Remote sensing data encryption method based on face image

Abstract

The invention discloses a remote sensing data encryption method based on a face image, and the method comprises the following steps: S1, extracting a plurality of face feature points of any face image, carrying out the normalization of the face feature points, and finally generating a feature point sequence A and blocks; s2, taking the feature point sequence A as a secret key, calculating a ciphertext coordinate based on the secret key to replace a source coordinate, and generating new remote sensing position data; s3, generating a chaos sequence of each block based on a chaos mapping formula, and combining the chaos sequences of the blocks to generate an integral chaos matrix AX; and S4, performing XOR on the normalized remote sensing image by using the overall chaos matrix AX to generate an encrypted image.

Description

Technical Field

[0001] The present invention relates to the technical field of remote sensing data encryption, and in particular to a remote sensing data encryption method based on face images. Background Art

[0002] In the field of remote sensing data encryption technology, with the increasing popularity of remote sensing applications, protecting the security of remote sensing data has become particularly important. Traditional encryption methods often rely on complex mathematical algorithms and randomly generated keys, but in practical applications, these methods may encounter problems such as difficult key management, time-consuming encryption process, and limited encryption strength. In addition, traditional encryption methods may be vulnerable to targeted attacks, such as brute force cracking and side channel attacks. Especially when processing large-scale, high-resolution remote sensing images, the balance between security and efficiency is particularly critical.

[0003] In recent years, the research on using biometrics as encryption keys has gradually increased. Face images have become a hot area in biometric encryption due to their uniqueness, non-replicability and universality. However, when using face images for remote sensing data encryption, especially when processing geographic location information, how to ensure encryption strength while being efficient and fast in encryption and decryption is a problem that has not yet been fully solved. Summary of the invention

[0004] In view of the above-mentioned prior art, the present invention provides a remote sensing data encryption method based on face images, which mainly solves the technical problems existing in the above-mentioned background technology.

[0005] To achieve the above object, the technical solution of the embodiment of the present invention is implemented as follows:

[0006] A remote sensing data encryption method based on face images, the method comprising the following steps:

[0007] S1, extracting multiple facial feature points of any face image, and normalizing the facial feature points, and finally generating a feature point sequence A and blocks;

[0008] S2, using the feature point sequence A as a secret key, calculating the ciphertext coordinates based on the secret key to replace the source coordinates, and generating new remote sensing position data;

[0009] S3, generating a chaotic sequence of each block based on a chaotic mapping formula, and merging the chaotic sequences of each block to generate an overall chaotic matrix AX;

[0010] S4. Use the overall chaotic matrix AX to perform XOR on the normalized remote sensing image to generate an encrypted image.

[0011] Optionally, the number of facial feature points extracted is 68.

[0012] Optionally, the specific process of step S2 includes:

[0013] S201, convert the feature point sequence A into a 4×34 matrix FL, and generate a corresponding block matrix FS:

[0014] S202, reading geographic coordinate system information in a specific remote sensing data file;

[0015] S203, creating a 2×8 matrix GL containing geographic coordinate system information, and then dividing it into a block matrix GS;

[0016] S204, using the discriminator to calculate the block H of the block matrix GS;

[0017] S205, constructing a geographic coordinate encryption algorithm F(H, GS[T]) based on the block H and the block matrix FS, and calculating the ciphertext coordinates {X', Y'} based on the geographic coordinate encryption algorithm;

[0018] S206. Replace the original geographic coordinates with the ciphertext coordinates to generate an encrypted file.

[0019] Optionally, the geographic coordinate system information includes location coordinates, width, height and resolution, and the geographic coordinate system information is matrixed.

[0020] Optionally, even point, odd point or cross point method is used for block segmentation.

[0021] Optionally, the elements in each block are subjected to Logistic chaotic mapping to generate a chaotic sequence, and the chaotic sequence of each block is synthesized into an overall chaotic matrix AX.

[0022] Optionally, the value of each channel of the remote sensing image is normalized to be between [0, 1], and then the remote sensing image is XORed with the generated overall chaotic matrix AX to generate an encrypted image.

