Remote sensing image encryption method for hierarchical access control

Abstract

The invention provides a hierarchical access control-based remote sensing image encryption method. The method comprises the following steps: S1, constructing a mathematical model of a novel spatiotemporal chaotic system; s2, judging the semantics of the remote sensing image, and dividing sensitive area grades according to the importance degree of sensitive information contained in the remote sensing image; s3, performing encryption processing on the divided sensitive areas at all levels to obtain ciphertext images of the sensitive areas at all levels; s4, carrying out hiding processing on the obtained ciphertext image of each level of sensitive area, carrying out XOR coupling on the ciphertext image and the newly generated image, and hiding the obtained new pseudo ciphertext information in the carrier image; and S5, taking the ciphertext image as input, generating different decryption keys through an SHA-512 algorithm, and respectively distributing the keys to users with different levels of permissions. According to the technical scheme, different decryption keys are distributed to users with different permissions, so that the users can access remote sensing images in different areas, sensitive information is protected, and encryption complexity and calculation time are effectively reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of image encryption, and in particular to a remote sensing image encryption method with hierarchical access control. Background Art

[0002] In the information age, remote sensing technology has become an indispensable tool for us to monitor and analyze the earth's surface and its changes. With the continuous development of satellites, aircraft and ground sensors, the acquisition and dissemination of remote sensing images have become increasingly widespread and frequent. Remote sensing images may contain sensitive geographic location information, such as military facilities, government agencies and personal residences, and the leakage of this information will pose a threat to national security and personal privacy. Secondly, commercial interests are often related to remote sensing images, such as land development, agricultural planning or natural resource exploration. In these cases, image encryption can effectively protect intellectual property rights and prevent unauthorized use and distribution. In addition, encryption also helps to protect the integrity of the data to ensure that the data has not been tampered with during transmission and storage, and improve the credibility and availability of the data.

[0003] At present, the research focus of remote sensing image processing is mainly on analyzing and extracting information, rather than protecting the image itself. This can be clearly seen in the published literature. Most of the research results focus on applications such as image segmentation, feature extraction, and target detection, while fewer studies emphasize the security and privacy protection of remote sensing images. The current encryption scheme generally adopts a method of fully encrypting the entire remote sensing image area to generate a ciphertext image, which is then used for transmission and storage. However, it has the following defects:

[0004] 1) Since remote sensing images are large in size and contain a wide range of information, there may be a lot of useless information. The global encryption method is complicated and time-consuming.

[0005] 2) Storing or transmitting the encrypted image in ciphertext form is likely to attract the attention of attackers and pose a risk of malicious attacks. Summary of the invention

[0006] According to the technical problems raised above, a remote sensing image encryption method with hierarchical access control is provided. The present invention divides the sensitive areas of the entire remote sensing image according to their importance, adopts different encryption schemes for different levels, and allocates different keys. Different keys can unlock remote sensing images with different permissions. The sensitive areas are extracted and encrypted separately; for the missing sensitive areas in the entire image, the Big-Lama model of deep learning is used to complete them through semantic learning of the background. In order to further ensure the security of the image in the sensitive area, the ciphertext image is coupled with the completed image, and the coupled image is hidden and embedded into the entire completed image using the CLSB algorithm.

[0007] The technical means adopted by the present invention are as follows:

[0008] A remote sensing image encryption method with hierarchical access control, comprising:

[0009] S1. Construct a mathematical model of a new spatiotemporal chaotic system;

[0010] S2. Make a judgment on the semantics of remote sensing images and classify sensitive areas according to the importance of the sensitive information they contain;

[0011] S3, encrypting the divided sensitive areas at each level respectively to obtain ciphertext images of the sensitive areas at each level;

[0012] S4, performing hiding processing on the obtained ciphertext images of sensitive areas at all levels, performing XOR coupling on the ciphertext image and the newly generated image, and hiding the obtained new pseudo-ciphertext information into the carrier image;

[0013] S5. Using the ciphertext image as input, different decryption keys are generated through the SHA-512 algorithm, and the keys are distributed to users with different levels of permissions.

