Image protection method suitable for medical internet of things

Abstract

The invention discloses an image protection method suitable for a medical internet of things. The method comprises the following steps: setting a secret key; acquiring initial parameters required by one-dimensional chaotic mapping by adopting a secret key, wherein the initial parameters comprise parameters and initial values of a chaotic mapping equation; respectively performing scrambling processing on two plaintext images acquired on the Internet of Things equipment to obtain two scrambled images; combining the two scrambled images to obtain a combined image; in combination with the obtained initial parameters required by the one-dimensional chaotic mapping, performing XOR diffusion on the merged image to obtain an XOR diffused image; splitting the image after the XOR diffusion to obtain two split images; performing re-scrambling processing on the two split images to obtain an encrypted image; and when the two plaintext images acquired on the Internet of Things equipment need to be checked, carrying out reverse processing on the encrypted images to finally obtain the two plaintext images. According to the invention, the encryption effect is good, and the security in the dual-image transmission process can be greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of information security, and in particular to an image protection method suitable for medical Internet of Things. Background Art

[0002] In recent years, there has been a surge in smart devices and related technologies such as the Internet of Things (IoT) and the Internet of Medical Things (IoMT). However, the limited processing power and storage capacity of smart devices make them vulnerable to cyber attacks, making traditional security and cryptographic technologies incapable of handling them. Image data information is different from traditional text messages and has the characteristics of large data capacity, which makes traditional encryption algorithms such as AES and DES face performance bottlenecks when processing large amounts of image data. To this end, the introduction of chaotic systems has become an effective solution. Chaotic systems are nonlinear dynamic systems that are known for their sensitivity to initial values, ergodicity, and non-periodicity, making them very suitable for achieving efficient and secure IoT data encryption. In particular, hyperchaotic systems have become a research hotspot in the field of digital IoT data encryption in recent years due to their stronger randomness and unpredictability.

[0003] In addition, in the context of the Internet of Things, real-time data processing and transmission have put forward higher requirements for security. Traditional Internet of Things image data encryption technology often fails to meet the requirements due to its high resource consumption and processing delay. Moreover, images in the Internet of Things often appear in the form of two pictures. For example, in the medical Internet of Things, doctors will analyze the pictures taken from two directions of the patient's condition to better judge the patient's condition. In this case, the pictures will be transmitted through the Internet of Things devices in the hospital, but the security of the traditional scrambling-diffusion framework is often insufficient. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and provide an image protection method suitable for medical Internet of Things.

[0005] To achieve the above purpose, the technical solution provided by the present invention is:

[0006] An image protection method applicable to medical Internet of Things, comprising:

[0007] Set the key;

[0008] The key is used to obtain the initial parameters required for the one-dimensional chaotic mapping, including the parameters and initial values ​​of the chaotic mapping equation;

[0009] Perform scrambling processing on the two plaintext images collected on the IoT device to obtain two scrambled images;

[0010] Merge the two scrambled images to obtain a merged image;

[0011] Combined with the initial parameters required for the acquired one-dimensional chaotic mapping, the combined image is XOR diffused to obtain an image after XOR diffusion;

[0012] Split the image after XOR diffusion to obtain two split images;

[0013] Re-scramble the two split images to obtain the encrypted image;

[0014] When it is necessary to view two plaintext images collected on the IoT device, the encrypted images are reversely processed to finally obtain two plaintext images.

[0015] Furthermore, the two plaintext images collected on the IoT device are scrambled by the positive DNA scanning scrambling algorithm. The scrambling process includes:

[0016] A1. Input the plaintext image img, obtain the length m and width n of the plaintext image img, initialize the loop parameter i to 1, and initialize the loop parameter j to 1;

[0017] A2. Check i. If i is m / 2+1, go to step A8; otherwise, go to step A2.

[0018] A3. Check j. If j is 2n+1, set the value of i to increment by 1, set j to 1, and return to step A2. Otherwise, go to step A4.

