Audio watermark embedding method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of artificial intelligence and the field of financial science and technology, and discloses an audio watermark embedding method and device, computer equipment and a storage medium, and the method comprises the steps: decomposing a host audio to be embedded with a watermark into a plurality of sub-bands with different frequency bands through discrete wavelet transform; performing layer-by-layer down-sampling on each sub-band by using an encoder in a U-Net network model, and extracting hidden space features of the host audio; determining watermark information to be embedded, and encoding the watermark information to obtain a watermark vector; embedding the watermark vector into the hidden space feature to obtain a fusion feature; carrying out layer-by-layer up-sampling on the fusion features by using a decoder in the U-Net network model, and reconstructing audio information; performing inverse discrete wavelet transform on the audio information to generate a target audio embedded with the watermark information; therefore, the flexibility, adaptability and robustness of audio watermark embedding can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the fields of artificial intelligence technology and financial technology, and in particular to an audio watermark embedding method, device, computer equipment and computer-readable storage medium. Background Art

[0002] At present, with the development of computer technology, more and more technologies are applied in the financial field. The traditional financial industry is gradually transforming to Fintech, and audio watermark embedding technology is no exception. For example, financial institutions (such as banks) often need to record customer service calls for quality control and compliance inspections. Audio watermark embedding technology can help ensure that the recording files have not been tampered with and provide a means to verify their authenticity. However, due to the security and real-time requirements of the financial industry, higher requirements are also placed on audio watermark embedding technology.

[0003] At the same time, with the rapid development of Internet technology and the continuous advancement of artificial intelligence (AI) technology, the production, editing, sharing and dissemination of digital audio have become more convenient than ever before. Audio files circulate rapidly through the Internet, greatly enriching people's daily lives. However, this convenience also brings challenges in audio copyright protection. Traditional audio copyright protection methods, such as relying on legal means and technical measures, have been unable to cope with copyright infringement issues in the current network environment. Against this background, digital watermarking technology has emerged as an effective means of copyright protection. Digital watermarking achieves copyright identification, content authentication and illegal distribution tracking by embedding hidden information in audio files. Although digital watermarking technology has shown great potential in copyright protection, most media digital watermarking implementations currently still rely on traditional methods, that is, watermark embedding algorithms designed based on expert experience. Traditional digital watermarking technology usually adopts rule-based methods, which need to consider the characteristics of audio information and the perceptual characteristics of the human ear when designing. Although these methods have achieved certain success in specific scenarios, they usually lack sufficient flexibility and adaptability and are difficult to cope with the increasingly complex network environment and the continuous advancement of audio processing technology. In addition, traditional methods often show limited robustness when facing complex transformations and attacks of audio information.

[0004] In summary, how to provide an audio watermark embedding method, apparatus, computer device and computer-readable storage medium that can effectively improve the flexibility, adaptability and robustness of audio watermark embedding is a problem that needs to be urgently solved by those skilled in the art. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, the object of the present invention is to provide an audio watermark embedding method, apparatus, computer device and computer-readable storage medium, aiming to solve the problem of how to effectively improve the flexibility, adaptability and robustness of audio watermark embedding.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides an audio watermark embedding method, which comprises:

[0008] Decomposing the host audio to be embedded with watermark into multiple sub-bands of different frequency bands by discrete wavelet transform;

[0009] Using an encoder in a U-Net network model to downsample each of the sub-bands layer by layer to extract latent space features of the host audio;

[0010] Determining watermark information to be embedded, and encoding the watermark information to obtain a watermark vector;

[0011] Embedding the watermark vector into the latent space feature to obtain a fusion feature;

[0012] Using the decoder in the U-Net network model to upsample the fusion features layer by layer to reconstruct the audio information;

[0013] The audio information is subjected to inverse discrete wavelet transform to generate target audio embedded with the watermark information.

[0014] In a second aspect, the present invention provides an audio watermark embedding device, comprising:

[0015] A decomposition module, used for decomposing the host audio to be embedded with watermark into a plurality of sub-bands of different frequency bands by discrete wavelet transform;

[0016] An extraction module, used to downsample each of the sub-bands layer by layer using an encoder in a U-Net network model to extract latent space features of the host audio;

[0017] The encoding module is used to determine the watermark information to be embedded and encode the watermark information to obtain a watermark vector;

[0018] An embedding module, used for embedding the watermark vector into the latent space feature to obtain a fusion feature;

[0019] A reconstruction module, used to upsample the fusion features layer by layer using the decoder in the U-Net network model to reconstruct the audio information;

[0020] The generating module is used to perform inverse discrete wavelet transform on the audio information to generate target audio embedded with the watermark information.