[0014] Furthermore, in step S1, the mathematical model of the novel spatiotemporal chaotic system is constructed as follows:

[0015]

[0016] Where n=1,2,3,...,; p and q represent Arnold coefficients; i,j,k∈[1,L]; μ1 and μ represent system parameters, μ∈[3.8,4].

[0017] Furthermore, in step S2, the sensitive area is divided into a primary sensitive area S1 and a secondary sensitive area S2 according to the importance of sensitive information contained in the semantics of the remote sensing image.

[0018] Further, step S3 includes:

[0019] S31, encrypting the primary sensitive area S1 to obtain a ciphertext image of the primary sensitive area S1, specifically including:

[0020] S311. Use the built-in function of Matlab to convert the three-dimensional matrix of the primary sensitive area S1 with a size of M×N×K into a one-dimensional matrix with a size of 1×MNK;

[0021] S312, combining the chaotic sequence A1, performing Arnold scrambling on the one-dimensional matrix; and then combining the chaotic sequence A2 to perform row and column index scrambling to obtain a scrambled one-dimensional matrix;

[0022] S313, performing a diffusion operation based on a circular left shift with addition and subtraction of a modulus on the one-dimensional matrix obtained in step S312 according to the following formula to obtain a diffused one-dimensional image;

[0023] C i (0) = mod (C i-1 (0)+S i (1)+P i (0),256)<<<LSB3(C i-1 (0))

[0024] S314, converting the one-dimensional image obtained in step S313 into a three-dimensional matrix C1 of size M×N×K, which is the final ciphertext image of the primary sensitive area S1;

[0025] S32, encrypting the secondary sensitive area S2 to obtain a ciphertext image of the secondary sensitive area S2, specifically including:

[0026] S321. Use the built-in function of Matlab to convert the three-dimensional matrix of the secondary sensitive area S2 into a one-dimensional matrix of size 1×MNK;

[0027] S322, combining the chaotic sequence A1, performing Arnold scrambling on the one-dimensional matrix to obtain a scrambled one-dimensional matrix;

[0028] S323, performing a diffusion operation based on a modulo-addition circular left shift on the one-dimensional matrix obtained in step S322 according to the following formula to obtain a diffused one-dimensional image;

[0029] C i (0) = mod (C i-1 (0)+S i (1)+P i (0),256)<<<LSB3(C i-1 (0))

[0030] S324, converting the one-dimensional image obtained in step S323 into a three-dimensional matrix C2 of size M×N×K, which is the final ciphertext image of the secondary sensitive area S2.

[0031] Further, step S4 specifically includes:

[0032] S41, XOR-couple the ciphertext image obtained in step S3 and the completed image P according to the following formula to obtain a coupled image H:

[0033]

[0034] S42, the coupled image H is embedded again, so that even if the attacker cracks the hidden embedding algorithm and obtains the embedded image, he cannot obtain the truly effective sensitive information;

[0035] S43, based on the lowest bit embedding algorithm, the chaotic sequence generated by the chaotic system is used to determine the position where the information to be embedded needs to be embedded;

[0036] S44, converting the information to be embedded into 8-bit binary, embedding each binary digit as an object, calculating the total length of all the information to be embedded, and then embedding from the lowest bit of the carrier image, after the lowest bit is completely embedded, start embedding the second to last bit, the third to last bit, and so on, to obtain the final image after embedding.

[0037] Furthermore, in step S5, encryption is divided into three levels of authority. Three keys are required to decrypt the three different levels of encryption areas. The keys are generated by the first-level sensitive area S1 and the second-level sensitive area S2 in the plaintext image through the SHA-512 algorithm, specifically including:

[0038] S51, using the ciphertext image C1 as input to generate a 512-bit key K1;

[0039] S52, using the ciphertext image C2 as input to generate a 512-bit key K2;

[0040] S53, perform an XOR operation on key K1 and key K2 to obtain a secondary encryption key K3.

