A reversible information hiding method and system based on image texture guided prediction

Abstract

The present invention belongs to the technical field of reversible information hiding and provides a reversible information hiding method and system based on image texture-guided prediction. The method includes allocating image pixels according to a checkerboard pattern to divide them into shadow layers and blank layers; calculating the prediction errors and fluctuation values ​​of all pixels in the shadow layer to construct a prediction error histogram for the shadow layer; calculating the prediction errors and fluctuation values ​​of all pixels in the blank layer to construct a prediction error histogram for the blank layer; obtaining a secret-carrying shadow layer based on the prediction error histogram of the shadow layer and the fluctuation sequence of the shadow layer; obtaining a secret-carrying blank layer based on the prediction error histogram of the blank layer and the fluctuation sequence of the blank layer; and combining the secret-carrying shadow layer and the secret-carrying blank layer to obtain a secret-carrying image.

Description

Technical Field

[0001] The present invention belongs to the technical field of reversible information hiding, and in particular relates to a reversible information hiding method and system based on image texture guided prediction. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the rapid development of multimedia network technology, information security has received widespread attention in various fields. In order to achieve the purpose of secure communication and realize more secure information transmission, reversible data hiding (RDH) technology has become a new branch of information hiding, which can play a vital role in the fields of confidential information protection, copyright protection, and secure communication. Therefore, reversible data hiding technology has become a research hotspot in the field of information security, among which improving the embedding capacity and ensuring the visual quality of the image have always been the focus of discussion. RDH mainly refers to the fact that when information is embedded in the original image, although it may cause certain damage to the quality of the host data, if the encrypted image does not change during the transmission process, then the legitimate users and authoritative institutions can extract the hidden information at the receiving end according to the extraction algorithm, and can repair the distortion of the original image to achieve accurate recovery of the original data.

[0004] In reversible information hiding algorithms, hiding secrets in image pixels takes advantage of the limitations of the human eye system and the redundancy in image pixel values. Secret information is hidden in image pixels by modifying and adjusting the pixel values ​​of the image within a certain range. In recent years, algorithms based on histogram shifting have been the most commonly used.

[0005] The histogram shift (HS) algorithm was first proposed in 2006. Its main idea is to embed additional data: keeping peak pixels unchanged to hide "0," shifting peak pixels by 1 to hide "1," and shifting pixels between the peak and zero points by adding or subtracting 1. However, this method's embedding capability is relatively low. Subsequently, a method for constructing a prediction error histogram (PEH) containing both negative and non-negative PEHs was proposed, effectively improving embedding capability. Because the distribution of prediction error values ​​is more compact than that of image pixel values, the PEH is steeper than the image pixel histogram, effectively increasing capacity. Subsequently, a method for embedding additional information by constructing an asymmetric histogram was proposed, characterized by quadratic embedding compensation. Asymmetric PEHs are formed using the maximum and minimum prediction errors of the image. The complementary effect of pixels generated during the embedding process reduces image distortion. Most current HS methods are the result of exploration based on these methods, with the primary goal of finding a histogram that is more conducive to information embedding.

[0006] However, most current methods embed additional information in a specific scanning order without considering the image's texture. Fundamentally, they fail to consider the impact of the number of invalid shifted pixels (ISPs) in the histogram shift on image distortion. For a set of additional data, the number of valid shifted pixels generated by the histogram shift is fixed during the embedding process, so image distortion is significantly affected by the number of ISPs. In other words, a greater number of ISPs results in greater image distortion. Summary of the Invention

[0007] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides a reversible information hiding method and system based on image texture-guided prediction. By preferentially embedding additional data in the smooth areas of the image, the proposed reversible information hiding scheme can reduce the invalid pixel displacement in the histogram displacement, improve the embedding performance under low distortion conditions, and achieve accurate prediction of image pixels.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] A first aspect of the present invention provides a reversible information hiding method based on image texture guided prediction.

[0010] A reversible information hiding method based on image texture guided prediction, comprising:

[0011] Distribute the image pixels in a checkerboard pattern, dividing them into shadow and blank layers;

[0012] Calculate the prediction error and fluctuation value of all pixels in the shadow layer and construct the prediction error histogram of the shadow layer; calculate the prediction error and fluctuation value of all pixels in the blank layer and construct the prediction error histogram of the blank layer;

[0013] According to the prediction error histogram of the shadow layer and the fluctuation sequence of the shadow layer, the dense shadow layer is obtained; according to the prediction error histogram of the blank layer and the fluctuation sequence of the blank layer, the dense blank layer is obtained;

[0014] The shadow layer of the dense layer and the blank layer of the dense layer are combined to obtain the dense image.

