Satellite image encryption and decryption method and system

Abstract

The application relates to the technical field of image information security, in particular to a satellite image encryption and decryption method and system, which comprises the following steps: acquiring an original color image, summing up pixel matrices of three color channels in the original color image one by one according to positions to obtain pixel values of the three color channels; performing normalization processing on the pixel values of the three color channels, taking the obtained normalized pixel values as initial values to substitute into a system state equation to generate a high-dimensional chaotic sequence, generating a key stream based on the high-dimensional chaotic sequence; converting the original color image into a byte stream after pixel diffusion and scrambling transformation based on the key stream and performing AES encryption to obtain an encrypted image; and the application can effectively inhibit noise interference in a complex space environment and improve the encryption and decryption security of the satellite image.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of image information security, and particularly relates to a satellite image encryption and decryption method and system. BACKGROUND

[0002] Satellite images usually contain a lot of sensitive information. Unlike traditional images, satellite images are affected by space noise during acquisition, transmission and processing, and have a large amount of data, which makes it necessary for image encryption and decryption technology to have three core requirements: high security, strong real-time performance and strong anti-noise capability. However, during the satellite-ground transmission and storage of images, they are often disturbed by space noise and active attacks, which makes the data vulnerable to interception or tampering, thereby causing a risk of leakage.

[0003] The chaotic system in the related art is inevitably disturbed by external disturbances in a complex space, which leads to instability of the chaotic system and degradation of the generated key stream, so that the encrypted image data is vulnerable to cracking during transmission. SUMMARY

[0004] The present application aims to provide a satellite image encryption and decryption method and system, which effectively suppresses noise interference in a complex space environment and improves the security of satellite image encryption and decryption.

[0005] In order to achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0006] In a first aspect, the present application provides a satellite image encryption method, which comprises the following steps:

[0007] An original color image is obtained, and the pixel matrices of three color channels in the original color image are summed one by one according to position to obtain pixel values of the three color channels;

[0008] The pixel values of the three color channels are normalized, and the normalized pixel values are taken as initial values to substitute into a system state equation to generate a high-dimensional chaotic sequence, and a key stream is generated based on the high-dimensional chaotic sequence;

[0009] After pixel diffusion and permutation transformation of the original color image based on the key stream, the original color image is converted into a byte stream and encrypted by AES to obtain an encrypted image.

[0010] Optionally, the generation of the key stream based on the high-dimensional chaotic sequence comprises:

[0011] The absolute value and the decimal part of the high-dimensional chaotic sequence are mapped into the key stream by the following formula:

[0012] ;

[0013] Wherein, f represents the absolute value of the high-dimensional chaotic sequence, floor(f) represents the absolute value and decimal part of the high-dimensional chaotic sequence, and Q represents the key stream.

[0014] Optionally, the slave system state equation is:

[0015] ;

[0016] Wherein, a, b, c, d, r are parameter coefficients, represents each state variable of the slave system, represents the differential form of each state variable of the slave system, represents the noise of each slave system, represents the gradient neural dynamic controller of each slave system;

[0017] The mathematical model of the gradient neural dynamic controller is:

[0018] ;

[0019] Wherein, , K represents the Jacobian matrix, and the superscript T represents the transpose, represents the convergence factor, represents a nonlinear mapping vector specific to the master system, represents the state variable of the master system, represents the state vector of the slave system, represents a nonlinear mapping vector specific to the slave system, represents the error function between the master system and the slave system.

[0020] Optionally, after the pixel diffusion and scrambling transformation of the original color image based on the key stream, the original color image is converted into a byte stream and encrypted by AES to obtain an encrypted image, comprising:

[0021] The original color image is subjected to a pixel-by-pixel XOR operation with the key stream to obtain a first image;

[0022] The pixels in the first image are subjected to a periodic pixel coordinate transformation to obtain a second image;

[0023] The second image is converted into a byte stream and encrypted by AES to obtain an encrypted image.

[0024] Optionally, the pixel coordinates of the second image are:

[0025] ;

[0026] Wherein, (x, y) represents pixel coordinates in the first image, p(x, y) represents pixel coordinates corresponding to pixel coordinates (x, y) in the first image in the second image, mod represents a modulus operation, and h and w represent height and width of the original color image respectively.

