Reversible information hiding method and system based on neural network and global pixel segmentation

Abstract

The purpose of the present invention is to provide a reversible information hiding method and system based on neural networks and global pixel segmentation. The present invention adopts a two-layer information embedding framework: first, the original image is divided into a first pixel set and a second pixel set according to a checkerboard pattern, and alternately used as a carrier pixel set in the two-layer information embedding. In each layer of information embedding operation, a predictor based on a two-branch neural network is used to predict the carrier pixel set with a reference pixel set and give an error estimate of the corresponding prediction value; the carrier pixel sequence is sorted according to the size of the predicted pixel value, and then the pixels with a prediction error greater than a preset error threshold are eliminated according to the estimated prediction error limit to obtain the final selected carrier pixel sequence, which is divided into sub-pixel sequence sets of equal length for embedding secret information. The present invention can better embed secret information into the carrier image, realize information hiding and reversible recovery, and ensure the security of information transmission.

Description

Technical Field

[0001] The present invention belongs to the field of information security technology and relates to a reversible information hiding method. The present invention uses a digital image as a cover for covert communication, hiding secret information in a stego-image. The receiver can extract the secret information from the stego-image while losslessly restoring the original carrier image. Background Art

[0002] Reversible information hiding is an important content security technology that embeds secret information into digital multimedia media while ensuring lossless recovery. One of the most commonly used multimedia media is digital images. Reversible information hiding is unique in that it protects the embedded data while preserving the integrity of the original media, which is crucial in scenarios where data authenticity and integrity must be guaranteed. Reversible information hiding based on digital images has been widely used in image authentication, medical image protection, and military communications.

[0003] With the rapid development of neural networks, applying neural networks to reversible information hiding has become a key research topic. Most early neural network-based digital image information hiding methods only ensure that the embedded information can be correctly extracted, but lack the property of reversibility, meaning that the original image cannot be restored losslessly after information extraction. These methods are therefore unsuitable for applications such as image authentication that require preserving the original image. An effective approach to achieving reversible information hiding based on neural networks is to apply neural networks to a reversible information hiding framework based on prediction error modulation. This model primarily consists of two components: a neural network-based pixel predictor and an information embedding and extraction method based on prediction error modulation. The neural network is primarily used to construct the pixel predictor, leveraging the deep learning capabilities of the neural network to improve the accuracy of pixel predictions, thereby enhancing the performance of the method. The reversibility of the method is ensured by the reversibility of the information embedding method based on prediction error modulation. This reversible information hiding method based on a neural network predictor and prediction error modulation leverages deep learning to improve steganographic performance while maintaining its reversibility.

[0004] It can be seen that how to implement a reversible information hiding method based on a neural network predictor and prediction error modulation is a technical problem that needs to be solved urgently. Summary of the Invention

[0005] The purpose of the present invention is to solve the problems existing in the prior art and provide a method and system for reversible information hiding based on a dual-branch prediction neural network and global pixel segmentation, so as to realize reversible information hiding based on a neural network.

[0006] The technical solution adopted by the present invention to solve the technical problem includes the following steps:

[0007] In a first aspect, the present invention provides a reversible information hiding method based on a neural network and global pixel segmentation, comprising:

[0008] S1. Divide the original carrier image I into two types of pixels, forming a first pixel set and a second pixel set respectively; wherein, for each pixel, among its eight neighboring pixels, the pixels on the four edges are all pixels of different types, and the pixels on the four diagonal corners are all pixels of the same type; first, use the first pixel set as the reference pixel set and the second pixel set as the carrier pixel set;

[0009] S2, using a prediction neural network with two output branches to predict the pixel value and error limit of each carrier pixel in the carrier pixel set based on the reference pixel set;

[0010] S3. Sort all carrier pixels in the carrier pixel set in ascending order according to the predicted pixel values ​​of the carrier pixels to obtain a first carrier pixel sequence; then, according to a preset error threshold, select carrier pixels whose prediction errors are less than the preset error threshold from the first carrier pixel sequence to form a second carrier pixel sequence, and sequentially divide the second carrier pixel sequence into a series of sub-pixel sequences of equal length;

[0011] S4. Perform a secret information embedding operation based on pixel value ordering (PVO) on each sub-pixel sequence. After all the secret information to be embedded is completely embedded, replace all sub-pixel sequences with the carrier pixel set to obtain the carrier pixel set after the information is embedded.

