CN117877497A - Time-domain audio steganography method based on generalized audio intrinsic energy and micro-amplitude suppression modification - Google Patents

Abstract

The invention discloses a time domain audio steganography method based on generalized audio internal energy and micro amplitude suppression modification, which comprises the following steps: acquiring carrier audio, carrying out segmentation pretreatment on the carrier audio, and cutting the carrier audio into audio fragments with set lengths; dividing and slicing secret information bits to be embedded into equal sections according to the number of the audio fragments to obtain secret information bit fragments; based on a complexity priority criterion, firstly constructing a steganography distortion cost function based on generalized audio internal energy, and then providing a distortion cost design criterion of micro amplitude suppression modification, wherein the steganography distortion cost function and the distortion cost design criterion are combined to obtain a final steganography distortion cost function; calculating the steganography distortion cost of each audio segment, and embedding the secret information bit segment into each audio segment based on a steganography coding method to obtain a plurality of carrier audio segments; and splicing all the encrypted audio fragments to obtain the final encrypted audio. The invention has the advantages of higher steganography safety and better carrier audio quality.

Description

Time domain audio steganography based on generalized audio intrinsic energy and micro-amplitude suppression modification Method

Technical Field

The present invention relates to the technical field of time domain audio steganography, and in particular to a time domain audio steganography method based on generalized audio intrinsic energy and micro-amplitude suppression modification.

Background technique

With the rapid development of computer and electronic technology in recent years, high-quality lossless audio (time domain audio) in WAV format has been widely disseminated and shared on the Internet. The audio steganography in temporal domain (ASTD) technology based on this medium is to embed secret information into the time domain audio in a certain way without arousing suspicion from the monitoring party. At present, the mainstream and practical time domain audio steganography schemes are all based on the design of the minimum distortion embedding framework, which mainly includes two parts: stego coding and distortion cost function design. With the introduction of efficient and practical stego coding such as STC and SPC, the research focus under this framework is currently on the design of the stego distortion cost function.

Under this framework, some scholars proposed a time-domain audio steganography method DFR based on derivative filter residual distortion cost. According to public reports, DFR is currently the best time-domain audio steganography algorithm with the best security performance. Specifically, under the guidance of the existing complexity priority criterion, DFR introduces derivative filter residual to characterize the complexity of the carrier and constructs the corresponding stego distortion cost function. However, the stego security performance of this method usually depends to a large extent on the selection of the filter used to obtain the residual. The final stego security performance obtained by different filters may be quite different, so the performance of this method is not stable; secondly, although the large amplitude priority (LAF) criterion in the DFR method can prevent the modification of small amplitude sampling points, it will conflict with the complexity priority criterion of content adaptive steganography to a certain extent, making the high-complexity low-amplitude (non-small amplitude) sampling points originally suitable for embedding less modified, while the low-complexity high-amplitude sampling points originally not suitable for embedding are more modified, which may cause a sharp decline in stego security performance.

Summary of the invention

In order to overcome the defects and shortcomings of the existing time-domain audio steganography algorithm DFR in the design of its steganalysis distortion cost function, the present invention provides a time-domain audio steganography method based on generalized audio intrinsic energy and micro-amplitude suppression modification, which uses the intrinsic energy of the carrier to characterize the complexity of the carrier, applies the micro-amplitude suppression criterion to the complexity priority criterion, and then sets the small amplitude sampling points as "wet points", which is committed to accurately preventing the small amplitude sampling points from being modified, while not affecting the embedded modification of other amplitude sampling points, which makes the secret audio obtained by the present invention more difficult to be detected by the steganalyzer, thereby improving the steganographic security of the present invention.

The second object of the present invention is to provide a time domain audio steganography system based on generalized audio intrinsic energy and micro-amplitude suppression modification;

A third object of the present invention is to provide a computer readable storage medium;

A fourth object of the present invention is to provide a computer device.

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

The present invention provides a time domain audio steganography method based on generalized audio intrinsic energy and micro-amplitude suppression modification, comprising the following steps:

Acquire carrier audio, perform segmentation preprocessing on the carrier audio, and cut the carrier audio into audio segments of a set length;

The secret information bits to be embedded are equally sliced according to the number of audio segments to obtain secret information bit segments;

Based on the complexity priority criterion, a steganographic distortion cost function based on generalized audio intrinsic energy is constructed. The steganographic distortion cost function based on generalized audio intrinsic energy is combined with the distortion cost design criterion of micro-amplitude suppression modification to obtain the final steganographic distortion cost function.

