Data transmission method and system for network security platform

Abstract

The invention belongs to the technical field of data transmission, and particularly relates to a data transmission method and system for a network security platform, and the method comprises the steps: obtaining a to-be-transmitted image; acquiring an LBP feature value of each pixel point in the image to be transmitted, and forming an LBP image by using the LBP feature values of all the pixel points; scrambling the LBP image to obtain a ciphertext image; according to the LBP characteristic values of the pixel points, reasoning the size relation of the gray values of the pixel points, selecting the pixel point with the local minimum gray value, the pixel point with the local maximum gray value and a plurality of middle pixel points as marked pixel points according to the size relation, and recording the coordinates of the marked pixel points and the gray values as supplementary information; and encrypting the supplementary information, and transmitting an encryption result and the ciphertext image to a receiving end together. According to the invention, the gray statistical information in the original image is destroyed, and the transmission security is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data transmission. More particularly, the present application relates to a data transmission method and system for a network security platform. BACKGROUND

[0002] In a network security platform, image data (such as face authentication photos, system topology diagrams, identity verification screenshots, etc.) as the core carrier of sensitive information, its secure transmission is a key link to ensure the overall security of the platform. Once these images are leaked, it may lead to invasion of personal privacy, exposure of system architecture or identity fraud, causing serious security incidents. Therefore, sensitive images must be encrypted to ensure their confidentiality during transmission.

[0003] Currently, many systems use image encryption schemes mainly rely on scrambling techniques, that is, through coordinate transformation, pixel position permutation and other methods to disrupt the pixel arrangement of the original image. Although this method can make the ciphertext image visually present a chaotic noise state, effectively hiding the intuitive content of the image, its essential defect is that it does not change the statistical properties of the image. For example, the statistical characteristics such as the gray level histogram and pixel value distribution of the ciphertext image remain highly consistent with the original image. Attackers can use this weakness to analyze the statistical distribution of the ciphertext image, combined with known image types, use statistical analysis, correlation attacks and other means, gradually infer the outline or even key features of the original image, thus achieving the cracking of the encryption system.

[0004] In addition, with the development of artificial intelligence and pattern recognition technology, attackers can use deep learning models to train large-scale ciphertext images to learn the mapping relationship from the statistical characteristics of a specific encryption algorithm to the original image, which makes attacks based on statistical characteristics more efficient and accurate. Therefore, traditional encryption methods relying solely on spatial scrambling are not secure enough to cope with modern complex network threats. There is an urgent need for an encryption scheme that can fundamentally destroy the statistical properties of images while ensuring high-fidelity recovery at the receiving end to meet the requirements of high-security level network platforms for image transmission. SUMMARY

[0005] To solve the technical problem that the existing image encryption method only changes the pixel position without changing the statistical properties of the image, which is difficult to resist statistical analysis attacks, the present application provides solutions in the following aspects.

[0006] In a first aspect, the present application provides a data transmission method for a network security platform, comprising: acquire an image to be transmitted; acquire an LBP feature value of each pixel point in the image to be transmitted, and construct an LBP image by using the LBP feature values of all the pixel points; obtain a ciphertext image by scrambling the LBP image; infer a size relationship of gray scale values of each pixel point according to the LBP feature value of the pixel point, select a pixel point with a local minimum gray scale value, a pixel point with a local maximum gray scale value, and a plurality of intermediate pixel points as marked pixel points according to the size relationship, and record coordinates and gray scale values of the marked pixel points as supplementary information; encrypt the supplementary information, and transmit the encrypted result and the ciphertext image to a receiving end.

[0007] The application fundamentally destroys the global gray scale statistical characteristics of the image by acquiring the LBP feature value of each pixel point in the image to be transmitted and constructing the LBP image, and converting the gray scale information of the original image into local texture structure features, so that an attacker cannot crack the image by statistical analysis; subsequently, the spatial correlation of the pixels is further disturbed by scrambling the LBP image, so that the ciphertext image visually presents a completely random noise shape, and the security in the transmission process is greatly enhanced; on this basis, the local extreme points and the key intermediate points are selected as the supplementary information according to the relative size relationship of the pixel gray scale values inferred from the LBP feature values, the supplementary information is encrypted and transmitted together with the ciphertext image, so that the receiving end can use the gray scale values of the pixel points recorded in the supplementary information and the size relationship of the pixel gray scale values inferred from the LBP image restored according to the ciphertext image to restore the original image with high fidelity by the interpolation method, thereby ensuring the security and high efficiency of the image transmission and realizing the high-fidelity restoration of the original image.

[0008] Preferably, acquiring the LBP feature value of each pixel point in the image to be transmitted comprises: sorting the 8 neighborhood pixel points of the pixel point according to a preset rule, comparing the gray scale value of each neighborhood pixel point with the gray scale value of the pixel point , marking 1 if the gray scale value of the neighborhood pixel point is greater than or equal to the gray scale value of the pixel point , otherwise marking 0; constructing an 8-bit binary number by using the obtained 8 marks in the order of the sorting of the neighborhood pixel points, converting the 8-bit binary number into a decimal number, and obtaining the LBP feature value of the pixel point .

[0009] The application converts absolute gray scale information of the image into relative texture relation with the surrounding neighborhood by calculating LBP feature value of each pixel point, which fundamentally destroys the gray scale statistical characteristics of the original image, so that the attacker cannot infer the original content by analyzing the statistical characteristics such as the gray scale histogram of the ciphertext image, thereby greatly enhancing the security of the image in the transmission process; meanwhile, the LBP feature value retains the key local structure and texture information of the image, which provides the possibility for subsequent recovery of the global gray scale of the image based on the relative relation at the receiving end, and realizes the effective balance between security and recoverability.

[0010] Preferably, the 8 neighborhood pixels of the pixel point are sorted according to a preset rule, including: obtaining a chaotic sequence by using a chaotic mapping method, multiplying each element in the chaotic sequence by 8 and then taking the upper integer to obtain an integer sorting sequence in the range of [1, 8]; for each pixel point in the image to be transmitted, the 8 neighborhood pixels of the pixel point are sorted according to the sorting sequence by using the variable-length Joseph algorithm.