[0019] A4. Select the operation to be performed according to the value of j, the vertical coordinate of the pixel value:

[0020]

[0021] A5. Select the operation to be performed according to the value of j. The horizontal coordinate of the pixel value is:

[0022]

[0023] A6. Check the values ​​of x and y. If x≤m&&y≤n&&x>0&&y>0, execute Line(num)=img(x,y), where num=num+1, and num represents the position number of the matrix Line of the pixel to be filled.

[0024] A7. Set the value of i to increase by 1, the value of j to increase by 1, and return to step A2;

[0025] A8. Use the Reshpe formula to convert the scanned matrix Line with a length of 1 and a width of m*n into a matrix Line with a length of m and a width of n.

[0026] Further, merge the two scrambled images, including:

[0027] Convert the two images img1 and img2 into two one-dimensional vectors P1 and P2 respectively, where the size of img1 is m1×n1, the size of img2 is m2×n2, the size of P1 is 1×(m1×n1), and the size of P2 is 1×(m2×n2);

[0028] Connect P1 and P2 end to end to form a one-dimensional vector P of 1×(m1×n1 + m2×n2).

[0029] Further, perform XOR diffusion on the merged image, including:

[0030] Suppose the one-dimensional vector P obtained by merging the two scrambled images has a size of 1×U, and its one-dimensional vector length L = 1×U;

[0031] Generate a one-dimensional chaotic sequence key_stream of length L using a chaotic system, where 1 < i < N0 + L and i is an int-type variable, and N0 is a set value;

[0032] Perform bitwise XOR on the one-dimensional vector P and the one-dimensional chaotic sequence key_strean to obtain the diffused sequence diffusion_img;

[0033] Convert the ciphertext one-dimensional vector diffusion_img back into an image of size U.

[0034] Further, the process of generating the one-dimensional chaotic sequence key_stream includes:

[0035] B1. Initialize variables: x(0) = x0, i = 1; x0 is the initial value obtained using the key, specifically the starting point of chaos generation;

[0036] B2. Iteratively loop the one-dimensional chaotic map N0 + L times to obtain an array sequence x composed of x(1)-x(N0 + L) with a size of 1*(N0 + L). Each specific step is divided into b1 and b2:

[0037] b1: Update the value of x(i) using the chaotic map formula:

[0038]

[0039] where b is the control parameter;

[0040] b2: i = i + 1;

[0041] B3. Intercept x(N0 + 1)-x(N0 + L) as the new x array sequence, and discard x(1)-x(N0);

[0042] B4. Perform the following processing on the x sequence, convert it to the interval [0,255] to obtain a one-dimensional chaotic sequence key_stream with a length of L;

[0043] key_stream(i)=mod(floor(x(i)×10^12),256); i=1:L

[0044] Among them, mod is the remainder operation, and floor is the rounding down operation.

[0045] Furthermore, the image after XOR diffusion is split, including:

[0046] Convert the XOR-diffused image into a one-dimensional vector P3 of size 1×(m1×n1+m2×n2);

[0047] Split the one-dimensional vector P3 into a one-dimensional vector P4 of size 1×(m1×n1) and a one-dimensional vector P5 of size 1×(m2×n2);

[0048] Use the Reshpe formula to convert the one-dimensional vector P4 of size 1×(m1×n1) and the one-dimensional vector P4 of size 1×

[0049] The one-dimensional vector P5 of (m2×n2) becomes the image img3 of size m1×n1 and the image img4 of size m2×n2 respectively.

[0050] Furthermore, the two split images are re-scrambled respectively by using the anti-DNA scanning scrambling algorithm, and the re-scrambling process includes:

[0051] C1. Input image Img, obtain the length M and width N of image Img, initialize loop parameter I to 1, and initialize loop parameter J to 1;

[0052] C2, check I, if I is M / 2+1, go to step C8, otherwise go to step C2;

[0053] C3. Check J. If J is 2N+1, set the value of I to increment by 1, set J to 1, and return to step C2. Otherwise, go to step C4.