[0015] A second aspect of the present invention provides a reversible information hiding system based on image texture guided prediction.

[0016] A reversible information hiding system based on image texture guided prediction, comprising:

[0017] an allocation module configured to: allocate image pixels according to a checkerboard pattern into shadow layers and blank layers;

[0018] A prediction error histogram calculation module is configured to: calculate the prediction error and fluctuation value of all pixels in the shadow layer and construct a prediction error histogram of the shadow layer; calculate the prediction error and fluctuation value of all pixels in the blank layer and construct a prediction error histogram of the blank layer;

[0019] The data embedding module is configured to: obtain a dense shadow layer according to a prediction error histogram of the shadow layer and a fluctuation sequence of the shadow layer; obtain a dense blank layer according to a prediction error histogram of the blank layer and a fluctuation sequence of the blank layer;

[0020] The confidentiality module is configured to combine the confidentiality layer shadow layer and the confidentiality layer blank layer to obtain a confidentiality image.

[0021] A third aspect of the present invention provides a computer-readable storage medium.

[0022] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the reversible information hiding method based on image texture guided prediction as described in the first aspect above.

[0023] A fourth aspect of the present invention provides a computer device.

[0024] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the reversible information hiding method based on image texture guided prediction as described in the first aspect above are implemented.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] By using the directional prediction errors (DPEs) designed by the present invention and constructing a single prediction error histogram (PEH) for data embedding, more accurate prediction values ​​can be obtained, thereby improving embedding performance.

[0027] Data embedding is restricted to pixels with the same DPE sign from horizontal and vertical predictions, achieving reversibility of data embedding and data extraction.

[0028] In order to reduce distortion, the present invention fully considers the influence of natural image texture on embedding distortion, that is, the image distortion caused by embedding data in the smooth area of ​​the image is much smaller than that in the non-smooth area, and data embedding is preferably embedded in the smooth area. This is mainly because embedding additional data in the smooth area corresponds to fewer invalid moved pixels (ISPs) in the histogram movement process. ISPs mean the movement of pixels, but are not selected to embed secret information. Therefore, in addition to using the previous method of measuring the complexity and non-complexity of images through local complexity (LC), LC is further used to calculate the fluctuation value of each pixel. The smaller the fluctuation, the smoother the pixel, and the smoother the image of this part. This method divides the image texture more finely and obtains as much embedding space as possible. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0030] Figure 1 is a framework diagram of a reversible information hiding method based on image texture guided prediction shown in the present invention;

[0031] Figure 2 Schematic diagram of double-layer partitioning of the original graph shown in the present invention;

[0032] Figure 3 This is an implementation of the present invention showing the use of a checkerboard pattern to allocate pixels. DETAILED DESCRIPTION

[0033] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0034] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0035] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0036] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the methods and systems according to the various embodiments of the present disclosure. It should be noted that each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code can include one or more executable instructions for implementing the logical functions specified in the various embodiments. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the flowchart and / or block diagram, and the combination of the boxes in the flowchart and / or block diagram, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.

[0037] Example 1

[0038] like Figure 1 As shown, this embodiment provides a reversible information hiding method based on image texture guided prediction. This embodiment uses the method applied to a server as an example for illustration. It is understandable that the method can also be applied to a terminal, and can also be applied to a system including a terminal, a server, and a server, and is implemented through the interaction between the terminal and the server. The server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network servers, cloud communications, middleware services, domain name services, security services CDN, and big data and artificial intelligence platforms. The terminal can be a smart phone, tablet computer, laptop computer, desktop computer, smart speaker, smart watch, etc., but is not limited to this. The terminal and the server can be directly or indirectly connected via wired or wireless communication, which is not limited in this application. In this embodiment, the method includes the following steps:

[0039] Distribute the image pixels in a checkerboard pattern, dividing them into shadow and blank layers;

[0040] Calculate the prediction error and fluctuation value of all pixels in the shadow layer and construct the prediction error histogram of the shadow layer; calculate the prediction error and fluctuation value of all pixels in the blank layer and construct the prediction error histogram of the blank layer;

[0041] According to the prediction error histogram of the shadow layer and the fluctuation sequence of the shadow layer, the dense shadow layer is obtained; according to the prediction error histogram of the blank layer and the fluctuation sequence of the blank layer, the dense blank layer is obtained;

[0042] The shadow layer of the dense layer and the blank layer of the dense layer are combined to obtain the dense image.