[0011] The application generates a sorting sequence by using chaotic mapping and sorts the 8 neighborhoods of the pixel point by combining the variable-length Joseph algorithm, so that the calculation process of the LBP feature value introduces high randomness and key dependence; the chaotic sequence is extremely sensitive to the initial parameters, and even if the attacker knows the LBP algorithm itself, the attacker cannot infer the sorting order of the neighborhood pixels without knowing the chaotic mapping parameters, so the LBP feature value cannot be correctly calculated, which fundamentally breaks the predictability between the LBP feature and the original image gray scale, greatly enhances the anti-attack ability of image encryption, and significantly improves the security of the transmission process.

[0012] Preferably, the size relation of the gray scale values of the pixels is inferred according to the LBP feature value of the pixel point, including: for any pixel point, constructing the inequality of the gray scale values between the pixel point and the neighborhood pixels according to the LBP feature value of the pixel point; by analyzing the LBP values of all pixel points, the inequality of the gray scale values between all adjacent pixel points is obtained; the inequalities of the gray scale values between all adjacent pixel points are concatenated to obtain the size relation of the gray scale values of the pixel points.

[0013] The present application can systematically deduce the relative size relationship of the pixel gray values in the whole image by analyzing the LBP characteristic value of each pixel point to construct the gray value inequality with the neighborhood pixel points and integrating the inequality between all the adjacent pixel points, fully utilizes the local gray relative information coded by the LBP feature, provides a reliable theoretical basis for the subsequent selection of local extreme points and intermediate pixel points as supplementary information, thereby significantly reduces the amount of supplementary data required to be transmitted under the premise of ensuring high-fidelity image recovery, and balances the safety, transmission efficiency and image quality.

[0014] Preferably, the process of obtaining the pixel point with the local minimum gray value and the pixel point with the local maximum gray value is as follows: constructing a gray directed graph according to the size relationship of the pixel gray values: regarding each pixel point as a node, if the gray values of two adjacent pixel points are the same in the deduced size relationship of the gray values, the two pixel points are merged into one node, if the gray values of two adjacent pixel points are different, a directed edge is constructed between the two pixel points, and the directed edge is directed from the pixel point with the smaller gray value to the pixel point with the larger gray value; regarding the pixel point corresponding to the node with only the out-degree in the gray directed graph as the pixel point with the local minimum gray value; regarding the pixel point corresponding to the node with only the in-degree in the gray directed graph as the pixel point with the local maximum gray value.

[0015] The present application can realize the visualization and structuring of the complex inequality system by constructing the gray directed graph, convert the relative size relationship between the pixel points into the topological structure of the graph, systematically identify the extreme points of the image, make the selection process of the marked pixel points objective and calculable, and provide a reliable theoretical basis and implementation path for the subsequent high-fidelity recovery of the image with the least amount of data.

[0016] Preferably, the method for obtaining the marked pixel points comprises: taking all the pixel points with the local minimum gray value and all the pixel points with the local maximum gray value as the initial marked pixel points; predicting the gray values of the remaining pixel points by the interpolation method based on the gray values of all the initial marked pixel points and the size relationship of the pixel gray values, obtaining the gray prediction values of the remaining pixel points, and calculating the gray loss values; in response to the average value of the gray loss values of all the pixel points being not less than a preset loss threshold, taking the pixel point with the largest gray loss value as a marked pixel point; repeating the process of obtaining and judging the gray loss values until the average value of the gray loss values is less than the loss threshold, and obtaining all the marked pixel points.

[0017] The application realizes adaptive optimization of the supplementary information by taking the local extreme point as an initial marked pixel point, performing interpolation prediction based on the size relationship between pixel points, and iteratively supplementing the pixel point with the largest prediction error as a new marked pixel point, dynamically balances the data amount of the supplementary information and the image fidelity under the premise of ensuring the quality of the finally restored image, avoids information redundancy or deficiency, thereby ensuring high-fidelity image restoration while minimizing the data amount of the supplementary information to be transmitted, and improving the transmission efficiency of the system.

[0018] Preferably, the supplementary information is encrypted, including: using an asymmetric encryption algorithm to encrypt the supplementary information.

[0019] Preferably, the LBP image is scrambled, including: using Arnold mapping to scramble the LBP image.

[0020] Preferably, if the image to be transmitted is an RGB image, each channel of the RGB image is regarded as a gray-scale image respectively to perform the operations of LBP image acquisition, scrambling and supplementary information acquisition.

[0021] In the second aspect, the application provides a data transmission system for a network security platform, including a processor and a memory, the memory stores computer program instructions, when the computer program instructions are executed by the processor, the above-mentioned data transmission method for a network security platform is realized.

[0022] By using the above technical solution, the above-mentioned data transmission method for a network security platform is generated into a computer program and stored in the memory to be loaded and executed by the processor, so that a terminal device is made according to the memory and the processor, and the use is convenient.

[0023] The application has the following beneficial effects: The application fundamentally destroys the statistical characteristics and spatial structure of the original image by combining the LBP feature conversion of the image with the scrambling operation, makes the ciphertext image visually present completely random noise, greatly improves the security in the transmission process, and effectively resists attacks based on statistical analysis. On this basis, the relative relationship of local gray scale contained in the LBP feature value is used to deduce the size relationship between pixels, and the local extreme point and the key intermediate point are selected as the supplementary information, and the supplementary information is encrypted and transmitted together with the ciphertext image, so that the receiving end can use the gray scale value of the pixel point recorded in the supplementary information and the size relationship between the pixel points deduced from the LBP image restored according to the ciphertext image to restore the original image with high fidelity. The application ensures the safe transmission of the image content while minimizing the additional data overhead. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 is a flow chart illustrating a data transmission method for a network security platform according to the present application; Figure 2 is a diagram illustrating a 3x3 pixel block; Figure 3 is a diagram illustrating a virtual neighborhood pixel setting; Figure 4 is a diagram illustrating an image to be transmitted; Figure 5 is a diagram illustrating an LBP image corresponding to embodiment one; Figure 6 is a diagram illustrating an LBP image corresponding to embodiment two; Figure 7 is a diagram illustrating a gray scale histogram of Figure 4 ; Figure 8 is a diagram illustrating a gray scale histogram of Figure 5 ; Figure 9 is a diagram illustrating a gray scale histogram of Figure 6 ; Figure 10 is a diagram illustrating a ciphertext image corresponding to Figure 5 ; Figure 11 is a diagram illustrating a ciphertext image corresponding to Figure 6 ; Figure 12 is a diagram illustrating a 4x4 pixel block; Figure 13 is a diagram illustrating an LBP image corresponding to Figure 12 ; Figure 14 is a diagram illustrating a gray scale directed graph corresponding to Figure 13 ; Figure 15 is a diagram illustrating a decrypted image of Figure 10 ; Figure 16 is a diagram illustrating a decrypted image of Figure 11 . DETAILED DESCRIPTION

[0025] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without any creative work fall within the scope of the present application.

