An image encryption and decryption method and system based on water bird-plant hyperchaotic system

Abstract

The present application relates to the technical field of image encryption and decryption, and discloses an image encryption and decryption method and system based on a water bird-plant hyperchaotic system. The method comprises: obtaining an original color image for preprocessing to obtain a color image to be encrypted; constructing a water bird-plant hyperchaotic system based on the relationship between consumers and resources in two habitats in ecology to generate a pseudo-random sequence; using the pseudo-random sequence to perform a scrambling operation on the color image to be encrypted to generate an intermediate encrypted image; and performing a scrambling operation on the color image to be encrypted according to a finite field GF(2 8 ) operation rules, the intermediate encrypted image is diffused to generate the final encrypted image; according to the finite field GF(2 8 ) is used to perform an inverse operation rule of the original image encryption algorithm, and an inverse diffusion operation is performed on the final encrypted image to generate an intermediate decrypted image; an inverse scrambling operation is performed on the intermediate decrypted image to generate the final decrypted image corresponding to the color image to be encrypted. This method combines chaos theory and finite field operations to enhance the security and complexity of image encryption and decryption.

Description

Technical Field

[0001] The present invention relates to the technical field of image encryption and decryption, and in particular to an image encryption and decryption method and system based on a water bird-plant hyperchaotic system. Background Art

[0002] Image data is widely used in medical diagnosis, monitoring systems, social media, digital photography, and various visual information processing fields. However, with the rapid development of data transmission networks and the popularization of the Internet, the security of image data has become increasingly prominent, especially in the context of cloud computing and big data. How to protect image data in storage and transmission from illegal access or tampering has become a problem that needs to be solved urgently.

[0003] Traditional image encryption technologies are mostly based on symmetric encryption and asymmetric encryption algorithms, such as DES and AES. These algorithms have certain advantages in terms of encryption speed and security, but they also have some obvious limitations. Algorithms based on symmetric encryption require both parties to share keys, which is prone to key management risks; while asymmetric encryption algorithms, although more secure, are less efficient when processing large-scale image data, which may result in longer encryption and decryption times. In addition, traditional encryption methods mostly rely on fixed encryption modes, are vulnerable to known attacks, brute force cracking or mathematical analysis methods, and lack sufficient anti-attack capabilities.

[0004] In recent years, the application of chaotic systems in image encryption has received widespread attention. Chaotic systems are considered to be ideal pseudo-random number generators because of their high nonlinearity, sensitive dependence on initial conditions, and long-term unpredictability. Chaotic encryption technology can generate high-quality key sequences by utilizing the randomness and complexity of chaotic systems, and use the sequences to encrypt and scramble images, effectively enhancing the strength of encryption. However, although chaotic encryption technology can provide certain security, it still has some shortcomings. Although the chaotic system itself has strong randomness, the selection of its initial conditions and the setting of system parameters have a crucial impact on the encryption effect. In practical applications, how to accurately control the generation process of the chaotic system and ensure that the generated sequence has sufficient randomness and unpredictability is still a challenge. In some cases, chaotic systems may have problems of instability or poor convergence, which will affect their application in image encryption. Chaotic sequences may become periodic or repetitive after a long time, resulting in reduced encryption strength and easy to be discovered and cracked by attackers.

[0005] Therefore, how to design an encryption technology that can provide high-intensity security and efficiently process large-scale image data has become a major challenge in the field of image encryption. Especially in the context of the growing development of modern attack technology and the emergence of various cracking methods, the vulnerability of traditional encryption technology is particularly prominent. Therefore, improving the security of image encryption technology and ensuring its anti-cracking ability has become an important topic in the current research of image encryption technology. Summary of the invention

[0006] Based on this, the present application proposes an image encryption and decryption method and system based on a water bird-plant hyperchaotic system, aiming to improve the security of image encryption technology and ensure its anti-cracking ability.

[0007] The first aspect of the present application provides an image encryption and decryption method based on a water bird-plant hyperchaotic system, the method comprising:

[0008] Acquire an original color image, and preprocess the original color image to obtain a color image to be encrypted;

[0009] Based on the relationship between consumers and resources in two habitats in ecology, a waterbird-plant hyperchaotic system is constructed to generate pseudo-random sequences.

[0010] Using the pseudo-random sequence to perform a scrambling operation on the color image to be encrypted to generate an intermediate encrypted image;

[0011] According to the finite field GF(2 8 ), performing a diffusion operation on the intermediate encrypted image to generate a final encrypted image;

[0012] According to the finite field GF(2 8 ), performing an inverse diffusion operation on the final encrypted image to generate an intermediate decrypted image;

[0013] An inverse scrambling operation is performed on the intermediate decrypted image to generate a final decrypted image corresponding to the color image to be encrypted.

[0014] As an optional implementation of the first aspect, based on the relationship between consumers and resources in two habitats in ecology, a water bird-plant hyperchaotic system is constructed to generate a pseudo-random sequence, including the steps of: determining the relationship between consumers and resources in two habitats in ecology; determining a water bird; Z , a kind of Vallisneria X i ( i =1,2) and the Vallisneria X i reproductive organs winter buds Y i The interaction between the two habitats and considering the waterbirdZ Foraging adaptability θ and abiotic environmental factors, including turbidity K bg , Water level L , light intensity I in ; Based on the consumer and resource relationship between the two habitats, a waterbird-plant hyperchaotic system is constructed; the waterbird-plant hyperchaotic system is solved by a numerical solution method to obtain the motion trajectories of multiple state variables, wherein the change of each state variable is mapped into a pseudo-random sequence.

[0015] As an optional implementation of the first aspect, the water bird-plant hyperchaotic system includes six state variables, and the initial conditions formed by the six state variables are: , the expression of each state variable is:

[0016] Habitat 1 Vallisneria X 1 : ,

[0017] Habitat 1 Winter Buds Y 1 : ,

[0018] Habitat 2 Vallisneria X 2 : ,

[0019] Habitat 2 Winter Buds Y 2 : ,

[0020] Waterbird Z: ,

[0021] Foraging Adaptability : ,

[0022] in, , ;

[0023] In the formula, represents the germination rate of Vallisneria sinensis, represents the half-saturation constant of winter bud growth, b 1 and b 2 represents the waterfowl feeding rate, and Indicates the mortality rate of Vallisneria, c 1 and c 2represents the reproductive investment rate of Vallisneria, h 1 、h 2 and h 3 Indicates the processing time, and represents the interference intensity, represents the assimilation rate, represents the mortality rate of waterfowl, represents the adaptation strength, represents the effective photosynthesis rate, represents the maximum photosynthesis rate, k represents the photon extinction rate, and Indicates the water level, K bg1 and K bg2 Indicates the turbidity of water. represents the light intensity half-saturation constant, represents the light intensity, Represents the total amount of winter buds potentially available to waterfowl.