[0041] Furthermore, each level of the key consists of two parts: key1 and key2; wherein:

[0042] key1 is a 512-bit binary array generated by the SHA-512 algorithm, which is used to generate the parameters of the chaotic system in the encryption process and the initial values ​​of e, t, μ and v; key1 and key2 are four decimal numbers, which are the position coordinates of the sensitive area, represented here as (x1, y1, w1, h1)(x2, y2, w2, h2), where x and y are the coordinates of the upper left corner of the sensitive area in the remote sensing image, w is the width of the entire area, and h is the height of the entire area.

[0043] Compared with the prior art, the present invention has the following advantages:

[0044] 1. The invention provides a remote sensing image encryption method with hierarchical access control, which allocates different decryption keys to users with different permissions, so that they can access remote sensing images of different areas. The invention effectively reduces encryption complexity and calculation time while protecting sensitive information.

[0045] 2. The present invention provides a remote sensing image encryption method with hierarchical access control. Aiming at the problem that the ciphertext images generated by the existing encryption methods are easily attacked by malicious attackers, it proposes hiding the information of sensitive areas and using deep learning background generation technology to fill the missing areas. Experiments have proved that the newly generated part and the original part are well integrated and are not easily detected by attackers.

[0046] 3. The remote sensing image encryption method with hierarchical access control provided by the present invention combines the idea of ​​chaos theory with encryption, embedding and key generation processes to further improve the security of the entire system.

[0047] Based on the above reasons, the present invention can be widely promoted in the fields of image encryption and the like. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0049] Figure 1 The figure is a flow chart of the overall framework of the method of the present invention.

[0050] Figure 2 A detailed implementation diagram of the hidden embedding algorithm provided in an embodiment of the present invention.

[0051] Figure 3 A schematic diagram of key generation provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0053] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0054] The present invention provides a remote sensing image encryption method with hierarchical access control, comprising:

[0055] S1. Construct a mathematical model of a new spatiotemporal chaotic system;

[0056] S2. Make judgments on the semantics of remote sensing images and classify sensitive areas according to the importance of the sensitive information they contain;

[0057] S3, encrypting the divided sensitive areas at each level respectively to obtain ciphertext images of the sensitive areas at each level;

[0058] S4, performing hiding processing on the obtained ciphertext images of sensitive areas at all levels, performing XOR coupling on the ciphertext image and the newly generated image, and hiding the obtained new pseudo-ciphertext information into the carrier image;

[0059] S5. Using the ciphertext image as input, different decryption keys are generated through the SHA-512 algorithm, and the keys are distributed to users with different levels of permissions.

[0060] In specific implementation, as a preferred embodiment of the present invention, in step S1, the mathematical model of the novel spatiotemporal chaotic system is constructed as follows:

[0061]

[0062] Where n=1,2,3,...,; p and q represent Arnold coefficients; i,j,k∈[1,L]; μ1 and μ represent system parameters, μ∈[3.8,4].

[0063] In specific implementation, as a preferred embodiment of the present invention, in step S2, the sensitive area is divided into a primary sensitive area S1 and a secondary sensitive area S2 according to the importance of the sensitive information contained in the semantics of the remote sensing image. In this embodiment, if a military airport is a sensitive area in a remote sensing image, then the aircraft in the military airport also belongs to the sensitive area in the military airport, that is, the military airport is divided into a secondary sensitive area, and the aircraft is divided into a primary sensitive area.

[0064] In specific implementation, as a preferred embodiment of the present invention, step S3 includes:

[0065] S31, encrypting the primary sensitive area S1 to obtain a ciphertext image of the primary sensitive area S1, specifically including:

[0066] S311. Use the built-in function of Matlab to convert the three-dimensional matrix of the primary sensitive area S1 with a size of M×N×K into a one-dimensional matrix with a size of 1×MNK;

[0067] S312, combining the chaotic sequence A1, performing Arnold scrambling on the one-dimensional matrix; and then combining the chaotic sequence A2 to perform row and column index scrambling to obtain a scrambled one-dimensional matrix;

[0068] S313, performing a diffusion operation based on a circular left shift with addition and subtraction of a modulus on the one-dimensional matrix obtained in step S312 according to the following formula to obtain a diffused one-dimensional image;