[0026] The specific embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0027] The embodiment of the present application discloses a data transmission method for a network security platform, referring to Figure 1 , comprising steps S1-S5: S1, acquiring an image to be transmitted.

[0028] It should be noted that in the network security platform, the image often contains sensitive information (such as face, system topology, etc.), and direct transmission has the risk of leakage. Therefore, the present application encrypts the image for transmission.

[0029] Specifically, the image to be transmitted is a sensitive image that needs to be transmitted in the network security platform, for example, a user's face authentication photo, a system topology diagram, or a screenshot containing sensitive information. This embodiment takes a gray-scale image with a size of as an example for illustration, wherein represents a pixel point located at the th row and the th column of the gray-scale image, , .

[0030] In other embodiments, if the sensitive image to be transmitted is an RGB image, the RGB image can be converted into a gray-scale image for subsequent encryption operation, or each channel of the RGB image can be regarded as a gray-scale image for subsequent encryption operation.

[0031] S2, acquiring the LBP feature value of each pixel point in the image to be transmitted, and constructing an LBP image by using the LBP feature values of all the pixel points.

[0032] It should be noted that at present, for image data, a method of scrambling is usually used to encrypt the image data, but the scrambling of the image data does not destroy the gray-scale statistical characteristics in the image data, and an attacker may use the gray-scale statistical characteristics to analyze and crack the image data. The local binary pattern (LBP, Local Binary Patterns) feature value is a local texture descriptor, which reflects the relative relationship between a pixel point and its neighborhood. By calculating the LBP feature value, the gray-scale information of the image data can be converted into its local structure feature. Therefore, the present application acquires the LBP feature value of each pixel point in the image to be transmitted, so as to subsequently scramble based on the LBP feature value, which fundamentally destroys the statistical characteristics of the image to be transmitted and improves the transmission security of the image to be transmitted.

[0033] Specifically, for each pixel point in the image to be transmitted The eight neighborhood pixels of the pixel point are sorted according to a preset rule, the gray value of each neighborhood pixel is compared with the gray value of the center pixel point , if the gray value of the neighborhood pixel is greater than or equal to the gray value of the center pixel point , then it is marked as 1, otherwise it is marked as 0. Eight marks obtained are constituted into an 8-bit binary number, the 8-bit binary number is converted into a decimal number, and the LBP feature value of the center pixel point is obtained.

[0034] In embodiment one, the eight neighborhood pixels of the pixel point are sorted according to a preset rule, including: The first row and the first column of the neighborhood pixels in the eight neighborhoods of the pixel point are taken as a starting point, the eight neighborhood pixels of the pixel point are sorted in a clockwise or counterclockwise order, and the marks corresponding to the eight neighborhood pixels are constituted into an 8-bit binary number in the order of sorting, so as to obtain the LBP feature value of the center pixel point .

[0035] Exemplarily, Figure 2 a 3*3 pixel block is represented, Figure 2 each box represents a pixel point, and the numerical value in the box represents the gray value of the pixel point. The gray value of the center pixel point is 80. After the eight neighborhood pixels are arranged in a clockwise order with the neighborhood pixel point in the upper left corner of the center pixel point (the first row and the first column) as a starting point, the gray values of the neighborhood pixel points are 78, 79, 82, 83, 81, 77, 76 and 79 respectively. The comparison results of the gray values of the eight neighborhood pixel points and the gray value of the center pixel point are 0, 0, 1, 1, 1, 0, 0 and 0 respectively, the 8-bit binary number is 00111000, and after conversion into a decimal number, it is 56. Therefore, the LBP feature value of the center pixel point is 56.

[0036] In embodiment two, the eight neighborhood pixels of the pixel point are sorted according to a preset rule, including: A chaotic sequence is obtained by using a chaotic mapping method, and the length of the chaotic sequence is , wherein represents the size of the image to be transmitted. After each element in the chaotic sequence is multiplied by 8 and rounded up, a total of integers in the range of [1, 8] are obtained, and the integers are constituted into a sorting sequence. For each pixel point in the image to be transmitted, the eight neighborhood pixels of the pixel point are sorted according to the sorting sequence by using a variable-length Joseph algorithm, the marks corresponding to the eight neighborhood pixels are constituted into an 8-bit binary number in the order of sorting, and the LBP feature value of the center pixel point is obtained.

[0037] It should be noted that the specific algorithm of the chaotic mapping is not limited in the embodiment, and the implementer can select a specific chaotic mapping algorithm according to the actual implementation, for example, a Logistic chaotic mapping, a Tent chaotic mapping, or the like. The parameters of the chaotic mapping algorithm are pre-agreed by the sending end and the receiving end. In the case that the attacker knows the chaotic mapping parameters, the chaotic sequence and the sorting sequence cannot be guessed, even if the attacker knows the LBP characteristic value adopted by the present application, the sorting order of the neighborhood pixel points cannot be guessed, so that the LBP characteristic value cannot be correctly calculated, and the security of the image to be transmitted is further improved on the basis of the first embodiment.