[0012] S5. Use the second pixel set after embedding the information as the reference pixel set and the first pixel set as the carrier pixel set, and repeat S2 to S4 to obtain the first pixel set after embedding the information; finally, map the pixel values ​​in the first pixel set after embedding the information and the second pixel set after embedding the information to each pixel position of the carrier image to form a carrier image with secret information.

[0013] As a preferred embodiment of the first aspect, the specific implementation method of S2 is as follows:

[0014] A pixel value matrix of the same size as the original carrier image I is constructed as the network input. The values ​​of all carrier pixels in the matrix maintain the original pixel values ​​in the original carrier image I, and the values ​​of all reference pixels are set to zero. Then, a trained prediction neural network with two branches is used to predict the pixel value of each carrier pixel in the carrier pixel set through one output branch based on the constructed network input, and the prediction error limit of each carrier pixel in the carrier pixel set is estimated through the other output branch.

[0015] As a preferred embodiment of the first aspect, the prediction neural network is composed of a feature extraction and fusion module, a pixel prediction module and a prediction error limit evaluation module;

[0016] The feature extraction and fusion module includes three parallel convolutional layers with different kernel sizes. The network input is spliced ​​after extracting features in parallel through the three convolutional layers. The spliced ​​features are then passed through multiple residual dense network blocks (RDBs) after convolution operations. The features extracted by each residual dense network block are spliced, convolved, and then element-by-element added to the spliced ​​features. Finally, the fusion result is output through the MDTA network.

[0017] The pixel prediction module and the prediction error limit evaluation module serve as two output branches respectively. Each output branch takes the fusion result as input and is composed of two convolutional layers connected with an activation function in the middle.

[0018] As a preferred embodiment of the first aspect, the prediction neural network needs to be trained on a data set before being used for actual reasoning.

[0019] As a preferred embodiment of the first aspect, the specific implementation method of S3 is as follows:

[0020] The carrier pixel set is converted into a carrier pixel sequence, and a predicted pixel value sequence and a predicted error limit sequence corresponding to the carrier pixel sequence are obtained at the same time; the predicted pixel value sequence is arranged in ascending order according to the pixel value size to obtain a new predicted pixel value sequence; the carrier pixel sequence is re-sorted according to the pixel index order in the new predicted pixel value sequence to obtain a first carrier pixel sequence; then, according to a preset error limit threshold value, carrier pixels with a prediction error limit greater than or equal to the error limit threshold value are eliminated from the first carrier pixel sequence to obtain a second carrier pixel sequence; finally, the second carrier pixel sequence is divided into a series of sub-pixel sequences according to a preset segment length.

[0021] In the second aspect, the present invention provides a reversible information recovery method based on neural networks and global pixel segmentation. For a carrier image with secret information obtained by the reversible information hiding method described in any one of the first aspects above, the second pixel set is first used as the reference pixel set and the first pixel set as the carrier pixel set, and the inverse operation of the secret information embedding operation described in S2 to S4 is performed to extract the embedded secret information and restore all the first pixels; then, the first pixel set is used as the reference pixel and the second pixel is used as the carrier pixel, and the inverse operation of the secret information embedding operation described in S2 to S4 is performed to extract the embedded secret information and restore all the second pixels.

[0022] In a third aspect, the present invention provides a reversible information hiding system based on a neural network and global pixel segmentation, comprising:

[0023] An information hiding module, configured to obtain a carrier image carrying secret information according to the reversible information hiding method based on a neural network and global pixel segmentation as described in any one of the first aspects above;

[0024] An information recovery module is used to extract secret information from a carrier image carrying secret information and restore the carrier image according to the reversible information recovery method based on neural network and global pixel segmentation as described in any one of the second aspects above.

[0025] In a fourth aspect, the present invention provides a computer program product, comprising a computer program / instruction, characterized in that when the computer program / instruction is executed by a processor, it can implement the reversible information hiding method based on neural network and global pixel segmentation as described in any one of the first aspects above, or implement the reversible information recovery method based on neural network and global pixel segmentation as described in the second aspect above.

[0026] In a fifth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it can implement the reversible information hiding method based on neural network and global pixel segmentation as described in any one of the first aspects above, or implement the reversible information recovery method based on neural network and global pixel segmentation as described in the second aspect above.

[0027] In a sixth aspect, the present invention provides a computer electronic device comprising a memory and a processor;

[0028] The memory is used to store computer programs;

[0029] The processor is used to implement the reversible information hiding method based on neural network and global pixel segmentation as described in any one of the first aspects above, or to implement the reversible information recovery method based on neural network and global pixel segmentation as described in the second aspect above, when executing the computer program.