[0021] In a third aspect, the present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the audio watermark embedding method as described above when executing the computer program.

[0022] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the audio watermark embedding method as described above.

[0023] Compared with the prior art, the present invention provides an audio watermark embedding method, apparatus, computer equipment and computer-readable storage medium, wherein the host audio to be embedded with a watermark is decomposed into a plurality of sub-bands of different frequency bands by discrete wavelet transform; each of the sub-bands is down-sampled layer by layer by an encoder in a U-Net network model to extract latent space features of the host audio; watermark information to be embedded is determined, and the watermark information is encoded to obtain a watermark vector; the watermark vector is embedded in the latent space features to obtain a fusion feature; the fusion feature is up-sampled layer by layer by a decoder in the U-Net network model to reconstruct the audio information; the audio information is inversely discretely transformed to generate a target audio embedded with the watermark information; thus, the present invention can effectively improve the flexibility, adaptability and robustness of audio watermark embedding. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0025] Figure 1 A schematic diagram of an application environment of an audio watermark embedding method provided by an embodiment of the present invention.

[0026] Figure 2 A flowchart of an audio watermark embedding method provided by an embodiment of the present invention.

[0027] Figure 3 A schematic diagram of program modules of an audio watermark embedding device provided by an embodiment of the present invention.

[0028] Figure 4A schematic diagram of the structure of a computer device provided by an embodiment of the present invention.

[0029] Figure 5 Another structural schematic diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0032] It should also be understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0033] As used in the present specification and the appended claims, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0034] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0035] References to "one embodiment" or "some embodiments" etc. described in the present specification mean that one or more embodiments of the present invention include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0036] It should be understood that the order of execution of the steps in the following embodiments does not imply a precedence of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0037] In order to illustrate the technical solution of the present invention, specific embodiments are provided below for illustration.

[0038] An audio watermark embedding method provided by an embodiment of the present invention can be applied in Figure 1 In the application environment shown, the client and the server communicate through the network. The client includes but is not limited to PDAs, desktop computers, laptops, ultra-mobile personal computers (UMPCs), netbooks, cloud computing devices, personal digital assistants (PDAs) and other computer devices. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0039] See also Figure 2 An embodiment of the present invention provides an audio watermark embedding method, wherein the method comprises the following steps:

[0040] S100, decomposing the host audio to be embedded with the watermark into a plurality of sub-bands of different frequency bands by discrete wavelet transform;

[0041] S200, using an encoder in a U-Net network model to downsample each of the sub-bands layer by layer to extract latent space features of the host audio;

[0042] S300, determining watermark information to be embedded, and encoding the watermark information to obtain a watermark vector;

[0043] S400, embedding the watermark vector into the latent space feature to obtain a fusion feature;

[0044] S500, using the decoder in the U-Net network model to upsample the fusion features layer by layer to reconstruct audio information;

[0045] S600: Perform inverse discrete wavelet transform on the audio information to generate target audio embedded with the watermark information.

[0046] In specific implementation, this embodiment realizes highly flexible, adaptable and robust audio watermark embedding by combining discrete wavelet transform (DWT) and U-Net network model. First, the host audio is decomposed into sub-bands of different frequency bands by DWT. This step allows the watermark information to select the most suitable embedding position according to the specific spectral characteristics of the host audio, thereby improving the flexibility of the embedding method. Secondly, the sub-bands are downsampled and latent space features are extracted by using the encoder of the U-Net network model. The deep learning mechanism of the U-Net network model enables the algorithm to adaptively learn audio features, thereby enhancing the adaptability of the embedding method. Then, the encoded watermark information is embedded into the latent space features. This process can be optimized during the network training process to ensure the concealment of the watermark and the automatic adjustment of the embedding strength, further improving the adaptability of the method. Next, the decoder of the U-Net network model is used to upsample and reconstruct the audio information. The self-learning characteristics of the network help to maintain the sound quality during the reconstruction process, and the embedded watermark information is also effectively retained. Finally, the execution of the inverse DWT restores the signal to the time domain to generate the target audio containing the watermark information. Throughout the implementation process, the introduction of deep learning models, especially the use of U-Net network models, enables the embedding method to not only cope with various audio characteristics and complex attacks, but also adaptively adjust the embedding strategy to maintain the concealment and robustness of the watermark, thereby achieving efficient and reliable audio watermark embedding in different scenarios and conditions.

[0047] Furthermore, in one embodiment, the audio watermark embedding method, wherein, before the step S100 of decomposing the host audio to be embedded with the watermark into a plurality of sub-bands of different frequency bands by discrete wavelet transform, specifically comprises the following steps:

[0048] Get the original audio uploaded by the target user;

[0049] The original audio is preprocessed to obtain the host audio to be embedded with a watermark.

