A wavelet packet domain adaptive quantization digital audio watermark embedding and extracting method based on Fibonacci sequence and particle swarm optimization

Abstract

A wavelet packet domain adaptive quantization digital audio watermark embedding and extracting method based on Fibonacci sequence and particle swarm optimization, the embedding method comprising: watermark information preprocessing, selecting watermark embedding position, embedding watermark information, optimizing embedding parameters, and obtaining the final audio signal containing watermark; the extracting method comprising: audio preprocessing, selecting watermark extracting position, extracting watermark information, statistically determining the watermark information in the current frame, splicing the watermark information in sequence, decrypting and recovering, and obtaining the final decrypted and recovered watermark image; the application introduces Fibonacci sequence and particle swarm optimization algorithm, which can well guarantee the security, transparency, adaptability and universality of the digital audio watermark, the watermark can effectively resist various attack means, and can adaptively balance the contradictory relationship between robustness and transparency.

Description

Technical Field

[0001] This invention belongs to the field of digital audio watermarking technology, and particularly relates to a wavelet packet domain adaptive quantization method for embedding and extracting digital audio watermarks based on the Fibonacci sequence and particle swarm optimization. Background Technology

[0002] With the rapid development of the internet and multimedia technologies, digital multimedia information has become an important source of information for people in their daily lives. This has brought with it issues of data security and copyright of electronic publications. As an effective means of addressing these problems, digital watermarking technology has received widespread attention from researchers and has developed rapidly, already being widely applied in areas such as copyright protection and content authentication.

[0003] In the field of digital audio watermarking, most existing watermarking algorithms already possess good performance. Since the transparency and robustness of a watermark are contradictory factors, how to balance these two aspects has always been a key issue in digital audio watermarking algorithm research. Furthermore, as the audio signal changes, the parameters involved in the algorithm may need to be adjusted accordingly to adapt to different audio signals. Common watermarking algorithms involve the concept of embedding strength and have corresponding parameters to control the embedding strength to address the aforementioned two issues. For example, quantization-based digital audio watermarking algorithms adjust the embedding strength of the watermark by controlling the quantization step size, thereby balancing the transparency and robustness of the watermark and adapting to different audio signals.

[0004] Many current algorithms lack good adaptability and cannot effectively balance robustness and transparency, requiring manual adjustment of algorithm parameters. Furthermore, some existing adaptive algorithms lack general applicability, focusing on optimizing a single parameter. For example, common quantization-based adaptive algorithms primarily aim to find the optimal quantization step size, and their ability to maintain similar or identical performance across different music genres is not significant.

[0005] Patent application CN106611601A discloses an audio watermarking method based on DWT-SVD and particle swarm optimization. In the audio watermark embedding process, the original audio signal is first divided into blocks, and each block is decomposed using SVD. Then, a vector is constructed from the maximum value in the resulting matrix, and the vector is subjected to three-level wavelet decomposition. The watermark is then embedded into the selected embedding position in the approximate components after wavelet decomposition using a quantization modulation method. Subsequently, the imperceptibility and robustness of the watermarked audio signal are evaluated using peak signal-to-noise ratio and normalized correlation coefficient, respectively. A particle swarm optimization algorithm is used to find the optimal watermark embedding strength. However, because the wavelet transform resolution used in this method is not fine enough, the quantization step size in the quantization modulation method is fixed, and the particle swarm optimization algorithm only optimizes the quantization step size, it suffers from drawbacks: the degree of modification is the same for coefficients of different sizes, lacking adaptability, and the trade-off between imperceptibility and robustness is insufficient. Summary of the Invention

[0006] To address the shortcomings and problems of existing technologies, the present invention aims to provide a wavelet packet domain adaptive quantization digital audio watermark embedding and extraction method based on the Fibonacci sequence and particle swarm optimization. By introducing the Fibonacci sequence, different degrees of quantization modification can be performed on wavelet packet domain coefficients of different sizes, achieving adaptive coefficient modification and reducing the impact of watermark information embedding on the original audio signal, thereby improving the transparency of the watermark. By introducing the particle swarm optimization algorithm, multiple parameters involved in the embedding method of the present invention can be optimized based on the objective function obtained when extracting the watermark from the audio signal under attack, so that the watermark can adaptively balance the robustness and transparency of the watermark for different types of music.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0008] A wavelet packet domain adaptive quantization digital audio watermarking embedding method based on Fibonacci sequence and particle swarm optimization includes the following steps:

[0009] Step 1, Watermark Information Preprocessing: The watermark information is scrambled, encrypted, and dimensionality reduced.

[0010] Step 2, Select the watermark embedding position: Select the watermark embedding position in the low-to-mid frequency region of the audio carrier signal;

[0011] Step 3, embed watermark information: The watermark information preprocessed in Step 1 is adaptively embedded into the watermark embedding position in the low-to-mid frequency region of the audio carrier signal selected in Step 2, to obtain the watermarked audio signal.

[0012] Step 4, optimize embedding parameters: Based on the particle swarm optimization algorithm, optimize multiple embedding parameters involved in the watermarked audio signal in Step 3 to obtain the final watermarked audio signal.

[0013] In step 1, the watermark information is the original binary watermark image information, which is M0 = {m0(i, j), 0 ≤ i < X, 0 ≤ j < Y}, where X and Y are the width and height of the original binary watermark image, respectively, and m0(i, j) ∈ {0, 1} represents the grayscale value of the pixel in the i-th row and j-th column of the original binary watermark image; the scrambling encryption and dimensionality reduction processing includes the following steps:

[0014] Step 1.1: Perform Arnold scrambling on the original binary watermark image M0. Use the number of iterations and control parameters selected during Arnold scrambling as the key K1. After Arnold scrambling, the scrambled binary watermark image M1 = {m1(i,j), 0≤i<X, 0≤j<Y} is obtained.

[0015] Step 1.2: Perform a dimensionality reduction operation on the scrambled binary watermark image M1 obtained in Step 1.1 to obtain a one-dimensional binary sequence w0 = {w(i), 0 ≤ i < MN}.

[0016] Step 1.3: Generate a Logistic chaotic sequence, using the method shown in Formula 1:

[0017] x i+1 =μx i (1-x i ) (Formula 1)

[0018] Where, x i This represents the current state, where μ is a control parameter, and 0 < x. i <1, when 3.569946 < μ ≤ 4, the Logistic mapping is in a chaotic state; x0 and μ that make the Logistic mapping in a chaotic state are selected together as the key K2;

[0019] Step 1.4: Based on the Logistic chaotic sequence obtained in Step 1.3, generate a binary chaotic sequence v using Formula 2;

[0020]

[0021] Where δ is the decision threshold, 0 < δ < 1, and v is the generated binary chaotic sequence;

[0022] Step 1.5: Perform an XOR operation between the one-dimensional binary sequence w0 obtained in step 1.2 and the binary chaotic sequence v generated in step 1.4 to obtain the final watermark sequence w to be embedded.