[0030] Compared with the prior art, the advantages and positive effects of the present invention are:

[0031] The present invention provides a reversible information hiding method based on a neural network and global pixel segmentation. The method adopts a two-layer information embedding framework to divide the original image into two pixel sets according to a checkerboard pattern, and sequentially serves as a reference pixel set and a carrier pixel set. In each layer of information embedding operation, a predictor based on a dual-branch neural network is used to predict the carrier pixel set with the reference pixel set and provide an error estimate of the corresponding predicted value; the carrier pixel sequence is sorted according to the size of the predicted pixel value, and then the pixels with a prediction error greater than a preset error threshold are eliminated according to the estimated prediction error limit to obtain the selected final carrier pixel sequence; finally, the selected carrier pixel sequence is divided into a set of sub-pixel sequences of equal length. In each sub-pixel sequence, a secret information embedding operation based on pixel value ordering (PVO) is used to embed secret information. The dual-branch predictor based on the neural network in the present invention can provide higher pixel value prediction accuracy and perform error estimation on the predicted value; at the same time, the global pixel segmentation based on pixel ordering and pixel selection can improve embedding efficiency. The reversible information hiding method based on a neural network predictor and prediction error modulation of the present invention utilizes deep learning to improve steganographic performance while maintaining the reversible property of the method. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 Schematic diagram of the steps of the reversible information hiding method based on neural network and global pixel segmentation;

[0033] Figure 2 Schematic diagram of the module composition of the reversible information hiding system based on neural network and global pixel segmentation;

[0034] Figure 3 It is a schematic diagram of the structure of computer electronic equipment;

[0035] Figure 4 This is a checkerboard pattern for dividing the carrier image in an embodiment of the present invention.

[0036] Figure 5 This is a dual-output prediction neural network diagram in an embodiment of the present invention.

[0037] Figure 6 This is a flowchart based on double-layer embedding in an embodiment of the present invention.

[0038] Figure 7 The following is a schematic diagram of the effect of the example. DETAILED DESCRIPTION

[0039] In order to make the above-mentioned objects, features and advantages of the present invention more clearly understood, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways than those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below. The technical features in the various embodiments of the present invention can be combined accordingly without conflicting with each other.

[0040] In the description of the present invention, it should be understood that the terms "first" and "second" are used solely for descriptive purposes and are not to be construed as indicating or implying relative importance or implicitly specifying the number of technical features being described. Therefore, features defined as "first" or "second" may explicitly or implicitly include at least one of such features.

[0041] The present invention provides a reversible information hiding method based on a neural network and global pixel segmentation. The method adopts a two-layer information embedding framework: first, the original image is divided into two parts according to a checkerboard pattern: a first pixel set and a second pixel set. In the first layer of information embedding, the first pixel set is used as the reference pixel set, and the second pixel set is used as the carrier pixel set. In the second layer of information embedding, the second pixel set after information embedding is used as the reference pixel set, and the first pixel set is used as the carrier pixel set. In each layer of information embedding operation, a predictor based on a two-branch neural network is used to predict the carrier pixel set using the reference pixel set and provide an error estimate of the corresponding predicted value. The carrier pixel sequence is sorted according to the size of the predicted pixel value, and then pixels with prediction errors greater than a preset error threshold are eliminated based on the estimated prediction error limit to obtain a selected final carrier pixel sequence. Finally, the selected carrier pixel sequence is segmented into a set of sub-pixel sequences of equal length. Secret information is embedded in each sub-pixel sequence using a secret information embedding operation based on pixel value ordering (PVO).

[0042] The specific implementation of the reversible information hiding method based on neural network and global pixel segmentation of the present invention is described below.

[0043] like Figure 1 As shown, in a preferred embodiment of the present invention, a reversible information hiding method based on neural network and global pixel segmentation is provided, which includes steps S1 to S5.

[0044] S1. Divide the original carrier image I into two types of pixels, forming a first pixel set and a second pixel set, respectively. For each pixel, the eight neighboring pixels on the four sides are all pixels of different types, while the pixels on the four diagonal corners are all pixels of the same type. The first pixel set is used as the reference pixel set, and the second pixel set is used as the carrier pixel set. This division of the first pixel set into the second pixel set is similar to treating the carrier image as a black and white checkerboard, with the first pixel set being the white pixel set and the second pixel set being the black pixel set.

[0045] S2. Use a prediction neural network with two output branches to predict the pixel value and error limit of each carrier pixel in the carrier pixel set based on the reference pixel set.

