Unmanned vehicle image sharing method, system, corresponding equipment and storage medium

Abstract

This application discloses a method, system, corresponding device, and storage medium for sharing images between unmanned vehicles, wherein the unmanned vehicles include at least a first and a second unmanned vehicle in the same local area network. The method comprises: in the first unmanned vehicle, performing i iterations of chaotic mapping encryption on an image captured by the first unmanned vehicle to obtain a chaotic unordered image and a chaotic mapping key; uploading the chaotic unordered image to a shared folder on the local area network; encrypting the file name of the chaotic unordered image and the chaotic mapping key using an encryption key of a symmetric or asymmetric encryption algorithm to obtain a ciphertext; and in response to receiving a request for access to the image from the second unmanned vehicle, sending the ciphertext and the symmetric encryption key or the asymmetric encryption decryption key to the second unmanned vehicle. The present invention can greatly increase the difficulty of deciphering encrypted images and ensure the security of the images.

Description

Technical Field

[0001] The present application relates to the field of electronic digital data processing, and in particular to an unmanned vehicle image sharing method, system, and corresponding equipment and storage medium. Background Art

[0002] For the encryption protection and secure sharing of confidential maps or other images, a single image encryption method cannot guarantee the security of the key or the integrity of the information during transmission between users. Traditional image encryption processes encrypt by replacing or transposing the image matrix according to a specific algorithm. Modern image encryption processes transform the image using more rigorous scientific theories. However, even with modern image encryption processes such as Henon chaotic map encryption, the probability of the encrypted chaotic state being deciphered is still high. In addition, there are two potential risks with image encryption alone: one is that the key is intercepted during transmission, making it possible to decipher the image; the other is that the key is tampered with during transmission, making it impossible for other users to obtain the original image using the original key. Summary of the Invention

[0003] The present invention provides an unmanned vehicle image sharing method, system, corresponding equipment and storage medium, which can greatly increase the difficulty of deciphering encrypted images and ensure the security of images.

[0004] In a first aspect of the present invention, a method for sharing images of unmanned vehicles is provided, wherein the unmanned vehicles include at least a first and a second unmanned vehicle in the same local area network. The method comprises: in the first unmanned vehicle,

[0005] Performing i-iteration chaotic map encryption on the image collected by the first unmanned vehicle to obtain a chaotic disordered graph and a chaotic map key of the image, where i is greater than or equal to 2;

[0006] Uploading the chaotic disorder graph to a shared folder on a local area network;

[0007] Encrypting the file name of the chaotic disordered graph and the chaotic map key using an encryption key of a symmetric or asymmetric encryption algorithm to obtain a ciphertext;

[0008] In response to receiving a request for access to the image sent by the second unmanned vehicle, the ciphertext and the symmetrically encrypted encryption key or the asymmetrically encrypted decryption key are sent to the second unmanned vehicle.

[0009] In a second aspect of the present invention, a system for sharing images of unmanned vehicles is provided, wherein the unmanned vehicles include at least first and second unmanned vehicles in the same local area network, and the first unmanned vehicle includes:

[0010] a chaotic encryption module, configured to perform i-iteration chaotic map encryption on the image collected by the first unmanned vehicle to obtain a chaotic disordered graph and a chaotic map key of the image, where i is greater than or equal to 2;

[0011] An uploading module, used for uploading the chaotic disorder graph to a shared folder in a local area network;

[0012] A symmetric or asymmetric encryption module, configured to encrypt the file name of the chaotic disordered graph and the chaotic map key using an encryption key of a symmetric or asymmetric encryption algorithm to obtain a ciphertext;

[0013] The sending module is used to send the ciphertext and the symmetrically encrypted encryption key or the asymmetrically encrypted decryption key to the second unmanned vehicle in response to receiving a request for accessing the image sent by the second unmanned vehicle.

[0014] In a third aspect of the present invention, a computer device is provided, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method according to the first aspect of the present invention when executing the computer program.

[0015] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method according to the first aspect of the present invention.