[0027] Optionally, the converting the second image into a byte stream and performing AES encryption to obtain an encrypted image comprises:

[0028] Generating an original key in the form of a byte matrix based on a key stream, and generating a plurality of round keys from the original key;

[0029] Converting the second image into a byte stream in the form of a byte matrix, and performing a bitwise XOR operation between the byte stream and each round key to obtain an initial matrix;

[0030] Performing byte substitution on each byte in the initial matrix using a fixed byte substitution matrix to obtain a first matrix;

[0031] Performing a shift operation on each row of the first matrix to obtain a second matrix;

[0032] Performing a linear transformation on each row of the second matrix through a coefficient matrix to obtain a third matrix;

[0033] Performing a bitwise XOR operation between the third matrix and each round key to obtain an encrypted matrix;

[0034] Converting the encrypted matrix into the format of an image to obtain an encrypted image.

[0035] In a second aspect, an embodiment of the present application provides a satellite image decryption method, and the method comprises the following steps:

[0036] Performing preliminary decryption on the encrypted image after converting the encrypted image into byte data to obtain a secondary plaintext image;

[0037] Performing a permutation transformation on the secondary plaintext image to obtain a plaintext image;

[0038] Performing an XOR operation between the plaintext image and a key stream to restore the original color image.

[0039] In a third aspect, an embodiment of the present application provides a satellite image encryption system, and the system comprises:

[0040] At least one processor;

[0041] At least one memory configured to store at least one program;

[0042] When the at least one program is executed by the at least one processor, the at least one processor implements the satellite image encryption method according to any one of the above.

[0043] In a fourth aspect, the embodiments of the present application provide a satellite image decryption system, the system comprising:

[0044] at least one processor;

[0045] at least one memory for storing at least one program;

[0046] When the at least one program is executed by the at least one processor, the at least one processor implements the satellite image decryption method according to any one of the above.

[0047] The present application has the following beneficial effects:

[0048] The present application provides a satellite image real-time chaotic encryption and decryption method based on noise-resistant gradient neural dynamics, belonging to the technical fields of information security and image processing. In view of the problems of insufficient noise resistance, poor key generation stability and low encryption efficiency in existing satellite image chaotic encryption technology, the present application provides an innovative hybrid encryption framework. The method realizes the noise robustness synchronization of the master-slave system by constructing a double-integral noise-resistant gradient neural dynamics model, effectively suppresses the noise interference in the complex space environment, and guarantees the randomness and stability of key generation. The core of the algorithm is to construct a good performance controller. Secondly, combined with Arnold mapping pixel scrambling and AES diffusion operation, a three-level encryption mechanism is formed, which significantly improves the encryption strength and security. Compared with traditional methods, the present application does not need to rely on a large amount of training data, and directly processes through physical layer information, has stronger generalization ability and real-time performance. Finally, experiments are carried out on the NWPU-RESISC45 dataset. The results prove that the model proposed in the present application provides a reliable technical solution for the information security problem of satellite images in complex space environment. BRIEF DESCRIPTION OF DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0050] Figure 1 Flowchart of the satellite image encryption method in the embodiments of the present application;

[0051] Figure 2 Flowchart of the satellite image decryption method in the embodiments of the present application;

[0052] Figure 3 Original image of the satellite image in the embodiments of the present application in the snow mountain area;

[0053] Figure 4 Fig. 13 is an encryption effect diagram of a satellite image of a snow mountain area in an embodiment of the present application;

[0054] Figure 5 Fig. 14 is a decryption effect diagram of a satellite image of a snow mountain area in an embodiment of the present application;

[0055] Figure 6 Fig. 15 is a synchronization effect diagram of a gradient neural dynamics controller in a chaotic system in an embodiment of the present application;

[0056] Figure 7 Fig. 16 is a structural schematic diagram of a satellite image encryption and decryption system in an embodiment of the present application. DETAILED DESCRIPTION