The steganalysis distortion cost of each audio segment is calculated, and the STC steganalysis coding method based on the minimum distortion steganography framework is used to embed the secret information bit segment into each audio segment to obtain multiple secret audio segments.

All the encrypted audio clips are spliced together to get the final encrypted audio.

As a preferred technical solution, a steganalysis distortion cost function based on generalized audio intrinsic energy is constructed, which specifically includes:

Acquire a local carrier audio signal, and obtain different carrier audio frequency components based on spectrum transformation;

Different weights are set for different carrier audio frequency components, and the weights are used to set exponential adjustment coefficients to calculate the intrinsic energy of the local carrier audio signal for steganography;

The intrinsic energy of the local carrier audio signal used for steganography is used as the complexity index of the center point of the local carrier audio signal. The steganalysis distortion cost of the carrier audio is inversely proportional to the complexity. A steganalysis distortion cost function based on the generalized audio intrinsic energy is constructed.

As a preferred technical solution, the local carrier audio signal is obtained by setting a sliding window to intercept or using mirror filling to obtain the local carrier audio signal.

As a preferred technical solution, the spectrum transformation adopts any one of discrete cosine transform, discrete Fourier transform or discrete wavelet transform.

As a preferred technical solution, a steganalysis distortion cost function based on generalized audio intrinsic energy is constructed, which is specifically expressed as:

Wherein, ρ _c represents the distortion cost of the center point of the local carrier audio signal, λ represents the equilibrium constant used to prevent ρ _c from being too small, ε represents the stability constant, n represents the number of different carrier audio frequency components, _fi represents the amplitude of the i-th carrier audio frequency component, _wi represents the weight corresponding to the i-th carrier audio frequency component, and p represents the exponential adjustment coefficient. represents the intrinsic energy of the local carrier audio signal x _l used for steganography; when calculating the intrinsic steganographic energy of the audio, the influence of the DC frequency component is excluded, that is, f ₁ is discarded.

As a preferred technical solution, the index adjustment coefficient is expressed as:

Among them, psehf represents the energy percentage of high-frequency components in each carrier, k represents the selection threshold of high-frequency components, k≥2, is the average amplitude of all carrier audio frequency components in each carrier.

As a preferred technical solution, the steganographic distortion cost function based on generalized audio intrinsic energy is combined with the distortion cost design criterion of micro-amplitude suppression modification to obtain the final steganographic distortion cost function, which is specifically expressed as:

Among them, | _xc | is the amplitude of the center point _xc of the local carrier audio signal, T is the amplitude threshold of the suppressed and modified sampling point, ψ is the distortion cost of the wet point under the micro-amplitude suppression criterion, _wi represents the weight corresponding to the i-th carrier audio frequency component, p represents the exponential adjustment coefficient, n represents the number of different carrier audio frequency components, ε represents the stability constant, and _ρc represents the distortion cost of the center point of the local carrier audio signal.

In order to achieve the above second purpose, the present invention adopts the following technical solutions:

The present invention provides a time domain audio steganography system based on generalized audio intrinsic energy and micro-amplitude suppression modification, comprising: a carrier audio acquisition module, a carrier audio preprocessing module, a secret information bit preprocessing module, a steganalysis distortion cost function construction module, a steganalysis distortion cost calculation module, a steganalysis encoding module, and an audio splicing module;

The carrier audio acquisition module is used to acquire carrier audio;

The carrier audio preprocessing module is used to perform segmentation preprocessing on the carrier audio, cutting the carrier audio into audio segments of a set length;

The secret information bit preprocessing module is used to equally slice the secret information bits to be embedded according to the number of audio segments to obtain secret information bit segments;

The steganalysis distortion cost function construction module is used to construct a steganalysis distortion cost function based on generalized audio intrinsic energy based on the complexity priority criterion, and combine the steganalysis distortion cost function based on generalized audio intrinsic energy with the distortion cost design criterion of micro-amplitude suppression modification to obtain the final steganalysis distortion cost function;

The steganalysis distortion cost calculation module is used to calculate the steganalysis distortion cost of each audio clip;

The stego-coding module is used to embed the secret information bit fragment into each audio fragment based on the STC stego-coding method of the minimum distortion steganographic framework to obtain multiple secret audio fragments;

The audio splicing module is used to splice all the encrypted audio clips to obtain the final encrypted audio.