[0016] According to the present invention, when the first and second unmanned vehicles are in the same local area network, the image collected by the first unmanned vehicle is encrypted by performing i-times iterative chaotic mapping in the first unmanned vehicle to obtain a chaotic disordered image and a chaotic mapping key of the image, and the chaotic disordered image is uploaded to a shared folder of the local area network. The file name of the chaotic disordered image and the chaotic mapping key are encrypted using an encryption key of a symmetric or asymmetric encryption algorithm to obtain a ciphertext. In response to a request for access to the image sent by the second unmanned vehicle, the ciphertext and the symmetric encryption encryption key or the asymmetric encryption decryption key are sent to the second unmanned vehicle. The i-times iterative chaotic mapping greatly increases the unpredictability of the image, thereby increasing the difficulty of deciphering the encrypted image. The symmetric or asymmetric encryption makes the chaotic mapping key used not afraid of being stolen during transmission, increases the difficulty of deciphering the key, and ultimately ensures the security of the image.

[0017] Other features and advantages of the present invention will become more apparent after reading the detailed description of the embodiments of the present invention in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a flow chart of an embodiment of a method according to the present invention;

[0019] Figure 2 shows a histogram of pixel values of the original image;

[0020] Figure 3 Shows the pixel value histogram after encryption by the classic Henon chaotic map;

[0021] Figure 4 The pixel value histogram after encryption by the improved Henon chaotic map of 2 iterations is shown;

[0022] Figure 5 The pixel value histogram after encryption of the improved Henon chaotic map with 100 iterations is shown;

[0023] Figure 6 The encrypted pixel value histogram of the improved Henon chaotic map after 500 iterations is shown;

[0024] Figure 7 shows the autocorrelation map of adjacent pixels of the original image;

[0025] Figure 8 The autocorrelation diagram of adjacent pixels after encryption of the classic Henon chaotic map is shown;

[0026] Figure 9 The autocorrelation graph of adjacent pixels after encryption by the improved Henon chaotic map of 2 iterations is shown;

[0027] Figure 10 The autocorrelation graph of adjacent pixels after encryption by the improved Henon chaotic map of 100 iterations is shown;

[0028] Figure 11 The autocorrelation graph of adjacent pixels after encryption by the improved Henon chaotic map of 500 iterations is shown;

[0029] Figure 12 FIG. 1 is a block diagram of an embodiment of a system according to the present invention.

[0030] The figures are schematic and simplified for clarity, and they merely show details which are essential to the understanding of the invention, while other details are left out. DETAILED DESCRIPTION

[0031] The following describes in detail the embodiments and examples of the present invention with reference to the accompanying drawings.

[0032] The scope of applicability of the present invention will become apparent from the detailed description given below. However, it should be understood that while the detailed description and specific examples indicate preferred embodiments of the invention, they are given for illustration purposes only.

[0033] Figure 1A flowchart of a preferred embodiment of the unmanned vehicle image sharing method according to the present invention is shown, wherein the unmanned vehicle includes at least a first and a second unmanned vehicle in the same local area network. A map of the working environment collected by the first unmanned vehicle is stored in the local memory of the first unmanned vehicle.

[0034] In the first unmanned vehicle:

[0035] In step S102, the size of the image captured by the first unmanned vehicle is compressed to reduce the workload of subsequent image processing. In other embodiments, this step may not be included.

[0036] In an embodiment, a method based on discrete cosine transform can be used to remove high-frequency details to achieve the purpose of image compression. Specifically:

[0037] The image collected by the first unmanned vehicle is converted into a frequency domain image through discrete cosine transform.

[0038] Take an 8×8 matrix as an example,

[0039] M=

[0040]

[0041] After discrete cosine transform,

[0042] D=

[0043]

[0044] After discrete cosine transform, low-frequency data, that is, data where adjacent pixel values change slowly, is in the upper left corner of the frequency domain matrix, and high-frequency data, that is, data where the values change dramatically, is in the lower right corner of the frequency domain matrix.

[0045] At this time, we can adopt different quantization matrices to achieve the purpose of removing some high-frequency details.