[0024] As an optional implementation of the first aspect, the pseudo-random sequence is used to perform a scrambling operation on the color image to be encrypted, and the step of generating an intermediate encrypted image includes: randomly selecting three state variables from the water bird-plant hyperchaotic system including six state variables as a three-dimensional pseudo-random sequence; sorting each dimension in the three-dimensional pseudo-random sequence to generate three scrambling indexes; according to the color image to be encrypted, extracting the first matrices of the three color channels, flattening the first matrices of the three color channels respectively, and obtaining three first one-dimensional arrays; using the three scrambling indexes to scramble the three first one-dimensional arrays respectively, to generate three scrambled one-dimensional arrays; and restoring the three scrambled one-dimensional arrays respectively to three first two-dimensional matrices to synthesize the intermediate encrypted image.

[0025] As an optional implementation of the first aspect, according to the finite field GF(2 8 ) operation rules, performing diffusion operation on the intermediate encrypted image, and generating the final encrypted image comprises the steps of: randomly selecting a state variable from the waterfowl-plant hyperchaotic system including six state variables as a one-dimensional pseudo-random sequence; performing diffusion mapping on the one-dimensional pseudo-random sequence to obtain a diffusion sequence; extracting the second matrices of the three color channels according to the intermediate encrypted image, flattening the three second matrices respectively, obtaining three second one-dimensional arrays, each element in the second one-dimensional array represents a pixel value; and mapping each pixel value and the diffusion sequence through a finite field GF(2 8) operation rules are used to obtain second two-dimensional matrices corresponding to the three color channels to synthesize the final encrypted image.

[0026] As an optional implementation of the first aspect, the one-dimensional pseudo-random sequence is absolutized, amplified, and the pixel value 256 is modulo-mapped to a range of [0, 255], and finally the diffusion sequence is obtained, wherein according to 2 8 =256 to indicate that there are 256 elements in total, corresponding to the range of 0 to 255, and each element is an 8-bit binary number, representing the image pixel value range.

[0027] As an optional implementation of the first aspect, each pixel value and the diffusion sequence are passed through a finite field GF(2 8 ) in the steps of the operation rule: the finite field GF(2 8 ) includes a multiplication operation whose modulus is a polynomial ,in, x represents a generator in a finite field.

[0028] The second aspect of the present application provides an image encryption and decryption system based on a water bird-plant hyperchaotic system, the system comprising:

[0029] A data acquisition module is used to acquire an original color image and preprocess the original color image to obtain a color image to be encrypted;

[0030] Construct a system module to construct a waterbird-plant hyperchaotic system based on the relationship between consumers and resources in two habitats in ecology to generate pseudo-random sequences;

[0031] The image encryption module is used to use the pseudo-random sequence to perform a scrambling operation on the color image to be encrypted to generate an intermediate encrypted image; according to the finite field GF(2 8 ), performing a diffusion operation on the intermediate encrypted image to generate a final encrypted image;

[0032] Image decryption module, used to decode the image according to the finite field GF(2 8 ), perform an inverse diffusion operation on the final encrypted image to generate an intermediate decrypted image; perform an inverse scrambling operation on the intermediate decrypted image to generate a final decrypted image corresponding to the color image to be encrypted.

[0033] The third aspect of the present application provides an electronic device, comprising: a processor; a memory for storing executable instructions of the processor; wherein the processor is configured to execute the executable instructions to implement the above-mentioned image encryption and decryption method based on the water bird-plant hyperchaotic system.

[0034] The fourth aspect of the present application provides a computer-readable storage medium. When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the above-mentioned image encryption and decryption method based on the water bird-plant hyperchaotic system.

[0035] Compared with the prior art, the present application provides an image encryption and decryption method based on a water bird-plant hyperchaotic system: first, a high-complexity pseudo-random sequence is generated by the water bird-plant hyperchaotic system, that is, a high-precision numerical integration method is used to solve the water bird-plant hyperchaotic system. In order to ensure the independence and randomness of the generated pseudo-random sequence, the initial conditions of the model are controlled by a key, and its security is further enhanced by combining the plaintext features of the input image. Secondly, two rounds of encryption operations are performed on the three channels (R, G, B) of the color image: scrambling and diffusion. The first encryption is the scrambling stage, that is, by sorting the pseudo-random sequence generated by the water bird-plant hyperchaotic system to form a permutation sequence of pixel indexes, thereby rearranging the pixel positions of the image; to ensure the randomness and complexity of the scrambling, different dimensions are randomly selected for different channels to generate a scrambled sequence, so that the pixel positions of the scrambled image are completely scrambled, so as to initially achieve the effect of preventing direct plaintext attacks. The second encryption is the diffusion stage, that is, using a pseudo-random sequence as a diffusion factor, combined with a finite field GF(2 8 ) operation rules, multiplying the image pixel value with the pseudo-random sequence. This diffusion process keeps the grayscale range of the image pixel value between [0,255], which is convenient for the storage and transmission of the encrypted image. Finally, image decryption is the reverse process of the encryption operation, that is, using the finite field GF(2 8 ) is used to restore the pixel values ​​in the diffusion phase, and then the pixel positions in the scrambling phase are restored through the inverse sequence of the pseudo-random sequence, and finally the original image is reconstructed. The entire decryption process is strictly symmetrical with the encryption process and has high key sensitivity. The premise for successful decryption is that the initial conditions are completely consistent with the key settings.

[0036] Additional aspects and advantages of the present application will be given in part in the following description, and in part will become apparent from the following description, or will be understood through the embodiments of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A flowchart of an image encryption and decryption method based on a water bird-plant hyperchaotic system proposed in the first embodiment of the present application;

[0038] Figure 2 This is a complete framework diagram of the water bird-plant hyperchaotic color image encryption and decryption method proposed in the second embodiment of the present application;

[0039] Figure 3This is a Lyapunov exponent distribution diagram based on the water bird-plant hyperchaotic system under specific parameter conditions in the second embodiment of the present application;

[0040] Figure 4 This is a chaotic attractor structure diagram based on the water bird-plant hyperchaotic system in the second embodiment of the present application;

[0041] Figure 5 It is a time series diagram of six state variables based on the water bird-plant hyperchaotic system in the second embodiment of the present application;

[0042] Figure 6 The experimental results of the water bird-plant hyperchaotic color image encryption and decryption method in the second embodiment of the present application;

[0043] Figure 7 This is a structural schematic diagram of an image encryption and decryption system based on a water bird-plant hyperchaotic system proposed in the third embodiment of the present application.

[0044] The following specific implementation methods will further illustrate the present application in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0046] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here. In addition, the "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally represents that the objects associated with each other are in an "or" relationship.