In order to achieve the third objective, the present invention adopts the following technical solutions:

A computer-readable storage medium stores a program, which, when executed by a processor, implements the time-domain audio steganography method based on generalized audio intrinsic energy and micro-amplitude suppression modification as described above.

In order to achieve the fourth objective, the present invention adopts the following technical solutions:

A computer device comprises a processor and a memory for storing a program executable by the processor. When the processor executes the program stored in the memory, the time-domain audio steganography method based on generalized audio intrinsic energy and micro-amplitude suppression modification as described above is implemented.

Compared with the prior art, the present invention has the following advantages and beneficial effects:

(1) The existing DFR method uses the derivative filter residual to characterize the complexity of the carrier and constructs its stegographic distortion cost function. The stegographic security performance under this method usually depends to a large extent on the selection of efficient filters. The present invention abandons the idea of using the carrier signal residual to characterize the carrier complexity, and uses the intrinsic energy of the carrier to characterize the carrier complexity. Based on this, a new stegographic distortion cost function is designed, which saves the trouble of carefully selecting efficient filters when applying the algorithm, thereby bringing better and more stable stegographic security to the present invention.

(2) The large amplitude priority criterion proposed in the existing DFR method can prevent the modification of small amplitude sampling points to a certain extent, but it will conflict with the complexity priority criterion of content adaptive steganography to a certain extent. The present invention proposes a small amplitude suppression criterion, which is applied to the complexity priority criterion, and then the small amplitude sampling points are set as "wet points", which is committed to accurately preventing the small amplitude sampling points from being modified, while not affecting the embedded modification of other amplitude sampling points. This makes the encrypted audio obtained by the present invention more difficult to be detected by the steganalyzer, thereby improving the steganographic security of the present invention, and has the advantages of higher steganographic security and better quality of encrypted audio.

(3) The present invention surpasses the most advanced similar algorithms in terms of security and audio quality of encrypted audio, achieves better performance, and has better steganographic imperceptibility.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a flow chart of a time domain audio steganography method based on generalized audio intrinsic energy and micro-amplitude suppression modification according to the present invention;

FIG. 2 is a schematic diagram of the framework of the implementation process of constructing a steganographic distortion cost function according to the present invention.

Detailed ways

In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

Example 1

As shown in FIG1 , this embodiment provides a time domain audio steganography method based on generalized audio intrinsic energy and micro-amplitude suppression modification, comprising the following steps:

S1: Acquire carrier audio, perform segmentation preprocessing on the carrier audio, and cut the carrier audio into audio segments of a set length. In this embodiment, the cutting length of each audio segment is preferably 1 second;

S2: the secret information bits to be embedded are also equally sliced according to the number of audio segments to obtain secret information bit segments;

S3: For each audio clip, a stego-distortion cost function is first constructed to calculate its stego-distortion cost, and then the secret information bit fragment is embedded into it based on the STC stego-coding method of the minimum distortion steganography framework to obtain multiple secret audio clips;

S4: All the encrypted audio clips are spliced together to obtain the final encrypted audio.

In this embodiment, the steganalysis distortion cost corresponding to the carrier is calculated based on the construction of the steganalysis distortion cost function. The construction process of the steganalysis distortion cost function is as follows: first, based on the complexity priority criterion, a steganalysis distortion cost function based on the generalized audio intrinsic energy is proposed. On this basis, a distortion cost design criterion of micro-amplitude suppression modification is proposed to supplement it.

In this embodiment, the design of the steganalysis distortion cost function based on the generalized audio intrinsic energy specifically includes:

The energy of a digital signal can be defined as the sum of the squares of the amplitudes of all its frequency components. For a one-dimensional audio signal, its frequency components can be easily obtained using a one-dimensional frequency transform function, such as DCT (discrete cosine transform), DFT (discrete Fourier transform), DWT (discrete wavelet transform), etc.