[0054] C4. Select the operation to be performed according to the value of J, the vertical coordinate of the pixel value:

[0055]

[0056] C5. Select the operation to be performed according to the value of J. The horizontal coordinate of the pixel value:

[0057]

[0058] C6. Check the values ​​of X and Y. If X≤M&&Y≤N&&X>0&&Y>0 is satisfied at the same time, then execute line(Num)=Img(X,1+NY), where Num=Num+1, and Num represents the position ordinal number of the matrix line of the pixel to be filled.

[0059] C7, set the value of I to increment by 1, the value of J to increment by 1, and return to step C2;

[0060] C8. Use the Reshpe formula to convert the scanned matrix line with a length of 1 and a width of M*N into a matrix line with a length of M and a width of N.

[0061] Furthermore, the encrypted image is reversely processed, and the process includes:

[0062] The two encrypted images are inversely scrambled by using the anti-DNA scanning scrambling algorithm to obtain two inversely scrambled images.

[0063] Merge the two inverse scrambled images to obtain a merged image;

[0064] Combined with the initial parameters required for the one-dimensional chaotic mapping obtained, the combined image obtained in the previous step is subjected to inverse XOR diffusion to obtain an image after inverse XOR diffusion;

[0065] Split the image after inverse XOR diffusion to obtain two split images;

[0066] When it is necessary to view the two plaintext images collected on the IoT device, the split images obtained in the previous step are respectively inversely scrambled using the positive DNA scanning scrambling algorithm to obtain two plaintext images.

[0067] Compared with the prior art, the principles and advantages of this technical solution are as follows:

[0068] 1. The encryption method of dual image scrambling-merging-diffusion-splitting-re-scrambling suitable for the Internet of Things is adopted, which has good encryption effect and can greatly improve the security of dual image transmission.

[0069] 2. The plaintext image is scrambled by the positive DNA scanning scrambling algorithm, and the split image is re-scrambled by the reverse DNA scanning scrambling algorithm. The combination of the two can significantly improve the scrambling effect and effectively resist attacks based on traditional scan line scrambling methods.

[0070] 3. Adoption A chaotic mapping is performed, which is sensitive enough to the key, difficult to crack, and can generate a sufficiently random sequence. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the services required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0072] Figure 1 This is a principle flow chart of an image protection method applicable to medical Internet of Things of the present invention;

[0073] Figure 2 It is a comparison picture of the dual images before and after encryption by the method of the present invention;

[0074] Figure 3 It is a schematic diagram of the principle of the positive DNA scanning scrambling algorithm;

[0075] Figure 4 Schematic diagram of the principle of anti-DNA scanning scrambling algorithm. DETAILED DESCRIPTION

[0076] The present invention will be further described below in conjunction with specific embodiments:

[0077] like Figure 1 As shown, the image protection method applicable to the medical Internet of Things described in this embodiment includes the following steps:

[0078] S1. Set the key;

[0079] S2, using the key to obtain the initial parameters required for the one-dimensional chaotic mapping, including the parameters and initial values ​​of the chaotic mapping equation;

[0080] S3, scrambling the two plaintext images collected on the IoT device by using the positive DNA scanning scrambling algorithm to obtain two scrambled images;

[0081] The scrambling process of this step is as follows:

[0082] A1. Input the plaintext image img, obtain the length m and width n of the plaintext image img, initialize the loop parameter i to 1, and initialize the loop parameter j to 1;

[0083] A2. Check i. If i is m / 2+1, go to step A8; otherwise, go to step A2.

[0084] A3. Check j. If j is 2n+1, set the value of i to increment by 1, set j to 1, and return to step A2. Otherwise, go to step A4.

