Face image data encryption method based on chaotic tent mapping

Abstract

The invention belongs to the field of data encryption, and discloses a face image data encryption method based on chaotic tent mapping, which comprises the following steps: S1, setting an initial state of a chaotic system; s2, receiving a face image and an initialization parameter, and generating an initial seed; s3, scrambling and diffusing the face image to obtain encrypted images R ', G' and B 'of R, G and B channels; s4, respectively carrying out DNA coding on R ', G' and B 'to obtain vectors RDNA, GDNA and BDNA; s5, respectively mixing the RDNA, the RDNA and the RDNA to obtain encrypted vectors R 'DNA, G' DNA and B 'DNA; and S6, decoding and combining the R 'DNA, the G' DNA and the B 'DNA to obtain an encrypted face image. According to the invention, the security and reliability of the algorithm are improved. And the complexity of the encryption process is remarkably improved while the algorithm efficiency is kept. According to the invention, through the nonlinear characteristic of the chaotic system and the randomness of DNA coding, a defense mechanism for a known attack mode is enhanced.

Description

Technical Field

[0001] The invention relates to the field of data encryption, and in particular to a method for encrypting face image data based on chaotic tent mapping. Background Art

[0002] With the advent of the information age, the rapid development of cutting-edge technologies such as 5G networks, cloud computing technology, and the Internet of Things has led to an explosive growth in the scale of facial image data. These data have become particularly critical due to their wide applications in personal identification, security monitoring, and other fields. Facial images not only have a huge amount of data, but also show a high degree of correlation between pixel values, often containing rich personal information. In view of this, ensuring the privacy and security of these images during network transmission has become a challenge that needs to be solved urgently. In the research landscape of information security, encryption technology plays a vital role. The high data volume of digital images in the spatial domain and the strong correlation between pixel values ​​require the development of an efficient pseudo-random number generation mechanism.

[0003] Although traditional block encryption methods (such as DES, IDEA, AES, etc.) are suitable for image encryption, they cannot resist the transmission noise that may increase in the image during the transmission process. Therefore, it is urgent to study new image encryption algorithms. The application of chaos theory in the field of cryptography provides new ideas for image encryption. Since digital images have the inherent characteristics of large volume and high correlation in the spatial domain, a unique pseudo-random number generator is required. Chaotic system is a good pseudo-random number generator. Chaotic system is highly sensitive to seeds and control factors, providing an ideal platform for generating high-quality pseudo-random numbers. This characteristic enables chaos-based encryption algorithms to effectively resist various cryptanalysis attacks and enhance the security of encryption systems. Considering the urgent need for digital image transmission security in the current network security environment, this paper is committed to in-depth research on image encryption methods based on chaos theory. This research direction not only opens up a new exploration path for information security disciplines in theory, but also shows broad application potential and important practical significance in actual network security applications.

[0004] However, although the current image encryption technology based on chaos theory has shown great potential, it still faces several challenges and limitations: (1) Insufficient security: The image encryption method based on a single chaotic mapping has security defects and is vulnerable to chosen plaintext attacks. In addition, the key space is small, which limits the flexibility and reliability of the encryption system. (2) The balance between the complexity and computational cost of the chaotic system: Although increasing the complexity of the chaotic system can improve security, it may also lead to an increase in computational cost and complexity in algorithm implementation. (3) Resistance to known attacks: Some existing chaotic systems have been shown to be insufficiently resistant to specific attacks (such as salt and pepper noise attacks). Summary of the invention

[0005] The purpose of the present invention is to disclose a face image data encryption method based on chaotic tent mapping to solve the technical problems raised in the background technology.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] The present invention provides a method for encrypting face image data based on chaotic tent mapping, comprising:

[0008] S1, set the initial state of the chaotic system;

[0009] S2, receives the face image and initialization parameters and generates an initial seed;

[0010] S3, scramble and diffuse the face image to obtain the encrypted images R′, G′, B′ of the three channels of R, G, and B;

[0011] S4, DNA encoding is performed on R′, G′, and B′ respectively to obtain vectors R_DNA, G_DNA, and B_DNA;

[0012] S5, confuse R_DNA, R_DNA, and R_DNA respectively to obtain encrypted vectors R′_DNA, G′_DNA, and B′_DNA;

[0013] S6, decode and merge R′_DNA, G′_DNA, and B′_DNA to obtain an encrypted face image.