[0069] C i (0) = mod (C i-1 (0)+S i (1)+P i (0),256)<<<LSB3(C i-1 (0))

[0070] S314, converting the one-dimensional image obtained in step S313 into a three-dimensional matrix C1 of size M×N×K, which is the final ciphertext image of the primary sensitive area S1;

[0071] In this embodiment, the first-level sensitive area S1 is smaller in scope and more important, so a more secure encryption algorithm is used. The scrambling algorithm combines the classic Arnold scrambling algorithm with the index scrambling algorithm, and the diffusion algorithm uses the addition and subtraction algorithm.

[0072] S32, encrypting the secondary sensitive area S2 to obtain a ciphertext image of the secondary sensitive area S2, specifically including:

[0073] S321. Use the built-in function of Matlab to convert the three-dimensional matrix of the secondary sensitive area S2 into a one-dimensional matrix of size 1×MNK;

[0074] S322, combining the chaotic sequence A1, performing Arnold scrambling on the one-dimensional matrix to obtain a scrambled one-dimensional matrix;

[0075] S323, performing a diffusion operation based on a modulo-addition circular left shift on the one-dimensional matrix obtained in step S322 according to the following formula to obtain a diffused one-dimensional image;

[0076] C i (0) = mod (C i-1 (0)+S i (1)+P i (0),256)<<<LSB3(C i-1 (0))

[0077] S324, converting the one-dimensional image obtained in step S323 into a three-dimensional matrix C2 of size M×N×K, which is the final ciphertext image of the secondary sensitive area S2.

[0078] In this embodiment, the secondary sensitive area S2 has a slightly larger image range and a relatively lower importance than the primary sensitive area, and is more suitable for an encryption algorithm with higher time efficiency. Therefore, the Arnold scrambling algorithm and the cyclic left shift diffusion algorithm are used here.

[0079] In specific implementation, as a preferred embodiment of the present invention, step S4 specifically includes:

[0080] S41, XOR-couple the ciphertext image obtained in step S3 and the completed image P according to the following formula to obtain a coupled image H:

[0081]

[0082] S42, the coupled image H is embedded again, so that even if the attacker cracks the hidden embedding algorithm and obtains the embedded image, he cannot obtain the truly effective sensitive information;

[0083] S43, based on the lowest bit embedding algorithm, the chaotic sequence generated by the chaotic system is used to determine the position where the information to be embedded needs to be embedded;

[0084] S44, converting the information to be embedded into 8-bit binary, embedding each binary digit as an object, calculating the total length of all the information to be embedded, and then embedding from the lowest bit of the carrier image, after the lowest bit is completely embedded, start embedding the second to last bit, the third to last bit, and so on, to obtain the final image after embedding.

[0085] In this embodiment, based on the chaotic mapping, a logical operation chaotic LSB embedding algorithm (LCLSB) is proposed in combination with the classical LSB embedding algorithm.

[0086] In specific implementation, as a preferred embodiment of the present invention, in step S5, encryption is divided into three levels of authority, and three keys are required to decrypt the three different levels of encryption areas. The keys are generated by the first-level sensitive area S1 and the second-level sensitive area S2 in the plaintext image through the SHA-512 algorithm, specifically including:

[0087] S51, using the ciphertext image C1 as input to generate a 512-bit key K1;

[0088] S52, using the ciphertext image C2 as input to generate a 512-bit key K2;

[0089] S53, perform an XOR operation on key K1 and key K2 to obtain a secondary encryption key K3.

[0090] In specific implementation, as a preferred implementation of the present invention, each level of the key consists of two parts: key1 and key2; wherein:

[0091] key1 is a 512-bit binary array generated by the SHA-512 algorithm, which is used to generate the parameters of the chaotic system in the encryption process and the initial values ​​of e, t, μ and v; key1 and key2 are four decimal numbers, which are the position coordinates of the sensitive area, represented here as (x1, y1, w1, h1)(x2, y2, w2, h2), where x and y are the coordinates of the upper left corner of the sensitive area in the remote sensing image, w is the width of the entire area, and h is the height of the entire area.