[0085] A4. Select the operation to be performed according to the value of j, the vertical coordinate of the pixel value:

[0086]

[0087] A5. Select the operation to be performed according to the value of j, abscissa of the pixel value:

[0088]

[0089] A6. Check the values of x and y. If x ≤ m && y ≤ n && x > 0 && y > 0 are satisfied simultaneously, then perform the operation Line(num) = img(x, y), num = num + 1, where num represents the position ordinal number of the matrix Line of the pixel to be filled;

[0090] A7. Set the value of i to increment by 1, the value of j to increment by 1, and return to step A2;

[0091] A8. Use the Reshpe formula to reshape the matrix Line with a length of 1 and a width of m * n obtained by scanning back into a matrix Line with a length of m and a width of n.

[0092] S4. Merge the two scrambled images to obtain the merged image;

[0093] The merging process of this step is as follows:

[0094] Convert the two images img1 and img2 into two one-dimensional vectors P1 and P2 respectively, where img1 has a size of m1 × n1, img2 has a size of m2 × n2, P1 has a size of 1 × (m1 × n1), and P2 has a size of 1 × (m2 × n2);

[0095] Connect P1 and P2 end to end to form a one-dimensional vector P of 1 × (m1 × n1 + m2 × n2).

[0096] S5. Combine the initial parameters required for the obtained one-dimensional chaotic map, and perform XOR diffusion on the merged image to obtain the XOR-diffused image;

[0097] The specific process of performing XOR diffusion on the merged image is as follows:

[0098] Suppose the size of the one-dimensional vector P obtained after merging the two scrambled images is 1 × U, and its one-dimensional vector length L = 1 × U;

[0099] Generate a one-dimensional chaotic sequence key_stream with a length of L using the chaotic system, where 1 < i < N0 + L and i is an int-type variable, and N0 is a set value;

[0100] Perform bitwise XOR on the one-dimensional vector P and the one-dimensional chaotic sequence key_stream to obtain the diffused sequence diffusion_img;

[0101] Convert the ciphertext one-dimensional vector diffusion_img back into an image of size U.

[0102] In the above, the process of generating the one-dimensional chaotic sequence key_strean includes:

[0103] B1. Initialize variables: x(0)=x0, i=1; x0 is the initial value obtained using the key, specifically the starting point of chaos generation;

[0104] B2. The one-dimensional chaotic mapping is iterated N0+L times to obtain an array sequence x of size 1*(N0+L) consisting of x(1)-x(N0+L). The specific steps of each time are divided into b1 and b2:

[0105] b1: Update the value of x(i) using the chaotic mapping formula:

[0106]

[0107] Among them, b is the control parameter;

[0108] b2: i=i+1;

[0109] B3. Take x(N0+1)-x(B0+L) as the new x array sequence and discard x(1)-x(B0);

[0110] B4. Perform the following processing on the x sequence, convert it to the interval [0,255] to obtain a one-dimensional chaotic sequence key_stream with a length of L;

[0111] key_stream(i)=mod(floor(x(i)×10^12),256); i=1:L

[0112] Among them, mod is the remainder operation, and floor is the rounding down operation.

[0113] S6, splitting the image after XOR diffusion to obtain two split images;

[0114] The process of splitting the image after XOR diffusion includes:

[0115] Convert the XOR-diffused image into a one-dimensional vector P3 of size 1×(m1×n1+m2×n2);

[0116] Split the one-dimensional vector P3 into a one-dimensional vector P4 of size 1×(m1×n1) and a one-dimensional vector P5 of size 1×(m1×n1).

[0117] One-dimensional vector P5 of (m2×n2);

[0118] Use the Reshpe formula to convert the one-dimensional vector P4 of size 1×(m1×n1) and the one-dimensional vector P4 of size 1×

[0119] The one-dimensional vector P5 of (m2×n2) becomes the image img3 of size m1×n1 and the image img4 of size m2×n2 respectively.

[0120] S7, re-scrambling the two split images respectively by using the anti-DNA scanning scrambling algorithm to obtain encrypted images;

[0121] The re-scrambling process includes:

[0122] C1. Assume the input image Img, obtain the length M and width N of the image Img, initialize the loop parameter I to 1, and initialize the loop parameter J to 1;

[0123] C2, check I, if I is M / 2+1, go to step C8, otherwise go to step C2;

[0124] C3. Check J. If J is 2N+1, set the value of I to increment by 1, set J to 1, and return to step C2. Otherwise, go to step C4.