[0047] In order to illustrate the technical solution described in this application, a specific embodiment is provided below for illustration.

[0048] Example 1

[0049] See also Figure 1 , which is a flow chart of an image encryption and decryption method based on a water bird-plant hyperchaotic system proposed in the first embodiment of the present application. The proposed method includes S01 to S06, as follows.

[0050] S01: Acquire an original color image, and preprocess the original color image to obtain a color image to be encrypted.

[0051] In some implementations, this step uses an image acquisition device or selects an original color image from an image database, performs a series of preprocessing on the acquired original color image to enhance image quality, reduce noise, or perform other necessary preprocessing operations. The preprocessed color image is used as the color image to be encrypted.

[0052] In this embodiment, for example, the size of the input color image is , recorded as a three-dimensional matrix The dimensions of the matrix are , where M is the image height, N is the image width, and C is the number of color channels (R, G, B). Each element in the matrix represents the pixel brightness value, ranging from [0,255] (M=144; N=223; C=3), and the total number of pixels is calculated as: .

[0053] S02: Based on the relationship between consumers and resources in two habitats in ecology, a waterbird-plant hyperchaotic system is constructed to generate pseudo-random sequences.

[0054] It should be noted that in image encryption technology, the most common methods include symmetric encryption, asymmetric encryption, chaotic encryption and other methods. Among them, the chaotic system has become an ideal encryption method due to its high sensitivity, unpredictability, complexity and other characteristics. The pseudo-random number sequence generated by the chaotic system can effectively realize the scrambling and diffusion of the image, thereby increasing the strength of image encryption. The encryption and decryption technology proposed in this application is based on the consumer-resource relationship between two habitats in ecology, and constructs a water bird-plant hyperchaotic system to generate pseudo-random sequences, which are used for key generation in the image encryption process. The water bird-plant hyperchaotic system can produce highly complex dynamic behaviors by characterizing the resource consumption and environmental feedback effects between different habitats, further enhancing the security of the encryption and decryption process. The output of the hyperchaotic system is random and unpredictable, and is suitable as a source of pseudo-random sequences in the encryption process. The following is a detailed introduction to the construction process and significance of the water bird-plant hyperchaotic system.

[0055] In this step, the two habitats in ecology are identified: consumer and resource relationships: Identify a water bird Z , a kind of Vallisneria X i ( i =1, 2) and Vallisneria X i reproductive organs winter buds Y i The interaction between the two habitats, and considering waterbirdsZ Foraging adaptability θ and abiotic environmental factors, including water turbidity K bg , Water level L , light intensity I in ; Based on the relationship between consumers and resources in the two habitats, a waterbird-plant hyperchaotic system is constructed; the waterbird-plant hyperchaotic system is solved by numerical solution method to obtain the motion trajectories of multiple state variables, in which the change of each state variable is mapped into a pseudo-random sequence.

[0056] The coupling relationship between variables in the water bird-plant hyperchaotic system makes the system highly complex and sensitive. Slight changes in initial conditions or parameter adjustments may lead to completely different dynamic behaviors. This makes the system have the potential to generate pseudo-random sequences. This sensitive dependence on initial values ​​ensures the randomness of the key space and provides a high degree of uncertainty for the image encryption process, making it impossible to recover the encrypted image through simple statistical analysis.

[0057] In some embodiments, the water bird-plant hyperchaotic system includes six state variables, and the initial conditions composed of the six state variables are , the expression of each state variable is:

[0058] Habitat 1 Vallisneria X 1 : ,

[0059] Habitat 1 Winter Buds Y 1 : ,

[0060] Habitat 2 Vallisneria X 2 : ,

[0061] Habitat 2 Winter Buds Y 2 : ,

[0062] Waterbird Z: ,

[0063] Foraging Adaptability : ,

[0064] in, , ;

[0065] In the formula, represents the germination rate of Vallisneria sinensis, represents the half-saturation constant of winter bud growth, b 1 and b 2 represents the waterfowl feeding rate, and Indicates the mortality rate of Vallisneria, c 1 and c 2 represents the reproductive investment rate of Vallisneria, h 1 、h 2 and h 3 Indicates the processing time, and represents the interference intensity, represents the assimilation rate, represents the mortality rate of waterfowl, represents the adaptation strength, represents the effective photosynthesis rate, represents the maximum photosynthesis rate, k represents the photon extinction rate, and Indicates the water level, K bg1 and K bg2 Indicates the turbidity of water. represents the light intensity half-saturation constant, represents the light intensity, Represents the total amount of winter buds potentially available to waterfowl.

[0066] In this embodiment, it is assumed that at a certain time step , the value of the state variable is: At this time, a part of these state variables or some combination of them is used as a component of the pseudo-random sequence. You can take one of the state variables, or combine multiple state variables in some way to form a new sequence. When generating a pseudo-random sequence, define the element This pseudo-random sequence is used for image scrambling and diffusion operations, making the encrypted image highly unpredictable and secure.

[0067] It should be noted that in the following encryption process (scrambling operation and diffusion operation), the pseudo-random sequence generated by the water bird-plant hyperchaotic system is first used to scramble the image pixels, disrupt the pixel positions, and eliminate the spatial correlation of the image. Then, the encryption effect is enhanced by nonlinear diffusion with the image pixels. Due to the nonlinearity and high dimensionality of the system, the encryption process can effectively prevent attackers from restoring the original image through brute force. The present invention relates to a two-habitat water bird-plant hyperchaotic system in ecology, which provides a key generation mechanism with high chaos and complexity. The pseudo-random sequence generated by this mechanism can effectively improve the security and anti-cracking ability of image encryption and decryption technology, and is widely used in image encryption, data security and other fields.

[0068] S03: Use a pseudo-random sequence to perform a scrambling operation on the color image to be encrypted to generate an intermediate encrypted image.

[0069] It should be noted that compared with traditional image encryption technology, the scrambling encryption based on the adaptive foraging waterfowl-plant hyperchaotic system has significant advantages. Due to the high complexity and nonlinear characteristics of the waterfowl-plant hyperchaotic system, the generated pseudo-random number sequence is extremely random and unpredictable, which makes the scrambling effect of the image very significant. The image encrypted in this way is not only visually unrecognizable from the original image information, but also has statistical characteristics that are quite different from the original image, making it difficult to crack using traditional image analysis methods. The waterfowl-plant hyperchaotic system provides a very large key space, making brute force cracking very difficult. By selecting different initial conditions and control parameters, different pseudo-random sequences can be generated, thereby ensuring the diversity and security of the encryption process. In addition, the dynamic behavior of the system also makes it so that even a small change in the initial conditions will lead to a huge difference in the key sequence, greatly increasing the difficulty of the attack.

