Medical image encryption algorithm based on spatiotemporal chaotic system

Abstract

The invention relates to a medical image encryption algorithm based on a spatiotemporal chaotic system, which comprises the following steps: constructing the spatiotemporal chaotic system based on a coupling mapping lattice CML model, and scrambling a plaintext by utilizing a designed protein sequence partition scrambling mode, setting a chaos secret key of an encryption algorithm: adopting a pseudorandom sequence CSS generated by a spatiotemporal chaos system and a sequence cu generated by a Logistic system as input of secret keys, and designing a diffusion algorithm based on the spatiotemporal chaos system; according to the method, the inherent characteristics of the medical image can be processed more comprehensively, the security and cracking resistance of the algorithm can be improved more effectively, the security performance is very high, and theoretical guidance and experimental verification are made for the fields of medical information security, secret communication, medical computer image encryption and the like.

Description

Technical Field

[0001] The present invention relates to the field of image encryption technology, and in particular to a medical image encryption algorithm based on a spatiotemporal chaotic system. Background Art

[0002] With the rapid progress of information technology, Internet medical care has become an important part of today's medical system. While enjoying the convenience of Internet medical care, people are more concerned about the security of medical information during storage. Due to the inherent characteristics of medical images, such as absorption and scattering of different tissues and structures during the imaging process, the grayscale values ​​of medical images are significantly different and the contrast is strong. These characteristics lead to a high correlation between adjacent pixels in the image. Although a variety of encryption algorithms have been proposed for medical images, due to the inherent characteristics of medical images, these algorithms still have deficiencies in processing efficiency and medical image security. Therefore, it is particularly important to improve the performance of encryption algorithms from multiple dimensions, including reducing the correlation between pixels, enhancing the anti-attack ability of encryption algorithms, and improving the security of key generation. At present, encryption schemes based on chaotic systems have attracted widespread attention due to their advantages. Chaotic system mapping has high randomness, sensitivity and complexity, so chaotic systems have been widely used in the field of cryptography. Compared with traditional encryption methods, encryption algorithms based on chaotic systems have unique advantages in processing data information. However, due to the small number of chaotic systems discovered so far, there are even fewer chaotic models that can be used for medical image encryption, which makes it increasingly difficult to crack ciphertext. Therefore, there is an urgent need for medical image encryption algorithms that use chaotic systems as the main design scheme.

[0003] In the prior art, when selecting a system to be applied to an encryption algorithm, the commonly used system is a low-dimensional static system, whose chaotic behavior is relatively simple, the chaotic sequence has strong periodicity and regularity, and is easy to retain certain statistical characteristics. This makes the system less secure and easy to be analyzed and predicted by attackers, especially when facing brute force cracking or dynamic analysis attacks. Therefore, in recent years, spatiotemporal chaos encryption has become one of the hot research topics in the fields of information security and image encryption. However, due to the limited number of existing spatiotemporal chaos models, the difficulty of parameter adjustment, the high implementation complexity, and the fact that they are not well integrated into the design of the algorithm. Summary of the invention

[0004] The present invention provides a medical image encryption algorithm based on a spatiotemporal chaotic system, which can more comprehensively process the inherent characteristics of medical images and more effectively improve the security of the algorithm and its ability to resist cracking. It has high security performance and provides theoretical guidance and experimental verification for the fields of medical information security, confidential communications, and medical computer image encryption.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A medical image encryption algorithm based on spatiotemporal chaotic system, comprising the following steps:

[0007] S1. Constructing a spatiotemporal chaotic system based on the coupled mapping lattice CML model:

[0008] x n+1 (i) = (1-ε n )f[x n (i)]+(ε n / 2)(f[x n (j)]+f[x n (k)] (1)

[0009] Among them, x n+1 (i) is the state of the i-th grid point at time n+1, x n (i) is the state of the i-th grid point at time n, x n (j) is the state of the jth grid point at time n, x n (k) is the state of the kth grid point at time n, ε n (0≤ε n ≤1) is the coupling coefficient at time n, f(x) is the Logistic chaotic system;

[0010]

[0011] Among them, ε n+1 is the coupling coefficient at time n+1, α is the control parameter, and its value range is (0,4);

[0012] i, j and k are spatial grid points and are determined by Arnold cat mapping, and are defined as follows:

[0013]

[0014] Among them, i ≥ 1 and j, k ≤ L, P and q are the horizontal and vertical coordinates of the original space grid respectively.