[0125] C4. Select the operation to be performed according to the value of J, the vertical coordinate of the pixel value:

[0126]

[0127] C5. Select the operation to be performed according to the value of J. The horizontal coordinate of the pixel value:

[0128]

[0129] C6. Check the values ​​of X and Y. If X≤M&&Y≤N&&X>0&&Y>0 is satisfied at the same time, then execute line(Num)=Img(X,1+NY), where Num=Num+1, and Num represents the position ordinal number of the matrix line of the pixel to be filled.

[0130] C7, set the value of I to increment by 1, the value of J to increment by 1, and return to step C2;

[0131] C8. Use the Reshpe formula to convert the scanned matrix line with a length of 1 and a width of M*N into a matrix line with a length of M and a width of N.

[0132] S8. When it is necessary to view two plaintext images collected on the IoT device, the encrypted images are reversely processed to finally obtain two plaintext images.

[0133] The encrypted image is reverse processed, the process includes:

[0134] The two encrypted images are inversely scrambled by using the anti-DNA scanning scrambling algorithm to obtain two inversely scrambled images.

[0135] Merge the two inverse scrambled images to obtain a merged image;

[0136] Combined with the initial parameters required for the one-dimensional chaotic mapping obtained, the combined image obtained in the previous step is subjected to inverse XOR diffusion to obtain an image after inverse XOR diffusion;

[0137] Split the image after inverse XOR diffusion to obtain two split images;

[0138] When it is necessary to view the two plaintext images collected on the IoT device, the split images obtained in the previous step are respectively inversely scrambled using the positive DNA scanning scrambling algorithm to obtain two plaintext images.

[0139] This embodiment adopts a dual-image scrambling-merging-diffusion-splitting-re-scrambling encryption method applicable to the Internet of Things, which has a good encryption effect and can greatly improve the security of the dual-image transmission process.

[0140] Specifically, the plaintext image is scrambled by the positive DNA scanning scrambling algorithm, and the split image is re-scrambled by the anti-DNA scanning scrambling algorithm. The combination of the two can significantly improve the scrambling effect and can effectively resist attacks based on traditional scan line scrambling methods.

[0141] Specifically, using A chaotic mapping is performed, which is sensitive enough to the key, difficult to crack, and can generate a sufficiently random sequence.

[0142] In order to prove the superiority and effectiveness of the method of the present invention, the following tests were performed:

[0143] 1. NIST test (if each index exceeds 0.01, it means that the chaos test has passed), and the NIST test data of the chaos map shown in Table 1 is obtained:

[0144] Test items P-value By situation Frequency test 0.085712 Successfully passed the test Within-block frequency test 0.623300 Successfully passed the test Accumulation and testing 0.515140 Successfully passed the test Run length test 0.283670 Successfully passed the test Longest run test 0.062820 Successfully passed the test Test for Matrix Rank 0.213300 Successfully passed the test Discrete Fourier Transform Test 0.233120 Successfully passed the test Non-overlapping template matching test 0.342115 Successfully passed the test Overlapping template matching test 0.494390 Successfully passed the test Common statistical tests 0.883160 Successfully passed the test Approximate Entropy Test 0.055670 Successfully passed the test Random walk test 0.543400 Successfully passed the test Random Walk Variable Test 0.549190 Successfully passed the test Continuity test 0.463260 Successfully passed the test Linear complexity test 0.893220 Successfully passed the test

[0145] Table 1

[0146] As can be seen from Table 1, the value of each test of the present invention exceeds 0.01, proving that the chaotic system adopted by the present invention can generate a sufficiently random sequence.

[0147] 2. Encryption visual test:

[0148] Figure 2For the double encrypted image obtained under the Internet of Things after the encrypted visualization test, the naked eye cannot recognize the original image on the left from the image on the right, so the encryption effect of the encryption algorithm adopted by the present invention is very good.