[0023] The low-frequency region in step 2 is located in the frequency range of 500Hz to 3000Hz.

[0024] Step 3 specifically includes:

[0025] Step 3.1: Normalize and frame the original audio signal. The frame length is N, and each frame of audio signal is embedded with 1 bit of binary watermark information.

[0026] Step 3.2: Perform a 6-level Discrete Wavelet Packet Transform (DWPT) on each frame of audio signal from Step 3.1. Based on the frequency range selected in Step 2, choose a suitable sub-band to embed the watermark sequence w; let the sub-band coefficient length be L.

[0027] The relationship between L and N is shown in Formula 3;

[0028]

[0029] Step 3.3: Select n coefficients on the sub-band obtained in Step 3.2 for watermark embedding. The value of n is an odd number less than the length of the sub-band coefficients, and n is at least 1. When n>1, watermark information will be redundantly embedded, that is, the same watermark information will be embedded at the positions of the n coefficients.

[0030] Step 3.4: Construct the Fibonacci sequence FIB0 = {0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, ...}. Divide the entire Fibonacci sequence FIB0 by a constant to ensure that the positions of all wavelet packet domain coefficients are distributed as evenly as possible within the sequence, thus obtaining the Fibonacci sequence FIB used in the adaptive quantization watermarking process. Assume that there are two adjacent Fibonacci numbers F in the Fibonacci sequence FIB. k and F k+1 And satisfy the condition that the wavelet packet domain coefficients are greater than F. k And less than F k+1 Then the position of the wavelet packet domain coefficient in the Fibonacci sequence FIB is the index k;

[0031] Step 3.5, define the quantization step size S, assuming the wavelet packet domain coefficients of the current frame are d = {d0, d1, d2, ..., d...} L-1 The watermark information embedded in the current frame is w(j)∈{0,1}. Based on the embedding strategies of Formulas 4 and 5, the n coefficients described in step 3.3 of the current frame are modified to adaptively quantize the embedded watermark information, using one of the n coefficients, d... i For example, the same applies to the remaining (n-1) coefficients. The embedding strategy for formulas 4 and 5 is as follows:

[0032]

[0033] d i =t i S (Formula 5)

[0034] Among them, t i It is the coefficient d i The position of L within the quantization interval fib It is the length of the Fibonacci sequence FIB in step 3.4, fib i It is d i The position in FIB, i.e., when d i elements greater than Fib k And smaller than Fib k+1 The index k is the time factor; a is a control factor, 0≤a≤1, used to control the intensity of the phenomenon that "coefficients of different sizes change their magnitude to different degrees depending on their position in the Fib"; l and r are safety factors, 0≤l≤0.5, 0.5≤r≤1, used to ensure the modified d i Within the range [l, r] of the corresponding quantization interval;

[0035] Step 3.6: Perform Inverse Discrete Wavelet Packet Transform (IDWPT) on each audio frame after embedding the watermark information in Step 3.5 to restore the audio frame to a time-domain signal.

[0036] Step 3.7: Combine all the audio frames obtained in step 3.6 in sequence and perform inverse normalization to obtain the watermarked audio signal.

[0037] In step 4, the optimization of the embedding parameters is achieved by using the particle swarm optimization algorithm, employing the signal-to-noise ratio (SNR) as a measure of transparency and the normalized correlation coefficient (NC) as a measure of robustness, to optimize the quantization step size S, the control factor a, and the safety factors l and r from step 3.5. The particle velocity and position update formulas in the particle swarm optimization algorithm are shown below:

[0038] v i (t+1)=wv i (t)+c1r1(p best (t)-x i (t))+c2r2(g best (t)-x i (t)) (Formula 6)

[0039] x i (t+1)=xi (t)+v i (t+1) (Formula 7)

[0040] In formulas 6 and 7, v i (t) and x i (t) represents the velocity and position of each particle i at time t, respectively, p best It is the optimal position of each particle itself, that is, its optimal solution in the search process, g best It is the global optimal position of the entire particle swarm, that is, the optimal solution of the entire particle swarm. w represents the inertia weight, which is used to adjust the search capability of the solution space. c1 and c2 represent learning factors, which are called cognitive parameters and social parameters, respectively. r1 and r2 are random numbers uniformly distributed in [0, 1], which can increase the randomness of the search process. The position of each particle can represent a set of parameters in step 3.5, namely the quantization step size S, the control factor a, and the safety factor l and r.

[0041] Specifically, the following steps are included:

[0042] Step 4.1: Initialize the population and set the relevant parameters for the particle swarm optimization algorithm;

[0043] Step 4.2: Based on the population initialized in Step 4.1, perform an iterative process of particle swarm optimization algorithm to find the optimal watermark embedding parameters, namely the quantization step size S, the control factor a, and the safety factors l and r. Then, embed the watermark into the audio according to the optimal watermark embedding parameters to obtain the final watermarked audio signal.

[0044] Step 4.2, the particle swarm optimization algorithm iteration process, includes the following sub-steps:

[0045] Step 4.2.1: Update the particle's velocity and position in each iteration;

[0046] Step 4.2.2: Update the individual optimal solution and the global optimal solution based on the updated particle positions obtained in Step 4.2.1;

[0047] Step 4.2.3: Based on the current particle position, obtain the corresponding quantization step size S, control factor a, and safety factor l and r. Embed watermark information into the audio signal and calculate the SNR value of the audio after embedding watermark information under the current parameters.

[0048] Step 4.2.4: Attack the watermarked audio signal obtained in step 4.2.3 after embedding the watermark information;

[0049] Step 4.2.5: Extract the watermark from the attacked audio signal obtained in step 4.2.4, and compare it with the original embedded watermark information in step 4.2.3 to obtain the NC value between the watermark information extracted under different attacks and the original watermark information.

[0050] Step 4.2.6: Based on the SNR value obtained in Step 4.2.3 and the NC value obtained in Step 4.2.5, construct the objective function. In each iteration, use the objective function to calculate the fitness of each particle and evaluate the population. When a sufficiently good satisfactory solution is found or the maximum number of iterations is reached, the optimal solution is considered to have been found. The objective function is shown in Equation 8.

[0051]

[0052] Where λ1 and λ2 are weighting factors, representing the importance of transparency and robustness to the objective function, respectively. A larger λ1 indicates a greater emphasis on transparency, and a larger λ2 indicates a greater emphasis on robustness. R represents the number of attack types, r i This is the weighting factor for each type of attack. When the algorithm considers the same weights for different attacks, r... i Can be a constant;

[0053] Step 4.2.7: Use the optimal solution obtained in step 4.2.6 as the final selected embedding parameters, and embed the watermark information according to step 3 to obtain the final watermarked audio signal.