[0015] S2. Scramble the plaintext using the designed protein sequence partition scrambling method: Use a partition algorithm, whose initial point is determined by the Logistic system, to divide the medical image into multiple groups of regions of different sizes and lengths. There are overlaps between the regions, and different encoding rules are set for regions of different sizes, and an encryption algorithm is set;

[0016] S3, set the chaotic key of the encryption algorithm: use the pseudo-random sequence CSS generated by the spatiotemporal chaotic system and the sequence cu generated by the Logistic system as the key input;

[0017] S4. Design a diffusion algorithm based on spatiotemporal chaotic system:

[0018]

[0019] Among them, LL(i) is the initial value of the chaotic sequence generated by the spatiotemporal chaotic system, D(i) is the coding sequence of the protein, sssk(i+1) is the pixel value after applying the diffusion algorithm, sssk(i) is the i-th pixel value of the ciphertext sequence, which is determined by the first two pixel values ​​and the chaotic sequence generated by the spatiotemporal chaotic system, SS(i), SS(i+2), SS(i+1) are the i-th, i+1, i+2 pixel values ​​of the first diffusion sequence, and ssk(i), ssk(i+1), ssk(i+2) are the i-th, i+1, i+2 pixel values ​​of the second diffusion sequence, respectively.

[0020] Furthermore, the encryption algorithm in step S2 includes the following steps:

[0021] S2.1. Generate m chaotic sequences (cs1, cs2, ..., cs m ),

[0022]

[0023] Where M is the length of each region, and N is the width of each region;

[0024] S2.2, let the one-dimensional sequence of m regions be (sp1, sp2, ..., sp m ), the length of the chaotic sequence cs(i) corresponds to the length of each segment sp(i), and the following operations are performed on each segment of the chaotic sequence cs(i):

[0025]

[0026] Among them, K(i) is the chaotic sequence after modulo operation;

[0027] S2.3, the chaotic sequence K1~K m The values ​​of and the pixel values ​​of each region sp1~spm are converted into binary, and the chaotic sequences cs and sp are converted into protein sequences a(i) and b(i) according to m protein encoding rules, and replaced with 4 elements;

[0028] S2.4, a(i) and b(i) are added according to the protein sequence addition rule. In the addition operation, a(i) and b(i) are converted into matrix y by binary left shift operation and table lookup;

[0029] S2.5. Convert the protein matrix y into decimal and encrypt the medical image.

[0030] Furthermore, the generation of the key in step S3 includes the following steps:

[0031] S3.1, convert the chaotic sequence css and sequence su into binary;

[0032] S3.2, perform XOR calculation on the sequence css and the sequence su;

[0033]

[0034] Among them, css(i) is the i-th value of the chaotic sequence, and su(i) is the i-th pixel value of the input sequence;

[0035] S3.3, in the sequence keysubse, b is the bit number (b≡1,2,3,…,128×8), map keysubse to the bit-level scrambling, and operate according to the designed bit-level scrambling algorithm;

[0036] S3.4, the final key sequence is obtained according to the designed bit-level arrangement, the key mapping is between [0,1], i is the spatial sequence number, i∈[1,128];

[0037] S3.5, Q(i) is the key sequence obtained by bit-level scrambling. In the diffusion algorithm, Q(i) is set to the initial value of the spatiotemporal chaotic system used in the pixel diffusion process.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] The space-time chaotic system not only introduces the space-time dimension, but also considers the complexity of space and time. It can describe the more complex behavior of the system. The space-time chaotic system not only has a larger parameter range, but also has more chaotic sequences. From the perspective of the anti-decryption ability of the encryption algorithm, the dynamic characteristics of the space-time chaotic system are very consistent with its core idea. The design of the medical image encryption algorithm based on the space-time chaotic system combined with other algorithms can not only more comprehensively process the inherent characteristics of medical images, but also more effectively improve the security of the algorithm and its ability to resist cracking; this is of great significance to the research in related fields such as medical confidential communication;