[0050] In specific implementation, this embodiment obtains the original audio uploaded by the target user and performs necessary preprocessing to obtain the host audio to be embedded with the watermark, thereby ensuring that the quality of the host audio meets the requirements of watermark embedding, and then uses discrete wavelet transform technology to decompose the host audio into multiple sub-bands of different frequency bands. This not only enables multi-resolution analysis of the host audio, but also can identify and focus on specific frequency bands for watermark embedding, thereby improving the pertinence and effectiveness of watermark embedding; the technical effect of this process is that it lays the foundation for subsequent watermark embedding steps, so that the watermark information can be embedded in the host audio more covertly and stably, while enhancing the robustness of the watermark to various possible signal processing and attacks.

[0051] The specific implementation process of the steps in this embodiment is roughly as follows:

[0052] 1. Get the original audio: First, receive and confirm the audio file (i.e. original audio) uploaded by the target user to ensure that its format and quality meet the standards for subsequent processing;

[0053] 2. Audio preprocessing: Perform preprocessing operations on the uploaded original audio, including denoising, format conversion, sampling rate adjustment, etc., to eliminate impurities in the recording and standardize the audio format to obtain clear host audio;

[0054] 3. Discrete wavelet transform: The pre-processed host audio is decomposed into multiple sub-bands of different frequency bands using the discrete wavelet transform method. Each sub-band contains detailed information of the host audio in a specific frequency band.

[0055] 4. Subband analysis: Analyze the characteristics of each subband, including frequency range and energy distribution, to provide a basis for determining the best location for watermark embedding;

[0056] 5. Watermark embedding preparation: According to the results of sub-band analysis, select the sub-band suitable for embedding watermarks, and ensure that the selected sub-band can carry watermark information without affecting the perceived quality of the host audio;

[0057] Through the above process, this embodiment can more accurately locate the frequency band in the host audio that is most suitable for embedding a watermark, thereby achieving efficient and covert watermark embedding and enhancing the concealment and robustness of the watermark.

[0058] Furthermore, in one embodiment, the audio watermark embedding method, wherein the step S200, using an encoder in a U-Net network model to downsample each of the sub-bands layer by layer to extract latent space features of the host audio, specifically comprises the steps of:

[0059] Pre-building the U-Net network model;

[0060] Taking each of the sub-bands as input, sequentially inputting them into the encoder for layer-by-layer downsampling, and obtaining a depth feature representation corresponding to each of the sub-bands;

[0061] All the obtained deep feature representations are combined to generate latent space features of the host audio.

[0062] In specific implementation, this embodiment realizes efficient feature extraction of host audio by pre-building a U-Net network model including an encoder and a decoder; wherein, by sequentially inputting multiple sub-bands of the host audio into the encoder for layer-by-layer downsampling, the U-Net network model can automatically learn and extract latent space features of the host audio, which capture the core representative information of the host audio and have a smaller dimension, thereby providing an optimized embedding space for subsequent watermark embedding; such processing not only improves the adaptability and flexibility of the watermark algorithm, but also enhances the concealment and robustness of the watermark, thereby ensuring that the watermark information can be stably embedded in the host audio and can be effectively extracted even when the audio is attacked or processed.

[0063] The specific implementation process of the steps in this embodiment is roughly as follows:

[0064] 1. Build a U-Net network model: Design and build a U-Net network model, which consists of two parts: encoder and decoder; the encoder is used to gradually reduce the spatial dimension of the input data, while the decoder is used to gradually restore the spatial dimension of the data;

[0065] 2. Initialize the encoder: Configure the encoder part, which usually includes multiple convolutional layers and pooling layers, as well as activation functions such as ReLU; these components are responsible for extracting features of the input subband and downsampling;

[0066] 3. Input subband: Each subband obtained by discrete wavelet transform is fed into the encoder of the U-Net network model as input in turn; these subbands contain detailed information of the host audio in different frequency bands;

[0067] 4. Downsampling layer by layer: The subband signal is transmitted layer by layer in the encoder. Each convolution layer may be followed by a pooling operation to gradually reduce the spatial resolution of the signal and increase the depth of the feature to extract the latent space features.