[0149] 3. Correlation test, and obtain the test data shown in Table 2:

[0150]

[0151] Table 2

[0152] As can be seen from Table 2, the values ​​of correlation tests of IoT image data in horizontal, vertical and diagonal directions after positive DNA scanning scrambling and reverse DNA scrambling are very low, which means that the correlation between adjacent pixels after image encryption is very low and statistical regularities are not easily detected, which means that the method proposed in the present invention is generally very secure.

[0153] 4. Encryption speed test:

[0154] After experimental testing, the encryption algorithm proposed in the invention takes an average of 0.012 seconds to process a 512*512 image. Therefore, the encryption efficiency is very high and suitable for image protection in the Internet of Things.

[0155] 5. Change the parameters (change each parameter b by 10^-15, the initial value x0 by 10^-15, and N0 by 1), and perform key sensitivity analysis on the encrypted images with and without the parameters changed, and obtain

[0156] The data shown in Table 3:

[0157]

[0158] Table 3

[0159] As can be seen from Table 3, the NPCR and UACI values ​​returned by the test are both very high, which indicates that the encryption algorithm proposed in the present invention is highly sensitive to the key and has sufficiently strong key sensitivity.

[0160] The embodiments described above are only preferred embodiments of the present invention and are not intended to limit the scope of implementation of the present invention. Therefore, all changes made according to the shape and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An image protection method suitable for medical Internet of Things, characterized in that: include: Set the key; The key is used to obtain the initial parameters required for the one-dimensional chaotic mapping, including the parameters and initial values ​​of the chaotic mapping equation; Perform scrambling processing on the two plaintext images collected on the IoT device to obtain two scrambled images; Merge the two scrambled images to obtain a merged image; Combined with the initial parameters required for the acquired one-dimensional chaotic mapping, the combined image is XOR diffused to obtain an image after XOR diffusion; Split the image after XOR diffusion to obtain two split images; Re-scramble the two split images to obtain the encrypted image; When it is necessary to view two plaintext images collected on the IoT device, the encrypted images are reversely processed to finally obtain two plaintext images.

2. The image protection method applicable to the medical Internet of Things according to claim 1, characterized in that: The two plaintext images collected on the IoT device are scrambled using the positive DNA scanning scrambling algorithm. The scrambling process includes: A1. Input the plaintext image img, obtain the length m and width n of the plaintext image img, initialize the loop parameter i to 1, and initialize the loop parameter j to 1; A2. Check i. If i is m / 2+1, go to step A8; otherwise, go to step A2. A3. Check j. If j is 2n+1, set the value of i to increment by 1, set j to 1, and return to step A2. Otherwise, go to step A4. A4. Select the operation to be performed according to the value of j, the vertical coordinate of the pixel value: A5. Select the operation to be performed according to the value of j. The horizontal coordinate of the pixel value is: A6. Check the values ​​of x and y. If x≤m&&y≤n&&x>0&&y>0 are satisfied at the same time, perform Line(hum)=img(x,y), num=num+1, hum represents the position ordinal number of the matrix Line of the pixel to be filled; A7. Set the value of i to increase by 1, the value of j to increase by 1, and return to step A2; A8. Use the Reshpe formula to convert the scanned matrix Line with a length of 1 and a width of m*n into a matrix Line with a length of m and a width of n.

3. The image protection method applicable to the medical Internet of Things according to claim 1, characterized in that: Merge the two scrambled images, including: Convert the two images img1 and img2 into two one-dimensional vectors P1 and P2 respectively, where the size of img1 is m1×n1, the size of img2 is m2×n2, the size of P1 is 1×(m1×n1), and the size of P2 is 1×(m2×n2); Connect P1 and P2 end to end to form a one-dimensional vector P of 1×(m1×n1+m2×n2).