[0046] In an embodiment of the present invention, the specific implementation method of the above step S2 is as follows:

[0047] A pixel value matrix of the same size as the original carrier image I is constructed as the network input. The values ​​of all carrier pixels in the matrix maintain the original pixel values ​​in the original carrier image I, and the values ​​of all reference pixels are set to zero. Then, a trained prediction neural network with two branches is used to predict the pixel value of each carrier pixel in the carrier pixel set through one output branch based on the constructed network input, and the prediction error limit of each carrier pixel in the carrier pixel set is estimated through the other output branch.

[0048] In an embodiment of the present invention, the above-mentioned prediction neural network is composed of a feature extraction and fusion module, a pixel prediction module and a prediction error limit assessment module. The feature extraction and fusion module includes three parallel convolutional layers with different kernel sizes. The network input is spliced ​​after extracting features in parallel through three convolutional layers. The spliced ​​features are passed through a convolution operation (the convolution operation consists of a 1×1 convolution layer, a ReLU activation function, and a 3×3 convolution layer cascade) and then pass through multiple residual dense network blocks (RDB). The features extracted by each residual dense network block are spliced ​​and then passed through a convolution operation (the convolution operation consists of a 1×1 convolution layer, a 3×3 convolution layer cascade) and then element-wise summed with the spliced ​​features. Finally, the fusion result is output through the MDTA network. The above-mentioned pixel prediction module and prediction error limit assessment module are respectively used as two output branches, each of which takes the above-mentioned fusion result as input and is composed of two convolution layers with a ReLU activation function connected in the middle.

[0049] It should be noted that the above prediction neural network needs to be trained on the dataset before being used for actual reasoning. The training process of the neural network belongs to the existing technology and will not be described in detail here.

[0050] S3. Sort all carrier pixels in the carrier pixel set in ascending order according to the predicted pixel values ​​of the carrier pixels to obtain a first carrier pixel sequence; then, according to a preset error threshold, select carrier pixels whose predicted error limit is less than the preset error threshold from the first carrier pixel sequence to form a second carrier pixel sequence, and divide the second carrier pixel sequence into a series of sub-pixel sequences of equal length in sequence.

[0051] In an embodiment of the present invention, the specific implementation method of the above step S3 is as follows:

[0052] The carrier pixel set is converted into a carrier pixel sequence, and a predicted pixel value sequence and a predicted error limit sequence corresponding to the carrier pixel sequence are obtained at the same time; the predicted pixel value sequence is arranged in ascending order according to the pixel value size to obtain a new predicted pixel value sequence; the carrier pixel sequence is re-sorted according to the pixel index order in the new predicted pixel value sequence to obtain a first carrier pixel sequence; then, according to a preset error limit threshold value, carrier pixels with a prediction error limit greater than or equal to the error limit threshold value are eliminated from the first carrier pixel sequence to obtain a second carrier pixel sequence; finally, the second carrier pixel sequence is divided into a series of sub-pixel sequences according to a preset segment length.

[0053] S4. Perform a secret information embedding operation based on pixel value ordering (PVO) on each sub-pixel sequence. After all the secret information that needs to be embedded is embedded, replace all sub-pixel sequences with the carrier pixel set to obtain the carrier pixel set after the information is embedded.

[0054] It should be noted that the secret information embedding operation is based on pixel value ordering (PVO). The secret information embedding operation based on PVO belongs to the information hiding technology in the prior art. It can rearrange the pixel values ​​through sorting to provide space for the embedding of secret information. The secret information is embedded in the sorting result of the image pixel value to achieve the secret transmission of information. The core of the present invention is to form a carrier sequence (i.e. the aforementioned sub-pixel sequence) for the secret information embedding operation, but embedding the secret information into the carrier sequence based on PVO can be achieved through existing technology. Theoretically, any secret information embedding algorithm based on PVO can be used in the present invention. In the embodiment of the present invention, it is preferred to use the information embedding method based on Multi-pass IPVO to embed secret information in each sub-pixel sequence.

[0055] S5. Use the second pixel set after embedding the information as the reference pixel set and the first pixel set as the carrier pixel set, and repeat S2 to S4 to obtain the first pixel set after embedding the information; finally, map the pixel values ​​in the first pixel set after embedding the information and the second pixel set after embedding the information to each pixel position of the carrier image to form a carrier image with secret information.