[0068] 5. Extract latent space features: In the last layer of the encoder, the deep feature representation corresponding to the sub-band (i.e., the latent space features of the sub-band) is obtained. These features capture the important information of the original audio information and are ready for embedding watermark information; then, the deep feature representations corresponding to all sub-bands are combined to generate the latent space features of the host audio;

[0069] 6. Skip connection (optional): U-Net network models usually contain skip connections to combine the downsampled features in the encoder with the upsampled features in the decoder to help restore contextual information during reconstruction;

[0070] Through the above process, the U-Net network model in this embodiment can extract useful latent space features for each sub-band, thereby forming the latent space features of the host audio, which can then be used in the watermark embedding process, making the embedding of watermark information more effective and robust.

[0071] Furthermore, in one embodiment, the audio watermark embedding method, wherein the step S300 of determining the watermark information to be embedded and encoding the watermark information to obtain a watermark vector, specifically comprises the steps of:

[0072] Selecting watermark information to be embedded according to the watermark embedding requirement of the host audio;

[0073] Encoding the watermark information to obtain an initial vector;

[0074] The length and / or strength of the initial vector is adjusted to obtain a watermark vector.

[0075] In specific implementation, the technical effect that can be achieved by this embodiment is that it can prepare suitable watermark information in a customized manner according to the characteristics of the host audio and the specific requirements of watermark embedding; first, the watermark information to be embedded is selected according to the watermark embedding requirements, ensuring that the watermark content matches the intended use; secondly, the watermark information is encoded to generate an initial vector, so that the watermark information is converted into a processable format; then, by adjusting the length and strength of the initial vector, the watermark vector is optimized to adapt to the audio characteristics and perception threshold, so as to ensure that the embedded watermark is not only sufficiently hidden and reduces the impact on the sound quality of the original audio, but also ensures the robustness of the watermark, ensuring that the watermark information can be stably extracted in the face of various signal processing and potential attacks; this embodiment improves the flexibility and adaptability of watermark embedding, so that it can be adjusted and optimized for different application scenarios and security requirements.

[0076] The specific implementation process of the steps in this embodiment is roughly as follows:

[0077] 1. Requirements analysis: Analyze and determine the requirements of the watermark information to be embedded according to the characteristics of the host audio and the purpose of watermark embedding (such as copyright protection, content authentication, etc.); this may include determining the type of watermark information (text, image, identifier, etc.) and the properties that need to be met (such as robustness, concealment, etc.);

[0078] 2. Watermark information selection: Select or generate watermark information that meets the requirements; for example, if the watermark is used for copyright statement, the copyright owner's identifier or a specific serial number may be selected;

[0079] 3. Watermark information encoding: Convert the selected watermark information into binary form; this can be achieved through various encoding techniques, such as ASCII encoding of text, binary encoding of images, etc., to generate the initial watermark vector;

[0080] 4. Initial vector processing: Perform necessary processing on the initial vector, such as adding redundant bits to enhance robustness, or applying specific coding techniques (such as Reed-Solomon coding) to improve error correction capabilities;

[0081] 5. Vector adjustment: adjust the length and strength of the watermark vector according to the characteristics and perception model of the host audio; this may include expanding or compressing the vector length, and adjusting the embedding strength to ensure the concealment of the watermark while ensuring that the watermark can still be reliably extracted under various possible audio processing and attacks;

[0082] 6. Generate watermark vector: After completing the vector adjustment, the final watermark vector is obtained; this vector will be used in the subsequent embedding process and embedded into the latent space features of the host audio;

[0083] Through the above process, this embodiment can ensure that the watermark vector matches the characteristics of the host audio and meets specific embedding requirements, thereby achieving effective watermark information embedding.

[0084] Furthermore, in one embodiment, the audio watermark embedding method, wherein the step S400, embedding the watermark vector into the latent space feature to obtain a fusion feature, specifically comprises the steps of:

[0085] Determining a watermark embedding strategy according to the content of the watermark information and the characteristics of the host audio;

[0086] Embedding the watermark vector into the latent space feature according to the watermark embedding strategy to obtain a fused preliminary feature;

[0087] The preliminary features are normalized to obtain fused features.

[0088] In specific implementation, the technical effect that can be achieved by this embodiment is that, first, a watermark embedding strategy is determined according to the content of the watermark information and the characteristics of the host audio, ensuring that the embedding of the watermark information meets both specific security requirements and adapts to the characteristics of the host audio; then, the watermark vector is embedded into the latent space features of the host audio according to the watermark embedding strategy to generate fused preliminary features. This process closely combines the watermark information with the audio content, improves the concealment of the watermark and the consistency of the overall audio information; finally, the preliminary features are normalized, and the obtained fused features not only ensure the balanced distribution of the watermark information in the host audio, but also help to improve the robustness of the watermark to various signal processing and potential attacks, making the watermark extraction process more reliable and accurate. This series of processing steps comprehensively improves the performance of the audio watermark embedding system, making it more effective and secure in practical applications.