[0070] In this embodiment, image scrambling is the first step of image encryption, and its main purpose is to disrupt the spatial arrangement of image pixels so that the encrypted image cannot be identified from its original content through simple statistical analysis or direct observation. To achieve this goal, the present invention performs a scrambling operation on the image by mapping the pseudo-random sequence generated by the adaptive foraging water bird-plant hyperchaotic system to the image pixel position.

[0071] It can be understood that before the scrambling operation, the three color channels of the color image to be encrypted are converted into one-dimensional vectors. The elements of each dimensional vector correspond to a pixel value of the color image to be encrypted, and all pixel values ​​are arranged in a certain order to form a one-dimensional array vector with a total length of M×N (the total number of pixels in the image). Then, by solving the dynamic equations of the six state variables in the water bird-plant hyperchaotic system, six pseudo-random sequences are obtained, in which three state variables are selected as three dimensions for scrambling operation.

[0072] Exemplarily, this step can be divided into sub-step S031, sub-step S032, sub-step S033, sub-step S034 and sub-step S035, as follows.

[0073] S031: Randomly select three state variables from the water bird-plant hyperchaotic system including six state variables as a three-dimensional pseudo-random sequence.

[0074] Specifically, first, using the initial conditions The value of is [6,5,2,1,4,0.5], and the water bird-plant hyperchaotic system is solved by the numerical solver (ode45). The result of the numerical solution Y is a matrix with a size of . Each column corresponds to a time series of a dynamic variable. The Y matrix is ​​as follows:

[0075]

[0076] Each column represents a state variable ( ) changes over time; each row represents the state of the six state variables at a certain point in time.

[0077] Secondly, three dimensions (such as the 6th, 2nd, and 4th columns) are randomly selected to form a pseudo-random sequence for scrambling the three RGB color channels. For ease of understanding, the following three dimensions (the 6th, 2nd, and 4th columns) are used to scramble the three color channels:

[0078] Red Channel: ;

[0079] Green Channel: ;

[0080] Blue Channel: .

[0081] S032: Sort each dimension in the three-dimensional pseudo-random sequence to generate three scrambled indexes.

[0082] In this sub-step, the scrambled index generated after sorting the selected pseudo-random sequence in ascending order is generated. For ease of understanding, the form of sorting each color channel to generate a scrambled index is expressed as:

[0083] Red Channel:

[0084] ;

[0085] Green Channel:

[0086] ;

[0087] Blue Channel:

[0088] .

[0089] S033: extracting first matrices of three color channels according to the color image to be encrypted, and flattening the first matrices of the three color channels respectively to obtain three first one-dimensional arrays.

[0090] First, extract the pixel matrix of each color channel as the first matrix, and the operation is as follows:

[0091] Red Channel: ;

[0092] Green Channel: ;

[0093] Blue Channel: ;

[0094] Secondly, flatten the first matrix to obtain the three first one-dimensional arrays. The operation is as follows:

[0095] Red Channel: ;

[0096] Green Channel: ;

[0097] Blue Channel: .

[0098] S034: Use three scrambling indexes to scramble the three first one-dimensional arrays respectively to generate three scrambled one-dimensional arrays.

[0099] Use scrambled indexes ( ) for the first one-dimensional array ( , , ) are rearranged, that is, each color channel is scrambled. .

[0100] Red Channel:

[0101]

[0102] Green Channel:

[0103]

[0104] Blue Channel:

[0105] .

[0106] S035: Restore the three scrambled one-dimensional arrays into three first two-dimensional matrices respectively to synthesize an intermediate encrypted image.

[0107] First, the scrambled one-dimensional array ( 、 、 ) are restored to the scrambled two-dimensional matrix. The operation process is as follows:

[0108] Red Channel:

[0109] The green channel and blue channel are the same as above, and the result is:

[0110]

[0111]

[0112]

[0113] Secondly, the two-dimensional matrix after the red, green and blue channels are restored ( , , ) is reassembled into a three-dimensional matrix. Let the three-dimensional matrix be , the size is , is used to store the pixel values ​​of the scrambled color image, and finally complete the first encryption of the color image. That is, the operation process is as follows:

[0114] ;

[0115] ;

[0116] .

[0117] S04: According to the finite field GF(2 8 ) operation rules, perform diffusion operation on the intermediate encrypted image to generate the final encrypted image.

[0118] It should be noted that in the image encryption process, diffusion is to transform the image data in a certain way so that each pixel in the image is widely affected, thereby effectively increasing the complexity and irreversibility of encryption. The goal of diffusion is to spread the data in the original image to the entire image, so that the change of any pixel can significantly affect the content of the final image. The diffusion operation can be implemented in many ways, based on GF(2 8 )The diffusion algorithm of finite fields is an efficient and commonly used method. GF(2 8 ) The diffusion algorithm processes the image pixels one by one by using multiplication and inverse operations in a finite field to achieve encryption. Its basic principle is to multiply the value of each pixel by a pseudo-random sequence generated by a hyperchaotic system to obtain a new diffused image. This diffusion operation ensures the strong coupling of image pixels, so that even if part of the information of the encrypted image is intercepted, it is impossible to restore the valid original image information.

[0119] Exemplarily, this step can be divided into sub-step S041, sub-step S042, sub-step S043 and sub-step S044, as follows.

[0120] S041: Randomly select a state variable from the water bird-plant hyperchaotic system including six state variables as a one-dimensional pseudo-random sequence.

[0121] S042: Perform diffusion mapping on the one-dimensional pseudo-random sequence to obtain a diffusion sequence.

[0122] Specifically, the one-dimensional pseudo-random sequence is absolutized, amplified, and the pixel value 256 is modulo-mapped to the range of [0,255], and finally the diffusion sequence is obtained. 8 =256 indicates that there are 256 elements in the domain, corresponding to the range of 0 to 255. Each element is an 8-bit binary number, representing the range of image pixel values. 8 ) requires the calculation of the modulo polynomial The result is guaranteed not to exceed 256, that is, the operation is restricted to the domain, achieving closure and randomization of the finite domain.

[0123] Randomly select a dimension (such as the 4th column) from the matrix Y of the solution of the water bird-plant chaotic system and let it be . That is, the corresponding pseudo-random sequence is obtained , and then The value is made absolute and enlarged by 10 6 times, and modulo 256 to map it to the range of [0,255], and the final diffusion sequence is obtained The operation process is as follows:

[0124] .

[0125] S043: Extract the second matrices of the three color channels according to the intermediate encrypted image, flatten the three second matrices respectively, and obtain three second one-dimensional arrays, where each element in the second one-dimensional array represents a pixel value.

[0126] In this sub-step, the red, green, and blue channels need to be processed channel by channel. For ease of understanding, this sub-step takes the red channel as an example and extracts the second matrix of the scrambled red channel: , and then the second matrix Flatten to the second 1D array .