[0040] A spatiotemporal chaotic system is constructed, and the chaotic sequence generated by the chaotic system is effectively combined with the original pixel sequence. A medical image partition encryption algorithm based on protein sequence is designed. The system equation depends on the chaotic key setting of the encryptor, and a new key bit-level scrambling algorithm is designed based on the constructed spatiotemporal chaotic system. After theoretical analysis and experimental verification, the image encryption algorithm designed by the invention has high security performance, which provides theoretical guidance and experimental verification for the fields of medical information security, confidential communication, and medical computer image encryption. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a schematic diagram of the chaotic key described in the present invention.

[0042] Figure 2 Schematic diagram of key bit-level scrambling principle in an embodiment of the present invention.

[0043] Figure 3 It is a spatiotemporal behavior diagram of a spatiotemporal chaotic system with a period of 1 in an embodiment of the present invention.

[0044] Figure 4 It is a time-space behavior diagram with a period of 8 in an embodiment of the present invention.

[0045] Figure 5 is a spatiotemporal behavior diagram with two chaotic bands in an embodiment of the present invention.

[0046] Figure 6 is a diagram of the space-time behavior of chaotic band interweaving in an embodiment of the present invention.

[0047] Figure 7 It is a spatiotemporal behavior diagram of the system entering intermittent chaos in an embodiment of the present invention.

[0048] Figure 8 It is a spatiotemporal behavior diagram of the system completely entering a chaotic turbulent state in an embodiment of the present invention.

[0049] Fig. 9 Schematic diagram of the protein sequence partition scrambling algorithm in an embodiment of the present invention.

[0050] Fig.10 It is a chest image diagram in an embodiment of the present invention.

[0051] Fig.11 This is an encrypted chest image after the encryption algorithm in the embodiment of the present invention.

[0052] Fig.12 It is a chest image decryption diagram in an embodiment of the present invention.

[0053] Fig.13 It is a foot image diagram in an embodiment of the present invention.

[0054] Fig.14 This is an encrypted image of the foot after the encryption algorithm in the embodiment of the present invention.

[0055] Fig.15 It is a decrypted diagram of the foot image in an embodiment of the present invention.

[0056] Fig.16 It is the plaintext chest image histogram test result of the encryption algorithm in the embodiment of the present invention.

[0057] Fig.17It is the ciphertext chest image histogram test result of the encryption algorithm in the embodiment of the present invention.

[0058] Fig.18 It is the decrypted chest image histogram test result of the encryption algorithm in the embodiment of the present invention.

[0059] Fig.19 It is the plaintext foot image histogram test result of the encryption algorithm in the embodiment of the present invention.

[0060] Fig. 20 It is the ciphertext foot image histogram test result of the encryption algorithm in the embodiment of the present invention.

[0061] Fig.21 It is the decrypted foot image histogram test result of the encryption algorithm in the embodiment of the present invention.

[0062] Fig. 22 This is a robustness test diagram of a 5% salt and pepper attack under the encryption algorithm in an embodiment of the present invention.

[0063] Fig.23 This is a test diagram of the robustness of foot image decryption under the salt and pepper attack under the encryption algorithm in an embodiment of the present invention.

[0064] Fig.24 This is a 25% reduction attack robustness test graph under the encryption algorithm in the embodiment of the present invention.

[0065] Fig.25 This is a robustness test graph of a chest image decryption graph subjected to a trimming attack under the encryption algorithm in an embodiment of the present invention. DETAILED DESCRIPTION

[0066] The specific implementation of the present invention will be further described below in conjunction with the accompanying drawings:

[0067] The present invention provides a medical image encryption algorithm based on a spatiotemporal chaotic system, comprising the following steps:

[0068] S1. Constructing a spatiotemporal chaotic system based on the coupled mapping lattice CML model:

[0069] x n+1 (i) = (1-ε)f[x n (i)]+(ε / 2)(f[x n (i+1)]+f[x n (i-1)]) (8)