[0089] The specific implementation process of the steps in this embodiment is roughly as follows:

[0090] 1. Determine the watermark embedding strategy: Determine the appropriate watermark embedding strategy based on the content of the watermark information, the requirements for watermark robustness, concealment and capacity, and the characteristics of the host audio (such as frequency range, etc.); this may include selecting the appropriate embedding location, embedding depth and embedding method;

[0091] 2. Embed watermark information: According to the determined watermark embedding strategy, the watermark vector is embedded into the latent space features of the host audio;

[0092] 3. Generate fused preliminary features: After embedding the watermark information, obtain the fused preliminary features; these features contain the information of the original host audio and the embedded watermark information;

[0093] 4. Normalization: Normalize the preliminary features to ensure that the distribution and strength of the features are within a reasonable range; this helps to improve the stability and robustness of watermark embedding and ensure that the watermark can be reliably extracted in different audio information and different playback environments;

[0094] 5. Obtain the final fusion features: After normalization, the final fusion features are obtained; these features will be used in the reconstruction process of audio information to generate the target audio embedded with watermark information;

[0095] Through the above process, this embodiment can obtain the final fusion features, which provide an input basis for the subsequent reconstruction process of audio information.

[0096] Furthermore, in one embodiment, the audio watermark embedding method, wherein the step S500, using the decoder in the U-Net network model to upsample the fusion features layer by layer to reconstruct the audio information, specifically further includes the steps of:

[0097] Performing a simulated attack test on the reconstructed audio information to generate a test result;

[0098] Based on the test results, the watermark embedding strategy and / or the decoder are adjusted.

[0099] In specific implementation, this embodiment uses a decoder to upsample the fusion features layer by layer, thereby effectively reconstructing the audio information embedded with the watermark, ensuring the quality of the audio information and the integrity of the watermark information; further, this embodiment can evaluate the robustness of the watermark through simulated attack tests, and adjust and optimize the watermark embedding strategy and decoder parameters according to the test results, thereby improving the reliability and practicality of subsequent watermark embedding, ensuring that the watermark can be effectively extracted in various actual application environments, and meeting the needs of application scenarios such as digital media copyright protection and authentication.

[0100] The specific implementation process of the steps in this embodiment is roughly as follows:

[0101] 1. Input fusion features to the decoder: The fusion features containing watermark information are provided as input to the decoder part of the U-Net network model; this step is the starting point of the audio information reconstruction process, and the task of the decoder is to gradually restore the original properties of the audio information;

[0102] 2. Upsampling layer by layer: The fused features are upsampled layer by layer in the decoder. Each layer may include convolution operations, upsampling operations, and activation functions. This process gradually increases the spatial resolution of the features and gradually restores them to a form close to the original audio information.

[0103] 3. Reconstruct the audio information: After the final processing by the decoder, the reconstructed audio information is generated; the audio information should be indistinguishable from the original audio information in theory, and contain the embedded watermark information;

[0104] 4. Conduct simulated attack tests: Conduct simulated attack tests on the reconstructed audio information, which may include adding noise, compression, resampling, editing and other operations to simulate various processing and damage that audio information may encounter in actual applications;

[0105] 5. Generate and analyze test results: After simulating the attack, test the audio information to generate results and evaluate the robustness of the watermark; analyze the test results to determine whether the watermark information can be accurately extracted and whether the sound quality meets the requirements;

[0106] 6. Adjust watermark embedding strategy and decoder: Based on the test results, optimize the watermark embedding strategy, such as modifying the strength of the watermark vector, adjusting the embedding position, etc., to improve the concealment and robustness of the watermark; at the same time, the structure and parameters of the decoder can also be adjusted to better reconstruct the audio information and improve the accuracy of watermark extraction;

[0107] 7. Iterative optimization (optional): If necessary, repeat the above process for multiple iterations, and continuously adjust the watermark embedding strategy and decoder settings until the watermark performance reaches a satisfactory level;

[0108] Through the above process, this embodiment can reconstruct audio information and ensure that the reconstructed audio information can adapt to potential attacks, thereby improving its reliability in practical applications.

[0109] Furthermore, in one embodiment, the audio watermark embedding method, wherein the step S600, performing an inverse discrete wavelet transform on the audio information to generate a target audio embedded with the watermark information, specifically comprises the steps of:

[0110] Performing an inverse discrete wavelet transform on the audio information to generate an initial audio embedded with the watermark information;

[0111] Performing denoising and equalization processing on the initial audio to obtain target audio, and identifying whether the watermark information is embedded in the target audio;

[0112] When the watermark information is embedded in the target audio, the target audio is output.