[0054] The extraction method based on the above-mentioned wavelet packet domain adaptive quantization digital audio watermark embedding method using Fibonacci sequence and particle swarm optimization includes the following steps:

[0055] Step 5, Audio preprocessing: Normalize and frame the audio signal from which the watermark is to be extracted. The frame length is the same as N in step 3.1 of the embedding method. Each frame of audio signal contains 1 bit of binary watermark information. Perform 6-level DWPT transformation on each frame of audio signal.

[0056] Step 6, Select watermark extraction location: Select the same sub-band as in step 3.2 of the embedding method, and select n coefficients in the sub-band that are in the same position as in step 3.3 of the embedding method;

[0057] Step 7, Extract watermark information: Perform watermark extraction operation on the n coefficients in the sub-frequency band described in Step 6, and use one of the n coefficients, d′ i For example, the remaining (n-1) coefficients are extracted in the same way, as shown in Formula 9:

[0058]

[0059] Step 8: Statistically determine the watermark information in the current frame: After extracting the watermark information in step 7, each frame of audio signal yields n binary watermark information; statistically count these n binary watermark information to obtain the number of "0"s zc and the number of "1"s oc, and use the method shown in Formula 10 to determine the watermark information contained in the current audio frame.

[0060]

[0061] Step 9, watermark information is concatenated in order: the watermark information contained in all audio frames obtained in step 8 is concatenated in order to obtain the final binary watermark sequence w′ extracted from the audio signal to be extracted;

[0062] Step 10, Decryption and Recovery: Using the key K1 obtained in the watermark information preprocessing in Step 1.1, the key K2 obtained in Step 1.3, and the binary chaotic sequence v obtained in Step 1.4, the binary watermark sequence w′ extracted in Step 9 is decrypted and recovered. During decryption, the binary watermark sequence w′ is first restored by Logistic scrambling with the key K2. The binary watermark sequence w′ and the binary chaotic sequence v are then XORed to obtain the decrypted and recovered binary sequence w″. The decrypted and recovered binary sequence w″ is then upgraded to obtain a binary image M′. Finally, the binary image M′ is restored by Arnold scrambling with the key K1 to obtain the final decrypted and recovered watermark image M″.

[0063] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0064] (1) This invention ensures the confidentiality and security of watermark information during transmission and extraction by scrambling and encrypting the watermark information.

[0065] (2) This invention utilizes the high flexibility and high resolution of wavelet packet transform, combined with the analysis of psychoacoustic models, to embed the watermark in the low-to-mid frequency region where the human ear is relatively insensitive, thus ensuring the transparency of the watermark.

[0066] (3) This invention introduces the Fibonacci sequence to guide the modification strategy of wavelet packet domain coefficients of different sizes, so that the degree of modification is adapted to the size of the coefficients, reducing the impact of watermark information embedding on the original audio signal, thereby improving the transparency of the algorithm.

[0067] (4) This invention introduces a particle swarm optimization algorithm to find the optimal watermark embedding parameters and optimizes multiple parameters involved in the embedding method. On the one hand, it can adaptively select the appropriate quantization step size for different audio signals without manual intervention. On the other hand, through the mutual coordination and optimization of multiple parameters, it effectively balances the relationship between watermark robustness and transparency.

[0068] (5) The watermark of this invention can effectively resist many attack methods, such as adding Gaussian noise, resampling, weighting, low-pass filtering, MP3 compression, etc. The watermark extracted after the attack is clear and highly identifiable. The watermark has good transparency. The audio with watermark information embedded is almost indistinguishable from the original audio before watermark information is embedded in terms of auditory perception. The signal-to-noise ratio between the two can be maintained at a high level under different music types.

[0069] (6) For different music types, the algorithm can adaptively adjust the relevant parameters to effectively balance the relationship between watermark robustness and transparency. The algorithm has good adaptability and universality.

[0070] In summary, this invention utilizes scrambling encryption technology, leverages the high flexibility and high resolution of wavelet packet transform, takes into account psychoacoustic models, and introduces the Fibonacci sequence and particle swarm optimization algorithm to effectively guarantee the security, transparency, adaptability, and universality of digital audio watermarks. The watermark can effectively resist various attack methods and adaptively balance the contradictory relationship between robustness and transparency. Attached Figure Description

[0071] Figure 1 This is a flowchart of the wavelet packet domain adaptive quantization digital audio watermarking embedding method based on Fibonacci sequence and particle swarm optimization provided in this embodiment of the invention.

[0072] Figure 2 This is a flowchart of an extraction method based on a wavelet packet domain adaptive quantization digital audio watermark embedding method based on Fibonacci sequence and particle swarm optimization, provided in an embodiment of the present invention.

[0073] Figure 3 This is the original binary watermark image provided in the embodiments of the present invention.

[0074] Figure 4 This is a scrambled binary watermark image provided in an embodiment of the present invention.

[0075] Figure 5 This is a schematic diagram of the sub-band frequency range after the 6-level wavelet packet transform of the audio signal provided in the embodiment of the present invention.

[0076] Figure 6 This is a schematic diagram of the coefficient modification strategy in the quantization interval in the embedding method provided in the embodiment of the present invention.

[0077] Figure 7 This is a flowchart of the optimization of embedding parameters based on the particle swarm optimization algorithm in the embedding method provided in the embodiments of the present invention.

[0078] Figure 8This is a schematic diagram showing how the objective function value of the global optimal solution changes during the iteration process in the particle swarm optimization algorithm provided in this embodiment of the invention.

[0079] Figure 9 This is an audio waveform diagram before and after watermark embedding provided in an embodiment of the present invention. Detailed Implementation

[0080] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0081] This invention aims to address the problems of many digital audio watermarking algorithms lacking adaptability, failing to properly balance robustness and transparency, and exhibiting poor universality when handling different music genres.

[0082] like Figure 1 As shown, a wavelet packet domain adaptive quantization digital audio watermarking embedding method based on the Fibonacci sequence and particle swarm optimization includes the following steps:

[0083] Step 1, Watermark Information Preprocessing: The watermark information is scrambled, encrypted, and dimensionality reduced.

[0084] Further, in step 1, the watermark information is the original binary watermark image information, the original binary watermark image is M0={m0(i,j),0≤i<X,0≤j<Y}, where X and Y are the width and height of the original binary watermark image, respectively, and m0(i,j)∈{0,1} represents the grayscale value of the pixel in the i-th row and j-th column of the original binary watermark image; the scrambling encryption and dimensionality reduction processing includes the following steps:

[0085] Step 1.1: Perform Arnold scrambling on the original binary watermark image M0. Use the number of iterations and control parameters selected during Arnold scrambling as the key K1. After Arnold scrambling, the scrambled binary watermark image M1 = {m i (i, j), 0≤i<X, 0≤j<Y};

[0086] Step 1.2: Perform a dimensionality reduction operation on the scrambled binary watermark image M1 obtained in Step 1.1 to obtain a one-dimensional binary sequence w0 = {w(i), 0 ≤ i < MN}.