[0127] S044: Each pixel value and diffusion sequence are passed through a finite field GF(2 8 ) operation rules are used to obtain the second two-dimensional matrices corresponding to the three color channels to synthesize the final encrypted image.

[0128] It should be noted that is a finite field that contains elements, used for diffusion encryption operations. Its elements are integers between 0 and 255. In this method, all addition and multiplication operations follow the operation rules of finite fields, especially multiplication operations, which involve polynomial operations. The multiplication operation is performed by a modular polynomial Implementation ensures that all calculation results are within the range of 0 to 255. The specific diffusion operation is described in detail as follows: Define the second one-dimensional array All elements in , Representative elements. Then, each pixel value will be represented by and the value of the diffusion sequence pass The calculation process is described as The result is that after diffusion ;

[0129] Then, the diffused One-dimensional array, restored to The second 2D matrix of the red channel .

[0130] Repeat the above steps for the green and blue channels respectively. , implement Finally, the diffused matrices of the red, green, and blue channels are combined into a three-dimensional matrix and stored in In the matrix, the diffusion operation of the color image is completed. It should be noted that The matrix is ​​a zero matrix with the same size as the scrambled color image, which is used to store the diffused image. The dimension of the zero matrix is .

[0131] The above GF(2 8 ) has the following advantages: (1) Irreversibility: The multiplication process ensures the irreversibility of the data. The product of any two pixels cannot simply restore the original data. (2) Efficiency: Through GF(2 8 ) multiplication, which can quickly perform encryption operations on large-scale image data and meet the needs of efficient processing. (3) Strong diffusion: GF(2 8 ) has a strong diffusion property, so that every change of the image data will affect the entire image, and the encrypted image is more difficult to restore.

[0132] The steps and significance of the above diffusion operation include: (1) Image reading. Read the image to be encrypted and convert it into a grayscale image. Each pixel in the image represents a grayscale value, usually an integer between 0 and 255. Each pixel of the image will be GF(2 8 ) multiplication operation to generate new pixel values. (2) Generate a chaotic sequence. Generate a series of pseudo-random numbers through the waterfowl-plant hyperchaotic system. These values ​​will be used as the "key" in the diffusion process and multiplied with each pixel of the image. (3) Generate a diffused image. After all pixel values ​​are diffused, a new image is obtained. The image has a high degree of randomness and cannot be directly restored by a simple inverse operation. The diffused image no longer has any characteristics of the original image, which increases the difficulty of cracking.

[0133] S05: According to the finite field GF(2 8 ) is used to perform an inverse diffusion operation on the final encrypted image to generate an intermediate decrypted image.

[0134] It should be noted that the key to the decryption process lies in the inverse diffusion operation. The inverse diffusion operation restores the original image by performing an inverse multiplication operation on each encrypted pixel. 8 ) is similar to multiplication, and the inverse operation uses GF(2 8 ) is calculated. For each encrypted pixel, the inverse element of its corresponding chaotic sequence value needs to be calculated first, and then multiplied with the encrypted pixel to obtain the restored pixel value. The inverse element calculation is done by GF(2 8) to calculate the inverse of each element in the chaotic sequence. The inverse element operation is implemented through multiple multiplication operations to ensure that the pixel values ​​of the original image can be restored after encryption. The inverse diffusion operation uses the calculated inverse element to multiply the encrypted image pixels to restore the pixel values ​​in the original image one by one. Finally, through the inverse diffusion operation, the result consistent with the original image is finally restored.

[0135] S06: Perform an inverse scrambling operation on the intermediate decrypted image to generate a final decrypted image corresponding to the color image to be encrypted.

[0136] It should be noted that the scrambled image can still be restored to its original state through the corresponding inverse operation. The decryption process requires the same water bird-plant hyperchaotic system and initial conditions as the encryption process. During decryption, the original order of pixels is restored by reversing the sequence generated by the hyperchaotic system, thereby restoring the original image. During the image decryption process, the scrambled sequence of each channel is first reversed, that is, each pixel is restored to its original position according to the reverse index. Since the scrambling operation is essentially a reversible process, the original content of the image can be effectively restored by applying the correct inverse scrambling sequence.

[0137] In summary, the implementation method of the present application combines the complex dynamic behavior of the hyperchaotic system and the dual encryption process, generates a pseudo-random number sequence through the waterfowl-plant adaptive dynamics model, and utilizes its highly nonlinear and sensitive characteristics to enhance the security and anti-cracking ability of the encryption and decryption process. First, by establishing a waterfowl-plant hyperchaotic system, the growth-reproduction conversion and consumer-resource relationship in ecology are simulated to generate a highly complex pseudo-random number sequence. The system includes dynamic equations of plant populations (Vallis and its reproductive organs-winter buds), waterfowl populations and their foraging preferences, and habitat resources, and the dynamic behavior of the system is obtained by numerical solution. The various variables in the system (such as Vallisneria population density and waterfowl number) interact with each other through nonlinear coupling relationships to produce complex chaotic behaviors. The change trajectories of these variables are used to generate pseudo-random sequences and as keys in the image encryption and decryption process. Secondly, in the image encryption process, the color image is decomposed into three channels of RGB, and the pixel unit of each channel is used as the basic unit of encryption. The pseudo-random number sequence generated by the waterfowl-plant hyperchaotic system is used to scramble the pixels of the image, that is, a mapping matrix with the same size as the image is generated using the pseudo-random number sequence, and then the pixel positions of the original image are randomly exchanged according to the matrix, thereby disrupting the spatial structure of the image and eliminating the original features of the image. Finally, the encryption effect is further enhanced by nonlinear diffusion. The value of each pixel is multiplicatively diffused according to the chaotic sequence, making the distribution of pixel values ​​more random. The diffusion operation ensures that each pixel in the image is affected by multiple surrounding pixels, increasing the complexity of the image and making it impossible to restore the encrypted image through simple statistical analysis. The image decryption process is basically symmetrical with the encryption process. The encrypted image is inversely diffused using the same pseudo-random number sequence as that used in encryption to restore the original spatial structure of the image. Then, the original value of each pixel is restored through the same inverse scrambling operation. Finally, after double decryption, the image can be completely restored to its state before encryption. The key in the decryption process is the same as that in encryption, ensuring the synchronization and efficiency of encryption and decryption. This scheme enhances the randomness and complexity of the encryption process by introducing the pseudo-random number sequence generated by the water bird-plant hyperchaotic system, ensuring the high security of the encryption system. In addition, the double encryption process not only increases the difficulty of cracking, but also improves the image's ability to resist attacks.