[0038] Exemplarily, assuming that the chaotic sequence is {0.125, 0.456, 0.789, 0.234, 0.567, 0.800, 0.345, 0.678,…}, the corresponding sorting sequence is {1, 4, 7, 2, 5, 7, 3, 6,…}, according to the sorting sequence, the variable-length Joseph algorithm is used to sort the 8 neighborhood pixel points in the image block. Figure 2 The 8 neighborhood pixel points in the image block are sorted by the variable-length Joseph algorithm as follows: first, taking the neighborhood pixel point in the upper left corner as the starting point, the 8 neighborhood pixel points are arranged in a clockwise order to form an initial sequence, the gray value sequence corresponding to the initial sequence is {78, 79, 82, 83, 81, 77, 76, 79}, an empty sequence is constructed, denoted as a result sequence, for storing the sorting result of the neighborhood pixel points according to the variable-length Joseph algorithm. According to the first element 1 in the sorting sequence, the first pixel point in the initial sequence is selected without replacement and added to the result sequence, then the gray value sequence corresponding to the result sequence is {78}, and the remaining 7 pixel points in the initial sequence correspond to the gray value sequence {79, 82, 83, 81, 77, 76, 79}. According to the second element 4 in the sorting sequence, the fourth pixel point in the initial sequence is selected without replacement and added to the result sequence, then the gray value sequence corresponding to the result sequence is {78, 81}, and the remaining 6 pixel points in the initial sequence correspond to the gray value sequence {79, 82, 83, 77, 76, 79}. According to the third element 7 in the sorting sequence, the seventh pixel point in the initial sequence is selected without replacement and added to the result sequence, but since the initial sequence only contains 6 pixel points, the seventh %6=1 pixel point in the initial sequence is selected without replacement and added to the result sequence, where % represents the modulus operation, then the gray value sequence corresponding to the result sequence is {78, 81, 79}, and the remaining 5 pixel points in the initial sequence correspond to the gray value sequence {82, 83, 77, 76, 79}. In this way, the final result sequence corresponds to the gray value sequence {78, 81, 79, 83, 82, 77, 76, 79}, that is, the variable-length Joseph algorithm sorts the 8 neighborhood pixel points in the image block. Figure 2 After the 8 neighborhood pixel points in the image block are sorted by the variable-length Joseph algorithm, the gray values of the neighborhood pixel points are 78, 81, 79, 83, 82, 77, 76, and 79, respectively.Figure 2 The gray value of the center pixel point in the image is 80, and the comparison results of the gray values of the 8 neighborhood pixel points with the gray value of the center pixel point are 0, 1, 0, 1, 1, 0, 0, and 0, respectively. Therefore, the 8-bit binary number is 01011000, and the conversion to the decimal number is 88. Therefore, the LBP feature value of the center pixel point in the image is 88. Figure 2 The LBP feature value of the center pixel point in the image is 88.

[0039] In other embodiments, the implementer can sort the 8 neighborhood pixel points of the pixel point in other ways, and the sorting rules need to be agreed in advance by the sending end and the receiving end, so that the receiving end can correctly restore the original image.

[0040] It should be particularly noted that for the pixel points in the first row, the pixel points in the first column, the pixel points in the last row, and the pixel points in the last column in the image to be transmitted, the number of neighborhood pixel points in the eight neighborhoods of the pixel points is less than 8. At this time, a virtual neighborhood pixel point is set to complete the 8 neighborhood pixel points, and the gray value of the virtual neighborhood pixel point is the same as that of the center pixel point, so as to ensure that the LBP feature value can be calculated for the pixel points in the first row, the pixel points in the first column, the pixel points in the last row, and the pixel points in the last column in the image to be transmitted.

[0041] Exemplarily, Figure 2 There are only 3 neighborhood pixel points in the eight neighborhoods of the pixel point in the first row and the first column in the image, and therefore a virtual neighborhood pixel point needs to be set. Figure 2 The schematic diagram of setting the virtual neighborhood pixel point for the pixel point in the first row and the first column in the image is shown in FIG. 2. Figure 3 , Figure 3 The solid line box in the image represents the actual pixel point in the image to be transmitted, and the dashed line box represents Figure 2 The virtual neighborhood pixel point of the pixel point in the first row and the first column in the image is set in the manner of the embodiment one. Figure 2 After the sorting of all the neighborhood pixel points of the pixel point in the first row and the first column in the image in the clockwise direction, the gray values of the neighborhood pixel points are 78, 78, 78, 79, 80, 79, 78, and 78, respectively. The comparison results of the gray values of the 8 neighborhood pixel points with the gray value of the center pixel point are 1, 1, 1, 1, 1, 1, 1, and 1, respectively. Therefore, the 8-bit binary number is 11111111, and the conversion to the decimal number is 255. Therefore, the LBP feature value of the center pixel point in the image is 255. Figure 2The LBP feature value of the pixel in the first row and first column is 255. Assuming the sorting sequence is {1,4,7,2,5,7,3,6,…}, after sorting the 8 neighboring pixels using the method described in Example 2, the grayscale values ​​of each neighboring pixel are 78, 80, 78, 79, 78, 79, 78, 78. The comparison results between the grayscale values ​​of the 8 neighboring pixels and the grayscale value of the center pixel are 1, 1, 1, 1, 1, 1, 1, 1. Therefore, the 8-bit binary number is 11111111, which is 255 in decimal. Figure 2 The LBP feature value of the pixel in the first row and first column is 255.

[0042] Furthermore, the LBP feature values ​​of each pixel are filled into an empty matrix of the same size as the image to be transmitted, according to the position of the pixel in the image to be transmitted, to obtain the LBP image.

[0043] For example, Figure 4 This is a schematic diagram of the image to be transmitted. The LBP image obtained using the method described in Example 1 is shown below. Figure 5 As shown. Assuming the Logistic chaotic mapping algorithm is used to obtain the chaotic sequence, and the chaotic mapping parameters are (3.698, 0.24), the LBP image obtained using the method in Example 2 is as follows. Figure 6 As shown. Figure 4 , Figure 5 , Figure 6 The corresponding grayscale histograms are as follows: Figure 7 , Figure 8 , Figure 9 As shown. It can be seen that... Figure 5 , Figure 6 The corresponding grayscale histogram ( Figure 8 , Figure 9 )and Figure 4 The corresponding grayscale histogram ( Figure 7 Completely inconsistent Figure 5 , Figure 6 Damaged Figure 4 The grayscale distribution characteristics are preserved, but the grayscale features are retained. Figure 4 It has certain texture features. Figure 6 Texture features in Figure 5 More ambiguous, and according to Figure 5 as well as Figure 6 The corresponding grayscale histogram ( Figure 8 , Figure 9 It can be seen that, Figure 6 The grayscale distribution in the middle is compared to Figure 5 The grayscale distribution in the image is more uniform, making it more uniform. Figure 6 It presents a more random visual effect.