[0087] Step 1.3: Generate a Logistic chaotic sequence, as shown in Formula 1.

[0088] x i+1 =μx i (1-x i ) (Formula 1)

[0089] Where, x i This represents the current state, where μ is a control parameter, and 0 < x. i <1, when 3.569946 < μ ≤ 4, the Logistic mapping is in a chaotic state; x0 and μ that make the Logistic mapping in a chaotic state are selected together as the key K2;

[0090] Step 1.4: Based on the Logistic chaotic sequence obtained in Step 1.3, generate a binary chaotic sequence v using Formula 2;

[0091]

[0092] Where δ is the decision threshold, 0 < δ < 1, and v is the generated binary chaotic sequence;

[0093] Step 1.5: Perform an XOR operation between the one-dimensional binary sequence w0 obtained in step 1.2 and the binary chaotic sequence v generated in step 1.4 to obtain the final watermark sequence w to be embedded.

[0094] The keys K1 and K2 together form the unique and correct decryption key for watermark extraction.

[0095] Step 2, Selecting the watermark embedding position: Based on the analysis of the psychoacoustic model, taking into account the sensitivity of the human ear to different frequencies and the impact of MP3 compression on audio and watermark, and considering that the robustness of digital audio watermarks embedded in the low-frequency region is higher than that embedded in the high-frequency region when subjected to common attacks, the selected watermark embedding position is in the mid-low frequency region of the audio carrier signal, in the frequency range of 500Hz to 3000Hz, with 1500Hz being the optimal frequency under normal circumstances.

[0096] Step 3, embed watermark information: The watermark information preprocessed in Step 1 is adaptively embedded into the watermark embedding position in the audio carrier signal selected in Step 2 to obtain the audio signal with watermark.

[0097] Step 3 specifically includes:

[0098] Step 3.1: Normalize and frame the original audio signal. The frame length is N, and each frame of audio signal is embedded with 1 bit of binary watermark information.

[0099] Step 3.2: Perform a 6-level Discrete Wavelet Packet Transform (DWPT) on each frame of audio signal from Step 3.1. Based on the frequency range selected in Step 2, select an appropriate sub-band to embed the watermark sequence w. Let the length of the sub-band coefficients be L, then the relationship between L and N is shown in Formula 3.

[0100]

[0101] Step 3.3: Select n coefficients on the sub-band obtained in Step 3.2 for watermark embedding. The value of n is an odd number less than the length of the sub-band coefficients, and n is at least 1. When n>1, watermark information will be redundantly embedded, that is, the same watermark information will be embedded at the positions of the n coefficients.

[0102] Step 3.4: Construct the Fibonacci sequence FIB0 = {0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, ...}. Divide the entire Fibonacci sequence FIB0 by a constant to ensure that the positions of all wavelet packet domain coefficients are distributed as evenly as possible within the sequence, thus obtaining the Fibonacci sequence FIB used in the adaptive quantization watermarking process. Assume that there are two adjacent Fibonacci numbers F in the Fibonacci sequence FIB. k and F k+1 And satisfy the condition that the wavelet packet domain coefficients are greater than F. k And less than F k+1 Then the position of the wavelet packet domain coefficient in the Fibonacci sequence FIB is the index k;

[0103] Step 3.5, define the quantization step size S, assuming the wavelet packet domain coefficients of the current frame are d = {d0, d1, d2, ..., d...} L-1 The watermark information embedded in the current frame is w(j)∈{0,1}. Based on the embedding strategies of Formulas 4 and 5, the n coefficients described in step 3.3 of the current frame are modified to adaptively quantize the embedded watermark information, using one of the n coefficients, d... i For example, the same applies to the remaining (n-1) coefficients. The embedding strategy for formulas 4 and 5 is as follows:

[0104]

[0105] d i =t i S (Formula 5)

[0106] Among them, t i It is the coefficient d i The position of L within the quantization interval fib It is the length of the Fibonacci sequence FIB in step 3.4, fib i It is d i The position in FIB, i.e., when d i elements greater than Fib k And smaller than Fib k+1The index k is the time; a is a control factor, 0≤a≤1, used to control that "coefficients of different sizes will change to different degrees depending on their position in the FIB".

[0107] The intensity of this phenomenon; l and r are safety factors, 0≤l≤0.5, 0.5≤r≤1, used to ensure the modified d i Within the corresponding quantization interval [l, r], the robustness of the algorithm is thus guaranteed;

[0108] Step 3.6: Perform Inverse Discrete Wavelet Packet Transform (IDWPT) on each audio frame after embedding the watermark information in Step 3.5 to restore the audio frame to a time-domain signal.

[0109] Step 3.7: Combine all the audio frames obtained in step 3.6 in sequence and perform inverse normalization to obtain the watermarked audio signal.

[0110] Step 4, optimize embedding parameters: Based on the particle swarm optimization algorithm, optimize multiple embedding parameters involved in the watermarked audio signal in Step 3 to obtain the final watermarked audio signal.

[0111] Furthermore, in step 4, the optimization of the embedding parameters is achieved by using a particle swarm optimization algorithm, employing the signal-to-noise ratio (SNR) as a measure of transparency and the normalized correlation coefficient (NC) as a measure of robustness, to optimize the quantization step size S, the control factor a, and the safety factors l and r from step 3.5. The particle velocity and position update formulas in the particle swarm optimization algorithm are shown below:

[0112] v i (t+1)=wv i (t)+c1r1(p best (t)-x i (t))+c2r2(g best (t)-x i (t)) (Formula 6)

[0113] x i (t+1)=x i (t)+v i (t+1) (Formula 7)

[0114] In formulas 6 and 7, v i (t) and x i (t) represents the velocity and position of each particle i at time t, respectively, p bestIt is the optimal position of each particle itself, that is, its optimal solution in the search process, g best It is the global optimal position of the entire particle swarm, that is, the optimal solution of the entire particle swarm. w represents the inertia weight, which is used to adjust the search capability of the solution space. c1 and c2 represent learning factors, which are called cognitive parameters and social parameters, respectively. r1 and r2 are random numbers uniformly distributed in [0, 1], which can increase the randomness of the search process. The position of each particle can represent a set of parameters in step 3.5, namely the quantization step size S, the control factor a, and the safety factor l and r.

[0115] Specifically, the following steps are included:

[0116] Step 4.1: Initialize the population and set the relevant parameters of the particle swarm optimization algorithm, including the number of particles in the population, inertia weight, cognitive parameters, social parameters, number of iterations, etc.

[0117] Step 4.2: Based on the population initialized in Step 4.1, perform an iterative process of particle swarm optimization algorithm to find the optimal watermark embedding parameters, namely the quantization step size S, the control factor a, and the safety factors l and r. Then, embed the watermark into the audio according to the optimal watermark embedding parameters to obtain the final watermarked audio signal.