[0113] In specific implementation, the technical effect that can be achieved by this embodiment is that the audio information integrated with the watermark information is restored to the time domain through inverse discrete wavelet transform to generate the initial audio containing the watermark information; then, the initial audio is optimized through denoising and equalization processing to improve the sound quality and ensure the concealment of the watermark, and finally the target audio is obtained; finally, it is identified whether the watermark information is embedded in the target audio. When the watermark information is embedded in the target audio, the target audio is output to the specified position, completing the entire watermark embedding and audio reconstruction process, ensuring the effective embedding of the watermark information and the integrity of the target audio, and meeting the needs of copyright protection and content authentication.

[0114] The specific implementation process of the steps in this embodiment is roughly as follows:

[0115] 1. Inverse discrete wavelet transform: The upsampled fusion features, i.e., the frequency domain representation containing the watermark information, are converted back to the time domain through the inverse discrete wavelet transform (IDWT); this step is to obtain a preliminary version of the audio information embedded with the watermark information, i.e., the initial audio;

[0116] 2. De-noising and equalization processing: A series of denoising and equalization processing is performed on the initial audio to improve the auditory quality of the reconstructed audio, ensure the concealment of the watermark, and meet specific technical standards or user needs;

[0117] 3. Quality assessment: During or after the denoising and equalization process, objective and subjective quality assessments are performed; objective assessments may use audio quality assessment algorithms such as PESQ (Perceptual Evaluation of Speech Quality), while subjective assessments may involve hearing tests; this step is to ensure that the audio information after the watermark is embedded maintains a high sound quality;

[0118] 4. Generate target audio: According to the results of quality assessment, fine-tune the parameters of denoising and equalization processing to obtain the final target audio; the target audio should effectively embed watermark information without significantly reducing the auditory experience;

[0119] 5. Output target audio: Identify whether the watermark information is embedded in the target audio. When the watermark information is embedded in the target audio, output the target audio to a specified location, such as saving it to the local file system, sending it to a remote server, or streaming it to other devices. This step marks the completion of the watermark embedding process, and the target audio is ready for distribution or further processing.

[0120] Through the above process, this embodiment can ensure that the target audio embedded with watermark information not only meets the technical requirements, but also is auditorily close to the original audio, providing users with a high-quality experience; at the same time, the watermark information is properly embedded, providing support for the protection of audio content.

[0121] It can be seen from the above method embodiments that the audio watermark embedding method provided by the present invention includes: decomposing the host audio to be embedded with watermark into multiple sub-bands of different frequency bands by discrete wavelet transform; using the encoder in the U-Net network model to downsample each of the sub-bands layer by layer to extract the latent space features of the host audio; determining the watermark information to be embedded, and encoding the watermark information to obtain a watermark vector; embedding the watermark vector into the latent space features to obtain fusion features; using the decoder in the U-Net network model to upsample the fusion features layer by layer to reconstruct the audio information; performing an inverse discrete wavelet transform on the audio information to generate a target audio embedded with the watermark information. In this way, the method of the present invention can effectively improve the flexibility, adaptability and robustness of audio watermark embedding.

[0122] It can be understood that the audio watermark embedding method provided by the embodiment of the present invention can be applied to audio watermark embedding scenarios related to the field of financial technology. Specifically, the audio watermark embedding function can be integrated into the corresponding quality control and compliance inspection scenarios of financial institutions (such as banks). Since financial institutions often need to record customer service calls for quality control and compliance inspections, the audio watermark embedding method provided by the embodiment of the present invention can help financial institutions ensure that the relevant recording files have not been tampered with and provide a means to verify their authenticity.

[0123] It should be understood that, although the present application provides method operation steps as described in the embodiments or flowcharts, more or less operation steps may be included based on conventional or non-creative labor, and these operation steps are not necessarily performed in sequence according to the embodiment or flowchart. The order of steps listed in the embodiment or flowchart is only one way of executing the order of many steps, and does not represent the only execution order. It should be noted that there is not necessarily a certain order between the above steps. A person of ordinary skill in the art can understand from the description of the embodiment of the present invention that in different embodiments, the above steps may have different execution orders, that is, they may be executed in parallel, or they may be executed in exchange, etc. Moreover, at least a part of the steps in the embodiment or flowchart may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but may be executed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but may be executed in turn, alternately or synchronously with other steps or at least a part of the sub-steps or stages of other steps.

