An image privacy security protection method based on RNA coding

Abstract

The present invention discloses an image privacy security protection method based on RNA coding, which relates to the field of image processing technology and is characterized in that it includes the following steps: S1: establishing an improved chaotic system model, an ELM model, an ESM model and an ETM model; S2: using a face image P as an information source to generate a secret key K and initial parameters that are highly sensitive to plaintext; S3: obtaining a coding matrix En_F according to RNA coding rules; S4: establishing another coding matrix En_S for subsequent pixel perturbations. The present invention proposes a key stream generation method based on an improved chaotic system (ELM, ESM and ETM), and introduces an RNA encryption algorithm to give priority to protecting key areas (such as face areas) to solve the above problems and enhance the security and flexibility of image encryption.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and in particular to an image privacy security protection method based on RNA coding. Background Art

[0002] The emergence of the Internet has made our lives more convenient, especially the iterative development of communication technology, which has further increased the usage of the Internet. However, the popularity of the Internet has also brought security risks to personal privacy. A large amount of personal privacy information is often stolen by hackers through various cracking techniques, thereby seeking improper benefits and causing damage. Therefore, how to resist hackers' cracking methods and improve the security of image protection has become particularly important and challenging. In recent years, scholars have proposed many image encryption methods that combine various information security technologies, such as information hiding, DNA coding, Boolean networks, steganography, chaos theory, etc. Since chaotic systems are highly sensitive to initial values ​​and control parameters, the ergodicity, local instability and global stability of chaotic sequences in phase space can make up for the shortcomings of traditional cryptography. Therefore, many scholars use the key stream generated by the chaotic system as an interference factor of the encryption algorithm to enhance the security of the encryption system. An encryption method combining sub-block scrambling and digital selective diffusion; three improved chaotic systems are mixed to avoid the shortcomings of a single chaotic system, and the key image is used as a mask for the XOR operation; the image is encrypted by KAA mapping and bit confusion, and the diffusion effect is improved by using KAA mapping. With the development of chaotic image encryption technology, a large number of encryption algorithms have been proposed. However, most existing image encryption algorithms select a narrow range of chaotic system parameters, have unstable chaotic characteristics, and only perform the same encryption operation on the entire image, resulting in potential security risks for the image and lack of priority screening and special protection for image information.

[0003] The main disadvantages of existing technologies include insufficient parameter range and stability of chaotic systems, lack of priority screening and protection of different areas of the image, resulting in a greater risk of exposure of key information. Image encryption technology has the following problems:

[0004] 1. Failure to prioritize and protect different parts of the image content, resulting in insufficient security for important information;

[0005] 2. The existing chaos system is unstable under certain conditions and may produce encryption vulnerabilities;

[0006] 3. The existing encryption algorithm has a narrow range of chaotic system parameters, which limits the flexibility and adaptability of the algorithm and affects the overall security. Summary of the invention

[0007] The technical problem to be solved by the present invention is to provide an image privacy security protection method based on RNA coding, propose a key stream generation method based on improved chaotic systems (ELM, ESM and ETM), and introduce an RNA encryption algorithm to give priority protection to key areas (such as face areas) to solve the above problems and enhance the security and flexibility of image encryption.

[0008] The present invention adopts the following technical solutions to achieve the invention objectives:

[0009] An image privacy security protection method based on RNA coding, characterized by comprising the following steps:

[0010] S1: Establish improved chaotic system models, ELM model, ESM model and ETM model;

[0011] S2: Face image P Generate secret key as information source K And the initial parameters that are highly sensitive to plaintext , , and ;

[0012] S3: Obtain the coding matrix according to RNA coding rules One_F ;

[0013] S4: Create another encoding matrix One_S Used for subsequent pixel perturbation;

[0014] S5: Encoding matrix One_F and the encoding matrix One_S Perform RNA operations to obtain encrypted RNA matrix RNA_en ;

[0015] S6: Yes RNA_en Matrix decoding to obtain the encrypted face area image F_c , reset it to the face image P Thus, we can further obtain the semi-encrypted image H ;

[0016] S7: Half Encrypted Image H Perform fast encryption to obtain a fully encrypted image C .