[0056] It should be noted that in the first-level information embedding, with the first pixel set as the reference pixel set and the second pixel set as the carrier pixel set, the first execution of steps S2 to S4 allows the secret information to be embedded in the second pixel set. In the second-level information embedding described in step S5 above, the second pixel set after the information is embedded is used as the reference pixel set, and the first pixel set is used as the carrier pixel set. S2 to S4 are then repeated. At this point, since the reference pixel set and the carrier pixel set are swapped, the secret information can be embedded in the first pixel set. However, for both levels of information embedding, the operations for embedding secret information are identical, namely, S2 to S4; the only difference is the different carrier pixel sets.

[0057] In addition, an embodiment of the present invention also provides a reversible information recovery method based on a neural network and global pixel segmentation, which can extract secret information and restore pixels from a carrier image containing secret information obtained by the reversible information hiding method. The secret information extraction and pixel restoration process is actually the inverse operation of the above steps S1 to S5. Since information embedding is performed in two layers, secret information extraction and pixel restoration also need to be performed in two layers. First, the first layer of secret information extraction and pixel restoration is performed, that is, the second pixel set is used as the reference pixel set and the first pixel set is used as the carrier pixel set. The inverse operation of the secret information embedding operation shown in S2 to S4 in the above step S5 is performed to extract the embedded secret information and restore all the first pixels. Then, the second layer of secret information extraction and pixel restoration is performed, that is, the first pixel set is used as the reference pixel set and the second pixel set is used as the carrier pixel set. The inverse operation of the secret information embedding operation shown in S2 to S4 is performed to extract the embedded secret information and restore all the second pixels.

[0058] Similarly, based on the same inventive concept, Figure 2 As shown, the present invention also provides a reversible information hiding system based on neural network and global pixel segmentation, which includes:

[0059] An information hiding module, configured to obtain a carrier image with secret information according to the above-mentioned reversible information hiding method based on neural network and global pixel segmentation;

[0060] The information recovery module is used to extract secret information from a carrier image with secret information and restore the carrier image according to the above-mentioned reversible information recovery method based on neural network and global pixel segmentation.

[0061] It should be noted that the information hiding module and the information recovery module can be respectively set on different computer devices.

[0062] Similarly, based on the same inventive concept, Figure 3 As shown, the present invention also provides a computer electronic device provided in the above embodiment, which includes a memory and a processor;

[0063] The memory is used to store computer programs;

[0064] The processor is used to implement the reversible information hiding method based on neural network and global pixel segmentation as described above, or to implement the reversible information recovery method based on neural network and global pixel segmentation as described above, when executing the computer program.

[0065] Furthermore, the logic instructions in the aforementioned memory can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention.

[0066] Therefore, based on the same inventive concept, the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it can implement the reversible information hiding method based on neural network and global pixel segmentation as described above, or implement the reversible information recovery method based on neural network and global pixel segmentation as described above.

[0067] Therefore, based on the same inventive concept, the present invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, can implement the reversible information hiding method based on neural network and global pixel segmentation as described above, or implement the reversible information recovery method based on neural network and global pixel segmentation as described above.

[0068] It is understood that the storage medium may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Furthermore, the storage medium may be any medium capable of storing program code, such as a USB flash drive, a mobile hard drive, a magnetic disk, or an optical disk.

[0069] It is understandable that the above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0070] It should also be noted that those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process of the system described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here. In the various embodiments provided in this application, the division of steps or modules in the system and method is only a logical function division. In actual implementation, there may be other division methods, for example, multiple modules or steps can be combined or integrated together, and a module or step can also be split.

[0071] In order to better understand the specific implementation process of the reversible information hiding method based on neural network and global pixel segmentation and the subsequent reversible information recovery method shown in S1 to S5 of the present invention, the specific steps and technical effects of the present invention are detailed below through an example.

[0072] Example

[0073] In this embodiment, the reversible information hiding method based on neural network and global pixel segmentation is shown in steps 1 to 5, and the subsequent reversible information recovery method is shown in step 6.

[0074] Step 1: Image division. Divide the carrier image I into Figure 4 The checkerboard pattern shown is divided into two parts: the white pixel set I W and black pixel set I B The specific division method can be formulated as follows:

[0075] I W ={I(x,y)|mod(x+y,2)=0},

[0076] I B ={I(x,y)|mod(x+y,2)=1},

[0077] Where (x, y) represents the coordinates of the pixel in image I.

