audio signal segment, and the historical watermark information adding bit is marked; the marked historical host audio signal is input into a preset initial audio watermark model for model training to obtain a trained audio watermark model.

[0152] Furthermore, the initial audio watermark model includes a message embedding network, a message extraction network and an attack simulation layer, and the training module is further used to:

[0153] The historical watermark information in the historical host audio signal is added with a bit-embedded historical watermark message through a message embedding network to obtain an audio signal carrying the historical watermark message, wherein the historical watermark message includes a historical watermark message body and a historical audio synchronization code; a historical interference audio signal is generated using an attack simulation layer, and the historical interference audio signal is added to the audio signal carrying the historical watermark message to generate a historical combined audio signal; audio signal separation and message extraction are performed on the historical combined audio signal through a message extraction network to obtain a host audio restoration signal and a restoration watermark message; a model prediction error is calculated according to the host audio restoration signal and the restoration watermark message, and the initial audio watermark model is adjusted based on the model prediction error to obtain a trained audio watermark model.

[0154] Furthermore, the training module is further used to specifically include:

[0155] Based on the preset first loss function, the error between the historical host audio signal and the host audio restoration signal is calculated to obtain a first prediction error; based on the preset second loss function, the error between the historical watermark message and the restored watermark message is calculated to obtain a second prediction error; based on the first prediction error and the second prediction error, the initial audio watermark model is iterated until the model is fitted to obtain a trained audio watermark model.

[0156] Furthermore, the expression of the first loss function is as follows:

[0157] L integrity =||(X syn ,X msg ),Ext(Emb(X host ,X syn ,X msg ))||2

[0158] Where, L integrity is the first loss function, X host , X syn , X msg They represent the historical host audio signal, the historical audio synchronization code, and the historical watermark message body respectively. Emb and Ext represent the message embedding operation and the message extraction operation respectively.

[0159] Furthermore, the expression of the second loss function is as follows:

[0160] L imperceptibility =||X host ,Emb(X host ,X syn ,X msg )||2

[0161] Where, L imperceptibility is the second loss function, X host , X syn , X msg They represent the historical host audio signal, historical audio synchronization code, and historical watermark message body respectively, and Emb represents the message embedding operation.

[0162] In the above embodiments, the present application discloses a watermark processing device for digital audio, which belongs to the field of artificial intelligence technology. The present application automatically identifies the watermark information addition position through the audio watermark model, realizes the addition and extraction of watermark information, significantly reduces the dependence on expert knowledge, and improves the speed and efficiency of watermark processing. Compared with the traditional method of manually selecting watermark embedding positions based on expert experience, the pre-trained model can more efficiently and accurately select the appropriate embedding position, thereby improving the processing speed and efficiency. At the same time, the audio signal is converted into a frequency domain signal through Fourier transform, which is conducive to embedding the watermark in the frequency domain, thereby improving the concealment and robustness of the watermark. In addition, the present application also combines the interference audio signal to enhance the anti-attack ability of the watermark, realizes rapid watermark extraction and signal separation, and improves the overall performance of the system.

[0163] To solve the above technical problems, the present application also provides a computer device. Figure 4 , Figure 4This is a basic structural block diagram of the computer device in this embodiment.

[0164] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected and communicated through a system bus. It should be noted that the figure only shows a computer device 4 with a memory 41, a processor 42, and a network interface 43, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0165] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0166] The memory 41 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 41 can be an internal storage unit of the computer device 4, such as a hard disk or memory of the computer device 4. In other embodiments, the memory 41 can also be an external storage device of the computer device 4, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. equipped on the computer device 4. Of course, the memory 41 can also include both the internal storage unit of the computer device 4 and its external storage device. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions of the watermark processing method of digital audio. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or are to be output.

[0167] The processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run the computer-readable instructions stored in the memory 41 or process data, such as running the computer-readable instructions of the watermark processing method for the digital audio.