[0070] Among them, x n+1 (i) is the state of the i-th grid point at time n+1, x n (i) is the state of the i-th grid point at time n, x n(i+1) is the state of the i+1th grid point at time n, x n (i-1) is the state of the i-1th grid point at time n, ε (0≤ε≤1) is the coupling coefficient, i+1 and i-1 are two adjacent grids, the coupling mode of the CML model is affected by the adjacent grids, f(x) is a Logistic chaotic system, which can be expressed as:

[0071] x n+1 =μx n (1-x n ), (μ∈(0,4],ε∈[0,1]) (9)

[0072] Where μ is the control parameter, and its value range is (0, 4]. When 3.57≤μ≤4, f(x) enters a completely chaotic state. We use the Arnold cat mapping to change the coupling mode of the CML model, which can be defined as:

[0073] x n+1 (i) = (1-ε)f[x n (i)]+(ε / 2)(f[x n (j)]+f[x n (k)]) (10)

[0074] Among them, x n (j) is the state of the jth grid point at time n, x n (k) is the state of the kth grid point at time n, i, j and k are spatial grid points and are determined by the Arnold cat map, which is defined as follows:

[0075]

[0076] Where i≥1 and j, k≤L, P and q are the horizontal and vertical coordinates of the original space grid respectively; the spatiotemporal behavior of the spatiotemporal chaotic system proposed in the present invention is shown in Figure 3-8 The introduction of Arnold cat mapping changes the coupling mode between grids, changing the interaction between adjacent grids to the interaction between non-adjacent grids, making the energy diffusion between grids more uniform, effectively improving the chaotic differences between grids, and making the system produce better chaotic behavior;

[0077] ε is the static coupling coefficient, which is applied to the self-mapping part and the non-adjacent grid point mapping part in formula (10), expressed as follows:

[0078] (1-ε)f[x n (i)] (12)

[0079] (ε / 2)(f[x n (j)]+f[xn (k)]) (13)

[0080] The static coupling coefficient ε is transformed into a dynamic coupling coefficient by introducing nonlinear changes. The nonlinear dynamic changes can be expressed as follows:

[0081]

[0082] Among them, ε n+1 is the coupling coefficient at time n+1, ε n is the coupling coefficient at time n, α is the control parameter, and its value range is (0, 4);

[0083] The final spatiotemporal chaotic system based on the coupled mapping lattice CML model is:

[0084] x n+1 (i) = (1-ε n )f[x n (i)]+(ε n / 2)(f[x n (j)]+f[x n (k)] (15)

[0085] S2. Use the designed protein sequence partition scrambling method to scramble the plaintext:

[0086] When encrypting medical images, the distribution of pixel values ​​in grayscale images has a strong correlation, which requires a high degree of randomness for the encryption algorithm. Since images with high correlation are often more vulnerable to attacks by certain means, the encryption scheme needs to have a strong anti-cracking capability. For this purpose, a partitioning algorithm is designed, such as Fig. 9 As shown in the figure, the initial point of the partitioning algorithm is determined by the Logistic system. Different from the traditional block algorithm, the proposed algorithm divides the medical image into multiple groups of regions of different sizes and lengths. There is overlap between the regions. According to the designed partitioning algorithm, the medical image is divided into 8 regions, two of which are square regions and two are non-square regions. This partitioning algorithm greatly improves the anti-decryption ability of the encryption algorithm.

[0087] The core of protein sequence encryption is based on the structural composition of its internal amino acids. Most amino acids are composed of an amino group NH2, a carboxyl group COOH and a hydrogen atom H. They are mainly composed of four elements C, H, O, and N. The atomic numbers of the four elements are converted into binary data to obtain hydrogen (01) and carbon (10), and nitrogen (11) and oxygen (00). The four elements are used for encoding, and 8 encoding rules that meet the requirements are generated. According to the 8 protein encoding rules, 8 protein sequence addition principles are designed and applied to 8 medical grayscale image regions. Region 1 adopts the addition rule numbered 1 (OHCN), and so on. Regions of different sizes have different encoding rules at the same time, which improves the complexity and anti-cracking ability of the encryption algorithm. The encryption algorithm is designed as follows:

[0088] S2.1, using the spatiotemporal chaotic system shown in formula (10) to generate 8 chaotic sequences (cs1, cs2, cs3, cs4, cs5, cs6, cs7, cs8),

[0089] cs1(i)={cs1(1), cs1(2),..., cs1(M1×N1))

[0090] cs2(i)={cs2(1), cs2(2),..., cs2(M2×N2)}

[0091] cs3(i)={cs3(1), cs3(2),…, cs3(M3×N3)}

[0092]

[0093] cs8(i)={cs8(1), cs8(2),..., cs8(M8×N8)} (16)

[0094] Where M is the length of each region, and N is the width of each region;

[0095] S2.2. Due to the inconsistency of region sizes, the values ​​of M and N are also different, and the generated chaotic sequences cs are also different. Suppose the one-dimensional sequences of the eight regions are (sp1, sp2, sp3, sp4, sp5, sp6, sp7, sp8). The length of the chaotic sequence cs(i) corresponds to the length of each segment sp(i). The following operations are performed on each segment of the chaotic sequence cs(i):

[0096] K1(i)=mod(floor(cs1(i)×10 13 ), 255)

[0097] K 2( i)=mod(floor(cs2(i)×10 13), 255)

[0098] K3(i)=mod(floor(cs3(i)×10 13 ), 255)

[0099]

[0100] K8(i)=mod(foor(cs8(i)×10 13 ), 255) (17)

[0101] Among them, K(i) is the chaotic sequence after modulo operation;

[0102] S2.3, the chaotic sequence K1~K m The values ​​of and the pixel values ​​of each region sp1 to sp8 are converted into binary, and the chaotic sequences cs and sp are converted into protein sequences a(i) and b(i) according to 8 protein coding rules, and replaced by 4 elements;

[0103] S2.4, a(i) and b(i) are added according to the protein sequence addition rule. In the addition operation, a(i) and b(i) are converted into matrix y by binary left shift operation and table lookup;

[0104] S2.5. Convert the protein matrix y into decimal and encrypt the medical image.

[0105] S3, set the chaos key of the encryption algorithm;

[0106] Since the correlation between adjacent pixel values ​​in medical images is high, a good encryption algorithm needs to be able to generate sufficiently random and unpredictable keys. Therefore, we use the pseudo-random sequence css generated by the spatiotemporal chaotic system and the sequence su generated by the Logistic system as the key input. The key of this algorithm is 1024 bits, including Key1, μ, ε, α, as shown in Figure 1 As shown, Key1 is divided into 16 groups, denoted as Key1 = (K2, K3, ..., K 17 ), each group contains K2=(K 21 , K 22 , …, K 28 ), K2 is 64 bits, and the keys μ, ε, and α correspond to formula (10). Taking 4 groups of key blocks as an example, the lengths of the sequences css and su are both 128, and the key generation process is as follows:

[0107] S3.1, convert the chaotic sequence css and sequence su into binary;

[0108] S3.2, perform XOR calculation on the sequence css and the sequence su;

[0109]

[0110] Among them, css(i) is the i-th value of the chaotic sequence, and su(i) is the i-th pixel value of the input sequence;

[0111] S3.3, in the sequence keysubse, b is the bit number (b≡1,2,3,…,128×8), map keysubse to the bit-level scrambling, and operate according to the designed bit-level scrambling algorithm;

[0112] S3.4, the final key sequence is obtained according to the designed bit-level arrangement, the key mapping is between [0,1], i is the spatial sequence number, i∈[1,128];

[0113] S3.5, Q(i) is the key sequence obtained by bit-level scrambling. In the diffusion algorithm, Q(i) is set as the initial value of the spatiotemporal chaotic system used in the pixel diffusion process;

[0114] Figure 2 The designed key bit-level scrambling process can be used for the bit-level arrangement of the key sequence, which starts from b1 to b4, and the path cycles 32 bits. The first and second rows are positive cycles, and they follow the cycle pattern (b1-b5-b2-b6-b9);

[0115] The third and fourth rows are reverse loops, which follow the loop pattern (b31-b27-b32-b28-b23). The path of b6-b9 is obtained by looking at the coordinates of b6(x, y). When x and y are both even, the loop follows this path. When the loop reaches the maximum number of columns b30(x, y) and x is even, the loop executes the path of b30-b31, and the forward loop becomes a reverse loop. The path of b28-b23 is taken through the coordinates of b28(x, y). When x is even and y is odd, the loop proceeds along this path, once forward and once reverse, completing a bit-level arrangement process. Through two complete forward and reverse cycles, 1024 key bit-level scrambling is achieved. The design of key bit-level arrangement provides stronger anti-cracking capabilities for medical image encryption.