[0118] Furthermore, step 4.2, the particle swarm optimization algorithm iteration process, includes the following sub-steps:

[0119] Step 4.2.1: Update the particle's velocity and position in each iteration;

[0120] Step 4.2.2: Update the individual optimal solution and the global optimal solution based on the updated particle positions obtained in Step 4.2.1;

[0121] Step 4.2.3: Based on the current particle position, obtain the corresponding quantization step size S, control factor a, and safety factor l and r. Embed watermark information into the audio signal and calculate the SNR value of the audio after embedding watermark information under the current parameters.

[0122] Step 4.2.4, attack the watermarked audio signal after embedding watermark information obtained in step 4.2.3. In this embodiment of the invention, the attack methods adopted include adding Gaussian white noise, bandpass filtering, resampling, weighting and MP3 compression.

[0123] Step 4.2.5: Extract the watermark from the attacked audio signal obtained in step 4.2.4, and compare it with the original embedded watermark information in step 4.2.3 to obtain the NC value between the watermark information extracted under different attacks and the original watermark information.

[0124] Step 4.2.6: Based on the SNR value obtained in Step 4.2.3 and the NC value obtained in Step 4.2.5, construct the objective function. In each iteration, use the objective function to calculate the fitness of each particle and evaluate the population. When a sufficiently good satisfactory solution is found or the maximum number of iterations is reached, the optimal solution is considered to have been found. The objective function is shown in Equation 8.

[0125]

[0126] Where λ1 and λ2 are weighting factors, representing the importance of transparency and robustness to the objective function, respectively. A larger λ1 indicates a greater emphasis on transparency, and a larger λ2 indicates a greater emphasis on robustness. R represents the number of attack types, r i This is the weighting factor for each type of attack. When the algorithm considers the same weights for different attacks, r... i Can be a constant;

[0127] Step 4.2.7: Use the optimal solution obtained in step 4.2.6 as the final selected embedding parameters, and embed the watermark information according to step 3 to obtain the final watermarked audio signal.

[0128] like Figure 2 As shown in the figure, the present invention provides a wavelet packet domain adaptive quantization digital audio watermark extraction method based on Fibonacci sequence and particle swarm optimization, which includes the following steps:

[0129] Step 5, Audio preprocessing: Normalize and frame the audio signal from which the watermark is to be extracted. The frame length is the same as N in step 3.1 of the embedding method. Each frame of audio signal contains 1 bit of binary watermark information. Perform 6-level DWPT transformation on each frame of audio signal.

[0130] Step 6, Select watermark extraction location: Select the same sub-band as in step 3.2 of the embedding method, and select n coefficients in the sub-band that are in the same position as in step 3.3 of the embedding method;

[0131] Step 7, Extract watermark information: Perform watermark extraction operation on the n coefficients in the sub-frequency band described in Step 6, and use one of the n coefficients, d′ i For example, the other (n-1) coefficients are extracted in the same way, as shown in Formula 9;

[0132]

[0133] Step 8, count and determine the watermark information in the current frame: After extracting the watermark information in step 7, each frame of audio signal can obtain n binary watermark information; count these n binary watermark information to obtain the number of "0"s zc and the number of "1"s oc, and use the method shown in formula 10 to determine the watermark information contained in the current audio frame.

[0134]

[0135] Step 9, watermark information is concatenated in order: the watermark information contained in all audio frames obtained in step 8 is concatenated in order to obtain the final binary watermark sequence w′ extracted from the audio signal to be extracted;

[0136] Step 10, Decryption and Recovery: Using the key K1 obtained in the watermark information preprocessing in Step 1.1, the key K2 obtained in Step 1.3, and the binary chaotic sequence v obtained in Step 1.4, the binary watermark sequence w′ extracted in Step 9 is decrypted and recovered. During decryption, the binary watermark sequence w′ is first restored by Logistic scrambling using key K2. Then, the binary watermark sequence w′ and the binary chaotic sequence v are XORed to obtain the decrypted and recovered binary sequence w″. Next, the decrypted and recovered binary sequence w″ is upgraded to obtain a binary image M′. Finally, the binary image M′ is restored by Arnold scrambling using key K1 to obtain the final decrypted and recovered watermark image M″.

[0137] This invention discloses a wavelet packet domain adaptive quantization digital audio watermark embedding and extraction method based on the Fibonacci sequence and particle swarm optimization. The method first normalizes and frames the audio carrier signal, then performs discrete wavelet packet transform to obtain several sub-bands. Next, based on a psychoacoustic model, a suitable sub-band is selected for watermark information embedding and extraction. This invention utilizes the Fibonacci sequence to modify the wavelet packet domain coefficients of different sizes to varying degrees. The core idea is to modify smaller coefficients to a smaller degree and larger coefficients to a larger degree, achieving adaptive coefficient modification and reducing the impact of watermark information embedding on the original audio signal, thereby improving the transparency of the watermark. This invention employs a particle swarm optimization algorithm. Based on the objective function obtained when extracting the watermark from the audio signal under attack, multiple parameters involved in this invention are optimized, enabling the watermark to adaptively balance robustness and transparency for different types of music. The digital audio watermark embedding and extraction method presented in this invention is a very effective method for embedding watermark information in audio, with outstanding advantages: it has good universality, can adaptively balance the contradictory relationship between the robustness and transparency of the watermark, and can resist various attack methods while having good transparency.

[0138] like Figure 3 As shown, the original binary watermark image M0 provided in this embodiment of the invention is a 32×32 binary image, and the image shown is the magnified effect.

[0139] like Figure 4As shown, the scrambled binary watermark image M1 provided in this embodiment of the invention is obtained by scrambling the image in step 1.1 as follows: Figure 3 The original binary watermark image M0 shown is scrambled using Arnold to obtain the scrambled binary watermark image M1.

[0140] In this embodiment of the invention, after step 1, the length of the final watermark sequence w to be embedded is 1024 bits.

[0141] like Figure 5 The diagram illustrates the frequency range of sub-bands after a 6-level wavelet packet transform of an audio signal, as provided in this embodiment of the invention. Taking an audio signal with a sampling rate of 44.1 kHz as an example, according to the sampling theorem, the sampling frequency must be at least twice the highest frequency of the signal; therefore, the highest frequency it can represent is 22.05 kHz. After performing a 6-level wavelet packet transform on the audio signal, a total of 64 sub-bands are obtained. The frequency range represented by each sub-band is as follows... Figure 5 As shown, sub-bands 4, 5, and 6 represent frequency ranges in the low-to-mid frequency region, relatively close to 1500Hz, and can be prioritized as candidate sub-bands. Based on the frequency domain characteristics of different types of audio signals, the selection position can be appropriately adjusted to reduce the impact of the watermark on the audio signal. Alternatively, multiple sub-bands can be selected to embed the watermark simultaneously for stronger robustness. When selecting sub-bands, a balance needs to be struck between watermark transparency and robustness. The selection should ideally be in an area relatively imperceptible to the human ear, while also fully considering the watermark's robustness under compression attacks. The selection of watermark embedding positions for audio signals at other sampling rates follows the same principle; the basic idea is to ensure that the selected range is as close to the low-to-mid frequency region as possible.