[0092] In this embodiment, the data set uses the 1511th remote sensing image P in the DOTA data set, with an image size of 1483×1732×3, and identifies two sensitive areas, namely the primary sensitive area S1 and the secondary sensitive area S2. The sizes of S1 and S2 are 216×200×3 and 543×681×3, respectively. The sensitive areas are extracted and encrypted for protection. During the encryption process, different scrambling algorithms are used for S1 and S2 in order to better protect important information while taking into account time efficiency. The remaining remote sensing image is called the background image B. The BigLama model is used to complete the missing image M after extracting sensitive information by learning the semantics of the background image, and a visually meaningful complete image is obtained, also called the desensitized image T. For the two encrypted sensitive areas C1 and C2, they are coupled with the newly generated complementary images I1 and I2, and the coupled information is hidden and embedded into the desensitized image T. The hidden encryption embedding algorithm includes three processes: scrambling, diffusion and coupled embedding. Combining the ideas of classical chaos theory, TSCML generates a chaotic sequence A, which is used in the processes of scrambling, diffusion and coupled embedding respectively.

[0093] Example

[0094] This embodiment uses the 606th image of MR20 multispectral remote sensing data as the input of the algorithm. First, the two-level sensitive areas in the image are identified and the two-level sensitive areas are processed separately. Each part of the processing includes three parts. The overall framework flow chart is as follows: Figure 1 As shown, the details are as follows:

[0095] The first part is feature extraction. The feature area is extracted to obtain two parts of the image, namely, masked image and extracted image. The masked image and extracted image are processed separately. The masked image is used as the input of the Big-Lama model. By learning the background, the missing part of the image is predicted and completed to obtain the predicted completed image Inpainted Image. The Extracted Image is the image of the sensitive area. Arnold scrambling and modulus diffusion operations are performed on it to obtain the ciphertext image of the first-level sensitive area.

[0096] The second part is to hide the ciphertext information. This embodiment changes the previous method of directly hiding the ciphertext information, performs XOR coupling on the ciphertext image and the newly generated image, and hides the obtained new pseudo ciphertext information into the carrier image. The hiding algorithm is based on the classic lowest bit embedding algorithm and combined with chaos theory. The position of the hidden information embedding is determined by the chaotic mapping. It is called the lowest bit embedding algorithm based on chaos theory (DRLSB). The schematic diagram of the embedding process is as follows Figure 2 shown.

[0097] The third part is to use the ciphertext image as input and generate the key through the SHA-512 algorithm. The operation process of the secondary sensitive area image is roughly the same as above, but the algorithm in the encryption stage pays more attention to time efficiency. The specific key composition is as follows Figure 3 shown.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A remote sensing image encryption method with hierarchical access control, characterized in that: include: S1. Construct a mathematical model of a new spatiotemporal chaotic system; S2. Make judgments on the semantics of remote sensing images and classify sensitive areas according to the importance of the sensitive information they contain; S3, encrypting the divided sensitive areas at each level respectively to obtain ciphertext images of the sensitive areas at each level; S4, performing hiding processing on the obtained ciphertext images of sensitive areas at all levels, performing XOR coupling on the ciphertext image and the newly generated image, and hiding the obtained new pseudo-ciphertext information into the carrier image; S5. Using the ciphertext image as input, different decryption keys are generated through the SHA-512 algorithm, and the keys are distributed to users with different levels of permissions.

2. According to the hierarchical access control remote sensing image encryption method of claim 1, it is characterized in that: In step S1, the mathematical model of the novel spatiotemporal chaotic system is constructed as follows: Where n=1,2,3,...,; p and q represent Arnold coefficients; i,j,k∈[1,L]; μ1 and μ represent system parameters, μ∈[3.8,4].

3. The remote sensing image encryption method with hierarchical access control according to claim 1 is characterized in that: In step S2, the sensitive area is divided into a primary sensitive area S1 and a secondary sensitive area S2 according to the importance of sensitive information contained in the semantics of the remote sensing image.