[0043] As introduced in the background technology, how to improve the embedding capacity while ensuring the visual quality of the image has always been the focus of research in RDH technology. In order to improve the embedding capacity, prediction error expansion (PEE) has attracted considerable attention in the past few decades. For PEE, the prediction error (PE) refers to the difference between the existing pixel value and the predicted pixel value. PE can be used to construct a prediction error histogram (PEH) in the difference domain. Compared with the grayscale histogram in the spatial domain, PEH has a clear distribution at the origin and is more suitable for data embedding. Therefore, RDH based on PEE usually obtains superior embedding performance.

[0044] Specifically, the specific solution of this embodiment is as follows:

[0045] Image pixels are arranged in a checkerboard pattern, divided into a shaded layer A and a blank layer B. Data embedding is performed separately on these two types of pixels. In the first layer, pixels in layer A are used to embed half of the payload, while pixels in layer B are used to calculate the predicted values ​​of these pixels, resulting in the prediction error and fluctuation values ​​for all pixels in layer A. In the second layer, pixels in layer B are used to embed the remaining information, and the embedded pixels in layer A are used to calculate the predicted values ​​of pixels in layer B, resulting in the prediction error and fluctuation values ​​for all pixels in layer B. Finally, the PEH for each layer is generated. By scanning each layer, the corresponding fluctuation sequence is obtained. The secret information is embedded in the order of embedding from the lowest to the highest fluctuation sequence, forming secret layers A and B. The two secret layers are then combined to form the final secret image. This completes the embedding of all secret information. The extraction process is the inverse of the embedding process. First, in the secret image, secret layer B is predicted based on the pixels in layer A. Data extraction is then performed on secret layer B. Secret layer A is predicted based on the recovered pixels in layer B, and data extraction is then performed on secret layer A.

[0046] The original image is divided into a shadow layer and a blank layer in a chessboard pattern (e.g. Figure 2 As shown in FIG, the pixels in layers A and B are processed in the same way, so here we will take layer A as an example.

[0047] 1. Directed Enclosed Prediction and Extension (DEPE) scheme.

[0048] set up Denotes direction prediction, where d∈dd,v} denotes horizontal or vertical direction. Covered pixel x can be predicted from horizontal and vertical directions using formula (1)

[0049]

[0050] Among them, v eDenote the adjacent pixel to the right of the covered pixel x, v w Denote the adjacent pixel to the left of the covered pixel x, v s Denote the adjacent pixel below the covered pixel x, v n Denote the adjacent pixel to the left of the covered pixel x, v s Denote the adjacent pixel above the covered pixel x

[0051] Then the directional prediction error (DPE) can be derived as

[0052]

[0053] Let e = {e h , e v} be a DPE vector, where, e h represents the horizontal vector of the directional prediction error, e v represents the vertical vector of the directional prediction error. Select the element with the smallest magnitude in e, denoted as e, as the PE of pixel x, and the direction of e is denoted as d.e. d can be derived as follows:

[0054]

[0055] Note that when e = f(e) = e d , the corresponding d = g(e) = d. When e = f(e) = e v ), there is d = g(e) = v. So far, the construction of PEH is based on e. In this way, all the PEs in the image can be drawn into a single PEH for data embedding.

[0056] Let e’ be the modified PE, m ∈ {0,1} be a data bit, and a and b (where one of a < b) be a space pair selected from the PEH to be expanded. The data embedding operation is performed by modifying e to e’ through the embedding formula. To make the DEPE reversible, the direction d’ of the predicted error e’ of the marked DPE vector e’ should be the same as the corresponding direction (e). However, since e and e‘ are respectively the DPEs with smaller magnitudes of e and e’, d’ may not be equal to d, that is, the extracted data bit is not the embedded data bit, and this embedding operation is irreversible. For the sake of convenience, this problem is called extraction ambiguity. Therefore, in DEPE, only part of the DPE vectors are suitable for data embedding without introducing extraction ambiguity. Let R denote the set of suitable DPE vectors:

[0057]

[0058] In addition, there are still some pixels not selected. Therefore, on this basis, we add a condition to the embedding process to make it located at e h ≤ 0, ev >0 and e h ≥0,e v Pixels that meet the embedding conditions within the range of <0 can be selected to improve the embedding capacity and thus improve the embedding performance. Assume that the pixel value of the covering pixel x is p, and the horizontal prediction value is p d , the vertical prediction value is p v , then the additional conditions are:

[0059] or

[0060] 2. Image texture division

[0061] Previous methods used local complexity (LC) to judge the complexity of the image and calculated the local complexity of each pixel in layer A. The formula is as follows:

[0062] n=|v n -v s |+|v e -v w |+|v n +v e -v s -v w |+|v e +v s -v n -v w | (6)

[0063] In this embodiment, in order to more accurately measure the smoothness of a pixel, the fluctuation value F of the current pixel value is calculated in combination with the local complexity of the adjacent pixel values. The smaller the pixel value fluctuation, the smoother it is. At this time, data embedding is limited to pixels whose fluctuation sequence is proportional to PE. Figure 3 Taking the pixel values ​​in as an example, the fluctuation value F of pixel point P1 is calculated as follows:

[0064] F p1 =n p1 +n p4 (7)

[0065] calculate Figure 3 The fluctuation value F of the pixel point P2 is:

[0066]

[0067] same, Figure 3 The calculation of the fluctuation value of pixel P4 should be:

[0068]

[0069] From the above, we can conclude that when a pixel in A has 1, 2, or 4 adjacent pixels, its fluctuation value is obtained by equation (7), (8), or (9), respectively.

[0070] The effectiveness of the technical solution of this embodiment is demonstrated by data experiments below:

[0071] Peak signal-to-noise ratio (PSNR) is commonly used as a metric for invisibility evaluation. The performance of the proposed method was evaluated through multiple experiments, and the experimental results were analyzed. Six standard 512×512 test images were used for comparison with three state-of-the-art techniques. The average PSNR gain (in dB) achieved by the proposed method with a payload of 0.01 to 0.05 bits per page (bpp) is shown in Table 1.

[0072] Table 1

[0073] Image Gain vs Lu et al. Gain vs Cden et al. Gain vs Jung Boat 2.72 1.73 2.10 Airplane 4.85 5.44 5.02 Elaine 4.51 2.26 4.15 Baboon 3.45 3.28 3.99 Man 5.11 4.60 6.74 Lena 1.62 2.09 1.23

[0074] Example 2

[0075] This embodiment provides a reversible information hiding system based on image texture guided prediction.

[0076] A reversible information hiding system based on image texture guided prediction, comprising:

[0077] an allocation module configured to: allocate image pixels according to a checkerboard pattern into shadow layers and blank layers;

[0078] A prediction error histogram calculation module is configured to: calculate the prediction error and fluctuation value of all pixels in the shadow layer and construct a prediction error histogram of the shadow layer; calculate the prediction error and fluctuation value of all pixels in the blank layer and construct a prediction error histogram of the blank layer;

[0079] The data embedding module is configured to: obtain a dense shadow layer according to a prediction error histogram of the shadow layer and a fluctuation sequence of the shadow layer; obtain a dense blank layer according to a prediction error histogram of the blank layer and a fluctuation sequence of the blank layer;

[0080] The confidentiality module is configured to combine the confidentiality layer shadow layer and the confidentiality layer blank layer to obtain a confidentiality image.

[0081] It should be noted that the examples and application scenarios implemented by the allocation module, prediction error histogram calculation module, data embedding module, and encryption module described above are the same as those in the steps of Example 1, but are not limited to the contents disclosed in Example 1. It should be noted that the above modules, as part of a system, can be executed in a computer system, such as a set of computer-executable instructions.

[0082] Example 3

[0083] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the steps of the reversible information hiding method based on image texture guided prediction as described in the first embodiment are implemented.

[0084] Example 4

[0085] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the reversible information hiding method based on image texture-guided prediction as described in the first embodiment are implemented.

[0086] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage and optical storage, etc.) containing computer-usable program code.