[0142] like Figure 6 As shown in the figure, the schematic diagram of the modification strategy of the coefficient in the quantization interval in the embedding method provided by the embodiment of the present invention is intended to better illustrate and explain the modification strategy in Formula 4, and to reflect the five modification rules in Formula 4 in the quantization interval.

[0143] Figure 6 The bolded section in the middle represents a complete quantization interval. Three complete intervals are shown, representing the watermark information "1", "0", and "1" respectively. Each quantization interval has a unit length of 1, and each interval represents either "0" or "1" watermark information, with the information alternating. The black dots in the diagram are the midpoints of the intervals, and the red dots are the positions of the safety factors l and r. Within the same interval, the area between l and r is within the safety interval, and the area outside l and r is outside the safety interval. Assuming the current coefficient is in a quantization interval of "0", the bolded section in the diagram represents the quantization interval of the current coefficient. Figure 6 The description and explanation of the five modification rules in Formula 4 are as follows:

[0144] Rule 1 states that the quantization interval of the current coefficient is the same as the watermark information to be embedded (the watermark information is "0"), and the current coefficient's position within the quantization interval is within the minimum acceptable range. Refer to orange lines 4 and 5 in the diagram. The modified strategy is to move the coefficient closer to the midpoint of its quantization interval based on its position in the Fib, increasing values ​​smaller than the midpoint and decreasing values ​​larger than the midpoint.

[0145] Rule 2 states that the quantization interval of the current coefficient is the same as the watermark information to be embedded (the watermark information is "0"), and the current coefficient's position within the quantization interval is to the left of the guaranteed interval. Refer to green line 3 in the diagram. The modification strategy in this case is to modify it to the guaranteed factor l.

[0146] Rule 3 states that the quantization interval of the current coefficient is the same as the watermark information to be embedded (the watermark information is "0"), and the current coefficient's position within the quantization interval is to the right of the guaranteed interval. Refer to blue line 7 in the diagram. The modification strategy in this case is to modify it to the guaranteed factor r.

[0147] Rule 4 states that the current coefficient's quantization interval is different from the watermark information to be embedded (the watermark information is "1"), and the current coefficient is located to the left of the midpoint of the quantization interval. Refer to red line 1 in the diagram. The modification strategy is to move the coefficient between the safety factor r and the midpoint of the adjacent left interval, based on its position in the FIB. The further back the coefficient is in the FIB, or the larger the control factor a, the closer the moved position will be to the midpoint of the adjacent left interval. This is reflected in the diagram where the displacement distance of red line 6 is greater than the displacement distance of red line 1.

[0148] Rule 5 states that the current coefficient's quantization interval differs from the watermark information to be embedded (the watermark information is "1"), and the current coefficient is located to the right of the midpoint of the quantization interval. Refer to black line 8 in the diagram. The modification strategy is to move the coefficient between the safety factor l and the midpoint of the adjacent interval to the right, based on its position in the FIB. The further back the coefficient is in the FIB, or the larger the control factor a, the closer the moved position will be to the midpoint of the adjacent interval to the right. This is reflected in the diagram where the displacement distance of black line 8 is greater than the displacement distance of black line 2.

[0149] In this embodiment of the invention, the original audio signal uses audio data from the GTZAN dataset, all of which are WAV format audio files with a sampling frequency of 22.05kHz and a length of 30 seconds. Compared to the common 44.1kHz audio sampling frequency, the 22.05kHz audio sampling frequency has fewer samples in the same time period, making watermark embedding more difficult. Therefore, using 22.05kHz audio as the embedding object in this embodiment of the invention better demonstrates the effectiveness and universality of the invention.

[0150] like Figure 7As shown in the figure, the flowchart of the optimization embedding parameters based on the particle swarm optimization algorithm provided in the embodiment of the present invention is shown in Table 1.

[0151] Table 1. Parameter Settings for Particle Swarm Optimization Algorithm

[0152]

[0153] like Figure 8 The diagram illustrates the change of the objective function value of the global optimal solution in the particle swarm optimization algorithm provided in this embodiment of the invention as the iteration process progresses. Since this embodiment uses the particle swarm optimization algorithm to solve for the minimum value, therefore... Figure 8 The objective function value curve shown is the result of taking the negative value of Formula 8. It can be seen that the curve basically converges to a stable value after the number of iterations reaches 50. Therefore, taking into account the time factor, in the step of optimizing the embedding parameters of the embedding method in this embodiment of the invention, the maximum number of iterations of the particle swarm optimization algorithm is 50.

[0154] like Figure 9 As shown in the figure, the audio waveforms before and after watermark embedding provided in this embodiment of the invention are as follows: The audio signal shown is the 8th song of the pop genre in the GTZAN dataset. The waveforms in the figure, from top to bottom, are the original audio signal, the watermarked audio signal, and the difference signal waveform between the two. Obviously, it is almost impossible to distinguish the difference before and after watermark embedding from the waveform diagram, indicating that the watermark has little impact on the original audio signal waveform.

[0155] To demonstrate the effectiveness and universality of this invention, the transparency and robustness of the watermark were tested and evaluated in this embodiment. The transparency of the watermark was evaluated using SNR and Mean Opinion Score (MOS). SNR provides an objective mathematical assessment of the difference between the watermarked audio and the original audio; generally, a higher SNR indicates better watermark transparency. MOS provides a better subjective evaluation of audio quality from a human perspective; a higher MOS score indicates better watermark transparency. The robustness of the watermark was evaluated using NC and Bit Error Rate (BER). A higher NC value indicates better robustness, and a lower BER value indicates better robustness. During the testing process, the embedding method of this invention was first used to embed watermarks into ten music genres. Then, the watermarked audio was processed using the following attack methods: (A) Adding 30dB Gaussian white noise; (B) Adding 20dB Gaussian white noise; (C) Resampling, with the sampling frequency first downsampled from 22.05kHz to 11.025kHz, then upsampled back to 22.05kHz; (D) Reweighting, with the sampling precision first quantized from 16-bit to 8-bit, then back to 16-bit; (E) Low-pass filtering, with a cutoff frequency of 4kHz; (F) MP3 compression, first performing MP3 compression at a bit rate of 128kbps, then decompressing to restore WAV format; (G) MP3 compression, first performing MP3 compression at a bit rate of 64kbps, then decompressing to restore WAV format. Finally, the extraction method of this invention was used to extract and evaluate the watermark. The test results for transparency and robustness are shown in Tables 2 and 3, respectively, where BER in Table 3 is the percentage result.