As shown in FIG2 , this embodiment first uses a sliding window SW _n of size n (n>1) to intercept a section of the local carrier audio signal, and represents it as x _l =[x ₁ ,x ₂ ...x _n ], where n is usually an odd number. For the extraction of the carrier audio frequency component, this embodiment preferably uses DCT transformation to achieve it. Then, by performing DCT transformation on the local carrier audio signal x _l , n different frequency components, namely F ₁ ,F ₂ ...F _n , can be obtained. Assuming that the intensity (amplitude) of the i-th frequency component F _i is _fi , then referring to the definition of digital signal energy, the intrinsic energy of the local carrier audio signal x _l is It can be expressed as:

Embedding modification in the high frequency region (high complexity region) of the carrier is generally safer than embedding modification in the low frequency region (low complexity region). This means that when the signal energy is equal in the above energy formula, the high frequency region is theoretically more suitable for embedding. Therefore, when calculating the intrinsic energy of the audio to design the steganographic distortion cost, different weights w should be assigned to different frequency components in formula (1). The principle is that the higher the frequency, the greater the weight. In addition, the steganographic distortion cost is generally related to the content complexity of the carrier, but not to the content of the carrier (i.e., the amplitude of the carrier signal). The results in the DFR_based method also confirm that the amplitude of the audio signal is not very suitable for the design of its steganographic distortion cost. Therefore, it is necessary to remove the direct current frequency component _f1 in the formula to eliminate the influence of the audio amplitude on its intrinsic energy calculation. On the other hand, in order to facilitate the adjustment of the weight w, an exponential adjustment coefficient p is introduced for the weight w; so far, in the scheme proposed in this embodiment, the local carrier audio signal _xl is used for the intrinsic energy of steganography. The construction can be further expressed in the following generalized form:

Without loss of generality, in order to meet the needs of audio steganography distortion cost function design, in the solution proposed in this embodiment, The complexity index of the center point _xc of _xl is considered, and the steganalysis distortion cost of the carrier audio is designed to be inversely proportional to its complexity. According to this paradigm, the distortion cost function of the proposed time-domain audio steganography based on generalized audio intrinsic energy is defined as:

Among them, ρ _c is the distortion cost of x _c , λ is the equilibrium constant to prevent ρ _c from being too small, and ε is the stability constant. Slide the SW _n window for each sampling point in turn, and then use the above formula to get the distortion cost of all sampling points in each audio carrier sample. When calculating the intrinsic steganographic energy of the audio, exclude the influence of the DC frequency component, that is, discard f ₁ ;

It is worth noting that, if necessary, mirror padding will be used to obtain x _l . Since the audio steganography distortion cost function proposed in the present invention is designed based on its generalized audio intrinsic energy, it is named GAIE (Generalized Audio Intrinsic Energy) method for ease of description.

In the above formula, the stability constant ε is taken as ^10-2 , and the weight w _i corresponding to the i-th frequency component is set to w _i =i (2≤i≤n). Taking into account the influence of the exponential weight w _i ^p , the equilibrium constant λ in the formula is set to n ^p *10 ^6. As for the parameter selection of the sliding window size n, experiments have found that as n increases, the steganographic security performance of the present invention will also increase accordingly. When n reaches 169, the performance tends to be stable, so the value of n is preferably fixed to 169. In addition, experiments show that the optimal value of the parameter p is closely related to the data set. After analysis, the parameter p mainly acts on the weight of the frequency component. Therefore, an attempt is made to determine the parameter p by the percentage of signal energy occupied by the high-frequency components in each carrier (percentage of signal energy occupied by the high-frequency components: pseh f). The specific definition of psehf is as follows:

Among them, k (k≥2) is the selection threshold of high-frequency components. Experiments show that the best value of k is 6. is the average intensity of the frequency F _i of all local signal fragments in each carrier, and the DC frequency also needs to be excluded. The larger the psehf of the sample, the greater the proportion of its high-frequency component. Experiments have shown that when the psehf is larger, a relatively large p can achieve better steganographic security. Therefore, the specific correspondence between psehf and parameter p is constructed as follows:

In this embodiment, the design of the steganalysis distortion cost function based on generalized audio intrinsic energy and micro-amplitude suppression modification specifically includes:

In view of the defect that the LAF criterion in the DFR algorithm is not universal, the present invention proposes a micro-amplitude suppression criterion (Micro-Amplitude-Suppression, MAS), and combines it with the complexity priority criterion, that is, after applying the complexity priority criterion, the micro-amplitude sampling points are set as "wet points" (representing unusable points), which is committed to accurately preventing the micro-amplitude sampling points from being modified, while not affecting the embedded modification of other amplitude sampling points. By combining this MAS criterion with the above GAIE, the key technology of the present invention can be obtained - the steganographic distortion cost function based on generalized audio intrinsic energy and micro-amplitude suppression modification (GAIE_MAS), which is in the following form:

Wherein, |x _c | is the amplitude of the sampling point x _c , and T is the amplitude threshold of the sampling point that is suppressed from modification. ψ is the distortion cost of the wet point under the MAS criterion. In this embodiment, the distortion cost of the wet point under the MAS criterion is preferably set to 10 ²⁰ . That is, when the amplitude of the sampling point is less than the threshold, its distortion cost will be very large to prevent the point from being modified.