[0046] C(x,y)=round(D(x,y) / Q(x,y))

[0047] Wherein, Q is the quantization matrix, C is the quantized matrix, and the matrix D is divided by the corresponding elements of the matrix Q and then rounded to obtain the matrix C.

[0048] For example, using

[0049]

[0050] Get the matrix C:

[0051]

[0052] Here we can see that many pixel values in the lower right corner become 0 after quantization, thereby achieving the purpose of removing some high-frequency details and compressing the image.

[0053] The quantization matrix Q used can be selected or designed based on the desired high-frequency detail removal / compression effect. For example, it can be selected from the quantization matrices recommended by the Picture Experts Group. In practice, removing 1% to 5% of the high-frequency data is sufficient.

[0054] In step S104, the compressed image is encrypted using Henon chaotic mapping iteratively i times to obtain a chaotic disordered graph and a chaotic mapping key of the compressed image, where i is greater than or equal to 2, for example, between 2 and 1000, such as 50, 100, 200, 500, 650, and so on.

[0055] The well-known image pixel transformation (mapping) formula of the Henon chaotic system is as follows:

[0056] X (n+1) =1–aX n 2 +Y n

[0057] Y (n+1) =b X n

[0058] Where a and b are parameters of the chaotic system. The values of a and b must ensure that the data generated by multiple encryptions of any pixel is non-periodic (periodicity means that a regular pattern applies and can be cracked). For example, a point (x, y) encrypted five times using the Henon chaotic map produces (x1, y1), (x2, y2)…(x5, y5). The resulting image or line segment is non-periodic. The typical values of a and b are a = 1.4 and b = 0.3.

[0059] The classic Henon chaotic map performs only one transformation, which makes the encrypted chaotic state still highly susceptible to decryption. Here, we improve the Henon chaotic map to make the encrypted chaotic state even more difficult to decrypt. Specifically, during the encryption operation, for each pixel in the image, we give it an initial coordinate origin (x0, y0), the parameters a and b of the chaotic system, and the number of iterations i. The coordinate origin (x0, y0), a, b, and i serve as the key for the first chaotic map encryption operation. The values of the coordinate origin x0 and y0 increase by a predetermined amount, such as 0.001, with each iteration. For example, if 500 iterations are performed, (x0, y0) = (0.001, 0.001), then (x1, y1) = (0.002, 0.002) ... (x500, y500) = (0.501, 0.501). Each iteration uses the four values a, b, x(i-1), and y(i-1) as the encryption key for the image (called the chaotic mapping key) and generates the i-th transformation matrix TMatrix(i) according to the Henon formula. The encrypted image matrix obtained after the i-1-th transformation operation is recorded as ImageMatrix(i-1). TMatrix(i) and ImageMatrix(i-1) are bitwise XORed to obtain the encrypted image matrix ImageMatrix(i) after the i-th transformation operation, which can be expressed as

[0060]

[0061] Until the i-th iteration is completed, each coordinate of the original image is rearranged according to the i-th chaotic map to obtain a chaotic disordered image encrypted with i different keys.

[0062] Since the two-dimensional Henon map is not only extremely sensitive to initial values, making the chaotic system more complex, but also disrupting the motion behavior in the dynamic system, as long as there is any slight difference between the selected decryption key group and the encryption key group, it is impossible to correctly decrypt the encrypted image, and it is highly immune to system model reconstruction attacks.