[0078] Step 2: Pixel prediction of the first layer. W As the reference pixel I R , I B As a carrier pixel I C . Using reference pixel set I R Construct the input image, use the prediction neural network to predict the carrier pixel set IC and estimate the prediction error limit. The detailed prediction steps are as follows:

[0079] Step 2-1: Construct a prediction neural network with two output branches, denoted as net(). The structure of the dual-branch prediction neural network used in this embodiment is as follows: Figure 5 As shown in (a) of the figure, the prediction neural network consists of a feature extraction and fusion module, a pixel prediction module, and a prediction error limit assessment module. The feature extraction and fusion module contains three parallel convolutional layers with different kernel sizes (7×7, 5×5, and 3×3, respectively). The network input is concatenated after extracting features through the three convolutional layers in parallel. The concatenated features are then concatenated through multiple residual dense blocks (RDBs). The concatenated features are then concatenated through a convolution operation (consisting of a 1×1 convolutional layer, a ReLU activation function, and a 3×3 convolutional layer in cascade). The concatenated features are then concatenated and concatenated, and then concatenated element-wise with the concatenated features. The fused results are finally output through the MDTA network. The pixel prediction module and prediction error limit assessment module serve as two output branches, each of which takes the fusion result as input and consists of two convolutional layers connected by a ReLU activation function.

[0080] It should be noted that the MDTA network belongs to the existing technology, and its structure is as follows Figure 5 As shown in (b) in .

[0081] Step 2-2: I R Based on, construct the input I of the prediction network in First, use I to I in Initialize and then keep I RThe corresponding pixel is then set to zero for the remaining half of the pixel values. This process can be formulated as follows:

[0082] I in =[I R ,0].

[0083] Step 2-3: I in As the input of the prediction neural network, the trained prediction network net() calculates the carrier pixel set I C The predicted value of each pixel in and the absolute error limit of the corresponding predicted value. The output predicted pixel value set is recorded as The corresponding prediction error set is I ε , then the prediction network process can be formulated as follows:

[0084]

[0085] Step 3: According to the predicted value and the estimated prediction error limit, the I C Split into a sequence of equal-length sub-pixel sequences, the specific steps are as follows:

[0086] Step 3-1: Set the carrier pixel set I C Convert to sequence (I c (1),I c (2),…,I c (L)), and its corresponding predicted value sequence is and the forecast error limit sequence is Where L represents I C The total number of pixels in .

[0087] Step 3-2: Forecast value sequence Arrange in ascending order to obtain The mapping σ represents the σ(i)th pixel in the original sequence to the i-th pixel in the new sequence.

[0088] Step 3-3: According to the permutation mapping σ, the carrier pixel sequence (I c (1),I c (2),…,I c (L)) are sorted to obtain (I c (σ(1)),I c (σ(2))…,I c (σ(L))).

[0089] Step 3-4: According to the preset error threshold value t ε , in the sorted carrier pixel sequence (I c (σ(1)),I c (σ(2)),…,Ic (σ(L))) eliminates prediction errors greater than t ε Pixels, get a new sequence (I c (σ(i1)),I c (σ(i2)),…,I c (σ(i T ), recorded as where index i j Indicates that the prediction error is less than or equal to t ε The position of the pixel, T represents the total number of pixels retained.

[0090] Step 3-5: Sequence Split into a sequence of carrier pixel subsequences of length t, that is in represents the k-th pixel subsequence, 1≤k≤L t .

[0091] Step 4: Using the information embedding method based on Multi-pass IPVO, the secret information s1 is embedded into the sub-pixel sequence in sequence until all the allocated secret information loads are embedded. Assume that the sub-sequence where p i yes The [(k-1)*t+i]th pixel of The information embedding process is as follows:

[0092] Step 4-1: Substitute the sequence (p1, p2, ..., p t ) are sorted in ascending order to obtain (p τ(1) ,p τ(2) ,…,p τ(t) ), where the mapping τ represents the τ(i)th pixel in the original sequence to the i-th pixel in the new sequence. Calculate p τ(t) and (p τ(1) ,p τ(2) ,…,p τ(t-1) ), the specific formula is as follows:

[0093] d L (i) = p x -p y ,

[0094] Among them, 1≤i≤t-1, x=min{τ(t),τ(i)}, y=max{τ(t),τ(i)}.

[0095] Step 4-2: For the sequence (p τ(1) ,p τ(2) ,…,pτ(t-1) ) are embedded as follows:

[0096]

[0097] Where b is the first bit of the information not embedded in the current s, 1≤i≤t-1. The new carrier pixel sequence obtained after this embedding operation is recorded as in

[0098] Step 4-3: Sequence Sort in ascending order again to get a new sequence The meaning of the mapping ω is similar to that in step 3. with sequence The difference of all pixels, the specific formula is as follows:

[0099]

[0100] Among them, x=min{ω(1),ω(i)}, y=max{ω(1),ω(i)}, 2≤i≤t.