4. The remote sensing image encryption method with hierarchical access control according to claim 1 is characterized in that: Step S3 includes: S31, encrypting the primary sensitive area S1 to obtain a ciphertext image of the primary sensitive area S1, specifically including: S311. Use the built-in function of Matlab to convert the three-dimensional matrix of the primary sensitive area S1 with a size of M×N×K into a one-dimensional matrix with a size of 1×MNK; S312, combining the chaotic sequence A1, performing Arnold scrambling on the one-dimensional matrix; and then combining the chaotic sequence A2 to perform row and column index scrambling to obtain a scrambled one-dimensional matrix; S313, performing a diffusion operation based on a circular left shift with addition and subtraction of a modulus on the one-dimensional matrix obtained in step S312 according to the following formula to obtain a diffused one-dimensional image; C i (0)=mod(C i-1 (0)+S i (1)+P i (0),256)<<<LSB3(C i-1 (0)) S314, converting the one-dimensional image obtained in step S313 into a three-dimensional matrix C1 of size M×N×K, which is the final ciphertext image of the primary sensitive area S1; S32, encrypting the secondary sensitive area S2 to obtain a ciphertext image of the secondary sensitive area S2, specifically including: S321. Use the built-in function of Matlab to convert the three-dimensional matrix of the secondary sensitive area S2 into a one-dimensional matrix of size 1×MNK; S322, combining the chaotic sequence A1, performing Arnold scrambling on the one-dimensional matrix to obtain a scrambled one-dimensional matrix; S323, performing a diffusion operation based on a modulo-addition circular left shift on the one-dimensional matrix obtained in step S322 according to the following formula to obtain a diffused one-dimensional image; C i (0)=mod(C i-1 (0)+S i (1)+P i (0),256)<<<LSB3(C i-1 (0)) S324, converting the one-dimensional image obtained in step S323 into a three-dimensional matrix C2 of size M×N×K, which is the final ciphertext image of the secondary sensitive area S2.

5. The remote sensing image encryption method with hierarchical access control according to claim 1 is characterized in that: Step S4 specifically includes: S41, XOR-couple the ciphertext image obtained in step S3 and the completed image P according to the following formula to obtain a coupled image H: S42, the coupled image H is embedded again, so that even if the attacker cracks the hidden embedding algorithm and obtains the embedded image, he cannot obtain the truly effective sensitive information; S43, based on the lowest bit embedding algorithm, the chaotic sequence generated by the chaotic system is used to determine the position where the information to be embedded needs to be embedded; S44, converting the information to be embedded into 8-bit binary, embedding each binary digit as an object, calculating the total length of all the information to be embedded, and then embedding from the lowest bit of the carrier image, after the lowest bit is completely embedded, start embedding the second to last bit, the third to last bit, and so on, to obtain the final image after embedding.

6. The remote sensing image encryption method with hierarchical access control according to claim 1 is characterized in that: In step S5, encryption is divided into three levels of authority. Three keys are required to decrypt the three different levels of encryption areas. The keys are generated by the first-level sensitive area S1 and the second-level sensitive area S2 in the plaintext image through the SHA-512 algorithm, specifically including: S51, using the ciphertext image C1 as input to generate a 512-bit key K1; S52, using the ciphertext image C2 as input to generate a 512-bit key K2; S53, perform an XOR operation on key K1 and key K2 to obtain a secondary encryption key K3.

7. The remote sensing image encryption method with hierarchical access control according to claim 6 is characterized in that: Each level of the key consists of two parts: key1 and key2; where: key1 is a 512-bit binary array generated by the SHA-512 algorithm, which is used to generate the parameters of the chaotic system in the encryption process and the initial values ​​of e, t, μ and v; key1 and key2 are four decimal numbers, which are the position coordinates of the sensitive area, represented here as (x1, y1, w1, h1)(x2, y2, w2, h2), where x and y are the coordinates of the upper left corner of the sensitive area in the remote sensing image, w is the width of the entire area, and h is the height of the entire area.

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