[0063] After iterative chaotic mapping, the pixel points can be quickly evenly distributed, eliminating the characteristic distribution of image pixels and greatly improving the difficulty of image deciphering. This can be clearly seen from the analysis of the pixel value histogram and the adjacent pixel autocorrelation point diagram, see Figure 2-11 ,in Figure 2 Figure 3 shows the pixel value histogram of the original image, and Figure 4 shows the pixel value histogram after encryption using the classic Henon chaotic map. Figure 4 The pixel value histogram after 2 iterations of the improved Henon chaotic map encryption is shown. Figure 5The encrypted pixel value histogram of the improved Henon chaotic map after 100 iterations is shown. Figure 6 The pixel value histogram after 500 iterations of the improved Henon chaos map encryption is shown. Figure 7 shows the autocorrelation diagram of adjacent pixels of the original image, Figure 8 The autocorrelation diagram of adjacent pixels encrypted by the classic Henon chaotic map is shown. Figure 9 The autocorrelation diagram of adjacent pixels after encryption of the improved Henon chaotic map after 2 iterations is shown. Figure 10 The autocorrelation diagram of adjacent pixels after 100 iterations of the improved Henon chaotic map encryption is shown. Figure 11 The autocorrelation diagram of adjacent pixels after 500 iterations of the improved Henon chaotic map encryption is shown. Figure 2-11 As can be seen, the histogram distribution of the encrypted image becomes more uniform with increasing recursion times, indicating a more randomized and unpredictable pixel distribution. Similarly, the autocorrelation distribution of the encrypted image becomes more randomized with increasing recursion times, demonstrating that the encrypted image's features are weakened, thereby reducing the response characteristics generated by external model attacks, making it impossible for external attacks to reconstruct the image based on these response characteristics.

[0064] Before performing the bitwise XOR operation, each pixel value must be converted to an 8-bit binary number. Therefore, accurately decrypting a pixel value requires approximately 255 operations. If an M×N matrix is to be accurately decrypted, for a digital image encrypted using the improved Henon chaotic map of the present invention, approximately 255(M+N)i attempts are required due to i iterations, greatly reducing the probability of decryption.

[0065] The Henon chaotic map is used in the preferred embodiment above because the Henon chaotic map formula is simple and easy to implement, but it is highly complex and suitable as a digital generator for encrypted images. In other embodiments, other chaotic maps can also be used for i-times iterative encryption, such as 2D / 3D Lorentz maps, Gaussian maps, regression maps, Arnold's Cat maps, Zaslavskii maps, etc. The iterative mapping process of chaotic maps other than Henon is similar to the iterative mapping process of the Henon chaotic map described above.

[0066] In step S106, the chaotic disordered graph encrypted by the Henon mapping is uploaded to a shared folder in the local area network. The chaotic disordered graph has a file name, such as "Unmanned Vehicle A_Current Position".PNG.

[0067] In step S108, the chaotic map file name and chaotic map key are encrypted using an AES (Advanced Encryption Standard) algorithm encryption key. The encrypted ciphertext is then converted from the matrix into a string and output. The chaotic map key is encrypted using the AES algorithm. This allows it to be publicly transmitted without risk of being stolen, preventing the image from being deciphered due to the leakage of the Henon chaotic map key.

[0068] In other embodiments, other known symmetric or asymmetric encryption algorithms other than AES may also be used.

[0069] In step S110, the established blockchain node writes the ciphertext into the blockchain. Ethereum-based blockchain nodes are established on both the first and second unmanned vehicles, forming a blockchain network. A private blockchain network can be established using the open-source Ethereum blockchain source code. A single computer establishes a node to maintain all blockchain information. An Ethereum-based chat program is created on this node. After the first and second unmanned vehicles are authorized to join the blockchain network, they can communicate and exchange information. While the AES-encrypted Henon chaotic map key can be tampered with during transmission, the tamper-proof nature of the blockchain ensures the integrity of information transmission.

[0070] In step S112, in response to receiving a request for access to the image sent by the second unmanned vehicle through blockchain communication, the ciphertext and the AES encryption key are sent to the second unmanned vehicle through blockchain communication.

[0071] In other embodiments, the above-mentioned blockchain communication may not be used, but the AES encryption key and ciphertext may be directly transmitted to the second unmanned vehicle upon request.

[0072] In the second unmanned vehicle:

[0073] In step S114, the ciphertext is decrypted using the AES encryption key to obtain the chaotic map key and file name.

[0074] In other embodiments, when an asymmetric encryption algorithm is used for encryption, the first unmanned vehicle sends the asymmetric encrypted decryption key to the second unmanned vehicle, and the second unmanned vehicle uses the decryption key to decrypt the ciphertext.

[0075] In step S116, the corresponding chaotic disorder graph is obtained from the shared folder according to the file name.