[0138] Therefore, this method is an efficient and reliable image encryption technology that integrates the complexity of hyperchaotic systems and the high security of multiple encryption strategies. It can be widely used in scenarios such as medical image protection, military communication encryption, and financial data security storage. This application is applied to the rearrangement of image pixels to ensure that the encrypted image is significantly different from the original image in terms of statistical characteristics and visual effects, greatly improving the security of the image, and ensuring the high confidentiality of image data during transmission and storage. At the same time, it ensures the high quality of the decrypted image, meeting the current strict requirements for secure image transmission.

[0139] Example 2

[0140] Figure 2 The complete framework of the water bird-plant hyperchaotic color image encryption and decryption method based on adaptive foraging in the second embodiment of the present application is presented. The framework covers the entire process from image input to decryption output, including image scrambling and diffusion processing, as well as key modules used in the encryption and decryption process. The following is a detailed description of each module in the framework.

[0141] The input image is generally a color image, usually with three channels of RGB. In actual operation, the original image can be directly used as input data, and enter the subsequent encryption operation after necessary preprocessing. The input image not only includes standard RGB image data, but may also involve grayscale image conversion processing, which is determined according to specific needs. After preprocessing, the input image will be sent to the scrambling module.

[0142] In the scrambling operation, the input image is spatially scrambled using a pseudo-random sequence generated by the waterfowl-plant hyperchaotic system. At this time, the scrambling does not change the pixel values ​​of the image, but rearranges the positions of the image pixels under the guidance of the chaotic sequence. This operation is based on the nonlinear and high sensitivity characteristics of the hyperchaotic system, which makes the position of each pixel chaotic during the encryption process. By performing this positional confusion on the image, a visually irregular intermediate ciphertext image is generated. Although the pixel values ​​of the image have not changed, its spatial structure has been effectively disrupted, increasing the difficulty of cracking. The scrambled image enters the diffusion module, which is the most critical step in the encryption and decryption process.

[0143] In the diffusion operation, each pixel value of the image is compared with the pseudo-random sequence generated by the water bird-plant chaotic system. 8 ) operation, that is, using finite field operations to diffuse the image. Through this operation, each pixel value of the image will change significantly, making each pixel value deeply encrypted, and it is impossible to restore the pixel value of the original image through simple statistical analysis. GF(2 8) operation utilizes the mathematical properties of finite fields, making the encrypted image more complex and secure, and highly unpredictable. The image after scrambling and diffusion is the output encrypted image, which is visually very different from the original image. Due to the dual effects of scrambling and diffusion, the encrypted image not only loses regularity in spatial arrangement, but also undergoes a deep transformation in pixel values, ensuring the security of the image. This encrypted image can be used as a ciphertext for secure storage or transmission to prevent unauthorized visitors from restoring the original image.

[0144] When the original image needs to be restored, the decryption process begins. First, the inverse diffusion operation is performed, and the pixel values ​​of the image are restored by performing the inverse operation through the same pseudo-random sequence as the chaotic sequence used in the encryption process. In this process, GF(2 8 )The inverse operation restores each pixel value in the encrypted image one by one, ensuring that the pixel value of the image can be restored to the state before scrambling. The key to the inverse diffusion operation is to use the properties of the chaotic system used in the encryption process and the corresponding inverse operation to restore the data, so that each pixel value of the image is restored to a reasonable range.

[0145] After the inverse diffusion, the image is still in a scrambled state, so the next step is the inverse scrambling operation. In this step, the pixel positions of the image are reversed using the same pseudo-random sequence as the scrambling process. Through the inverse scrambling, the originally scrambled positions are restored, and the spatial structure of the image returns to its original state. Finally, through the dual operations of inverse scrambling and inverse diffusion, the decryption process successfully restores the original image.

[0146] The core of this technology is to combine the evolution of waterfowl foraging preferences with the waterfowl-plant hyperchaotic system to generate pseudo-random sequences for scrambling and diffusion. The adaptive foraging waterfowl-plant hyperchaotic system itself has a high degree of sensitivity and unpredictability, which makes the generated pseudo-random sequence have strong randomness and chaos in the encryption process, ensuring the security of the image encryption and decryption process. In addition, the GF(2 8 ) finite field operations further increase the complexity of the encryption process and prevent simple mathematical deduction and statistical analysis from being able to crack image data.

[0147] Figure 3 is the Lyapunov index distribution diagram of the water bird-plant hyperchaotic system under specific parameter conditions. Figure 4 is the chaotic attractor structure diagram, Figure 5 The time series diagram of the six state variables. In order to determine whether the system has hyperchaotic dynamics, the system is simulated on the Matlab software platform. Set the initial value of the system is (6, 5, 2, 1, 4, 0.5), let =0.09( Figure 3 The vertical dotted line in the figure) sets the simulation time interval to [0,20000]. The results show that the Lyapunov exponents (LE) are: LE 1 =5.8356 10 -3 , L.E. 2 =1.4581 10 -4 , L.E. 3 =-4.3208 10 -3 , L.E. 4 =-1.1301 10 -2 , L.E. 5 =-1.3086 10 -2 , L.E. 6 =-7.8065 10 -2 ; There are two positive LE curves (LE 1 and LE 2 ), which shows that the water bird-plant system is a hyperchaotic system, and the model parameter settings are shown in Table 1:

[0148] Table 1 Parameters of the waterbird-plant adaptation dynamics model

[0149]

[0150] Figure 3 In the figure, the ordinate is the Lyapunov index (LE), and the abscissa is the key parameter (adaptation intensity ) range. Through numerical simulation and calculation, at different adaptation intensities Under this condition, six groups of Lyapunov exponential curves of the six-dimensional dynamic model can be obtained (such as Figure 3 Each exponent measures the rate at which a small perturbation in phase space evolves exponentially with time (LE>0) or shrinks (LE<0). Figure 3 The red curve in the figure is above the zero line), the system shows chaotic characteristics, which means that any small disturbance will be amplified, making long-term behavior difficult to predict. When there are two or more positive Lyapunov exponents (such as Figure 3 The red and blue curves in the figure are both above the zero line), and the system shows hyperchaotic characteristics. This higher level of chaos means that the dynamic trajectory of the system is highly sensitive to the initial value in at least two positive growth directions, thus presenting a more complex, multi-sensitive and more difficult to predict dynamic behavior pattern. It can be seen from the results obtained by the present invention that when =0.09( Figure 3 The vertical dashed line in the figure shows the Lyapunov exponent. 1 With LE 2 Both are positive (LE 1 =5.8356×10 -3 >0;LE 2 =1.4581×10 -4 >0), which clearly indicates that the system has entered a hyperchaotic state. Due to the existence of the positive LE index, the system does not tend to a simple attractor or steady state in the phase space, but maintains a high sensitivity to initial values ​​and external perturbations, forming a highly complex chaotic attractor structure ( Figure 4 ).when =0.09( Figure 3 The vertical dashed line in the figure) is used to solve the water bird-plant adaptive dynamics model using Matlab (ode45), and the obtained six-dimensional state variable sequence is recorded as y(t)=[y 1 (t),y 2 (t), y 3 (t),y 4 (t),y 5 (t),y 6 (t)], t∈[0, 20000]. In the three-dimensional chaotic attractor structure diagram ( Figure 4 ), with y(:,1), y(:,3) and y(:,5) as the X, Y and Z axes (corresponding to the X defined in the present invention) 1 , Y 1 , Z variables), through this different three-dimensional projection method, we can compare the impact of different state variables on the system dynamics structure, help observe whether the trajectory of the system under different phase space projections has similarities and symmetries, or what kind of asymmetric attractor distribution is presented on different state components. This three-dimensional phase diagram is not a simple regular geometric shape, but presents highly coiled, overlapping, and bifurcated trajectory characteristics. This complex and diverse trajectory morphology is a typical manifestation of chaotic attractors. Similar to chaotic attractors ( Figure 4 ) data calculation method, the experiment selected the time interval [15000,20000] as the drawing interval to ensure that the initial transition state of the system (that is, the part that did not reach the steady-state attractor in the early stage) has decayed or eliminated, thereby highlighting the chaotic attractor form after the system enters the steady state. Then, from the time domain diagram of the six state variables ( Figure 5 ) It can be seen from the figure that the sequence iteratively generated by the water bird-plant hyperchaotic system presents irregular periodicity.