[0156] Based on the transparency test results in Table 2, it can be seen that the present invention has excellent transparency. The SNR values ​​of all ten song types are above 36dB, with an average of 38.10dB, far exceeding the IFPI requirement of 20dB, objectively demonstrating the excellent transparency of the present invention. The MOS scores of all ten song types are 4.7 or above, with an average of 4.83. According to the MOS evaluation criteria, the audio quality after embedding the watermark is close to excellent, subjectively indicating good transparency of the present invention, which is not easily detected by the human ear. Furthermore, the SNR values ​​and MOS scores of different music types are relatively similar, verifying the universality of the present invention in terms of transparency.

[0157] Table 2. Transparency Test Results

[0158]

[0159] Table 3. Robustness Test Results

[0160]

[0161]

[0162] According to the robustness test results in Table 3, it can be seen that for different music genres, the NC value of the watermark under various attack methods is higher than 0.96, with an average of 0.993, and the BER is less than 2.2%, with an average of 0.396%. The NC value is 1 under the two attack methods of adding 30dB Gaussian white noise and weighting, indicating that the invention is very robust to these two attack methods. The robustness to MP3 compression attacks at 64kbps is relatively weaker, with an average BER of 1.432%. The test results demonstrate that the invention can resist various attack methods and has good universality in terms of robustness.

[0163] The above results and analysis were obtained by balancing transparency and robustness, with each attack method receiving equal weight in the objective function of the particle swarm optimization algorithm. In practical applications, the parameters can be adjusted appropriately to make the algorithm prioritize transparency or robustness, achieving results that better suit the specific needs of the scenario.

[0164] The above process encompasses the entire process of the wavelet packet domain adaptive quantization digital audio watermarking embedding and extraction method based on the Fibonacci sequence and particle swarm optimization proposed in this invention. The above description is merely a specific embodiment of this invention, but the scope of protection of this invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the technical scope disclosed in this invention, and within the spirit and principles of this invention, should be included within the scope of protection of this invention.

Claims

1. A wavelet packet domain adaptive quantization digital audio watermarking embedding method based on Fibonacci sequence and particle swarm optimization, characterized in that, It includes the following steps: Step 1, preprocessing of watermark information: perform scrambling encryption and dimensionality reduction on the watermark information; Step 2, select the watermark embedding position: select the watermark embedding position in the mid-low frequency region of the audio carrier signal; Step 3, embed the watermark information: adaptively embed the preprocessed watermark information in Step 1 into the watermark embedding position in the mid-low frequency region of the audio carrier signal selected in Step 2 to obtain the watermarked audio signal; Step 4, optimize the embedding parameters: based on the particle swarm optimization algorithm, optimize multiple embedding parameters involved in the watermarked audio signal in Step 3 to obtain the final watermarked audio signal.

2. The wavelet packet domain adaptive quantization digital audio watermarking embedding method based on Fibonacci sequence and particle swarm optimization as described in claim 1, characterized in that, In Step 1, the watermark information is the original binary watermark image information. The original binary watermark image is M0 = {m0(i,j), 0 ≤ i < X, 0 ≤ j < Y}, where X and Y are the width and height of the original binary watermark image respectively, and m0(i,j) ∈ {0,1} represents the gray value of the pixel at the i-th row and j-th column of the original binary watermark image; the scrambling encryption and dimensionality reduction processing include the following steps: Step 1.1, perform Arnold scrambling on the original binary watermark image M0, and use the selected iteration number and control parameter during Arnold scrambling as the key K1 together. After Arnold scrambling is completed, the scrambled binary watermark image M1 = {m1(i,j), 0 ≤ i < X, 0 ≤ j < Y} is obtained; Step 1.2, perform a dimensionality reduction operation on the scrambled binary watermark image M1 obtained in Step 1.1 to obtain a one-dimensional binary sequence w0 = {w(i), 0 ≤ i < MN}; Step 1.3, generate a Logistic chaotic sequence, and the generation method is shown in Formula 1: x i+1 =μx i (1-x i ) (Formula 1) Where, x i This indicates the current state, where μ is a control parameter, 0 <x i <1, when 3.569946<μ≤4, the Logistic mapping is in a chaotic state; x0 and μ that make the Logistic mapping in a chaotic state are selected together as the key K2; Step 1.4, according to the Logistic chaotic sequence obtained in Step 1.3, generate a binary chaotic sequence v using Formula 2; where, δ is the decision threshold, 0 < δ < 1, and v is the generated binary chaotic sequence; Step 1.5, perform an exclusive OR operation on the one-dimensional binary sequence w0 obtained in Step 1.2 and the binary chaotic sequence v generated in Step 1.4 to obtain the final watermark sequence w to be embedded.

3. The wavelet packet domain adaptive quantization digital audio watermarking embedding method based on Fibonacci sequence and particle swarm optimization as described in claim 1, characterized in that, The frequency range of the mid-low frequency region in Step 2 is within 500Hz to 3000Hz.

4. The wavelet packet domain adaptive quantization digital audio watermarking embedding method based on Fibonacci sequence and particle swarm optimization as described in claim 1, characterized in that, Step 3 specifically includes: Step 3.1, perform normalization and framing processing on the original audio signal. The frame length is N, and 1 bit of binary watermark information is embedded in each frame of audio signal; Step 3.2, perform 6-level discrete wavelet packet transform (Discrete Wavelet Packet Transform, DWPT) on each frame of audio signal in Step 3.

1. According to the frequency range selected in Step 2, select an appropriate sub-band to embed the watermark sequence w; assume the length of the sub-band coefficient is L, and the relationship between L and N is shown in Formula 3; Step 3.3, select n coefficients on the sub-band obtained in Step 3.2 for watermark embedding operation. The value of n is an odd number less than the length of the sub-band coefficient, and the minimum value of n is 1; when n > 1, redundant watermark information will be embedded at this time, that is, the same watermark information is embedded at n coefficient positions. Step 3.4: Construct the Fibonacci sequence FIB0 = {0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, ...}. Divide the entire Fibonacci sequence FIB0 by a constant to ensure that the positions of all wavelet packet domain coefficients are distributed as evenly as possible within the sequence, thus obtaining the Fibonacci sequence FIB used in the adaptive quantization watermarking process. Assume that there are two adjacent Fibonacci numbers F in the Fibonacci sequence FIB. k and F k+1 And satisfy the condition that the wavelet packet domain coefficients are greater than F. k And less than F k+1 Then the position of the wavelet packet domain coefficient in the Fibonacci sequence FIB is the index k; Step 3.5, define the quantization step size S, assuming the wavelet packet domain coefficients of the current frame are d = {d0, d1, d2, ..., d...} L-1 The watermark information embedded in the current frame is w(j)∈{0,1}. Based on the embedding strategies of Formulas 4 and 5, the n coefficients described in step 3.3 of the current frame are modified to adaptively quantize the embedded watermark information, using one of the n coefficients, d... i For example, the same applies to the remaining (n-1) coefficients. The embedding strategy for formulas 4 and 5 is as follows: d i =t i S (Formula 5) Among them, t i It is the coefficient d i The position of Lf within the quantization interval ib It is the length of the Fibonacci sequence FIB in step 3.4, fib i It is d i The position in FIB, i.e., when d i elements greater than Fib k And smaller than Fib k+1 The index k is the time factor; a is a control factor, 0≤a≤1, used to control the intensity of the phenomenon that "coefficients of different sizes change their magnitude to different degrees depending on their position in the Fib"; l and r are safety factors, 0≤l≤0.5, 0.5≤r≤1, used to ensure the modified d i Within the range [l, r] of the corresponding quantization interval; Step 3.6: Perform Inverse Discrete Wavelet Packet Transform (IDWPT) on each audio frame after embedding the watermark information in Step 3.5 to restore the audio frame to a time-domain signal. Step 3.7: Combine all the audio frames obtained in step 3.6 in sequence and perform inverse normalization to obtain the watermarked audio signal.