The existing DFR method uses the derivative filter residual to characterize the complexity of the carrier and construct its steganalysis distortion cost function. The steganalysis security performance under this method usually depends to a large extent on the selection of efficient filters. To this end, the present invention abandons the idea of using the carrier signal residual to characterize the complexity of the carrier, uses the intrinsic energy of the carrier to characterize the complexity of the carrier, and designs a new steganalysis distortion cost function based on this, which saves the trouble of carefully selecting efficient filters when applying the algorithm, thereby bringing better and more stable steganalysis security to the present invention.

In addition, the large amplitude priority criterion proposed in the existing DFR method can prevent the modification of small amplitude sampling points to a certain extent, but it will conflict with the complexity priority criterion of content adaptive steganography to a certain extent. For this reason, the present invention proposes a micro-amplitude suppression criterion (Micro-Amplitude-Suppression, MAS), which is applied to the complexity priority criterion, and then the small amplitude sampling points are set as "wet points", which is committed to accurately preventing the small amplitude sampling points from being modified, while not affecting the embedded modification of other amplitude sampling points. This makes the secret audio obtained by the present invention more difficult to be detected by the steganalyzer, thereby improving the steganalysis security of the present invention. The present invention has the advantages of higher steganalysis security and better quality of secret audio.

In order to verify that the steganographic method of the present invention has better steganographic security, tests are conducted on four audio datasets: CMU_ARCTIC (CMU), LJSpeech (LJ), TED-LIUM corpus (TED), and Arabic Speech (ARABIC);

For each of the above four datasets, some audio clips will be randomly selected from them, and then they will be cropped into a series of shorter clips of 1 second in length and stored in WAV format. 10,000 1-second audio clips are selected from each dataset for experiments, thus forming four audio datasets;

During testing, all steganographic schemes are simulated under their corresponding load-distortion bounds, with relative loads α∈{0.1, 0.2, 0.3, 0.4, 0.5} bps. For the design of the steganalyzer, the currently optimal steganalysis feature TMF is used to evaluate the empirical security performance of the steganographic scheme, and FLD is used to train the ensemble classifier. Half of the original carrier/secret carrier audio pairs in the dataset will be used to train the classifier, and the other half will be used as a test set. The security performance is defined as the average classification error probability under the same prior conditions obtained by performing 20 random tests on the test set, denoted as Average classification error probability/> The closer it is to 50%, the better the security of the steganography scheme.

In order to reflect the advantages of the proposed GAIE_MAS scheme, the security performance is compared with the existing most advanced steganography schemes AAC and DFR, as shown in Tables 1 and 2 below, which show the performance comparison of the tested steganography schemes on four data sets. It can be found from the table that the performance of the AAC and DFR methods on different data sets is not stable and fluctuates greatly. The comprehensive scheme GAIE_MAS proposed in this invention has achieved the best or almost the same performance as the best scheme on almost all data sets.

Table 1 Security performance of steganographic schemes against TMF feature detection on CMU and LJ datasets Compared

Table 2 Security performance of steganographic schemes against TMF feature detection on TED and ARABIC datasets Compared

In addition to steganographic security, imperceptibility is also an important indicator for evaluating steganographic quality. In order to compare the imperceptibility of different time-domain audio steganographic schemes, the perceptual evaluation of audio quality (PEAQ) index is introduced, which is an objective evaluation standard for audio perception recommended by ITU-RBS.1387. It first uses the subjective perception characteristics of the human ear to calculate the masking value and distortion value of the audio signal, and then adopts an artificial neural network to fuse the evaluation parameter ODG (object difference level). Among them, the higher the ODG score, the better the quality of the audio. This embodiment selects 2000 audio clips from CMU and LJ respectively, and then compares the audio quality of the steganographic schemes tested under different loads. As shown in Table 3 below, it can be seen from the ODG score comparison results that the GAIE_MAS scheme proposed in the present invention has the highest ODG score, that is, it has the best steganographic imperceptibility.