[0087] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0088] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0089] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0090] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware through a computer program. The program can be stored in a computer-readable storage medium, and when executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0091] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A reversible information hiding method based on image texture guided prediction, characterized in that: include: Distribute the image pixels in a checkerboard pattern, dividing them into shadow and blank layers; Calculating the prediction errors and fluctuation values ​​of all pixels in the shadow layer and constructing a prediction error histogram of the shadow layer, specifically including: obtaining a prediction error histogram of the shadow layer according to the prediction errors and fluctuation values ​​of all pixels in the shadow layer; calculating the prediction errors and fluctuation values ​​of all pixels in the blank layer and constructing a prediction error histogram of the blank layer, specifically including: obtaining a prediction error histogram of the blank layer according to the prediction errors and fluctuation values ​​of all pixels in the blank layer; According to the prediction error histogram of the shadow layer and the fluctuation sequence of the shadow layer, a secret shadow layer is obtained, specifically comprising: scanning each layer of the shadow layer to obtain a corresponding fluctuation sequence, and embedding the secret information in an embedding order from low to high of the fluctuation sequence of each layer, thereby obtaining a secret shadow layer; according to the prediction error histogram of the blank layer and the fluctuation sequence of the blank layer, a secret blank layer is obtained, scanning each layer of the blank layer to obtain a corresponding fluctuation sequence, and embedding the secret information in an embedding order from low to high of the fluctuation sequence of each layer, thereby obtaining a blank shadow layer; The shadow layer of the dense layer and the blank layer of the dense layer are combined to obtain the dense image.

2. The reversible information hiding method based on image texture guided prediction according to claim 1, characterized in that: The calculation of the prediction error and fluctuation value of all pixels in the shadow layer specifically includes: in the shadow layer, the pixels of the shadow layer are used to embed half of the effective pixels, and the blank layer pixels are used to calculate the prediction value of the shadow layer pixels to obtain the prediction error and fluctuation value of all pixels in the shadow layer.

3. The reversible information hiding method based on image texture guided prediction according to claim 2, characterized in that: The calculation of the prediction errors and fluctuation values ​​of all pixels in the blank layer specifically includes: In the blank layer, the pixels of the blank layer are used to embed the other half of the valid pixels, and the shadow layer pixels after embedding information are used to calculate the predicted values ​​of the blank layer pixels to obtain the prediction errors and fluctuation values ​​of all pixels in the blank layer.

4. The reversible information hiding method based on image texture guided prediction according to claim 2 or 3, characterized in that: The embedding of the valid pixels is limited to pixels with the same sign of directional prediction errors from horizontal and vertical predictions, wherein the directional prediction error is the prediction of the predicted pixels from the vertical and horizontal directions respectively to obtain the prediction values ​​in the two directions, and the predicted pixels are respectively subtracted from the prediction values ​​in the two directions, and the one with the smaller difference in the two directions is selected as the prediction error, that is, the prediction of the pixel is directional.

5. The reversible information hiding method based on image texture guided prediction according to claim 4, characterized in that: The conditions for embedding pixels include: The horizontal vector of the directional prediction error is greater than or equal to zero, and the vertical vector of the directional prediction error is greater than or equal to zero; The horizontal vector of the directional prediction error is less than zero, and the vertical vector of the directional prediction error is less than zero.

6. A reversible information hiding system based on image texture guided prediction, characterized in that: include: an allocation module configured to: allocate image pixels according to a checkerboard pattern into shadow layers and blank layers; The prediction error histogram calculation module is configured to: calculate the prediction error and fluctuation value of all pixels in the shadow layer and construct a prediction error histogram of the shadow layer, specifically including: obtaining the prediction error histogram of the shadow layer according to the prediction error and fluctuation value of all pixels in the shadow layer; calculate the prediction error and fluctuation value of all pixels in the blank layer and construct a prediction error histogram of the blank layer, specifically including: obtaining the prediction error histogram of the blank layer according to the prediction error and fluctuation value of all pixels in the blank layer; The data embedding module is configured to: obtain a secret-carrying shadow layer based on a prediction error histogram of the shadow layer and a fluctuation sequence of the shadow layer, specifically comprising: scanning each layer of the shadow layer to obtain a corresponding fluctuation sequence, and embedding the secret information in an embedding order from low to high of the fluctuation sequence of each layer to obtain a secret-carrying shadow layer; obtain a secret-carrying blank layer based on a prediction error histogram of the blank layer and a fluctuation sequence of the blank layer, scanning each layer of the blank layer to obtain a corresponding fluctuation sequence, and embedding the secret information in an embedding order from low to high of the fluctuation sequence of each layer to obtain a blank shadow layer; The confidentiality module is configured to combine the confidentiality layer shadow layer and the confidentiality layer blank layer to obtain a confidentiality image.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the reversible information hiding method based on image texture guided prediction as described in any one of claims 1 to 5 are implemented.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the reversible information hiding method based on image texture guided prediction are implemented as described in any one of claims 1 to 5.

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