[0151] Combine the following Figure 6The experimental results of the water bird-plant hyperchaotic color image encryption and decryption method based on adaptive foraging of the present invention are described in detail. Figure 6 The present invention presents a complete set of experimental processes for image encryption and decryption analysis. First, an original color image is imported (such as a color image of the dual parameters affecting the stability of the system generated based on the simulation of the waterfowl-plant adaptive dynamics model). During the encryption process, the present invention generates a chaotic sequence by constructing a high-dimensional complex waterfowl-plant hyperchaotic system. The initial conditions and parameter settings of the system (such as the adaptive strength , interference intensity w , Processing time h , germination rate and environmental factors turbidity K bg , Water level L and Rizhao I in The key is composed of the initial state vector of the water bird-plant hyperchaotic system, the control parameters, and the pseudo-random sequence generated by the mapping. The precise combination of these parameters and the initial conditions makes the chaotic trajectory generated by the system unpredictable, thereby ensuring the security of image encryption.

[0152] After obtaining the chaotic sequence, the encryption process is mainly divided into two stages: confusion and diffusion. First, the original image is scrambled at the channel level. Specifically, the three channels of R, G, and B of the color image are flattened into a one-dimensional array, and each channel is sorted and mapped using multiple independent chaotic sequences generated by the water bird-plant hyperchaotic system. Three non-repeating chaotic subsequences are selected from them to rearrange the order of the pixels of the R, G, and B channels respectively. This scrambled index generated according to the chaotic sequence completely disrupts the original spatial distribution law of the image, forming an intermediate image without obvious structural features. After the scrambling is completed, the system enters the diffusion stage. In this step, one dimension of output is selected from the six-dimensional chaotic sequence again and mapped to GF(2 8 ) is used as the diffusion factor in the finite domain. The diffusion process is carried out through GF(2 8 ) domain, multiplying each scrambled image pixel value with the corresponding diffusion factor to change the statistical distribution of the pixel value.

[0153] The decryption process is the reverse process of the encryption process. It is necessary to accurately restore the key parameters (including initial conditions and control parameters) and then use the same chaotic sequence as encryption. When decrypting, first apply GF(2 8) domain multiplication inverse operation to restore the pixel value to the intermediate state of the scrambling stage; then use the previously recorded inverse scrambling index to rearrange the pixels back to their original positions, thereby completely restoring a color image consistent with the original image. The unpredictability of the key parameters ensures that a third party without the key cannot complete the inverse process.

[0154] Example 3

[0155] See also Figure 7 , which is a schematic diagram of the structure of an image encryption and decryption system based on a water bird-plant hyperchaotic system proposed in the third embodiment of the present application, and the system includes:

[0156] The data acquisition module 100 is used to acquire an original color image and preprocess the original color image to obtain a color image to be encrypted;

[0157] A system module 200 is constructed to construct a water bird-plant hyperchaotic system based on the relationship between consumers and resources in two habitats in ecology to generate a pseudo-random sequence;

[0158] The image encryption module 300 is used to perform a scrambling operation on the color image to be encrypted using the pseudo-random sequence to generate an intermediate encrypted image; 8 ), performing a diffusion operation on the intermediate encrypted image to generate a final encrypted image;

[0159] The image decryption module 400 is used to decode the image according to the finite field GF(2 8 ), perform an inverse diffusion operation on the final encrypted image to generate an intermediate decrypted image; perform an inverse scrambling operation on the intermediate decrypted image to generate a final decrypted image corresponding to the color image to be encrypted.

[0160] On the other hand, the present application also proposes an electronic device, comprising: a processor; a memory for storing executable instructions of the processor; wherein the processor is configured to execute the executable instructions to implement the above-mentioned image encryption and decryption method based on the water bird-plant hyperchaotic system.

[0161] On the other hand, the present application also proposes a computer-readable storage medium. When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device can execute the above-mentioned image encryption and decryption method based on the water bird-plant hyperchaotic system.

[0162] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0163] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0164] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. An image encryption and decryption method based on a water bird-plant hyperchaotic system, characterized in that: The method comprises: Acquire an original color image, and preprocess the original color image to obtain a color image to be encrypted; Based on the relationship between consumers and resources in two habitats in ecology, a waterbird-plant hyperchaotic system is constructed to generate pseudo-random sequences, including: Identifying two-habitat consumer and resource relationships in ecology: Identifying a waterbird Z , a kind of Vallisneria X i ( i =1,2) and the Vallisneria X i reproductive organs winter buds Y i The interaction between the two habitats and considering the waterbird Z Foraging adaptability θ and abiotic environmental factors, including turbidity K bg , Water level L , light intensity I in ; Based on the consumer and resource relationship of the two habitats, a water bird-plant hyperchaotic system is constructed; the water bird-plant hyperchaotic system is solved by a numerical solution method to obtain the motion trajectories of multiple state variables, wherein the change of each state variable is mapped into a pseudo-random sequence; The water bird-plant hyperchaotic system includes six state variables, and the initial conditions formed by the six state variables are: , the expression of each state variable is: Habitat 1 Vallisneria X 1: , Habitat 1 Winter Buds Y 1: , Habitat 2 Vallisneria X 2: , Habitat 2 Winter Buds Y 2: , Waterbird Z: , Foraging Adaptability : , in, , ; In the formula, represents the germination rate of Vallisneria sinensis, represents the half-saturation constant of winter bud growth, b 1 and b 2 represents the water bird feeding rate, and Indicates the mortality rate of Vallisneria, c 1 and c 2 represents the investment rate of Vallisneria reproduction, h 1 、h 2 and h 3 represents the processing time, and represents the interference intensity, represents the assimilation rate, represents the mortality rate of waterfowl, represents the adaptation strength, represents the effective photosynthesis rate, represents the maximum photosynthesis rate, k represents the photon extinction rate, and Indicates the water level, K bg1 and K bg2 Indicates the turbidity of water. represents the light intensity half-saturation constant, represents the light intensity, It represents the total amount of winter buds potentially available to waterfowl; Assume that at a certain time step , the value of the state variable is: , using a part or a combination of the state variables as a component of a pseudo-random sequence; Using the pseudo-random sequence to perform a scrambling operation on the color image to be encrypted to generate an intermediate encrypted image; According to the finite field GF(2 8 ), performing a diffusion operation on the intermediate encrypted image to generate a final encrypted image; According to the finite field GF(2 8 ), performing an inverse diffusion operation on the final encrypted image to generate an intermediate decrypted image; An inverse scrambling operation is performed on the intermediate decrypted image to generate a final decrypted image corresponding to the color image to be encrypted.