[0044] It should be noted that since the LBP feature value of the pixel point is converted from an 8-bit binary number, and the range of the decimal number corresponding to the 8-bit binary number is [0, 255], the pixel value range of the obtained LBP image is [0, 255]. The LBP image is completely different from the original image in vision, destroys the overall gray distribution information of the original image, but retains the key structural information of the original image, so that the LBP image becomes an ideal encryption object, and the attacker is difficult to intuitively understand the content even if the ciphertext image is obtained, and it is also difficult to crack the original image by using the gray distribution information in the ciphertext image, thereby improving the security of image data transmission.

[0045] S3, scrambling the LBP image to obtain a ciphertext image.

[0046] Specifically, the Arnold mapping is used to scramble the LBP image, and the LBP image after scrambling is taken as the ciphertext image.

[0047] It should be noted that the Arnold mapping is a chaotic mapping method for repeated folding and stretching transformation in a limited area. The scrambling of the image pixel position is realized by coordinate transformation and modular operation, so that the scrambled image presents a completely random state in vision. The Arnold mapping has the typical characteristics of chaotic system: sensitive to initial conditions, long-term behavior unpredictable, and topological transitivity. These characteristics make it difficult for an attacker to recover the original image without knowing the key even if the scrambling algorithm is known. The specific implementation process of the Arnold mapping is a known technology, and will not be described in detail here. The chaotic parameters and the number of iterations involved in the Arnold mapping are pre-agreed by the sender and the receiver.

[0048] In other embodiments, the implementer can also use other scrambling methods, such as a scrambling algorithm based on the Logistic chaotic system and a scrambling algorithm based on the Tent chaotic system. These methods are also based on the characteristics of the chaotic system, and realize the rearrangement of the image pixel position through different mathematical transformations, and have similar scrambling effects and security. Whether to choose a scrambling method can be weighed according to the security requirements and computing resources of the actual application scenario. It should be emphasized that no matter which scrambling method is used, the parameters and the number of iterations should be securely shared by the sender and the receiver as a key to ensure the correctness of the encryption and decryption process.

[0049] Exemplarily, the Arnold mapping is used to scramble the LBP image shown in FIG. 4, and the obtained ciphertext image is as shown in FIG. 5. Figure 5 Exemplarily, the Arnold mapping is used to scramble the LBP image shown in FIG. 4, and the obtained ciphertext image is as shown in FIG. 5. Figure 10 Exemplarily, the Arnold mapping is used to scramble the LBP image shown in FIG. 4, and the obtained ciphertext image is as shown in FIG. 5. Figure 6 Exemplarily, the Arnold mapping is used to scramble the LBP image shown in FIG. 4, and the obtained ciphertext image is as shown in FIG. 5. Figure 11As shown. It can be seen that... Figure 10 and Figure 5 In contrast, the spatial structure of the image is completely disrupted, presenting a completely random noise appearance, making it impossible to identify any meaningful texture or edge information. Figure 4 compared to, Figure 10 Not only are the grayscale distribution features of the original image lost, but the spatial correlation is also completely destroyed, making it impossible for attackers to infer the content of the original image through visual recognition or statistical analysis. Figure 11 and Figure 6 In comparison, it also presents a completely random visual effect, and Figure 4 compared to, Figure 11 and Figure 4 The correlation between them is further reduced, and even if the attacker knows the LBP algorithm and the Arnold mapping, they will not be able to recover the original image without knowing the chaotic sequence parameters and the number of scrambling iterations.

[0050] S4. Based on the LBP feature value of the pixel, infer the relationship between the gray values ​​of each pixel. Based on the relationship, select the pixel with the local minimum gray value, the pixel with the local maximum gray value, and several intermediate pixels as marked pixels. Record the coordinates and gray values ​​of the marked pixels as supplementary information.

[0051] It should be noted that transmitting only the encrypted image is insufficient to fully recover the original image. While LBP feature transformation and scrambling operations destroy the statistical properties and spatial structure of the original image, they also lose its global grayscale information. To ensure that the receiving end can recover the original image with high fidelity, supplementary information needs to be acquired and transmitted. Since LBP feature values ​​encode the relative relationships of local gray levels, the relative magnitudes of gray values ​​between pixels can be inferred from the LBP feature values.

[0052] Specifically, the relationship between the grayscale values ​​of each pixel is inferred based on the LBP feature values ​​of the pixels, including: For any given pixel, construct inequalities in grayscale values ​​between that pixel and its neighboring pixels based on its LBP feature value. By analyzing the LBP values ​​of all pixels, inequalities in grayscale values ​​between all adjacent pixels can be obtained. By connecting all the inequalities in grayscale values ​​between adjacent pixels, the relationship between the grayscale values ​​of the pixels can be solved.

[0053] In the specific implementation process, if step S2 uses the method of obtaining LBP feature values ​​as described in Example 1, then starting from the first row and first column of the 8-neighborhood of a pixel, the 8 neighboring pixels are sorted in a clockwise or counterclockwise order. According to the sorted order, the LBP binary bit corresponding to each neighboring pixel is determined in turn. If the bit is 1, then an inequality is constructed: , if the bit is 0, then the inequality is constructed: ; if step S2 adopts the LBP feature value acquisition method in Example 2, then the 8 neighborhood pixels of the pixel point are sorted by using the variable-length Joseph algorithm according to the sorting sequence generated by the chaotic sequence, and according to the sorted sequence, the LBP binary bit corresponding to each neighborhood pixel point is judged in turn, if the bit is 1, then the inequality is constructed: , if the bit is 0, then the inequality is constructed: . Wherein, represents the gray value of the pixel point , H represents the gray value of the pixel point , H represents the gray value of the pixel point , H represents the gray value of the pixel point , and the pixel point is the neighborhood pixel point of the pixel point .