[0057] The concept, specific structure and generated technical effects of the present application will be described clearly and completely in combination with embodiments and drawings, so as to fully understand the purpose, scheme and effects of the present application. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0058] For the characteristics of satellite image "large data, high correlation, real-time", the traditional block-by-block encryption method (such as DES) processing speed is slow and the security is difficult to meet the high strength demand, need to design special efficient, safe encryption scheme, to protect the confidentiality and integrity of image information. Researchers have proposed many image encryption methods, such as DNA encoding-based image encryption, quantum cryptography-based image encryption, compression sensing-based image encryption and chaos system-based image encryption. In the early stage of satellite image encryption, the main satellite image encryption method is SCAN language scanning mode data encryption technology, with the continuous expansion of satellite image application, the confidentiality and security requirements of satellite image are increasing, and the traditional image encryption technology cannot meet the high security demand. In this period, image encryption technology has developed greatly, and chaos system has become the core direction of researchers' attention. On the one hand, the initial value of chaos system is sensitive, which makes the decryption result completely wrong in the chaos encryption system with slight key difference, greatly increasing the difficulty of brute force cracking; on the other hand, the calculation complexity of chaos system is low, which supports custom nonlinear function. Through simple algebraic operation to generate chaotic sequence, combined with dynamic key to realize "one-time one-key" chaos encryption, improve the encryption efficiency and decryption difficulty. In recent years, with the vigorous development of artificial intelligence and neural network, its application has penetrated into various fields, and has achieved fruitful results in image encryption, optimization calculation, intelligent control and other aspects. Deep learning has great potential in image encryption, and its advantages lie in adaptive learning of noise distribution and end-to-end optimization of encryption and denoising. Many researchers use convolutional neural network (CNN) as the main tool for image denoising and encryption. In addition, the classic zero neural dynamics ZND, as a branch of neural dynamics (ND), generates chaotic sequences through hyperchaotic systems to encrypt images, improves the stability and noise resistance of image encryption, and improves the noise resistance of the model by introducing single integral ZND. However, satellite image encryption and decryption still face the following challenges:

[0059] (1) Learning-based neural networks have good encryption effect. However, when the data is insufficient, the generalization ability decreases significantly, and its performance is strongly dependent on the consistency of the distribution of training data and real scene. Satellite images often appear distribution deviation due to imaging conditions and regional differences, which leads to learning false features. Before training the model, data preprocessing is often required, which introduces redundant calculation, and it is difficult to construct multi-scale features. When facing insufficient data, overfitting problem occurs, which makes the model perform well in training but not in actual effect.

[0060] (2) Noise interference in the image transmission process can significantly reduce the reliability of the chaotic system. Because the chaotic system is sensitive to the initial value, even a weak change can cause a large change in the system, and the existence of external noise can cause the key to be destroyed, thereby causing encryption and decryption to fail. The single-integral neural dynamic model does not have the ability to completely suppress linear noise, and under the interference of quadratic noise, the residual error of the single-integral neural dynamic model even diverges. In practical applications, noise interference in a complex space environment often leads to instability of the chaotic system, degradation of the key, and makes the ciphertext easy to be cracked.

[0061] (3) Satellite image encryption often faces insufficient ciphertext attack resistance. Most traditional encryption algorithms are based on position permutation. This encryption method has the advantage of fast encryption speed, but its security depends on the security of the encryption algorithm, which cannot meet the requirements of modern encryption systems. And the chaotic encryption on the satellite image is limited by the key space, and the generated ciphertext is vulnerable to brute force cracking and statistical attacks, and the key can be inversely deduced by analyzing the characteristics of the ciphertext, such as the distribution of pixel values and the strong correlation between pixels.

[0062] In view of the above challenges, the present application proposes a double-integral anti-noise gradient neural dynamic model (gradient neural dynamic controller), constructs a controller based on the gradient neural dynamic controller, and realizes stable synchronization of the chaotic system under external noise. In addition, due to the nonlinear, unpredictable and sensitive to initial value characteristics of the chaotic system, a pseudo-random key stream can be generated. Encryption and decryption experiments are carried out on the satellite images of the NWPU-RESISC45 dataset. The main improvements of the present application are as follows:

[0063] A double-integral anti-noise gradient robust neural dynamic model (gradient neural dynamic controller) is proposed, a noise tolerance mechanism and a speed compensation parameter are introduced to construct a second-order neural dynamic system, and the noise tolerance ability of the model is enhanced.

[0064] The coupling effect of two identical chaotic systems is designed to design a noise suppression neural dynamic controller with good performance, realize robust synchronization of the chaotic system in a noisy environment, and effectively ensure the randomness and stability of the key stream.