[0124] Based on the above method embodiment, please refer to Figure 3 Another embodiment of the present invention further provides an audio watermark embedding device, wherein the device comprises:

[0125] A decomposition module 11, used for decomposing the host audio to be embedded with the watermark into a plurality of sub-bands of different frequency bands by discrete wavelet transform;

[0126] An extraction module 12 is used to downsample each of the sub-bands layer by layer using an encoder in a U-Net network model to extract latent space features of the host audio;

[0127] The encoding module 13 is used to determine the watermark information to be embedded and encode the watermark information to obtain a watermark vector;

[0128] An embedding module 14, used for embedding the watermark vector into the latent space feature to obtain a fusion feature;

[0129] A reconstruction module 15 is used to upsample the fusion features layer by layer using the decoder in the U-Net network model to reconstruct the audio information;

[0130] The generating module 16 is used to perform an inverse discrete wavelet transform on the audio information to generate a target audio embedded with the watermark information.

[0131] Furthermore, in one embodiment, the audio watermark embedding device further comprises:

[0132] The acquisition module is used to obtain the original audio uploaded by the target user;

[0133] The preprocessing module is used to preprocess the original audio to obtain the host audio to be embedded with the watermark.

[0134] Furthermore, in one embodiment, in the audio watermark embedding device, the extraction module 12 is specifically used for:

[0135] Pre-building the U-Net network model;

[0136] Taking each of the sub-bands as input, sequentially inputting them into the encoder for layer-by-layer downsampling, and obtaining a depth feature representation corresponding to each of the sub-bands;

[0137] All the obtained deep feature representations are combined to generate latent space features of the host audio.

[0138] Furthermore, in one embodiment, in the audio watermark embedding device, the encoding module 13 is specifically used for:

[0139] Selecting watermark information to be embedded according to the watermark embedding requirement of the host audio;

[0140] Encoding the watermark information to obtain an initial vector;

[0141] The length and / or strength of the initial vector is adjusted to obtain a watermark vector.

[0142] Furthermore, in one embodiment, in the audio watermark embedding device, the embedding module 14 is specifically used for:

[0143] Determining a watermark embedding strategy according to the content of the watermark information and the characteristics of the host audio;

[0144] Embedding the watermark vector into the latent space feature according to the watermark embedding strategy to obtain a fused preliminary feature;

[0145] The preliminary features are normalized to obtain fused features.

[0146] Furthermore, in one embodiment, in the audio watermark embedding device, the reconstruction module 15 is further configured to:

[0147] Performing a simulated attack test on the reconstructed audio information to generate a test result;

[0148] Based on the test results, the watermark embedding strategy and / or the decoder are adjusted.

[0149] Furthermore, in one embodiment, in the audio watermark embedding device, the generating module 16 is specifically used for:

[0150] Performing an inverse discrete wavelet transform on the audio information to generate an initial audio embedded with the watermark information;

[0151] Performing denoising and equalization processing on the initial audio to obtain target audio, and identifying whether the watermark information is embedded in the target audio;

[0152] When watermark information is embedded in the target audio, the target audio is output.

[0153] It should be noted that in the embodiment of the device of the present invention, the information interaction, execution process and other contents between the above-mentioned modules are based on the same concept as the embodiment of the method of the present invention. Their specific functions and technical effects can be found in the aforementioned method embodiment part and will not be repeated here.

[0154] Based on the above method embodiment, another embodiment of the present invention further provides a computer device, which may be a server, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the functions or steps of the audio watermark embedding method server side in any of the above method embodiments are implemented.

[0155] Based on the above method embodiment, another embodiment of the present invention further provides a computer device, which may be a client, and its internal structure diagram may be as follows: Figure 5As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the functions or steps of the client side of the audio watermark embedding method in any of the above method embodiments are implemented.

[0156] Those skilled in the art will understand that Figure 4 and Figure 5 The structural schematic diagram shown in the figure is only a schematic diagram of a partial structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0157] The processor may be a CPU, or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, etc.

[0158] Among them, the memory includes a readable storage medium, an internal memory, etc., wherein the internal memory can be the memory of a computer device, and the internal memory provides an environment for the operation of an operating system and computer-readable instructions in the readable storage medium. The readable storage medium can be a hard disk of a computer device, and in other embodiments, it can also be an external storage device of a computer device, for example, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on a computer device. Further, the memory can also include both an internal storage unit of a computer device and an external storage device. The memory is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of a computer program, etc. The memory can also be used to temporarily store data that has been output or is to be output.

[0159] Based on the above method embodiments, another embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the audio watermark embedding method in any of the above method embodiments is implemented. The computer-readable storage medium may be non-volatile or volatile.