[0116] S4, diffusion algorithm based on spatiotemporal chaotic system;

[0117] In the designed diffusion algorithm, each pixel value is determined by the chaotic sequence generated by the previous two pixel values ​​and the spatiotemporal chaotic system. Compared with other diffusion algorithms in which each pixel value only depends on the previous pixel value, the diffusion algorithm proposed in the present invention has more complex structural characteristics and better encryption effect. After two diffusion operations, the correlation between adjacent pixels is greatly reduced, and the encryption effect of pixels is significantly enhanced;

[0118]

[0119] Among them, the initial value LL(i) is the chaotic sequence generated by the spatiotemporal chaotic system, D(i) is the coding sequence of the protein, the value of SS(i+2) depends on the values ​​of SS(i), SS(i+1), D(i) and LL(i), in the XOR algorithm, we introduce two perturbation parameters s1 and s6 to enhance the anti-cracking ability of the algorithm, sssk(i+1) is the pixel value after applying the diffusion algorithm, sssk(i) is the i-th pixel value of the ciphertext sequence, which is determined by the first two pixel values ​​and the chaotic sequence generated by the spatiotemporal chaotic system, SS(i), SS(i+2), SS(i+1) are the i-th, i+1, i+2 pixel values ​​of the first diffusion sequence respectively, ssk(i), ssk(i+1), ssk(i+2) are the i-th, i+1, i+2 pixel values ​​of the second diffusion sequence respectively, the complexity of the diffusion algorithm structure effectively reduces the correlation of medical images.

[0120] like Figure 10-15 As shown in Figure 2, we performed encryption and decryption tests on chest medical images and foot medical images. In addition, we performed histogram tests on the two medical images. The results are shown in Figure 2. Figure 16-21 As shown in the figure, the results show that the encryption algorithm we proposed can evenly reduce the frequency of pixel appearance. Figure 22-25 In this paper, we conducted salt and pepper noise attack and letter occlusion and clipping attack on two medical images, and the results show that the encryption algorithm we proposed has excellent ability to resist noise attacks.

[0121] In order to further compare and analyze the security performance of the encryption algorithm proposed in the present invention, the average change intensity UACI and the pixel change rate NPCR of the encryption algorithm are tested. The closer the values ​​of NPCR and UACI are to the ideal values ​​(99.6094070% and 33.4635070%), the better the security of the algorithm. As shown in Table 1, compared with the five encrypted pictures in other algorithms, see Lena, Elaine, Barb, Hill, Baboon in the table, the values ​​of NPCR and UACI of the proposed encryption algorithm are closer to the ideal values, that is, the encryption algorithm based on the spatiotemporal chaotic system proposed in the present invention has better security and stronger anti-decryption ability;

[0122] Table 1 Comparative analysis of UACI and NPCR

[0123]

[0124] Information entropy is often used as a quantitative indicator of image information content. For a 256-grayscale image, 8 usually represents the maximum uncertainty and the maximum amount of information. If the value is closer to 8, the randomness of the ciphertext data is higher and the encryption effect is better. As shown in Table 2, the information entropy of Lena, Elaine, Barb, Hill, and Baboon is relatively low, but after encryption by the algorithm proposed in the present invention, the information entropy is close to the ideal value.

[0125] Table 2 Information entropy

[0126]

[0127] The above embodiments are implemented based on the technical solution of the present invention, and detailed implementation methods and specific operation processes are given, but the protection scope of the present invention is not limited to the above embodiments. The methods used in the above embodiments are conventional methods unless otherwise specified.