[0014] Preferably, S1 includes:

[0015] The chaotic tent mapping system, the piecewise linear chaotic mapping system, the Lorenz chaotic mapping system and the Chen chaotic mapping system are selected as the chaotic systems for encrypting the face images;

[0016] Initialize the above four types of chaotic systems respectively:

[0017] Initialize the chaos tent mapping system, its basic form is:

[0018]

[0019] x represents a state variable in the mapping, representing the state of the chaotic tent mapping system at a certain moment. n represents the state of the chaotic tent mapping system in the nth iteration, f represents a constant, and s represents the threshold parameter in the chaotic tent mapping system;

[0020] Wherein, f=0.8, s=0.5, and the starting value of the chaotic sequence x0=0.1 are set. For each pixel value pixels[i-1] in the pixel value array, its corresponding chaotic sequence value x[i-1] is calculated, and the chaotic sequence value x[i-1] is used to perform an XOR operation with the pixel value pixels[i-1] to obtain the pixel value encrypted_arrary[i-1] after chaotic tent mapping encryption; the next value x[i] of the chaotic sequence is updated according to the value of x[i-1] to ensure the dynamic and unpredictable nature of the sequence; this process is repeated on each element of the pixel value array, thereby realizing the chaotic tent mapping encryption of the entire image to be encrypted;

[0021] Initialize the piecewise linear chaotic mapping system, and its iteration formula is:

[0022]

[0023] p0 represents the threshold parameter in the piecewise linear chaotic mapping system;

[0024] Through the above iterative formula, a chaotic sequence a is generated i , the length of the sequence is 3×w×h, where w and h represent the width and height of the image to be encrypted respectively;

[0025] The chaotic sequence a i Combined with the pixel value list pixels after the first tent mapping scrambling, a tuple containing chaotic values ​​and corresponding pixel values ​​is formed, and according to the chaotic value a i Sort them; after sorting, reorganize the pixel values ​​to obtain a second scrambled pixel value list pixels; divide the encrypted pixel value list according to the three color channels of RGB to obtain a pixel value list of the three color channels of RGB;

[0026] Initialize the Lorentz chaotic mapping system, which is defined by the following three nonlinear differential equations:

[0027]

[0028] The variable y represents the intensity of the convective motion in the fluid, which is used to quantify the difference in amplitude between the rising and falling air currents in the air pool. The variable z represents the temperature gradient in the fluid, which is used to reflect the temperature difference between the rising fluid and the sinking fluid. The user describes the degree of deviation of the temperature inside the air pool relative to the average temperature of the environment. The parameters f, g, and r are constants representing the physical properties of the airflow.

[0029] Initialize the initial state y0, z0, q0 of the Lorentz chaotic mapping system, call the Lorentz model function, generate the chaotic sequence Y, Z, Q, discard the first 100 values ​​of each sequence to remove the initial transient effect, and then use the chaotic sequence to sort the pixel values ​​of the RGB channels of the image to be encrypted to enhance the randomness and unpredictability of encryption;

[0030] Initialize the Chen chaotic map system, which is defined by the following four nonlinear differential equations:

[0031]

[0032] Among them, a, b, c, d, k are system parameters, u, v, w, x are system state variables; when parameters a=36, b=3, c=28, d=16 and the value range of parameter k is -0.7≤k≤0.7, the Chen chaotic system exhibits typical chaotic dynamic behavior and has the ability to generate multiple chaotic sequences; within this parameter range, the Chen chaotic mapping system can generate four independent chaotic sequences, and the parameter k=0.2 is selected to construct the Chen chaotic sequence to achieve specific dynamic characteristics and controllability;

[0033] The fourth-order Runge–Kutta numerical method is used to perform an iterative numerical solution process on the nonlinear dynamic system equations consisting of the above four nonlinear differential equations; under given initial conditions and parameter settings, four chaotic sequences U, V, W, X with different dynamic characteristics are generated. In order to remove the initial transient effect, the first 100 values ​​of each sequence are discarded.