[0054] Exemplarily, Figure 12 is a 4x4 image block, Figure 12 each box in the figure represents a pixel point, and the numerical value in the box represents the gray value of the pixel point. The corresponding LBP image of Figure 12 obtained by the method in step S2 in Example 1 is shown in Figure 13 , Figure 13 the numerical value in the box represents the LBP feature value of the pixel point. For the pixel point (1, 1) in Figure 13 , the LBP feature value 251 converted into binary is 11111011, the values of the first 3 bits and the 7th and 8th bits in 11111011 reflect the size relationship between the virtual neighborhood pixel point and the gray value of the pixel point (1, 1), the values of these bits can be ignored, the 4th bit 1 in 11111011 reflects that the gray value of the pixel point (1, 2) is greater than or equal to the gray value of the pixel point (1, 1), so H(1, 2) H(1, 1), the 5th bit 1 in 11111011 reflects that the gray value of the pixel point (2, 2) is greater than or equal to the gray value of the pixel point (1, 1), so H(2, 2) H(1, 1), the 6th bit 0 in 11111011 reflects that the gray value of the pixel point (2, 1) is less than the gray value of the pixel point (1, 1), so H(2, 1)<H(1, 1); similarly, for the pixel point (1, 2) in Figure 13 , the LBP feature value 252 converted into binary is 11111100, so H(1, 1), H(2, 1), H(2, 2)<H(1, 2) H(1, 3), H(2, 3); for Figure 13For the middle pixel point (1,3), its LBP feature value 254 is converted into binary as 11111110, and thus H(1,2) < H(1,3) H(1,4), H(2,2), H(2,3), H(2,4); for Figure 13 For the middle pixel point (1,4), its LBP feature value 254 is converted into binary as 11111110, and thus H(1,3) < H(1,4) H(2,3), H(2,4); and so on, for Figure 13 For the remaining pixel points, the inequalities of the gray values obtained according to the LBP feature values are respectively: H(2,1) H(1,1), H(1,2), H(2,2), H(3,1), H(3,2); H(1,1), H(1,2), H(1,3), H(2,1), H(3,1) < H(2,2) H(2,3), H(3,2), H(3,3); H(1,2), H(1,3), H(1,4), H(2,1) < H(2,3) H(2,4), H(3,2), H(3,3), H(3,4); H(1,3), H(1,4), H(2,3), H(3,4) < H(2,4) H(3,3); H(2,1) < H(3,1) H(2,2), H(3,2), H(4,1), H(4,2); H(2,1), H(2,2), H(2,3), H(3,1), H(3,3), H(4,1), H(4,2), H(4,3) < H(3,2); H(2,2), H(2,3), H(2,4), H(3,4), H(4,2), H(4,3) < H(3,3) H(3,2), H(4,4); H(3,4) H(2,3), H(2,4), H(3,3), H(4,3), H(4,4); H(3,1) < H(4,1) H(3,2), H(4,2); H(3,1), H(4,1) < H(4,2) H(3,2), H(3,3), H(4,3); H(3,2), H(3,3), H(4,4); H(3,2), H(3,3), H(4,4); H(3,2), H(3,3), H(4,4);

[0055] According to the above inequalities, the size relationship between the gray values of the pixel points can be derived as follows: H(2,1) < H(1,1) < H(1,2) < H(1,3) < H(2,2), H(1,4) < H(2,3) = H(3,4) < H(2,4), H(4,3) < H(3,3) < H(3,2), H(4,4); H(2,1) < H(3,1) < H(2,2) < H(2,3) = H(3,4) < H(2,4), H(4,3) < H(3,3) < H(3,2), H(4,4); H(2,1) < H(3,1) < H(4,1) < H(4,2) < H(4,3) < H(3,3) < H(3,2), H(4,4).

[0056] Further, the method for obtaining the pixel point with the local minimum gray value, the pixel point with the local maximum gray value and the intermediate pixel point comprises: Specifically, a gray directed graph is constructed according to the size relationship between the gray values of the pixel points: each pixel point is regarded as a node, for any two adjacent pixel points in the size relationship between the gray values of the pixel points, if the gray values of the two pixel points are the same in the size relationship obtained by solving, the two pixel points are merged into one node, if the gray values of the two pixel points are different in the size relationship obtained by solving, a directed edge is constructed between the two pixel points, the directed edge is directed from the pixel point with the smaller gray value to the pixel point with the larger gray value.

[0057] Exemplarily, Figure 14 The corresponding gray directed graph is shown in Figure 14 . Figure 13 Each circle in the figure represents a node, and the numerical value in the circle represents the position of the pixel point corresponding to the node in the image. Figure 14

[0058] ​It should be noted that if a node in the gray scale directed graph only has an in-degree without an out-degree, it indicates that the gray scale value of the pixel point corresponding to the node is greater than the gray scale values of all neighboring pixel points. If a node in the gray scale directed graph only has an out-degree without an in-degree, it indicates that the gray scale value of the pixel point corresponding to the node is less than the gray scale values of all neighboring pixel points. If a node in the gray scale directed graph has both an in-degree and an out-degree, it indicates that the gray scale value of the pixel point corresponding to the node is in the middle of the gray scale values of all pixel points in the neighborhood range. Therefore, the present application obtains the pixel point with a local minimum gray scale value, the pixel point with a local maximum gray scale value and a plurality of intermediate pixel points according to the in-degree and the out-degree of the node.

[0059] Specifically, all nodes in the gray scale directed graph with only an out-degree without an in-degree are taken as the pixel points with a local minimum gray scale value. All nodes in the gray scale directed graph with only an in-degree without an out-degree are taken as the pixel points with a local maximum gray scale value. The nodes in the gray scale directed graph with both an out-degree and an in-degree are taken as intermediate pixel points.