Claims

1. A medical image encryption algorithm based on spatiotemporal chaotic system, characterized in that: The steps include: S1. Constructing a spatiotemporal chaotic system based on the coupled mapping lattice CML model: x n+1 (i)=(1-ε n )f[x n (i)]+(ε n / 2)(f[x n (j)]+f[x n (k)] (1) Among them, x n+1 (i) is the state of the i-th grid point at time n+1, x n (i) is the state of the i-th grid point at time n, x n (j) is the state of the jth grid point at time n, x n (k) is the state of the kth grid point at time n, ε n (0≤ε n ≤1) is the coupling coefficient at time n, f(x) is the Logistic chaotic system; Among them, ε n+1 is the coupling coefficient at time n+1, α is the control parameter, and its value range is (0,4); i, j and k are spatial grid points and are determined by Arnold cat mapping, and are defined as follows: Among them, i ≥ 1 and j, k ≤ L, P and q are the horizontal and vertical coordinates of the original space grid respectively. S2. Scramble the plaintext using the designed protein sequence partition scrambling method: Use a partition algorithm, whose initial point is determined by the Logistic system, to divide the medical image into multiple groups of regions of different sizes and lengths. There are overlaps between the regions, and different encoding rules are set for regions of different sizes, and an encryption algorithm is set; S3, set the chaotic key of the encryption algorithm: use the pseudo-random sequence CSS generated by the spatiotemporal chaotic system and the sequence cu generated by the Logistic system as the key input; S4. Design a diffusion algorithm based on spatiotemporal chaotic system: Among them, LL(i) is the initial value of the chaotic sequence generated by the spatiotemporal chaotic system, D(i) is the coding sequence of the protein, sssk(i+1) is the pixel value after applying the diffusion algorithm, sssk(i) is the i-th pixel value of the ciphertext sequence, which is determined by the first two pixel values ​​and the chaotic sequence generated by the spatiotemporal chaotic system, SS(i), SS(i+2), SS(i+1) are the i-th, i+1, i+2 pixel values ​​of the first diffusion sequence, and ssk(i), ssk(i+1), ssk(i+2) are the i-th, i+1, i+2 pixel values ​​of the second diffusion sequence, respectively.

2. According to claim 1, a medical image encryption algorithm based on spatiotemporal chaotic system is characterized in that: The encryption algorithm in step S2 includes the following steps: S2.

1. Generate m chaotic sequences (cs1, cs2, ..., cs m ), Where M is the length of each region, and N is the width of each region; S2.2, let the one-dimensional sequence of m regions be (sp1, sp2, ..., sp m ), the length of the chaotic sequence cs(i) corresponds to the length of each segment sp(i), and the following operations are performed on each segment of the chaotic sequence cs(i): Among them, K(i) is the chaotic sequence after the modulo operation; S2.3, the chaotic sequence K1~K m The values ​​of and the pixel values ​​of each region sp1~spm are converted into binary, and the chaotic sequences cs and sp are converted into protein sequences a(i) and b(i) according to m protein encoding rules, and replaced with 4 elements; S2.4, a(i) and b(i) are added according to the protein sequence addition rule. In the addition operation, a(i) and b(i) are converted into matrix y by binary left shift operation and table lookup; S2.

5. Convert the protein matrix y into decimal and encrypt the medical image.

3. According to claim 1, a medical image encryption algorithm based on spatiotemporal chaotic system is characterized in that: The generation of the key in step S3 includes the following steps: S3.1, convert the chaotic sequence css and sequence su into binary; S3.2, perform XOR calculation on the sequence css and the sequence su; Among them, css(i) is the i-th value of the chaotic sequence, and su(i) is the i-th pixel value of the input sequence; S3.3, in the sequence keysubse, b is the bit number (b≡1,2,3,…,128×8), map keysubse to the bit-level scrambling, and operate according to the designed bit-level scrambling algorithm; S3.4, the final key sequence is obtained according to the designed bit-level arrangement, the key mapping is between [0,1], i is the spatial sequence number, i∈[1,128]; S3.5, Q(i) is the key sequence obtained by bit-level scrambling. In the diffusion algorithm, Q(i) is set to the initial value of the spatiotemporal chaotic system used in the pixel diffusion process.

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