[0168] The network interface 43 may include a wireless network interface or a wired network interface. The network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0169] In the above embodiment, the present application discloses a computer device, which belongs to the field of artificial intelligence technology. The present application automatically identifies the watermark information addition position through the audio watermark model, realizes the addition and extraction of watermark information, significantly reduces the dependence on expert knowledge, and improves the speed and efficiency of watermark processing. Compared with the traditional method of manually selecting watermark embedding positions based on expert experience, the pre-trained model can more efficiently and accurately select the appropriate embedding position, thereby improving the processing speed and efficiency. At the same time, the audio signal is converted into a frequency domain signal through Fourier transform, which is conducive to embedding the watermark in the frequency domain, thereby improving the concealment and robustness of the watermark. In addition, the present application also combines the interference audio signal to enhance the anti-attack ability of the watermark, realizes rapid watermark extraction and signal separation, and improves the overall performance of the system.

[0170] The present application also provides another embodiment, namely, providing a computer-readable storage medium, wherein the computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the digital audio watermark processing method as described above.

[0171] In the above embodiments, the present application discloses a computer-readable storage medium, which belongs to the field of artificial intelligence technology. The present application automatically identifies the watermark information addition position through the audio watermark model, realizes the addition and extraction of watermark information, significantly reduces the dependence on expert knowledge, and improves the speed and efficiency of watermark processing. Compared with the traditional method of manually selecting watermark embedding positions based on expert experience, the pre-trained model can more efficiently and accurately select the appropriate embedding position, thereby improving the processing speed and efficiency. At the same time, the audio signal is converted into a frequency domain signal through Fourier transform, which is conducive to embedding the watermark in the frequency domain, thereby improving the concealment and robustness of the watermark. In addition, the present application also combines the interference audio signal to enhance the anti-attack ability of the watermark, realizes rapid watermark extraction and signal separation, and improves the overall performance of the system.

[0172] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0173] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0174] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A watermark processing method for digital audio, characterized in that: include: Receiving a watermark adding instruction, obtaining an original host voice, and performing Fourier transform on the original host voice to obtain an original host audio signal; Identifying watermark information addition bits in the original host audio signal through a pre-trained audio watermark model, and embedding a watermark message in the watermark information addition bits to obtain an audio signal carrying the watermark message; Acquire an interference audio signal, combine the interference audio signal with the audio signal carrying the watermark message to obtain a combined audio signal, and output the combined audio signal; Receiving a message extraction signal, acquiring the combined audio signal, separating the interfering audio signal from the combined audio signal, and obtaining the audio signal carrying the watermark message; The watermark information added bits in the audio signal carrying the watermark message are identified, and the watermark message is extracted from the watermark information added bits.

2. The watermark processing method for digital audio according to claim 1, characterized in that: The watermark information adding bit includes a first adding bit and a second adding bit, the watermark message includes a watermark message body and an audio synchronization code matching the watermark message body, and the step of identifying the watermark information adding bit in the original host audio signal by using a pre-trained audio watermark model and embedding the watermark message in the watermark information adding bit to obtain an audio signal carrying the watermark message specifically includes: Dividing the original host audio signal to obtain a plurality of audio signal segments; Counting the frequency range of each of the audio signal segments, and determining the insensitive audio signal segment based on the frequency range of each of the audio signal segments; Embedding the watermark information adding bit in the non-sensitive audio signal segment by using the audio watermark model; embedding the watermark message body in the first adding position; The audio synchronization code is embedded in the second adding position to generate an audio signal carrying a watermark.