[0023] The beneficial effects of the present invention are as follows: by using the facial feature points of the face image as the encryption key, the complexity of the encryption process and the randomness of the key are increased, the encryption strength is effectively improved, and the difficulty of cracking is greatly increased. Secondly, the chaotic mapping technology is used in the encryption process, which not only ensures the randomness and unpredictability of the encryption, but also realizes the rapid execution of the encryption process through matrix operations and block processing, and improves the efficiency of processing large-scale remote sensing data. In addition, by matrixing the geographic coordinate system information and encrypting it with the key generated by the facial feature points, the location information in the remote sensing data is effectively protected, and the leakage of sensitive geographic information is prevented, which can adapt to the encryption needs in different scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 Schematic diagram of the process of encrypting remote sensing data based on face images in an embodiment of the present application;

[0025] Figure 2 A schematic diagram of a model for encrypting geographic location coordinates in an embodiment of the present application;

[0026] Figure 3 A schematic diagram of a model for encrypting remote sensing images in an embodiment of the present application. DETAILED DESCRIPTION

[0027] The technical solution of the present invention is further elaborated in detail below in conjunction with the drawings and specific embodiments of the specification. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention. In the following description, the expression "some embodiments" is related to a subset of all possible embodiments, but it should be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict.

[0028] In the following description, a large number of specific details are provided to provide a more thorough understanding of the present invention. However, it is apparent to those skilled in the art that the present invention can be implemented without one or more of these details. In other examples, in order to avoid confusion with the present invention, some technical features well known in the art are not described.

[0029] It should be understood that the present invention can be implemented in different forms and should not be interpreted as being limited to the embodiments proposed herein. On the contrary, providing these embodiments will make the disclosure thorough and complete, and the scope of the present invention will be fully conveyed to those skilled in the art. And the purpose of the terms used herein is only to describe specific embodiments and is not intended to be a limitation of the present invention. When used herein, the singular forms of "one", "one" and "said / the" are also intended to include plural forms, unless the context clearly indicates another way. It should also be understood that the terms "compose" and / or "include" when used in this specification determine the presence of the features, integers, steps, operations, elements and / or parts, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, parts and / or groups. When used herein, the term "and / or" includes any and all combinations of the relevant listed items.

[0030] It should also be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "inside", "outside", "left", "right" and similar expressions used herein are for illustrative purposes only and are not intended to be the only implementation method.

[0031] In order to fully understand the present invention, a detailed structure will be proposed in the following description to illustrate the technical solution proposed by the present invention. The optional embodiments of the present invention are described in detail as follows, but in addition to these detailed descriptions, the present invention may also have other implementations.

[0032] Please refer to the attached Figure 1 The present application provides a remote sensing data encryption method based on face images, the method comprising the following steps:

[0033] S1, extracting multiple facial feature points of any face image, and normalizing the facial feature points, and finally generating a feature point sequence A and blocks;

[0034] S2, using the feature point sequence A as a secret key, calculating the ciphertext coordinates based on the secret key to replace the source coordinates, and generating new remote sensing position data;

[0035] S3, generating a chaotic sequence of each block based on a chaotic mapping formula, and merging the chaotic sequences of each block to generate an overall chaotic matrix AX;

[0036] S4. Use the overall chaotic matrix AX to perform XOR on the normalized remote sensing image to generate an encrypted image.

[0037] In one possible implementation, 68 facial feature points are extracted from the facial image and organized into a data structure called a "face matrix". Subsequently, in order to ensure a consistent reference frame for these feature points in subsequent processing, normalization is performed. This process involves calculating the minimum value of the x and y coordinates (min{xi} and min{yi}) and converting the feature point coordinates to relative positions relative to this minimum coordinate. Thus, a new matrix "Face xym ”, which contains the normalized feature point coordinates.

[0038]

[0039]

[0040] Subsequently, we build a repository to store the normalized feature points and their corresponding facial image data. This repository allows for uniform processing of facial feature points, ultimately generating a sequence S = {x 1 ,y 1 ,…,x 68 ,y 68}, which will become the basis for subsequent facial feature point verification. 1 ,y 1 )、(x 2 ,y 2 ),...(x 68 ,y 68 ) refers to the facial coordinate points of a human face.