[0065] Further combined with Arnold mapping pixel scrambling and AES diffusion operation, a three-level hybrid encryption framework is constructed: first, a high-dimensional chaotic key is generated using the Chen hyperchaotic system, then the image pixels are scrambled, and finally the AES diffusion is introduced to enhance the dynamic key space, which greatly improves the anti-attack ability and real-time performance of the encrypted image.

[0066] Referring to Figure 1 The present application provides a satellite image encryption method, which comprises the following steps:

[0067] S100, obtaining an original color image, summing up pixel matrices of three color channels in the original color image one by one according to positions to obtain pixel values of the three color channels;

[0068] S200, performing normalization processing on the pixel values of the three color channels, taking normalized pixel values as initial values to substitute into a system state equation to generate a high-dimensional chaotic sequence, and generating a key stream based on the high-dimensional chaotic sequence;

[0069] S300, performing pixel diffusion and permutation transformation on the original color image based on the key stream, converting into a byte stream and performing AES encryption to obtain an encrypted image.

[0070] The application gives a formula for realizing synchronization of a master system and a slave system of a chaotic system under external noise interference. The chaotic synchronization is essentially to construct a controller to make the motion state trajectory of the slave system coupled with the master system. In the application, the expression of the master system is defined as:

[0071] ;

[0072] wherein, represents a vector of the master system state equation, represents a vector form of the master system state equation, represents an n-th component of the master system state equation, represents a nonlinear mapping vector specific to the master system. In addition, a general expression of the slave system is:

[0073] ;

[0074] wherein, represents a vector of the slave system equation, represents a vector form of the slave system state equation, represents an n-th component of the slave system state equation, represents a nonlinear mapping vector specific to the slave system. represents a controller, represents an n-th component of a vector form of the controller.

[0075] Since external noise is inevitable, the application discusses the influence of external interference on the chaotic system synchronization. An expression of the slave system under external interference is:

[0076] ;

[0077] wherein, represents noise, represents a component of an n-th noise vector.

[0078] The chaotic master-slave system is a nonlinear equation, therefore, the neural dynamics algorithm can be designed, firstly, in order to measure the error generated in the synchronization process of the chaotic system, the traditional gradient neural dynamics model is introduced, and the error function between the master system and the slave system is defined For:

[0079] ;

[0080] Wherein, represents the vector two norm.

[0081] Then, in order to make the energy function gradually converge to 0, the error function The gradient direction of f is:

[0082] ;

[0083] Wherein, the superscript T represents transposition, and K represents the Jacobian matrix, as follows:

[0084] ;

[0085] Therefore, the traditional gradient-based neural dynamics (GNN) is as follows:

[0086] ;

[0087] Wherein, represents the differential form of the state solution of the chaotic master-slave system, represents a convergence factor. Since the GNN model lacks speed compensation of time-varying variables, the speed compensation is added to the GNN model:

[0088] ;

[0089] The above two sides are multiplied by the Jacobian matrix K, and the following can be obtained:

[0090] ;

[0091] Let , the following can be obtained represents the introduced speed compensation parameter;

[0092] Then, the derivative of is obtained .

[0093] Therefore, the following can be obtained: ;

[0094] The above formula is substituted into formula (9), and the improved GNN model is obtained, as follows:

[0095] ;

[0096] Further converting the above formula into GNN dynamic error The expression is: ;

[0097] In order to make the above model formula (11) have anti-noise ability, the present application improves it with integral term, and obtains a single integral anti-noise gradient neural dynamic (SINGNN) model as follows:

[0098] ;

[0099] Wherein, Convergence factor, t represents time, Integral time. The dynamic error function of the SINGNN model is:

[0100] ;

[0101] Based on the dynamic formula of the model formula (12) of GNN dynamic error A new error function is designed:

[0102] ;

[0103] Substitute the above formula into the model formula (12), and obtain:

[0104] ;

[0105] Then, let , and extend the above formula:

[0106] By reordering these terms, the present application can redesign the error function: ;

[0107] Substitute the above formula into formula (12), and the present application obtains , and extends it as:

[0108] ;

[0109] Then, the above formula is simplified, and the following can be obtained: ;

[0110] After two integrations, the present application obtains a new GNN design formula with double integrals, which is expressed as:

[0111] ;

[0112] Considering the double integral GNN design formula with noise, it is expressed as:

[0113] ;

[0114] represents noise.