3. The watermark processing method for digital audio according to claim 2, characterized in that: Before the step of identifying the watermark information addition bit in the original host audio signal by using the pre-trained audio watermark model and embedding the watermark message in the watermark information addition bit to obtain the audio signal carrying the watermark message, the method further includes: Acquire historical host voice, and perform Fourier transform on the historical host voice to obtain a historical host audio signal; Dividing the historical host audio signal to obtain a plurality of historical audio signal segments; Counting the frequency range of each of the historical audio signal segments, and determining the historical insensitive audio signal segment based on the frequency range of each of the historical audio signal segments; Embedding a historical watermark information adding bit in the historical non-sensitive audio signal segment, and marking the historical watermark information adding bit; The marked historical host audio signal is input into a preset initial audio watermark model for model training to obtain a trained audio watermark model.

4. The watermark processing method for digital audio according to claim 3, characterized in that: The initial audio watermark model includes a message embedding network, a message extraction network and an attack simulation layer. The step of inputting the marked historical host audio signal into a preset initial audio watermark model for model training to obtain a trained audio watermark model specifically includes: Adding a historical watermark message to the historical watermark information in the historical host audio signal through the message embedding network to obtain an audio signal carrying the historical watermark message, wherein the historical watermark message includes a historical watermark message body and a historical audio synchronization code; Using the attack simulation layer to generate a historical interference audio signal, and adding the historical interference audio signal to the audio signal carrying the historical watermark message to generate a historical combined audio signal; Performing audio signal separation and message extraction on the historical combined audio signal through the message extraction network to obtain a host audio restoration signal and a restoration watermark message; A model prediction error is calculated according to the host audio restoration signal and the restoration watermark message, and the initial audio watermark model is adjusted based on the model prediction error to obtain a trained audio watermark model.

5. The watermark processing method for digital audio according to claim 4, characterized in that: The step of calculating the model prediction error according to the host audio restoration signal and the restoration watermark message, and adjusting the initial audio watermark model based on the model prediction error to obtain the trained audio watermark model specifically includes: Calculate the error between the historical host audio signal and the restored host audio signal based on a preset first loss function to obtain a first prediction error; Calculate the error between the historical watermark message and the restored watermark message based on a preset second loss function to obtain a second prediction error; The initial audio watermark model is iterated based on the first prediction error and the second prediction error until the model is fitted, thereby obtaining a trained audio watermark model.

6. The method for watermarking digital audio according to claim 5, characterized in that: The expression of the first loss function is as follows: L integrity =||(X syn ,X msg ),Ext(Emb(X host ,X syn ,X msg ))||2 Where, L integrity is the first loss function, X host , X syn , X msg They represent the historical host audio signal, the historical audio synchronization code, and the historical watermark message body respectively. Emb and Ext represent the message embedding operation and the message extraction operation respectively.

7. The watermark processing method for digital audio according to claim 5, characterized in that: The expression of the second loss function is as follows: L imperceptibility =||X host ,Emb(X host ,X syn ,X msg )||2 Where, L imperceptibility is the second loss function, X gost , X syn , X msg They represent the historical host audio signal, historical audio synchronization code, and historical watermark message body respectively, and Emb represents the message embedding operation.

8. A digital audio watermark processing device, characterized in that: include: An audio conversion module, used to receive a watermark adding instruction, obtain an original host voice, and perform Fourier transform on the original host voice to obtain an original host audio signal; A watermark adding module, used for identifying watermark information adding bits in the original host audio signal through a pre-trained audio watermark model, and embedding a watermark message in the watermark information adding bits to obtain an audio signal carrying the watermark message; A simulation attack module, used for acquiring an interference audio signal, combining the interference audio signal with the audio signal carrying the watermark message to obtain a combined audio signal, and outputting the combined audio signal; An audio separation module, configured to receive a message extraction signal, obtain the combined audio signal, separate an interfering audio signal from the combined audio signal, and obtain the audio signal carrying the watermark message; The watermark extraction module is used to identify the watermark information added bits in the audio signal carrying the watermark message, and extract the watermark message from the watermark information added bits.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the digital audio watermark processing method as described in any one of claims 1 to 7 when executing the computer-readable instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the steps of the digital audio watermark processing method according to any one of claims 1 to 7 are implemented.

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