[0017] As a further limitation of the present technical solution, the ELM model, ESM model and ETM model established in step S1 are specifically:

[0018] (1)

[0019] (2)

[0020] (3)

[0021] Among them: Control parameters , and In range Inside;

[0022] Represents the operation of taking modulo 1;

[0023] Indicates that the current time series is i The value at time;

[0024] Indicates that the current time series is i The value at +1.

[0025] As a further limitation of the present technical solution, step S2 specifically includes:

[0026] S21: For a picture with w OK h Column face images P , using SHA-512 algorithm to generate the key K The first part K 1;

[0027] S22: K The 128-bit hexadecimal number contained in 1 is divided into four 32-bit hexadecimal numbers and converted into four decimal numbers, which are used as the initial parameters , , and ;

[0028] S23: From face image P Extracting face area from image F The location information is used as the key K Part II K 2 ( x， y, r, c );

[0029] in:( x,y ) represents the position of the pixel at the lower left corner of the face;

[0030] r , c Represents the number of rows and columns respectively.

[0031] As a further limitation of the present technical solution, step S3 specifically includes:

[0032] The initial parameters and Substitute into the ETM model and iterate times, get the sequence R ;

[0033] The face area image F Each element is converted into a binary number by S_ru The corresponding numbers select the corresponding encoding RNA rules, and encode according to the RNA rules to obtain the encoding matrix One_F ;

[0034] (4).

[0035] As a further limitation of the present technical solution, step S4 specifically includes:

[0036] The image matrix in step S3 F Replace with S_ma Matrix and repeat step S3 to obtain the encoding matrix One_S ,in S_ma The matrix is ​​calculated as follows:

[0037] (5).

[0038] As a further limitation of the present technical solution, step S5 specifically includes:

[0039] The operators include addition, subtraction, and XOR. S_oper Select the value of

[0040] (6).

[0041] As a further limitation of the present technical solution, step S6 specifically includes:

[0042] Depend on S_ru The selected rule pair RNA_en Decode the binary matrix and convert it into a decimal column, and then convert it into a ciphertext image of size according to the row priority order. F_c And restore it to its original position to get the semi-ciphertext image H .

[0043] As a further limitation of the present technical solution, step S7 specifically includes:

[0044] S71: Initial parameters and Substitute into the ESM mapping and iterate times, get the sequence R 1;

[0045] S72: R 1 Sort from small to large and record its index sequence Qs;

[0046] S73: The semi-ciphertext image H Converted to a sequence, restored to its original size through the following operations: P_half Perform dynamic diffusion driven by ELM mapping to obtain the final ciphertext C :

[0047] (7)

[0048] in: Indicates rounding down;

[0049] (8).

[0050] Compared with the prior art, the advantages and positive effects of the present invention are:

[0051] The device establishes a more stable and wider parameter range chaotic system (ELM, ESM and ETM), generates a secure key stream, and enhances the security of the encryption process; introduces the RNA encryption algorithm to specially encrypt important areas (such as faces) in the image, enhance the information security of these areas and improve the overall encryption efficiency. Priority detection and special protection are performed on the face area in the image, and the face area is specially encrypted using the RNA encryption algorithm. These core innovations improve the flexibility and security of the encryption method and effectively protect sensitive information. The RNA-based face image protection method provided by the present invention includes establishing ELM, ESM and ETM models with a larger range and better chaos characteristics to improve the security of encryption. The RNA-based face image protection method provided by the present invention detects and locates face information and performs priority screening and special protection according to different parts of the image content. The RNA-based face image protection method provided by the present invention includes establishing RNA operation rules and applying them to the protection of the face area, which effectively improves the pixel confusion degree of the face area. Under the premise of ensuring the privacy and security of the face, the designed face image protection method can be applied in engineering fields such as image encryption and has important value. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a schematic diagram of the process of the present invention.