[0115] Subtract the main system from the system:

[0116] ;

[0117] Finally, the double-integral noise-tolerant gradient neural dynamics controller is obtained, and the mathematical model is represented as:

[0118] ;

[0119] wherein, , represents the state variable of the main system, represents the state vector of the slave system, represents the nonlinear mapping vector specific to the slave system.

[0120] The general form of the Chen chaotic system of the hyperchaotic system is given by the following formula:

[0121] ;

[0122] The parameters are a=35, b=3, c=12, d=7, and 0.085 represents each state variable of the Chen chaotic system, represents the differential form of each state variable.

[0123] The state equation of the main system is as follows:

[0124] ;

[0125] The parameters are a=35, b=3, c=12, d=7, and 0.085 represents each state variable of the main system. represents the differential form of each state variable of the slave system.

[0126] The state equation of the system is represented as follows:

[0127] ;

[0128] The parameters are a=35, b=3, c=12, d=7, and 0.085 represents each state variable of the slave system. represents the differential form of each state variable of the slave system, represents the noise of each slave system, represents the gradient neural dynamics controller of each slave system.

[0129] The present application applies the mathematical model of the gradient neural dynamics controller in image encryption in chaotic systems. A size of h x w original color image, three channels (red, green, blue) pixel matrix respectively represents .

[0130] The image encryption steps are as follows:

[0131] The original color image is summed up one by one according to the position The pixel value of the channel.

[0132] ;

[0133] In order to strengthen the correlation between the image and the encryption key, the normalized pixel value of the image The chaotic sequence of the slave system is assigned to the initial value corresponding to formula (26) as follows:

[0134] ;

[0135] The above initial value is substituted into the state equation of the slave system to generate a high-dimensional chaotic sequence f, and the absolute value and the decimal part of the sequence are mapped into the key stream to generate 16 keys Q B， for AES encryption. The generated key stream Q is used for subsequent encryption process.

[0136] ;

[0137] ;

[0138] The original color image is subjected to pixel by pixel XOR operation with the key stream Q generated based on chaos to obtain a first image, so as to realize the encryption of the original color image.

[0139] ;

[0140] Then, the Arnold permutation is used to disturb the pixel position of the first image . By performing periodic pixel coordinate transformation on the first image, a second image is obtained, which can further disturb the spatial correlation of the first image, thereby increasing the complexity of the first image. Its mapping formula is as follows:

[0141]

[0142] where (x, y) represents the pixel value coordinates in the first image of the chaotic encryption, p(x, y) represents the corresponding pixel coordinates in the second image after transformation, mod represents the modulo operation, and h and w represent the height and width of the original color image, respectively.

[0143] The second image P after Arnold transformation is converted into P B The byte stream is then AES encrypted. AES is a symmetric encryption algorithm that encrypts data through a series of byte substitution, row shifting, column mixing, and round key operations.

[0144] ;

[0145] where, is the byte of the plaintext after Arnold transformation. Similarly, the key Q B is a byte matrix, represented as:

[0146] ;

[0147] In AES, the key is divided into multiple round keys . The original key is Q B , and the C round keys are generated through the key expansion algorithm, where each Q BC is used for the corresponding encryption round.

[0148] The byte stream is subjected to a bitwise XOR (exclusive OR) operation with the current round key to obtain the initial data block:

[0149] ;

[0150] where Q BC is the c-th round key, and M0 is the initial matrix of the encryption process.

[0151] Byte substitution is performed through a fixed S (byte substitution matrix), which is a nonlinear operation where each byte is substituted for another byte.

[0152] ;

[0153] where, is the substituted byte, represent the byte positions of the data block, respectively, and S is a fixed matrix that maps each byte to another byte.

[0154] Shift operation is performed on each row of the matrix, with the i-th row being shifted left by i bits.

[0155] ;

[0156] Wherein, M2 is the matrix after row displacement.

[0157] Linear transformation is performed on each row of the matrix, and the linear transformation is realized through matrix multiplication, and the formula is: ; wherein, A is a fixed coefficient matrix, M2 is the matrix after row displacement. M3 is the matrix after column mixing.