Table 3 Comparison of ODG scores of steganographic schemes tested on CMU and LJ

Example 2

This embodiment provides a time-domain audio steganography system based on generalized audio intrinsic energy and micro-amplitude suppression modification, which is used to implement the time-domain audio steganography method based on generalized audio intrinsic energy and micro-amplitude suppression modification of the above-mentioned embodiment 1. The system includes: a carrier audio acquisition module, a carrier audio preprocessing module, a secret information bit preprocessing module, a stego distortion cost function construction module, a stego distortion cost calculation module, a stego encoding module, and an audio splicing module;

In this embodiment, the carrier audio acquisition module is used to acquire the carrier audio;

In this embodiment, the carrier audio preprocessing module is used to perform segmentation preprocessing on the carrier audio, cutting the carrier audio into audio segments of a set length;

In this embodiment, the secret information bit preprocessing module is used to equally slice the secret information bits to be embedded according to the number of audio segments to obtain secret information bit segments;

In this embodiment, the steganalysis distortion cost function construction module is used to construct a steganalysis distortion cost function based on generalized audio intrinsic energy based on the complexity priority criterion, and combine the steganalysis distortion cost function based on generalized audio intrinsic energy with the distortion cost design criterion of micro-amplitude suppression modification to obtain the final steganalysis distortion cost function;

In this embodiment, the steganalysis distortion cost calculation module is used to calculate the steganalysis distortion cost of each audio clip;

In this embodiment, the stego-coding module is used to embed the secret information bit fragment into each audio fragment based on the STC stego-coding method of the minimum distortion steganographic framework, so as to obtain a plurality of secret audio fragments;

In this embodiment, the audio splicing module is used to splice all the encrypted audio clips to obtain the final encrypted audio.

Example 3

This embodiment provides a storage medium, which may be a storage medium such as ROM, RAM, disk, or CD. The storage medium stores one or more programs. When the program is executed by a processor, the time-domain audio steganography method based on generalized audio intrinsic energy and micro-amplitude suppression modification of Embodiment 1 is implemented.

Example 4

This embodiment provides a computing device, which can be a desktop computer, a laptop computer, a smart phone, a PDA handheld terminal, a tablet computer or other terminal device with a display function. The computing device includes a processor and a memory, and the memory stores one or more programs. When the processor executes the program stored in the memory, the time-domain audio steganography method based on generalized audio intrinsic energy and micro-amplitude suppression modification of Embodiment 1 is implemented.

The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.

Claims (10)