[0053] Figure 2 is the bifurcation diagram of the model of the present invention;

[0054] Figure 2 (a) is the performance test result of the ELM model; Figure 2 (b) is the performance test result of the ESM model. Figure 2 (c) is the performance test result of the ETM model. DETAILED DESCRIPTION

[0055] A specific implementation of the present invention is described in detail below in conjunction with the accompanying drawings, but it should be understood that the protection scope of the present invention is not limited by the specific implementation.

[0056] The present invention comprises the following steps:

[0057] S1: Establish improved chaotic system models, including ELM model (enhanced Logistic model), ESM model (enhanced Sine model) and ETM model (enhanced Tent model);

[0058] S2: Face image P Generate secret key as information source K And the initial parameters that are highly sensitive to plaintext , , and ;

[0059] S3: Obtain the coding matrix according to RNA coding rules One_F ;

[0060] S4: Create another encoding matrix One_S Used for subsequent pixel perturbation;

[0061] S5: Encoding matrix One_F and the encoding matrix One_S Perform RNA operations to obtain encrypted RNA matrix RNA_en ;

[0062] S6: Yes RNA_en Matrix decoding to obtain the encrypted face area image F_c , reset it to the face image P Thus, we can further obtain the semi-encrypted image H ;

[0063] S7: Half Encrypted Image H Perform fast encryption to obtain a fully encrypted image C .

[0064] The ELM model, ESM model and ETM model established in step S1 are specifically:

[0065] (1)

[0066] (2)

[0067] (3)

[0068] Among them: Control parameters , and In range Inside;

[0069] Represents the operation of taking modulo 1;

[0070] Indicates that the current time series is i The value at time;

[0071] Indicates that the current time series is i The value at +1.

[0072] Figure 2 It is the performance test result of ELM model, ESM model and ETM model. Figure 2 Middle (a), Figure 2 (b) and Figure 2 (c) In the interval [0, 1000], all three models show excellent chaotic state.

[0073] The step S2 specifically includes:

[0074] First, the face image P Convert it into a grayscale image, then binarize the image; then calculate the range of face information to get x , y , r and c .

[0075] S21: For a picture with w OK h Column face images P In order to make the algorithm more sensitive to plaintext images, the SHA-512 algorithm is used to generate the key. K The first part K 1;

[0076] S22: K The 128-bit hexadecimal number contained in 1 is divided into four 32-bit hexadecimal numbers and converted into four decimal numbers, which are used as the initial parameters , , and ;

[0077] S23: From face image P Extracting face area from image F The location information is used as the key K Part II K 2 ( x， y, r, c );

[0078] in:( x,y ) represents the position of the pixel at the lower left corner of the face;

[0079] r , c Represents the number of rows and columns respectively.

[0080] The step S3 specifically includes:

[0081] The initial parameters and Substitute into the ETM model and iterate times, get the sequence R ;

[0082] The face area image F Each element is converted into a binary number by S_ru The corresponding numbers are selected from the corresponding encoding RNA rules in Table 1, and the encoding matrix is ​​obtained according to the RNA rules in Table 1. One_F , where A, C, G, and U represent adenine, cytosine, guanine, and uracil, respectively.

[0083] (4).

[0084] Table 1 RNA rule encoding table

[0085]

[0086] The step S4 specifically includes:

[0087] The image matrix in step S3 F Replace with S_ma Matrix and repeat step S3 to obtain the encoding matrix One_S ,in S_ma The matrix is ​​calculated as follows:

[0088] (5).

[0089] The step S5 specifically includes:

[0090] The operators include addition, subtraction, and XOR (as shown in Table 2, Table 3, and Table 4). S_oper Select the value of

[0091] (6).

[0092] Table 2 Addition Operator

[0093]

[0094] Table 3 Subtraction operator

[0095]

[0096] Table 4 XOR operator

[0097]

[0098] The step S6 specifically includes:

[0099] Depend on S_ru The selected rule pair RNA_en Decode the binary matrix and convert it into a decimal column, and then convert it into a ciphertext image of size according to the row priority order. F_c And restore it to its original position to get the semi-ciphertext image H .