[0060] Exemplarily, Figure 12 The node (2, 1) has only an out-degree without an in-degree, so the pixel point (2, 1) is a pixel point with a local minimum gray scale value. The nodes (3, 2) and (4, 4) have only an in-degree without an out-degree, so the pixel points (3, 2) and (4, 4) are pixel points with a local maximum gray scale value. The remaining nodes have both an in-degree and an out-degree, and the corresponding pixel points are intermediate pixel points.

[0061] Further, the pixel points with a local minimum gray scale value, the pixel points with a local maximum gray scale value and a plurality of intermediate pixel points are selected as the marked pixel points according to the size relationship, and the coordinates and the gray scale values of the marked pixel points are recorded as the supplementary information, including: 1. The pixel points with a local minimum gray scale value and all pixel points with a local maximum gray scale value are taken as the marked pixel points.

[0062] 2. Based on the size relationship between the gray scale values of all the marked pixel points and the gray scale values of the pixel points obtained, the gray scale prediction values of the remaining pixel points are obtained by an interpolation method. The difference between the gray scale prediction value of a pixel point and the gray scale value of the pixel point is taken as the gray scale loss value of the pixel point.

[0063] 3. In response to the average value of the gray scale loss values of all the pixel points being less than a preset loss threshold, the coordinates and the gray scale values of all the marked pixel points are taken as the supplementary information. In response to the average value of the gray scale loss values of all the pixel points not being less than the preset loss threshold, the pixel point with the maximum gray scale loss value is also taken as a marked pixel point. Steps 2-3 are repeated until the average value of the gray scale loss values of all the pixel points is less than the preset loss threshold, and the iteration is stopped.

[0064] Wherein, the loss threshold is set by the implementer according to the actual implementation situation, when high-fidelity recovery image is needed, a smaller loss threshold can be set, for example, 5, when a certain degree of image distortion is allowed, a larger loss threshold can be set, for example, 10, but in order to ensure the basic usability of the image, it is required that the loss threshold cannot exceed 20.

[0065] It should be noted that since the LBP feature value only retains the local gray size relationship of the pixel point, the size relationship between the gray values of the pixel points obtained based on the LBP feature value may have multiple components, and the gray values of the remaining pixel points predicted based on each component and the gray value of the marked pixel point may have multiple results, therefore, the present application takes the average of multiple prediction results of the gray value of the same pixel point after rounding off to the nearest integer as the gray prediction value of the pixel point.

[0066] Exemplarily, assuming that the loss threshold is set to 5, the gray values of the pixel points are as follows Figure 15 The pixel point (2, 1) is the pixel point with the local minimum gray value, the gray value is 83, the pixel points (3, 2) and (4, 4) are the pixel points with the local maximum gray value, the gray values are both 99, the size relationship between the gray values of the pixel points obtained is: component 1: H(2, 1)<H(1, 1)<H(1, 2)<H(1, 3)<H(2, 2), H(1, 4)<H(2, 3)=H(3, 4)<H(2, 4), H(4, 3)<H(3, 3)<H(3, 2), H(4, 4); component 2: H(2, 1)<H(3, 1)<H(2, 2)<H(2, 3)=H(3, 4)<H(2, 4), H(4, 3)<H(3, 3)<H(3, 2), H(4, 4); component 3: H(2, 1)<H(3, 1)<H(4, 1)<H(4, 2)<H(4, 3)<H(3, 3)<H(3, 2), H(4, 4). Taking (2, 1), (3, 2) and (4, 4) as the marked pixel points, according to component 1, the interpolation method can obtain 83<85<87<89<91<93<95<97<99; according to component 2, the interpolation method can obtain 83<85.67<88.34<91.04<93.68<96.35<99; according to component 3, the interpolation method can obtain 83<85.67<88.34<91.04<93.68<96.35<99. Therefore, the gray prediction value of the pixel point (1, 1) is 85, the gray loss value is , the gray prediction value of the pixel point (1, 2) is 87, the gray loss value is , the gray prediction value of the pixel point (1, 3) is 89, the gray loss value is , the gray prediction value of the pixel point (1, 4) is 91, the gray loss value is , the gray prediction value of the pixel point (2, 2) is the gray scale loss value of (2, 1) is the gray scale prediction value of (2, 3) is the gray scale loss value of (2, 3) is the gray scale prediction value of (2, 4) is the gray scale loss value of (2, 4) is the gray scale prediction value of (3, 1) is the gray scale loss value of (3, 1) is the gray scale prediction value of (3, 3) is the gray scale loss value of (3, 3) is the gray scale prediction value of (3, 4) is the gray scale loss value of (3, 4) is the gray scale prediction value of (4, 1) is the gray scale loss value of (4, 1) is the gray scale prediction value of (4, 2) is the gray scale loss value of (4, 2) is the gray scale prediction value of (4, 3) is the gray scale loss value of (4, 3) is wherein represents a rounding symbol, represents an absolute value symbol. The average value of the gray scale loss values of all pixel points is less than a loss threshold value 5. Therefore, the supplementary information includes the coordinates of the marked pixel points (2, 1), (3, 2) and (4, 4) and the gray scale values 83, 99 and 99 of the marked pixel points.

[0067] S5, encrypting the supplementary information and transmitting the encryption result and the ciphertext image to a receiving end.

[0068] Specifically, in order to ensure the confidentiality and integrity of the supplementary information in the transmission process, the supplementary information is encrypted by using an asymmetric encryption algorithm. The encryption result and the ciphertext image are transmitted to the receiving end.

[0069] It should be noted that the asymmetric encryption algorithm has high security but large calculation overhead and is suitable for encryption of small data volume. The data volume of the supplementary information is very small, and the data volume of the sensitive image to be transmitted is large. Therefore, the supplementary information is encrypted by using the asymmetric encryption algorithm, and the sensitive image to be transmitted is not encrypted by using the asymmetric encryption algorithm.

[0070] Specifically, the asymmetric encryption algorithm includes but is not limited to RSA, ECC (elliptic curve encryption) and the like. In this embodiment, the RSA algorithm is used to encrypt the supplementary information: the sending end encrypts the supplementary information by using the public key of the receiving end, and the receiving end decrypts the encryption result by using the private key of itself. The key length of the RSA algorithm can be set according to security requirements, for example, 2048 bits or 4096 bits.