[0101] Step 4-4: Sequence The pixels in are embedded as follows:

[0102]

[0103] Where b is the first bit of the information not embedded in the current s, 2≤i≤t. The pixel sequence after this embedding operation is recorded as in This sequence is for The final result after information embedding is recorded as

[0104] Step 5: Execute the second layer of information embedding. The black pixel set after embedding information is used as the reference pixel and the white pixel is used as the carrier pixel. Refer to step 2 to use the dual-branch prediction neural network to predict the carrier pixel value and estimate the prediction error, and then perform steps 3 and 4 in sequence. The two-layer embedding process used in this method is as follows Figure 6 shown.

[0105] Step 6: Information extraction and image restoration. The receiver extracts and restores the carrier pixel set based on the inverse operation of the information embedding operation, performing two layers of information extraction and carrier pixel restoration. In the first layer of information extraction and carrier pixel restoration, pixel prediction is performed using the black pixel set as the reference pixel, and the white pixel set is segmented before information extraction and carrier pixel restoration are performed. In the second layer of information extraction and carrier pixel restoration, pixel prediction is performed using the restored white pixel set as the reference pixel, and the black pixel set is segmented before information extraction and carrier pixel restoration are performed. The specific operations for information extraction and carrier pixel restoration for the embedding operation in step 4 are as follows:

[0106] Step 6-1: Sort by calculate with sequence The difference of all pixels is as follows:

[0107]

[0108] Among them, x=min{ω′(1),ω′(i)}, y=max{ω′(1),ω′(i)}, 2≤i≤t.

[0109] Step 6-2: From the sequence Any pixel in The operation of extracting secret information b is as follows:

[0110]

[0111] and will Perform the following recovery operations:

[0112]

[0113] The result after this recovery operation is in

[0114] Step 6-3: Sequence Sort by calculate with sequence The difference of all pixels in is as follows:

[0115]

[0116] Among them, x=min{τ′(t),τ′(i)}, y=max{τ′(t),τ′(i)}, 1≤i≤t-2.

[0117] Step 6-4: From Any pixel in The operation of extracting secret information b is as follows:

[0118]

[0119] And the pixels Perform the following recovery operations:

[0120]

[0121] The final recovered carrier pixel subsequence is (p1, p2, ..., p t ),in

[0122] Figure 7 The experimental results of this embodiment on the test image are given, where (a) is the input of the prediction neural network, (b) is the output of the pixel prediction module, (c) is the statistical distribution of the output of the prediction error limit evaluation module, and (d) is the steganographic image after embedding information (load: 5000 bits, t ε =1). Experimental verification has shown that the reversible information hiding method based on a neural network and global pixel segmentation in this embodiment can effectively embed secret information with high efficiency, thanks to the neural network-based dual-branch predictor, which provides higher pixel value prediction accuracy and performs error estimation on the predicted values. Furthermore, step 6 allows the image to be restored and the secret information to be extracted. This reversible information hiding method based on a neural network predictor and prediction error modulation utilizes deep learning to improve steganographic performance while maintaining its reversibility.

[0123] The embodiment described above is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Persons skilled in the art may make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, any technical solution obtained by equivalent substitution or equivalent transformation falls within the scope of protection of the present invention.

Claims

1. A reversible information hiding method based on neural network and global pixel segmentation, characterized in that: include: S1, dividing the original carrier image I into two types of pixels, forming a first pixel set and a second pixel set respectively; Among the eight neighboring pixels of each pixel, the pixels on the four sides are all different types of pixels, and the pixels on the four diagonal corners are all the same type of pixels; first, the first pixel set is used as the reference pixel set, and the second pixel set is used as the carrier pixel set; S2, using a prediction neural network with two output branches to predict the pixel value and error limit of each carrier pixel in the carrier pixel set based on the reference pixel set; S3. Sort all carrier pixels in the carrier pixel set in ascending order according to the predicted pixel values ​​of the carrier pixels to obtain a first carrier pixel sequence; then, according to a preset error threshold, select carrier pixels whose prediction errors are less than the preset error threshold from the first carrier pixel sequence to form a second carrier pixel sequence, and sequentially divide the second carrier pixel sequence into a series of sub-pixel sequences of equal length; S4. Perform a secret information embedding operation based on pixel value ordering (PVO) on each sub-pixel sequence. After all the secret information to be embedded is completely embedded, replace all sub-pixel sequences with the carrier pixel set to obtain the carrier pixel set after the information is embedded. S5. Use the second pixel set after embedding the information as the reference pixel set and the first pixel set as the carrier pixel set, and repeat S2 to S4 to obtain the first pixel set after embedding the information; finally, map the pixel values ​​in the first pixel set after embedding the information and the second pixel set after embedding the information to each pixel position of the carrier image to form a carrier image with secret information.