[0100] The step S7 specifically includes:

[0101] S71: Initial parameters and Substitute into the ESM mapping and iterate times, get the sequence R 1;

[0102] S72: R 1 Sort from small to large and record its index sequence Qs ;

[0103] S73: The semi-ciphertext image H Converted to a sequence, restored to its original size through the following operations: P_half Perform dynamic diffusion driven by ELM mapping to obtain the final ciphertext C :

[0104] (7)

[0105] in: Indicates rounding down;

[0106] (8).

[0107] The above disclosure is only a specific embodiment of the present invention, but the present invention is not limited thereto, and any changes that can be conceived by those skilled in the art should fall within the protection scope of the present invention.

Claims

1. A method for protecting image privacy security based on RNA coding, characterized in that: The following steps are involved: S1: Establish improved chaotic system models, ELM model, ESM model and ETM model; S2: Face image P Generate secret key as information source K And the initial parameters that are highly sensitive to plaintext , , and ; S3: Obtain the coding matrix according to RNA coding rules En_F ; S4: Create another encoding matrix En_S Used for subsequent pixel perturbation; S5: Encoding matrix En_F and the encoding matrix En_S Perform RNA operations to obtain encrypted RNA matrix RNA_en ; S6: Yes RNA_en Matrix decoding to obtain the encrypted face area image F_c , reset it to the face image P Thus, we can further obtain the semi-encrypted image H ; S7: Half Encrypted Image H Perform fast encryption to obtain a fully encrypted image C ; The ELM model, ESM model and ETM model established in step S1 are specifically: （1） （2） （3） Among them: Control parameters , and In range Inside; Represents the operation of taking modulo 1; Indicates that the current time series is i The value at time; Indicates that the current time series is i The value at +1; The step S2 specifically includes: S21: For a picture with w OK h Column face images P , using SHA-512 algorithm to generate the key K The first part K 1; S22: K The 128-bit hexadecimal number contained in 1 is divided into four 32-bit hexadecimal numbers and converted into four decimal numbers, which are used as the initial parameters , , and ; S23: From face image P Extracting face area from image F The location information is used as the key K Part II K 2 ( x, y, r, c ); in:( x,y ) represents the position of the pixel at the lower left corner of the face; r , c Represents the number of rows and columns respectively.

2. The image privacy security protection method based on RNA coding according to claim 1 is characterized in that: The step S3 specifically includes: The initial parameters and Substitute into the ETM model and iterate times, get the sequence R ; The face area image F Each element is converted into a binary number by S_ru The corresponding numbers select the corresponding encoding RNA rules, and encode according to the RNA rules to obtain the encoding matrix En_F ; （4）。 3. The image privacy security protection method based on RNA coding according to claim 2 is characterized in that: The step S4 specifically includes: The image matrix in step S3 F Replace with S_ma Matrix and repeat step S3 to obtain the encoding matrix En_S ,in S_ma The matrix is ​​calculated as follows: （5）。 4. The image privacy security protection method based on RNA coding according to claim 3 is characterized in that: The step S5 specifically includes: The operators include addition, subtraction, and XOR. S_oper Select the value of （6）。 5. The image privacy security protection method based on RNA coding according to claim 4 is characterized in that: The step S6 specifically includes: Depend on S_ru The selected rule pair RNA_en Decode the binary matrix and convert it into decimal columns, and then convert it into a ciphertext image of size according to row priority F_c And restore it to its original position to get the semi-ciphertext image H .

6. The image privacy security protection method based on RNA coding according to claim 5 is characterized in that: The step S7 specifically includes: S71: Initial parameters and Substitute into the ESM mapping and iterate times, get the sequence R 1; S72: R 1 Sort from small to large and record its index sequence Q ; S73: The semi-ciphertext image H Converted to a sequence, restored to its original size through the following operations: P_half Perform dynamic diffusion driven by ELM mapping to obtain the final ciphertext C : （7） in: Indicates rounding down; （8）。

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