4. The image protection method applicable to the medical Internet of Things according to claim 1, characterized in that: Perform XOR diffusion on the merged image, including: Assume that the size of the one-dimensional vector P obtained by merging the two scrambled images is 1×U, and its one-dimensional vector length L=1×U; A one-dimensional chaotic sequence key_stream with a length of L is generated by using a chaotic system, where 1<i<N0+L and i is an int variable, and N0 is a set value; Perform bit-by-bit XOR on the one-dimensional vector P and the one-dimensional chaotic sequence key_stream to obtain the diffused sequence diffusion_img; Convert the ciphertext one-dimensional vector diffusion_img back into an image of size U.

5. The image protection method applicable to the medical Internet of Things according to claim 4, characterized in that: The process of generating a one-dimensional chaotic sequence key_stream includes: B1. Initialize variables: x(0)=x0, i=1; x0 is the initial value obtained using the key, specifically the starting point of chaos generation; B2. The one-dimensional chaotic mapping is iterated N0+L times to obtain an array sequence x of size 1*(N0+L) composed of x(1)-x(N0+L). The specific steps of each time are divided into b1 and b2: b1: Update the value of x(i) using the chaotic mapping formula: Among them, b is the control parameter; b2: i=i+1; B3. Take x(N0+1)-x(N0+L) as the new x array sequence and discard x(1)-x(N0); B4. Perform the following processing on the x sequence, convert it to the interval [0, 255] to obtain a one-dimensional chaotic sequence key_stream with a length of L; key_stream(i)=mod(floor(x(i)×10^12), 256); i=1: L Among them, mod is the remainder operation, and floor is the rounding down operation.

6. The image protection method applicable to the medical Internet of Things according to claim 1, characterized in that: Split the image after XOR diffusion, including: Convert the XOR-diffused image into a one-dimensional vector P3 of size 1×(m1×n1+m2×n2); Split the one-dimensional vector P3 into a one-dimensional vector P4 of size 1×(m1×n1) and a one-dimensional vector P5 of size 1×(m2×n2): Use the Reshpe formula to convert the one-dimensional vector P4 of size 1×(m1×n1) and the one-dimensional vector P5 of size 1×(m2×n2) into an image img3 of size m1×n1 and an image img4 of size m2×n2 respectively.

7. The image protection method applicable to the medical Internet of Things according to claim 1, characterized in that: The two split images are re-scrambled by the anti-DNA scanning scrambling algorithm. The re-scrambling process includes: C1. Assume the input image Img, obtain the length M and width N of the image Img, initialize the loop parameter I to 1, and initialize the loop parameter J to 1; C2, check I, if I is M / 2+1, go to step C8, otherwise go to step C2; C3. Check J. If J is 2N+1, set the value of I to increment by 1, set J to 1, and return to step C2. Otherwise, go to step C4. C4. Select the operation to be performed according to the value of J, the vertical coordinate of the pixel value: C5. Select the operation to be performed according to the value of J. The horizontal coordinate of the pixel value: C6. Check the values ​​of X and Y. If X≤M&&Y≤N&&X>0&&Y>0 is satisfied at the same time, then execute line(Num)=Img(X,1+NY), where Num=Num+1, and Num represents the position ordinal number of the matrix line of the pixel to be filled. C7, set the value of I to increment by 1, the value of J to increment by 1, and return to step C2; C8. Use the Reshpe formula to convert the scanned matrix line with a length of 1 and a width of M*N into a matrix line with a length of M and a width of N.

8. The image protection method applicable to the medical Internet of Things according to claim 1, characterized in that: The encrypted image is reverse processed, the process includes: The two encrypted images are inversely scrambled by using the anti-DNA scanning scrambling algorithm to obtain two inversely scrambled images. Merge the two inverse scrambled images to obtain a merged image; Combined with the initial parameters required for the one-dimensional chaotic mapping obtained, the combined image obtained in the previous step is subjected to inverse XOR diffusion to obtain an image after inverse XOR diffusion; Split the image after inverse XOR diffusion to obtain two split images; When it is necessary to view the two plaintext images collected on the IoT device, the split images obtained in the previous step are respectively inversely scrambled using the positive DNA scanning scrambling algorithm to obtain two plaintext images.

Images