5. The wavelet packet domain adaptive quantization digital audio watermarking embedding method based on Fibonacci sequence and particle swarm optimization as described in claim 1, characterized in that, In step 4, the optimization of the embedding parameters is achieved by using the particle swarm optimization algorithm, employing the signal-to-noise ratio (SNR) as a measure of transparency and the normalized correlation coefficient (NC) as a measure of robustness, to optimize the quantization step size S, the control factor a, and the safety factors l and r from step 3.

5. The particle velocity and position update formulas in the particle swarm optimization algorithm are shown below: v i (t + 1) = wv i (t) + c1r1(p best (t) - x i (t)) + c2r2(g best (t) - x i (t)) (Equation 6) x i (t+1)=x i (t)+v i (t+1) (Formula 7) In formulas 6 and 7, v i (t) and x i (t) represents the velocity and position of each particle i at time t, respectively, p best It is the optimal position of each particle itself, that is, its optimal solution in the search process, g best It is the global optimal position of the entire particle swarm, that is, the optimal solution of the entire particle swarm. w represents the inertia weight, which is used to adjust the search capability of the solution space. c1 and c2 represent learning factors, which are called cognitive parameters and social parameters, respectively. r1 and r2 are random numbers uniformly distributed in [0,1], which can increase the randomness of the search process. The position of each particle can represent a set of parameters in step 3.5, namely the quantization step size S, the control factor a, and the safety factor l and r. Specifically, the following steps are included: Step 4.1: Initialize the population and set the relevant parameters for the particle swarm optimization algorithm; Step 4.2: Based on the population initialized in Step 4.1, perform an iterative process of particle swarm optimization algorithm to find the optimal watermark embedding parameters, namely the quantization step size S, the control factor a, and the safety factors l and r. Then, embed the watermark into the audio according to the optimal watermark embedding parameters to obtain the final watermarked audio signal.

6. The wavelet packet domain adaptive quantization digital audio watermarking embedding method based on Fibonacci sequence and particle swarm optimization as described in claim 5, characterized in that, Step 4.2, the particle swarm optimization algorithm iteration process, includes the following sub-steps: Step 4.2.1: Update the particle's velocity and position in each iteration; Step 4.2.2: Update the individual optimal solution and the global optimal solution based on the updated particle positions obtained in Step 4.2.1; Step 4.2.3: Based on the current particle position, obtain the corresponding quantization step size S, control factor a, and safety factor l and r. Embed watermark information into the audio signal and calculate the SNR value of the audio after embedding watermark information under the current parameters. Step 4.2.4: Attack the watermarked audio signal obtained in step 4.2.3 after embedding the watermark information; Step 4.2.5: Extract the watermark from the attacked audio signal obtained in step 4.2.4, and compare it with the original embedded watermark information in step 4.2.3 to obtain the NC value between the watermark information extracted under different attacks and the original watermark information. Step 4.2.6: Based on the SNR value obtained in Step 4.2.3 and the NC value obtained in Step 4.2.5, construct the objective function. In each iteration, use the objective function to calculate the fitness of each particle and evaluate the population. When a sufficiently good satisfactory solution is found or the maximum number of iterations is reached, the optimal solution is considered to have been found. The objective function is shown in Equation 8. Where λ1 and λ2 are weighting factors, representing the importance of transparency and robustness to the objective function, respectively. A larger λ1 indicates a greater emphasis on transparency, and a larger λ2 indicates a greater emphasis on robustness. R represents the number of attack types, r i This is the weighting factor for each type of attack. When the algorithm considers the same weights for different attacks, r... i Can be a constant; Step 4.2.7: Use the optimal solution obtained in step 4.2.6 as the final selected embedding parameters, and embed the watermark information according to step 3 to obtain the final watermarked audio signal.

7. An extraction method based on the wavelet packet domain adaptive quantization digital audio watermark embedding method according to any one of claims 1 to 6, characterized in that, Includes the following steps: Step 5, Audio preprocessing: Normalize and frame the audio signal from which the watermark is to be extracted. The frame length is the same as N in step 3.1 of the embedding method. Each frame of audio signal contains 1 bit of binary watermark information. Perform 6-level DWPT transformation on each frame of audio signal. Step 6, Select watermark extraction location: Select the same sub-band as in step 3.2 of the embedding method, and select n coefficients in the sub-band that are in the same position as in step 3.3 of the embedding method; Step 7, Extract watermark information: Perform watermark extraction operation on the n coefficients in the sub-frequency band described in Step 6, and use one of the n coefficients, d' i For example, the remaining (n-1) coefficients are extracted in the same way, as shown in Formula 9: Step 8: Statistically determine the watermark information in the current frame: After extracting the watermark information in step 7, each frame of audio signal yields n binary watermark information. Count the number of "0"s (zc) and the number of "1"s (oc) among these n binary watermark information, and use the method shown in Formula 10 to determine the watermark information contained in the current audio frame; Step 9, watermark information is concatenated in order: the watermark information contained in all audio frames obtained in step 8 is concatenated in order to obtain the final binary watermark sequence w' extracted from the audio signal to be extracted; Step 10, Decryption and Recovery: Using the key K1 obtained in the watermark information preprocessing in Step 1.1, the key K2 obtained in Step 1.3, and the binary chaotic sequence v obtained in Step 1.4, the binary watermark sequence w' extracted in Step 9 is decrypted and recovered. During decryption, the binary watermark sequence w' is first restored by Logistic scrambling with the key K2. The binary watermark sequence w' and the binary chaotic sequence v are then XORed to obtain the decrypted and recovered binary sequence w”. The decrypted and recovered binary sequence w” is then upgraded to obtain a binary image M'. Finally, the binary image M' is restored by Arnold scrambling with the key K1 to obtain the final decrypted and recovered watermark image M.

Images