1. A time domain audio steganography method based on generalized audio intrinsic energy and micro-amplitude suppression modification, characterized in that it includes the following steps: Acquire carrier audio, perform segmentation preprocessing on the carrier audio, and cut the carrier audio into audio segments of a set length; The secret information bits to be embedded are equally sliced according to the number of audio segments to obtain secret information bit segments; Based on the complexity priority criterion, a steganographic distortion cost function based on generalized audio intrinsic energy is constructed. The steganographic distortion cost function based on generalized audio intrinsic energy is combined with the distortion cost design criterion of micro-amplitude suppression modification to obtain the final steganographic distortion cost function. The steganalysis distortion cost of each audio segment is calculated, and the STC steganalysis coding method based on the minimum distortion steganography framework is used to embed the secret information bit segment into each audio segment to obtain multiple secret audio segments. All the encrypted audio clips are spliced together to get the final encrypted audio. 2. According to claim 1, the time domain audio steganography method based on generalized audio intrinsic energy and micro-amplitude suppression modification is characterized in that a steganographic distortion cost function based on generalized audio intrinsic energy is constructed, specifically comprising: Acquire a local carrier audio signal, and obtain different carrier audio frequency components based on spectrum transformation; Different weights are set for different carrier audio frequency components, and the weights are used to set exponential adjustment coefficients to calculate the intrinsic energy of the local carrier audio signal for steganography; The intrinsic energy of the local carrier audio signal used for steganography is used as the complexity index of the center point of the local carrier audio signal. The steganalysis distortion cost of the carrier audio is inversely proportional to the complexity. A steganalysis distortion cost function based on the generalized audio intrinsic energy is constructed. 3. According to claim 2, the time-domain audio steganography method based on generalized audio intrinsic energy and micro-amplitude suppression modification is characterized in that the local carrier audio signal is obtained by setting a sliding window to intercept or using mirror filling to obtain the local carrier audio signal. 4. According to claim 2, the time-domain audio steganography method based on generalized audio intrinsic energy and micro-amplitude suppression modification is characterized in that the spectrum transformation adopts any one of discrete cosine transform, discrete Fourier transform or discrete wavelet transform. 5. The time-domain audio steganography method based on generalized audio intrinsic energy and micro-amplitude suppression modification according to claim 1 or 2 is characterized in that a steganographic distortion cost function based on generalized audio intrinsic energy is constructed, which is specifically expressed as: Wherein, ρ _c represents the distortion cost of the center point of the local carrier audio signal, λ represents the equilibrium constant used to prevent ρ _c from being too small, ε represents the stability constant, n represents the number of different carrier audio frequency components, _fi represents the amplitude of the i-th carrier audio frequency component, _wi represents the weight corresponding to the i-th carrier audio frequency component, and p represents the exponential adjustment coefficient. represents the intrinsic energy of the local carrier audio signal x _l used for steganography; when calculating the intrinsic steganographic energy of the audio, the influence of the DC frequency component is excluded, that is, f ₁ is discarded. 6. The time-domain audio steganography method based on generalized audio intrinsic energy and micro-amplitude suppression modification according to claim 2, characterized in that the exponential adjustment coefficient is expressed as: Among them, psehf represents the energy percentage of high-frequency components in each carrier, k represents the selection threshold of high-frequency components, k≥2, is the average amplitude of all carrier audio frequency components in each carrier. 7. The time-domain audio steganography method based on generalized audio intrinsic energy and micro-amplitude suppression modification according to claim 1 is characterized in that the steganographic distortion cost function based on generalized audio intrinsic energy is combined with the distortion cost design criterion of micro-amplitude suppression modification to obtain the final steganographic distortion cost function, which is specifically expressed as: Among them, | _xc | is the amplitude of the center point _xc of the local carrier audio signal, T is the amplitude threshold of the suppressed and modified sampling point, ψ is the distortion cost of the wet point under the micro-amplitude suppression criterion, _wi represents the weight corresponding to the i-th carrier audio frequency component, p represents the exponential adjustment coefficient, n represents the number of different carrier audio frequency components, ε represents the stability constant, and _ρc represents the distortion cost of the center point of the local carrier audio signal. 8. A time-domain audio steganography system based on generalized audio intrinsic energy and micro-amplitude suppression modification, characterized by comprising: a carrier audio acquisition module, a carrier audio preprocessing module, a secret information bit preprocessing module, a steganalysis distortion cost function construction module, a steganalysis distortion cost calculation module, a steganalysis encoding module, and an audio splicing module; The carrier audio acquisition module is used to acquire carrier audio; The carrier audio preprocessing module is used to perform segmentation preprocessing on the carrier audio, cutting the carrier audio into audio segments of a set length; The secret information bit preprocessing module is used to equally slice the secret information bits to be embedded according to the number of audio segments to obtain secret information bit segments; The steganalysis distortion cost function construction module is used to construct a steganalysis distortion cost function based on generalized audio intrinsic energy based on the complexity priority criterion, and combine the steganalysis distortion cost function based on generalized audio intrinsic energy with the distortion cost design criterion of micro-amplitude suppression modification to obtain the final steganalysis distortion cost function; The steganalysis distortion cost calculation module is used to calculate the steganalysis distortion cost of each audio clip; The stego-coding module is used to embed the secret information bit fragment into each audio fragment based on the STC stego-coding method of the minimum distortion steganographic framework to obtain multiple secret audio fragments; The audio splicing module is used to splice all the encrypted audio clips to obtain the final encrypted audio. 9. A computer-readable storage medium storing a program, characterized in that when the program is executed by a processor, the time-domain audio steganography method based on generalized audio intrinsic energy and micro-amplitude suppression modification as described in any one of claims 1 to 7 is implemented. 10. A computer device comprising a processor and a memory for storing a program executable by the processor, wherein when the processor executes the program stored in the memory, the time domain audio steganography method based on generalized audio intrinsic energy and micro-amplitude suppression modification as described in any one of claims 1 to 7 is implemented.