2. The reversible information hiding method based on neural network and global pixel segmentation according to claim 1, characterized in that: The specific implementation method of S2 is as follows: A pixel value matrix of the same size as the original carrier image I is constructed as the network input. The values ​​of all carrier pixels in the matrix maintain the original pixel values ​​in the original carrier image I, and the values ​​of all reference pixels are set to zero. Then, a trained prediction neural network with two branches is used to predict the pixel value of each carrier pixel in the carrier pixel set through one output branch based on the constructed network input, and the prediction error limit of each carrier pixel in the carrier pixel set is estimated through the other output branch.

3. The reversible information hiding method based on neural network and global pixel segmentation according to claim 2, characterized in that: The prediction neural network consists of a feature extraction and fusion module, a pixel prediction module and a prediction error limit evaluation module; The feature extraction and fusion module includes three parallel convolutional layers with different kernel sizes. The network input is spliced ​​after extracting features in parallel through the three convolutional layers. The spliced ​​features are then passed through multiple residual dense network blocks (RDBs) after convolution operations. The features extracted by each residual dense network block are spliced, convolved, and then element-by-element added to the spliced ​​features. Finally, the fusion result is output through the MDTA network. The pixel prediction module and the prediction error limit evaluation module serve as two output branches respectively. Each output branch takes the fusion result as input and is composed of two convolutional layers connected with an activation function in the middle.

4. The reversible information hiding method based on neural network and global pixel segmentation according to claim 1, characterized in that: The prediction neural network needs to be trained on the dataset in advance before being used for actual reasoning.

5. The reversible information hiding method based on neural network and global pixel segmentation according to claim 1, characterized in that: The specific implementation method of S3 is as follows: The carrier pixel set is converted into a carrier pixel sequence, and a predicted pixel value sequence and a predicted error limit sequence corresponding to the carrier pixel sequence are obtained at the same time; the predicted pixel value sequence is arranged in ascending order according to the pixel value size to obtain a new predicted pixel value sequence; the carrier pixel sequence is re-sorted according to the pixel index order in the new predicted pixel value sequence to obtain a first carrier pixel sequence; then, according to a preset error limit threshold value, carrier pixels with a prediction error limit greater than or equal to the error limit threshold value are eliminated from the first carrier pixel sequence to obtain a second carrier pixel sequence; finally, the second carrier pixel sequence is divided into a series of sub-pixel sequences according to a preset segment length.

6. A reversible information recovery method based on neural network and global pixel segmentation, characterized in that: For the carrier image with secret information obtained according to the reversible information hiding method according to any one of claims 1 to 5, first, the second pixel set is used as the reference pixel set, the first pixel set is used as the carrier pixel set, and the inverse operation of the secret information embedding operation described in S2 to S4 is performed to extract the embedded secret information and restore all the first pixels; then, the first pixel set is used as the reference pixel set, the second pixel is used as the carrier pixel, and the inverse operation of the secret information embedding operation described in S2 to S4 is performed to extract the embedded secret information and restore all the second pixels.

7. A reversible information hiding system based on neural network and global pixel segmentation, characterized in that: include: An information hiding module, configured to obtain a carrier image with secret information according to the reversible information hiding method based on a neural network and global pixel segmentation according to any one of claims 1 to 5; An information recovery module is used to extract secret information from a carrier image carrying secret information and restore the carrier image according to the reversible information recovery method based on neural network and global pixel segmentation as described in claim 6.

8. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, it can implement the reversible information hiding method based on a neural network and global pixel segmentation as described in any one of claims 1 to 5, or implement the reversible information recovery method based on a neural network and global pixel segmentation as described in claim 6.

9. A computer-readable storage medium, characterized in that The storage medium stores a computer program. When the computer program is executed by a processor, it can implement the reversible information hiding method based on a neural network and global pixel segmentation as described in any one of claims 1 to 5, or implement the reversible information recovery method based on a neural network and global pixel segmentation as described in claim 6.

10. A computer electronic device, characterized in that: including memory and processor; The memory is used to store computer programs; The processor is configured to, when executing the computer program, implement the reversible information hiding method based on a neural network and global pixel segmentation as described in any one of claims 1 to 5, or implement the reversible information recovery method based on a neural network and global pixel segmentation as described in claim 6.

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