[0076] In step S118, the chaotic map is decrypted using the chaotic map key using the inverse Henon chaotic map step for i iterations to obtain the requested image. Specifically, using the inverse coordinate origin (x0', y0'), the values of x0', y0' are reduced by a predetermined amount, 0.001, used during encryption, in each iteration. For example, if we perform 500 iterations, the values of x0', y0' are (x0, y0) + 500*0.001, (x0', y0') = (0.501, 0.501), and thus (x1', y1') = (0.500, 0.500) ... (x500', y500') = (0.001, 0.001). Each iteration uses the four values a, b, x(i-1)', and y(i-1)' as the encryption key for the image. The i-th inverse transformation matrix TMatrix(i)' is generated in the same way as the encryption according to the Henon formula. The decrypted image matrix obtained after the i-1-th inverse transformation operation is recorded as ImageMatrix(i-1)'. TMatrix(i)' is used to perform a bitwise XOR operation with ImageMatrix(i-1)' to obtain ImageMatrix(i)', which can be expressed as:

[0077]

[0078] Until the i iterations are completed, each coordinate of the chaotic disordered map is rearranged by i inverse chaotic mapping to obtain the original map image. The pixels and resolution of the decrypted image are consistent with the original image.

[0079] The above embodiment combines the characteristics of image compression, improved chaotic mapping, advanced encryption standard, and blockchain, ensuring the reliability of image encryption processing, publicly transmitting image encryption keys, and preventing information from being stolen or tampered with, thereby ensuring information security.

[0080] In one embodiment, the processes in the first and second unmanned vehicles can be interchangeable. For example, the second unmanned vehicle is responsible for collecting, encrypting, and uploading images, while the first unmanned vehicle requests the collected images from the second unmanned vehicle and decrypts them. In another embodiment, a single unmanned vehicle can include a combination of the processes described above for the first and second unmanned vehicles.

[0081] Figure 12 A block diagram of a preferred embodiment of an unmanned vehicle image sharing system according to the present invention is shown. The unmanned vehicle includes at least a first and a second unmanned vehicle in the same local area network. The first unmanned vehicle includes:

[0082] a chaotic encryption module 122 for performing i-iteration chaotic map encryption on the image collected by the first unmanned vehicle to obtain a chaotic disordered graph and a chaotic map key of the image, where i is greater than or equal to 2;

[0083] An uploading module 124 is used to upload the chaotic disorder graph to a shared folder in a local area network;

[0084] A symmetric or asymmetric encryption module 126 is configured to encrypt the file name of the chaotic disordered graph and the chaotic map key using an encryption key of a symmetric or asymmetric encryption algorithm to obtain a ciphertext;

[0085] The sending module 128 is used to send the ciphertext and the symmetrically encrypted encryption key or the asymmetrically encrypted decryption key to the second unmanned vehicle in response to receiving the request for accessing the image sent by the second unmanned vehicle.

[0086] In an embodiment, the second unmanned vehicle includes: a symmetric or asymmetric decryption module, used to decrypt the ciphertext using a symmetric encryption key or an asymmetric encryption decryption key to obtain the chaotic mapping key and the file name; an acquisition module, used to obtain the chaotic disordered graph from the shared folder through the file name; a chaotic decryption module, used to use the chaotic mapping key to perform i iterative chaotic mapping decryption on the chaotic disordered graph to obtain the requested image.

[0087] In an embodiment, the system further includes a compression module for compressing the size of the image collected by the first unmanned vehicle before performing the i-th iterative chaotic map encryption.

[0088] In an embodiment, the system also includes: a first blockchain module, which is used for the first unmanned vehicle to write the ciphertext into the blockchain through the established blockchain node; a second blockchain module, which is used for the second unmanned vehicle to send a request for access to the image to the first unmanned vehicle through blockchain communication through the established blockchain node; and a third blockchain module, which is used for the first unmanned vehicle to send the ciphertext to the second unmanned vehicle through blockchain communication.

[0089] In another embodiment, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the Figure 1 The steps of the method embodiment or other corresponding method embodiments described are not repeated here.