[0158] The matrix after column mixing is again subjected to bitwise XOR operation with the key of the current round, and the formula is: ; wherein, D is the final encryption result.

[0159] Finally, the encrypted byte stream is converted into the format of an image, so that the encrypted data can be restored into the same dimension as the original color image, facilitating display and saving.

[0160] Corresponding to the method of Figure 1 , refer to Figure 2 The application provides a satellite image decryption method, and the method comprises the following steps:

[0161] S400, the encrypted image is converted into byte data and then subjected to preliminary decryption to obtain a secondary plaintext image;

[0162] S500, the secondary plaintext image is subjected to Arnold transformation to obtain a plaintext image;

[0163] S600, the plaintext image is subjected to XOR operation with a key stream to be restored into an original color image.

[0164] The image decryption steps are as follows:

[0165] Decryption is the inverse process of encryption. Firstly, the encrypted image is converted into byte data again, and steps (a)-(e) are repeated to perform preliminary decryption, so as to obtain a secondary plaintext image.

[0166] The secondary plaintext image subjected to preliminary decryption is subjected to Arnold transformation to obtain a plaintext image.

[0167] ;

[0168] Wherein, represents the pixel coordinates in the secondary plaintext image, represents the plaintext image subjected to Arnold inverse transformation.

[0169] Finally, the plaintext image subjected to Arnold inverse transformation is subjected to XOR operation with a chaotic key Q to be restored into an original color image S2.

[0170] ;

[0171] Please refer to Figure 3 ,Figure 4 and Figure 5 The present application is verified by experiments on the NWPU-RESISC45 public data set. Figure 3 、 Figure 4 and Figure 5 The encryption and decryption effect diagram of the satellite image in the Chen chaotic system based on the DNRGND model is shown.

[0172] Please refer to Figure 6 , Figure 6 The synchronization effect diagram of the gradient neural dynamics controller in the chaotic system is shown. Figure 6 In (a), no noise, (b) constant noise, (c) linear noise, (d) random noise.

[0173] For the chaotic system, the importance of state synchronization is self-evident. The states of the master and the slave system in the Chen chaotic system under four noise conditions are all synchronized tracking. The offset and error of the four states are extremely small. At the same time, due to the sensitivity of the chaotic system to random initial values, the noise has absolute neural state control ability under the adjustment of the double-integral gradient neural dynamics controller.

[0174] Conclusion: The present application proposes a double-integral anti-noise gradient neural dynamics model (DNRGND). The model introduces velocity compensation and secondary dynamic error tolerance mechanism, which can realize fast response of chaotic synchronization. At the same time, it can significantly suppress the noise interference in the complex space environment and improve the robustness of the chaotic system in the noise environment. Then, the controller of the chaotic system is constructed by neural dynamics. The controller method based on dynamics driving can realize the synchronization coupling effect of the chaotic system. By constructing a robust controller, the performance under external noise interference can be realized, which can significantly improve the robustness of the chaotic system and provide guarantee for the key generation of image encryption. The chaotic sequence generated by the chaotic system is used as the key to encrypt the original image.

[0175] Corresponding to the method of Figure 1 , with reference to Figure 7 , the present application provides a satellite image encryption and decryption system, comprising:

[0176] At least one processor;

[0177] At least one memory for storing at least one program;

[0178] When the at least one program is executed by the at least one processor, the at least one processor implements the satellite image encryption method or the satellite image decryption method described above.

[0179] It can be seen that the contents in the method embodiments described above are applicable to the system embodiments, the system embodiments specifically implement the same functions as the method embodiments described above, and achieve the same beneficial effects as the method embodiments described above.

[0180] In addition, the embodiments of the present application further disclose a computer program product or a computer program, which is stored in a computer readable storage medium. The processor of the computer device can read the computer program from the computer readable storage medium, and the processor executes the computer program, so that the computer device executes the method described above. Similarly, the contents in the method embodiments described above are applicable to the storage medium embodiments, the storage medium embodiments specifically implement the same functions as the method embodiments described above, and achieve the same beneficial effects as the method embodiments described above.

[0181] Those of ordinary skill in the art can understand that all or some of the methods disclosed above and the system can be implemented as software, firmware, hardware, and appropriate combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software can be distributed on a computer readable medium, which can include computer storage media (or non-transitory media) and communication media (or transitory media). As known to those of ordinary skill in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. In addition, it is known to those of ordinary skill in the art that communication media typically includes computer readable instructions, data structures, program modules or other data in modulated data signals such as carrier waves or other transport mechanisms, and can include any information delivery medium.