[0160] It should be noted that the above-mentioned functions or steps that can be implemented by the computer-readable storage medium or computer device, and the technical effects brought about by the functions / steps, can be found in the relevant descriptions in the aforementioned method embodiments. To avoid repetition, they will not be described one by one here.

[0161] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM). The disclosed memory components or memories of the operating environments described herein are intended to comprise one or more of these and / or any other suitable types of memory.

[0162] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, in the embodiment of the device of the present invention, only the division of the above-mentioned functional units and modules is used as an example. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned device can refer to the corresponding process in the above-mentioned method embodiment, which will not be repeated here. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0163] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0164] In the embodiments provided by the present invention, it should be understood that the disclosed devices / computer equipment and methods can be implemented in other ways. For example, the device / computer equipment embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0165] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0166] It should be noted that if software tools or components other than those of the Company appear in the embodiments of the present application, they are only used for illustration and do not represent actual use. The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the above embodiments, a person of ordinary skill in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents; and these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. An audio watermark embedding method, characterized in that: include: Decomposing the host audio to be embedded with watermark into multiple sub-bands of different frequency bands by discrete wavelet transform; Using an encoder in a U-Net network model to downsample each of the sub-bands layer by layer to extract latent space features of the host audio; Determining watermark information to be embedded, and encoding the watermark information to obtain a watermark vector; Embedding the watermark vector into the latent space feature to obtain a fusion feature; Using the decoder in the U-Net network model to upsample the fusion features layer by layer to reconstruct audio information; The audio information is subjected to inverse discrete wavelet transform to generate target audio embedded with the watermark information.

2. The audio watermark embedding method according to claim 1, characterized in that: Before the step of decomposing the host audio to be embedded with watermark into a plurality of sub-bands of different frequency bands by discrete wavelet transform, the method comprises: Get the original audio uploaded by the target user; The original audio is preprocessed to obtain the host audio to be embedded with a watermark.

3. The audio watermark embedding method according to claim 1, characterized in that: The method of downsampling each of the sub-bands layer by layer by using an encoder in the U-Net network model to extract latent space features of the host audio includes: Pre-building the U-Net network model; Taking each of the sub-bands as input, sequentially inputting them into the encoder for layer-by-layer downsampling, and obtaining a depth feature representation corresponding to each of the sub-bands; All the obtained deep feature representations are combined to generate latent space features of the host audio.

4. The audio watermark embedding method according to claim 1, characterized in that: The step of determining the watermark information to be embedded and encoding the watermark information to obtain a watermark vector includes: Selecting watermark information to be embedded according to the watermark embedding requirements of the host audio; Encoding the watermark information to obtain an initial vector; The length and / or strength of the initial vector is adjusted to obtain a watermark vector.

5. The audio watermark embedding method according to claim 1, characterized in that: The step of embedding the watermark vector into the latent space feature to obtain a fusion feature includes: Determining a watermark embedding strategy according to the content of the watermark information and the characteristics of the host audio; Embedding the watermark vector into the latent space feature according to the watermark embedding strategy to obtain a fused preliminary feature; The preliminary features are normalized to obtain fused features.

6. The audio watermark embedding method according to claim 5, characterized in that: The step of using the decoder in the U-Net network model to upsample the fusion features layer by layer to reconstruct the audio information also includes: Performing a simulated attack test on the reconstructed audio information to generate a test result; Based on the test results, the watermark embedding strategy and / or the decoder are adjusted.

7. The audio watermark embedding method according to any one of claims 1 to 6, characterized in that: The step of performing an inverse discrete wavelet transform on the audio information to generate a target audio embedded with the watermark information comprises: Performing an inverse discrete wavelet transform on the audio information to generate an initial audio embedded with the watermark information; Performing denoising and equalization processing on the initial audio to obtain target audio, and identifying whether the watermark information is embedded in the target audio; When the watermark information is embedded in the target audio, the target audio is output.

8. An audio watermark embedding device, characterized in that: include: A decomposition module, used for decomposing the host audio to be embedded with watermark into a plurality of sub-bands of different frequency bands by discrete wavelet transform; An extraction module, used to downsample each of the sub-bands layer by layer using an encoder in a U-Net network model to extract latent space features of the host audio; The encoding module is used to determine the watermark information to be embedded and encode the watermark information to obtain a watermark vector; An embedding module, used for embedding the watermark vector into the latent space feature to obtain a fusion feature; A reconstruction module, used to upsample the fusion features layer by layer using the decoder in the U-Net network model to reconstruct the audio information; The generating module is used to perform inverse discrete wavelet transform on the audio information to generate target audio embedded with the watermark information.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the audio watermark embedding method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the audio watermark embedding method according to any one of claims 1 to 7 is implemented.

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