[0090] In another embodiment, the present invention provides a computer device comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the combined Figure 1 The steps of the method embodiment or other corresponding method embodiments described are not repeated here.

[0091] The multiple different embodiments described herein or their specific features, structures or characteristics can be appropriately combined in one or more embodiments of the present invention. In addition, in some cases, as long as it is appropriate, the order of steps in the flow chart and / or the pipeline process description can be modified and does not have to be performed in the exact order described. In addition, the multiple different aspects of the present invention can be implemented using software, hardware, firmware or a combination thereof and / or other computer-implemented modules or devices that perform the functions described. The software implementation of the present invention may include executable code stored in a computer-readable medium and executed by one or more processors. The computer-readable medium may include a computer hard drive, ROM, RAM, flash memory, portable computer storage medium such as CD-ROM, DVD-ROM, flash drive and / or other devices with a universal serial bus (USB) interface, and / or any other suitable tangible or non-transient computer-readable medium or executable code can be stored thereon and a computer memory executed by a processor. The present invention can be used in conjunction with any suitable operating system.

[0092] Unless otherwise specified, the singular forms "a," "an," and "the" as used herein include the plural meaning (i.e., meaning "at least one"). It should be further understood that the terms "having," "including," and / or "comprising" as used in this specification indicate the presence of the recited features, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof. As used herein, the term "and / or" includes any and all combinations of one or more of the listed items.

[0093] While some preferred embodiments of the present invention have been described above, it should be emphasized that the present invention is not limited to these embodiments and may be implemented in other ways within the scope of the present invention. Those skilled in the art may make various variations and modifications to the present invention based on the technical concept of the present invention and without departing from the scope of the present invention, and such variations and modifications shall still fall within the scope of protection of the present invention.

Claims

1. A method for sharing images of unmanned vehicles, wherein the unmanned vehicles include at least a first and a second unmanned vehicle in the same local area network, characterized in that: The method comprises: In the first unmanned vehicle, Performing i-iteration chaotic map encryption on the image collected by the first unmanned vehicle to obtain a chaotic disordered graph and a chaotic map key of the image, where i is greater than or equal to 2; Uploading the chaotic disorder graph to a shared folder on a local area network; Encrypting the file name of the chaotic disordered graph and the chaotic map key using an encryption key of a symmetric or asymmetric encryption algorithm to obtain a ciphertext; In response to receiving a request for access to the image sent by the second unmanned vehicle, sending the ciphertext and the symmetrically encrypted encryption key or the asymmetrically encrypted decryption key to the second unmanned vehicle; Wherein, the chaotic map is a Henon chaotic map, and the i-time iterative chaotic map encryption includes: for each pixel point of the image, an initial coordinate origin (x0, y0) is given, and based on the parameters a and b of the Henon chaotic system and the values of x0 and y0 of the coordinate origin, i-time iterative mapping is performed according to the Henon mapping formula, wherein the values of x0 and y0 of the coordinate origin are increased by a predetermined amount in each iteration, and each iteration uses the four values a, b, x(i), and y(i) as the chaotic map key of the image, and generates the i-th transformation matrix TMatrix(i) according to the Henon formula. The encrypted image obtained after the i-1-th transformation operation is ImageMatrix(i-1), and TMatrix(i) and ImageMatrix(i-1) are used to perform a bitwise XOR operation to obtain the image ImageMatrix(i) after the i-th transformation operation, until the i-th iteration is completed, and each coordinate of the original image is rearranged according to the i-th chaotic map to obtain the chaotic disordered image.

2. The method according to claim 1, characterized in that The method further comprises: In the second unmanned vehicle, Decrypting the ciphertext using a symmetric encryption key or an asymmetric encryption decryption key to obtain the chaotic map key and the file name; Obtain the chaotic disorder graph from the shared folder according to the file name; The chaotic map is decrypted by performing i iterations of chaotic mapping on the chaotic disordered image using the chaotic mapping key to obtain the requested image.

3. The method according to claim 1 or 2, characterized in that The method further comprises: Before performing the i-th iterative chaotic map encryption, the size of the image collected by the first unmanned vehicle is compressed.