[0182] The above is a specific description of the preferred implementation of the present disclosure, but the present disclosure is not limited to the above implementation, and those skilled in the art can make various equivalent modifications or replacements without departing from the spirit of the present disclosure, and these equivalent modifications or replacements are all included in the scope defined by the claims of the present disclosure.

Claims

1. A method of encrypting satellite images, characterized in that, The method comprises the following steps: Obtaining an original color image, summing up pixel matrices of three color channels in the original color image one by one according to positions to obtain pixel values of the three color channels; Normalizing the pixel values of the three color channels, taking the normalized pixel values as initial values to substitute into a system state equation to generate a high-dimensional chaotic sequence, and generating a key stream based on the high-dimensional chaotic sequence; After pixel diffusion and permutation transformation of the original color image based on the key stream, converting the original color image into a byte stream and performing AES encryption to obtain an encrypted image; The system state equation is: where a, b, c, d, r are parameter coefficients, denotes the differential form from each state variable of the system, denotes the differential form from each state variable of the system, denotes the noise from each slave system, denotes the gradient neural dynamic controller from each slave system; The mathematical model of the gradient neural dynamics controller is: ； wherein, , denotes the Jacobian matrix, the superscript denotes the transpose, denotes the convergence factor, denotes time, denotes the integration time, denotes the non-linear mapping vector specific to the master system, denotes the state vector of the master system, denotes the state vector of the slave system, denotes the non-linear mapping vector specific to the slave system, denotes the error function between the master and slave systems; denotes the gradient neural dynamic controller, denotes the nth component of the gradient neural dynamic controller in vector form; After pixel diffusion and permutation transformation of the original color image based on the key stream, converting the original color image into a byte stream and performing AES encryption to obtain an encrypted image, comprising: Performing a pixel-by-pixel exclusive OR operation on the original color image and the key stream to obtain a first image; Performing a periodic pixel coordinate transformation on the pixels in the first image to obtain a second image; Converting the second image into a byte stream and performing AES encryption to obtain an encrypted image.

2. The method of claim 1, wherein, The high-dimensional chaotic sequence is mapped into the key stream through the following formula: The pixel coordinates of the second image are: ； wherein, denotes an absolute value of the high-dimensional chaotic sequence, denotes a floor of the absolute value f of the high-dimensional chaotic sequence, denotes a key stream.

3. The method of claim 1, wherein, Converting the second image into a byte stream and performing AES encryption to obtain an encrypted image, comprising: ； wherein, denotes a pixel coordinate in the first image, denotes a pixel coordinate in the first image corresponding pixel coordinate in the second image, denotes a modulo operation, denotes a height and a width of the original color image, respectively.

4. The method of claim 3, wherein, Generating an original key in the form of a byte matrix based on the key stream, generating a plurality of rounds of keys from the original key; Converting the second image into a byte stream in the form of a byte matrix, performing a bitwise exclusive OR operation on the byte stream and each round of key to obtain an initial matrix; Performing byte substitution on each byte in the initial matrix using a fixed byte substitution matrix to obtain a first matrix; Performing a shift operation on each row of the first matrix to obtain a second matrix; Performing a linear transformation on each row of the second matrix through a coefficient matrix to obtain a third matrix; Performing a bitwise exclusive OR operation on the third matrix and each round of key to obtain an encrypted matrix; Converting the encrypted matrix into the format of an image to obtain an encrypted image. The method comprises the following steps:

5. A method of decrypting a satellite image, characterized by, Converting the encrypted image into byte data to perform preliminary decryption and obtain a secondary plaintext image; the encrypted image is generated based on the satellite image encryption method of any one of claims 1 to 4; Performing a permutation transformation on the secondary plaintext image to obtain a plaintext image; Performing an exclusive OR operation on the plaintext image and the key stream to restore the original color image. The system comprises:

6. A satellite image encryption system characterized by, At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the satellite image encryption method of any one of claims 1 to 4. The system comprises:

7. A satellite image decryption system characterized by comprising: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the satellite image decryption method of claim 5. ​

Images