[0041] See also Figure 2 , further, the specific process of step S2 includes:

[0042] S201, convert the feature point sequence A into a 4×34 matrix FL, and generate a corresponding block matrix FS:

[0043]

[0044]

[0045] S202, reading geographic coordinate system information in a specific remote sensing data file;

[0046] For a specific remote sensing data file, the geographic coordinate system information (L = {X, Y, Width, Height, Res}) is opened and read. This information includes parameters such as location coordinates (X, Y), width (Width), height (Height) and resolution (Res). These latitude and longitude coordinates are treated as plain text and adjusted to L x and L y The matrix is ​​used for subsequent calculations.

[0047]

[0048]

[0049] S203, creating a 2×8 matrix GL containing geographic coordinate system information, which includes position coordinates (X, Y), width (Width), height (Reigh t) and resolution (Res), and then dividing it into a block matrix GS;

[0050]

[0051]

[0052] S204, using the discriminator to calculate the block H of the block matrix GS;

[0053] Specifically, the discriminator is used to determine the encryption and decryption flags. The discriminator calculates T x and Ty, thereby generating H.

[0054]

[0055]

[0056] T = mod(Res, 4)

[0057] H=[GS[T x ][0],GS[T y ][1]]

[0058] S205, constructing a geographic coordinate encryption algorithm F(H, GS[T]) based on the block H and the block matrix FS, and calculating the ciphertext coordinates {X', Y'} based on the geographic coordinate encryption algorithm;

[0059] The generated geographic coordinate encryption algorithm is:

[0060]

[0061] S206. Replace the original geographic coordinates with the ciphertext coordinates to generate an encrypted file.

[0062] See also Figure 3 , further, in step S3, the parameters required for subsequent calculation are calculated through the distribution of feature points in each block (Ai x0 , Aiu) and (AiL and AiW), the elements in each block are subjected to Logistic chaotic mapping to generate a chaotic sequence, and then the chaotic sequence is merged in blocks to achieve the chaotic sequence generation of the overall remote sensing image. (i∈(1,…,m*n)).

[0063] The chaotic mapping formula used in this algorithm combines the characteristics of Logistic and PWLCM chaotic mapping to form a new chaotic mapping formula. The specific formula and the rules set by the research are as follows:

[0064] The Logistic chaotic mapping formula is:

[0065]

[0066] where ux i is the state variable at step i, x i+1 is the state variable for the next step and u is the control parameter.

[0067] The PWLCM chaotic mapping formula can be further expanded on this basis to form a new chaotic mapping rule. The PWLCM chaotic mapping formula is:

[0068]

[0069] According to the PWLCM chaos mapping, in some cases (x i-1 =x i =x i+1 ), which greatly affects the subsequent chaotic sequence values ​​and increases the error. This application designs a rule that combines the advantages of both:

[0070] 1. Set the initial x i =Ai x0 , P = Aiu, when there is no feature point in the block or the feature point is located at the edge, that is, when the area is the largest or smallest ((Ai x0 =0) or (Ai x0 =1)), take the following measures:

[0071] A) If it is the first block, P=1-Ai x0 , Ai x0 Keep numbers until the first non-zero digit after the decimal point;

[0072] B) If it is not the first block, then Ai x0 =A(i-1) x0 , P = A(i-1)u;

[0073] 2. If (x i-1 =x i =x i+1 ), replace the working formula with the Logistic chaotic mapping formula, set u = 4 (full mapping state), and replace the formula with (11) until (x i-1 ≠x i ≠x i+1 ) is replaced by the PWLCM chaotic mapping formula;

[0074] This design can generate more random and uniform chaotic sequences, improving their sensitivity.

[0075] Where u is the control parameter, and its value is in (0, 4]. The larger the value of u, the higher the chaos. When u = 4, it is in a complete chaotic state. The chaotic trajectory state value range is in (0, 1), so it is only necessary to confirm the initial time Ai x0 That's it.