4. The method according to claim 3, characterized in that The compression includes: Before performing the i-th iterative chaotic map encryption, converting the spatial domain image collected by the first unmanned vehicle into a frequency domain image; quantizing the frequency domain image using a quantization matrix to remove high-frequency data in the frequency domain image; Convert the quantized frequency domain image into a spatial domain image.

5. The method according to claim 1 or 2, characterized in that The method further comprises: The first unmanned vehicle writes the ciphertext into the blockchain via the established blockchain node; The second unmanned vehicle sends a request for access to the image to the first unmanned vehicle via the established blockchain node through blockchain communication; The first unmanned vehicle sends the ciphertext to the second unmanned vehicle through blockchain communication.

6. An unmanned vehicle image sharing system, wherein the unmanned vehicles include at least a first and a second unmanned vehicle in the same local area network, characterized in that: The first unmanned vehicle includes: a chaotic encryption module, configured to perform i-iteration chaotic mapping encryption on the image collected by the first unmanned vehicle to obtain a chaotic disordered graph and a chaotic mapping key of the image, where i is greater than or equal to 2; An uploading module, used for uploading the chaotic disorder graph to a shared folder in a local area network; A symmetric or asymmetric encryption module, configured to encrypt the file name of the chaotic disordered graph and the chaotic map key using an encryption key of a symmetric or asymmetric encryption algorithm to obtain a ciphertext; a sending module, configured to send the ciphertext and the symmetrically encrypted encryption key or the asymmetrically encrypted decryption key to the second unmanned vehicle in response to receiving a request for accessing the image sent by the second unmanned vehicle; The chaotic map is a Henon chaotic map, and the chaotic encryption module is used to: for each pixel point of the original image, give an initial coordinate origin (x0, y0), based on the parameters a and b of the Henon chaotic system and the values of x0 and y0 of the coordinate origin, perform i iterative mappings according to the Henon mapping formula, wherein the values of x0 and y0 of the coordinate origin increase by a predetermined amount in each iteration, and each iteration uses the four values a, b, x(i), and y(i) as the chaotic mapping key of the image, generate the i-th transformation matrix TMatrix(i) according to the Henon formula, and the encrypted image obtained after the i-1-th transformation operation is ImageMatrix(i-1), use TMatrix(i) and ImageMatrix(i-1) to perform a bitwise XOR operation to obtain the image ImageMatrix(i) after the i-th transformation operation, until the i-th iteration is completed, and rearrange each coordinate of the original image according to the i-th chaotic map to obtain the chaotic disordered image.

7. The system according to claim 6, characterized in that The second unmanned vehicle includes: A symmetric or asymmetric decryption module, configured to decrypt the ciphertext using a symmetric encryption key or an asymmetric encryption decryption key to obtain the chaotic map key and the file name; An acquisition module, configured to acquire the chaotic disorder graph from the shared folder according to the file name; The chaotic decryption module is used to perform i-times iterative chaotic map decryption on the chaotic disordered image using the chaotic map key to obtain the requested image.

8. The system according to claim 6 or 7, characterized in that The system further comprises: A compression module is used to compress the size of the image collected by the first unmanned vehicle before performing the i-th iterative chaotic map encryption.

9. The system according to claim 8, characterized in that The compression module is used to: Before performing the i-th iterative chaotic map encryption, converting the spatial domain image collected by the first unmanned vehicle into a frequency domain image; quantizing the frequency domain image using a quantization matrix to remove high-frequency data in the frequency domain image; Convert the quantized frequency domain image into a spatial domain image.

10. The system according to claim 6 or 7, characterized in that The system further comprises: A first blockchain module, configured for the first unmanned vehicle to write the ciphertext into the blockchain via an established blockchain node; A second blockchain module is configured for the second unmanned vehicle to send a request for accessing the image to the first unmanned vehicle via the established blockchain node through blockchain communication; The third blockchain module is used for the first unmanned vehicle to send the ciphertext to the second unmanned vehicle through blockchain communication.

11. A computer device comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method according to any one of claims 1 to 5 when executing the computer program.

12. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

Images