[0071] Further, after the receiving end receives the ciphertext image and the encryption result of the supplementary information, the ciphertext image is decrypted, specifically: 1. The receiving end uses the private key to decrypt the received encryption result of the supplementary information to obtain the coordinates and the gray values of the marked pixel points.

[0072] 2. The receiving end uses the scrambling parameters (for example, the chaos parameters and the iteration numbers involved in the Arnold mapping) agreed with the sending end in advance to perform the inverse scrambling operation on the ciphertext image to restore the LBP image.

[0073] 3. Based on the restored LBP image, the method in step S4 is used to infer the size relationship of the gray values of the pixel points according to the LBP feature values of the pixel points.

[0074] 4. According to the coordinates and the gray values of the marked pixel points obtained by decryption and the size relationship of the gray values of the pixel points, the gray values of all the pixel points are restored by the interpolation method to obtain the decrypted image.

[0075] Exemplarily, Figure 10 is Figure 16 the corresponding decrypted image, Figure 11 is ​ the corresponding decrypted image.

[0076] The embodiment of the application further discloses a data transmission system for a network security platform, comprising a processor and a memory, and the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a data transmission method for a network security platform according to the application is realized.

[0077] The system further comprises a communication bus and a communication interface and other components familiar to those skilled in the art, the settings and functions of which are known in the art, and thus will not be described here.

Claims

1. A data transmission method for a network security platform, characterized by, The method comprises the following steps: acquiring an image to be transmitted; acquiring LBP feature values of each pixel point in the image to be transmitted, and constructing an LBP image by using the LBP feature values of all the pixel points; performing scrambling on the LBP image to obtain a ciphertext image; inference the size relationship of the gray values of each pixel point according to the LBP feature values of the pixel points, selecting a pixel point with a local minimum gray value, a pixel point with a local maximum gray value and a plurality of intermediate pixel points as marker pixel points according to the size relationship, and recording the coordinates and gray values of the marker pixel points as supplementary information; encrypting the supplementary information, and transmitting the encrypted result and the ciphertext image to a receiving end.

2. The data transmission method for a network security platform according to claim 1, wherein, The method for acquiring the LBP feature values of each pixel point in the image to be transmitted comprises the following steps: For each pixel point in the image to be transmitted , the 8 neighborhood pixel points of the pixel point are sorted according to a preset rule, the gray values of each neighborhood pixel point are compared with the gray value of the pixel point , if the gray value of the neighborhood pixel point is greater than or equal to the gray value of the pixel point , it is marked as 1, otherwise it is marked as 0; according to the order of sorting the neighborhood pixel points, the obtained 8 marks form an 8-bit binary number, and the 8-bit binary number is converted into a decimal number to obtain the LBP feature value of the pixel point .

3. The data transmission method for a network security platform according to claim 2, wherein, the method for sorting the eight neighborhood pixel points of the pixel point according to a preset rule comprises the following steps: a chaotic sequence is acquired by using a chaotic mapping method, each element in the chaotic sequence is multiplied by 8 and then rounded up to obtain an integer sequence in the range of [1, 8]; for each pixel point in the image to be transmitted, the eight neighborhood pixel points of the pixel point are sorted according to the sorting sequence by using a variable-length Joseph algorithm.

4. The data transmission method for a network security platform according to claim 1, wherein, The method for inferring the size relationship of the gray values of each pixel point according to the LBP feature values of the pixel points comprises the following steps: for any pixel point, an inequality of the gray values between the pixel point and the neighborhood pixel points is constructed according to the LBP feature value of the pixel point; the inequalities of the gray values between all the adjacent pixel points are obtained by analyzing the LBP values of all the pixel points; and the size relationship of the gray values between the pixel points is acquired by concatenating the inequalities of the gray values between all the adjacent pixel points.

5. The data transmission method for a network security platform according to claim 1, wherein, The method for acquiring the pixel point with the local minimum gray value and the pixel point with the local maximum gray value comprises the following steps: a gray directed graph is constructed according to the size relationship of the pixel gray values: each pixel point is regarded as a node, if the gray values of two adjacent pixel points are the same in the size relationship of the gray values obtained by solving, the two pixel points are merged into one node, and if the gray values of two adjacent pixel points are different, a directed edge is constructed between the two pixel points, and the directed edge points from the pixel point with the smaller gray value to the pixel point with the larger gray value; the pixel point corresponding to the node with only an out-degree but no in-degree in the gray directed graph is regarded as the pixel point with the local minimum gray value, and the pixel point corresponding to the node with only an in-degree but no out-degree in the gray directed graph is regarded as the pixel point with the local maximum gray value.

6. The data transmission method for a network security platform according to claim 1, wherein, The method for acquiring the marker pixel points comprises the following steps: all the pixel points with the local minimum gray value and all the pixel points with the local maximum gray value are regarded as initial marker pixel points; the gray prediction values of the remaining pixel points are obtained by predicting the gray values of the remaining pixel points by using an interpolation method based on the gray values of all the initial marker pixel points and the size relationship of the pixel gray values, and the gray loss values are calculated; in response to the fact that the average value of the gray loss values of all the pixel points is not less than a preset loss threshold value, the pixel point with the largest gray loss value is also regarded as a marker pixel point; the acquisition and judgment processes of the gray loss values are repeated until the average value of the gray loss values is less than the loss threshold value, and all the marker pixel points are obtained.

7. The data transmission method for a network security platform according to claim 1, wherein, The supplement information is encrypted, including: using an asymmetric encryption algorithm to encrypt the supplement information.

8. The data transmission method for a network security platform according to claim 1, wherein, The LBP image is scrambled, including: using Arnold mapping to scramble the LBP image.

9. The data transmission method for a network security platform according to claim 1, wherein, If the image to be transmitted is an RGB image, each channel of the RGB image is regarded as a gray image respectively to perform the LBP image acquisition, scrambling and supplement information acquisition operations.

10. A data transmission system for a cyber-security platform, characterized by, The method comprises the steps that: A processor and a memory, the memory stores computer program instructions, when the computer program instructions are executed by the processor, a network security platform data transmission method according to any one of claims 1-9 is realized.

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