2. According to claim 1, the image encryption and decryption method based on the water bird-plant hyperchaotic system is characterized in that: The steps of using the pseudo-random sequence to perform a scrambling operation on the color image to be encrypted to generate an intermediate encrypted image are: Randomly selecting three state variables from the water bird-plant hyperchaotic system including six state variables as a three-dimensional pseudo-random sequence; Sorting each dimension in the three-dimensional pseudo-random sequence to generate three scrambling indexes; Extracting first matrices of three color channels according to the color image to be encrypted, and flattening the first matrices of the three color channels respectively to obtain three first one-dimensional arrays; Use the three scrambling indexes to scramble the three first one-dimensional arrays respectively to generate three scrambled one-dimensional arrays; The three scrambled one-dimensional arrays are restored to three first two-dimensional matrices respectively to synthesize the intermediate encrypted image.

3. The image encryption and decryption method based on the water bird-plant hyperchaotic system according to claim 2 is characterized in that: According to the finite field GF(2 8 ), performing a diffusion operation on the intermediate encrypted image to generate a final encrypted image comprises: Randomly selecting a state variable from the water bird-plant hyperchaotic system including six state variables as a one-dimensional pseudo-random sequence; Performing diffusion mapping on the one-dimensional pseudo-random sequence to obtain a diffusion sequence; Extracting second matrices of three color channels according to the intermediate encrypted image, flattening the three second matrices respectively to obtain three second one-dimensional arrays, each element in the second one-dimensional array represents a pixel value; Each pixel value and the diffusion sequence are passed through a finite field GF(2 8 ) operation rules are used to obtain second two-dimensional matrices corresponding to the three color channels to synthesize the final encrypted image.

4. The image encryption and decryption method based on the water bird-plant hyperchaotic system according to claim 3 is characterized in that: The step of performing diffusion mapping on the one-dimensional pseudo-random sequence to obtain a diffusion sequence includes: The one-dimensional pseudo-random sequence is absolutized, amplified, and the pixel value 256 is modulo-mapped to the range of [0, 255], and finally the diffusion sequence is obtained, wherein according to 2 8 =256 to indicate that there are 256 elements in the domain, corresponding to the range of 0 to 255, and each element is an 8-bit binary number, representing the image pixel value range.

5. The image encryption and decryption method based on the water bird-plant hyperchaotic system according to claim 4 is characterized in that: Each pixel value and the diffusion sequence are passed through a finite field GF(2 8 ) in the steps of the operation rule: The finite field GF(2 8 ) includes a multiplication operation whose modulus is a polynomial ,in, x represents a generator in a finite field.

6. An image encryption and decryption system based on a water bird-plant hyperchaotic system, characterized in that: The system comprises: A data acquisition module is used to acquire an original color image and preprocess the original color image to obtain a color image to be encrypted; The system module is used to construct a waterbird-plant hyperchaotic system based on the relationship between consumers and resources in the two habitats in ecology to generate pseudo-random sequences, including: Identifying two-habitat consumer and resource relationships in ecology: Identifying a waterbird Z , a kind of Vallisneria X i ( i =1,2) and the Vallisneria X i reproductive organs winter buds Y i The interaction between the two habitats and considering the waterbird Z Foraging adaptability θ and abiotic environmental factors, including turbidity K bg , Water level L , light intensity I in ; Based on the consumer and resource relationship of the two habitats, a water bird-plant hyperchaotic system is constructed; the water bird-plant hyperchaotic system is solved by a numerical solution method to obtain the motion trajectories of multiple state variables, wherein the change of each state variable is mapped into a pseudo-random sequence; The water bird-plant hyperchaotic system includes six state variables, and the initial conditions formed by the six state variables are: , the expression of each state variable is: Habitat 1 Vallisneria X 1: , Habitat 1 Winter Buds Y 1: , Habitat 2 Vallisneria X 2: , Habitat 2 Winter Buds Y 2: , Waterbird Z: , Foraging Adaptability : , in, , ; In the formula, represents the germination rate of Vallisneria sinensis, represents the half-saturation constant of winter bud growth, b 1 and b 2 represents the water bird feeding rate, and Indicates the mortality rate of Vallisneria, c 1 and c 2 represents the investment rate of Vallisneria reproduction, h 1 、h 2 and h 3 represents the processing time, and represents the interference intensity, represents the assimilation rate, represents the mortality rate of waterfowl, represents the adaptation strength, represents the effective photosynthesis rate, represents the maximum photosynthesis rate, k represents the photon extinction rate, and Indicates the water level, K bg1 and K bg2 Indicates the turbidity of water. represents the light intensity half-saturation constant, represents the light intensity, It represents the total amount of winter buds potentially available to waterfowl; Assume that at a certain time step , the value of the state variable is: , using a part or a combination of the state variables as a component of a pseudo-random sequence; The image encryption module is used to use the pseudo-random sequence to perform a scrambling operation on the color image to be encrypted to generate an intermediate encrypted image; according to the finite field GF(2 8 ), performing a diffusion operation on the intermediate encrypted image to generate a final encrypted image; Image decryption module, used to decode the image according to the finite field GF(2 8 ), perform an inverse diffusion operation on the final encrypted image to generate an intermediate decrypted image; perform an inverse scrambling operation on the intermediate decrypted image to generate a final decrypted image corresponding to the color image to be encrypted.

7. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; Wherein, the processor is configured to execute the executable instructions to implement an image encryption and decryption method based on a water bird-plant hyperchaotic system as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute an image encryption and decryption method based on a water bird-plant hyperchaotic system as described in any one of claims 1 to 5.

Images