[0076] Through the parameters of each block (Ai x0, Aiu) generates the chaotic sequence of each block and finally synthesizes the overall chaotic matrix AX we need.

[0077]

[0078]

[0079] Furthermore, for the generation of the block fractal matrix, the size of (m×n) is determined during the generation. If We choose 4 k point method to generate the fractal matrix; if Then we choose 9 k point method to generate the fractal matrix; if Then choose the Odd commutation point method to generate the fractal matrix; if none of them meet the requirements, choose the intersection point method to generate the matrix.

[0080] In a possible implementation, the value of each channel of the remote sensing image is normalized to be between [0, 1], and then the remote sensing image is XORed with the generated overall chaotic matrix AX to generate an encrypted image.

[0081] Correspondingly, in the decryption process, the corresponding face image is first obtained, and the feature points of the face image are extracted, and the extracted face information is matched with the feature points in the database, and the Euclidean distance is calculated based on the distance between the face feature points. For the feature point sequence A to be verified (where P i A represents the coordinates of the i-th feature point), and compares it with the sequence B in the repository (where P i B represents the coordinates of the i-th feature point in the repository). We calculate the Euclidean distance D and compare it with a predefined threshold T. If D is less than T, it means that the verified face is similar to the current compared face.

[0082] On the contrary, if D is greater than or equal to T, it means that the verified face is not similar to the currently compared face.

[0083]

[0084] When the face being verified is similar to the face being compared, the corresponding key can be obtained based on the feature points, and the following formula is used to restore the coordinate position:

[0085]

[0086] [X, Y] = [H[0] - L x [T x],H[1]-L y [T y ]]

[0087] In the remote sensing image decryption process, it is only necessary to perform inverse operations on the encrypted image to obtain the decrypted result.

[0088] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. The protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A remote sensing data encryption method based on face image, characterized in that: The method comprises the following steps: S1, extracting multiple facial feature points of any face image, and normalizing the facial feature points, and finally generating a feature point sequence A and blocks; S2, using the feature point sequence A as a secret key, calculating the ciphertext coordinates based on the secret key to replace the source coordinates, and generating new remote sensing position data; S3, generating a chaotic sequence of each block based on a chaotic mapping formula, and merging the chaotic sequences of each block to generate an overall chaotic matrix AX; S4. Use the overall chaotic matrix AX to perform XOR on the normalized remote sensing image to generate an encrypted image.

2. The remote sensing data encryption method based on face image according to claim 1 is characterized in that: The number of facial feature points extracted is 68.

3. The remote sensing data encryption method based on face image according to claim 1 is characterized in that: The specific process of step S2 includes: S201, convert the feature point sequence A into a 4×34 matrix FL, and generate a corresponding block matrix FS: S202, reading geographic coordinate system information in a specific remote sensing data file; S203, creating a 2×8 matrix GL containing geographic coordinate system information, and then dividing it into a block matrix GS; S204, using the discriminator to calculate the block H of the block matrix GS; S205, constructing a geographic coordinate encryption algorithm F(H, GS[T]) based on the block H and the block matrix FS, and calculating the ciphertext coordinates {X', Y'} based on the geographic coordinate encryption algorithm; S206. Replace the original geographic coordinates with the ciphertext coordinates to generate an encrypted file.

4. The remote sensing data encryption method based on face image according to claim 3 is characterized in that: The geographic coordinate system information includes position coordinates, width, height and resolution, and the geographic coordinate system information is matrixed.

5. The remote sensing data encryption method based on face image according to claim 4 is characterized in that: Use Even point, Odd point or Cross point method to divide the blocks.

6. The remote sensing data encryption method based on face image according to claim 5, characterized in that: The elements in each block are subjected to Logistic chaotic mapping to generate a chaotic sequence, and the chaotic sequence of each block is synthesized into the overall chaotic matrix AX.

7. The remote sensing data encryption method based on face image according to claim 6 is characterized in that: The value of each channel of the remote sensing image is normalized to be in [0, 1], and then the remote sensing image is XORed with the generated overall chaotic matrix AX to generate an encrypted image.

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