[0034] Preferably, S2 includes:

[0035] Receive an original color face image I, and a series of parameter sets for initializing and controlling chaotic systems, including a0, p0, u0, v0, w0, x0, y0, z0, q0, and four initialized chaotic systems Tent, PWLCM, Lorenz and Chen;

[0036] Split the message digest generated by SHA-256 into eight equal-sized sub-blocks, denoted as m j , where j = 1, 2, ... 8, each sub-block consists of seven hexadecimal digits, and the hexadecimal values ​​are converted into decimal floating point numbers according to the following formula, and their value range is in the interval [0, 0.0156):

[0037] m j =hex2dec(m1,...,m8) / 2 34

[0038] The second group is directly converted to floating point values ​​in the range (0, 0.0156):

[0039] d=hex2dec(d) / 2 38

[0040]

[0041] a′0 and p′0 represent the initial seeds of the newly generated piecewise linear chaotic mapping system;

[0042] Assuming that the seed values ​​of the Chen chaotic system are u0, v0, w0, x0, the following formula is used to generate a new initial seed:

[0043]

[0044] For the Lorenz chaotic system, three seeds are required, which are calculated as follows:

[0045]

[0046] The above SHA-256 generates a 64-bit message digest, which is divided into nine sub-blocks. The first eight sub-blocks are of equal size, each with a seven-digit hexadecimal number, denoted as m. j , the remaining sub-block is an eight-digit hexadecimal number, denoted as d;

[0047] CK is the generated public key, as shown below:

[0048] CK=a0+p0+u0+v0+w0+x0+y0+z0+q0mod 1.

[0049] Preferably, S3 includes:

[0050] The images of the three color channels of the face image, R, G, and B, are combined and flattened into a one-dimensional array of size 1×3MN, and an XOR operation is performed with the chaotic sequence T generated by the chaotic tent mapping to achieve preliminary encryption of the pixel values:

[0051]

[0052] The chaotic sequence A is obtained by iterating PWLCM 3MN times. The pixel values ​​of I are arranged as follows according to the chaotic sequence A to form I′:

[0053] A={a i ,a i+1 ,...,a 3MN}

[0054] [valueA,indexA]=order(A)

[0055] I′=I(indexA)

[0056] order indicates a sorting operation; [valueA, indexA] indicates an index-value pair, valueA is an element in the chaotic sequence, and indexA is the index corresponding to this element in the pixel array; I() indicates a reference to the original pixel array;

[0057] Divide I′ into the following three 1×MN one-dimensional arrays R1, G1, B1 for the third permutation:

[0058] R1=[I′(1),I′(2),...,I′(MN)]

[0059] G1=[I′(MN+1),I′(MN+2),...,I′(2MN)]

[0060] B1=[I′(2MN+1),I′(2MN+2),...,I′(3MN)]

[0061] A pseudo-random number generation strategy based on the initial seeds y′0, z′0, q′0 is used to construct three chaotic sequences Y, Z, Q of length t+MN; these sequences are used to perform chaotic permutation and diffusion operations on the three RGB pixel arrays of the face image; in order to eliminate the transient effect of the initial sequence, a truncation strategy is adopted to discard the first t values ​​in the sequence; then, the remaining sequences are sorted to achieve a specific pixel value permutation pattern, as shown in the following formula:

[0062] [valueY,indexY]=order(Y)

[0063] [valueZ,indexZ]=order(Z)

[0064] [valueQ,indexQ]=order(Q)

[0065] In the proposed image encryption framework, the sequences Y, Z, Q generated by chaos theory are sorted to obtain the corresponding index sets indexY, indexZ, indexQ; using these index values, the pixel value elements of the three color channels R, G, and B of the image are subjected to index-based rearrangement operations; this operation follows a specific permutation rule to ensure the remapping of pixel values, thereby generating a new permutation image, as follows:

[0066]

[0067] Among them, f R , f G , f BThey represent the rearrangement functions applied to the three color channels R, G, and B respectively. The function replaces the original pixel values ​​according to the index generated by the chaotic sequence to obtain the encrypted images R′, G′, and B′.

[0068] Preferably, S4 includes:

[0069] The encryption process of the three color channels of the face image is converted using four specific DNA coding rules to achieve binary-based pixel value representation;

[0070] The specific DNA coding rules are as follows:

[0071] 00: rule[0]

[0072] 01: rule[1]

[0073] 10: rule[2]

[0074] 11: rule[3]

[0075] 00, 01, 10, 11 represent 4 groups of 2-bit binary numbers extracted after the decimal pixel value is converted to an 8-bit binary number; the 8-bit binary number is intercepted with 2 bits as the interception interval to obtain 4 groups of 2-bit binary numbers;

[0076] rule is the DNA coding rule determined by the sequence U generated in Chen chaotic mapping;

[0077] Using Chen hyperchaotic system, combined with initial key vectors u′0, v′0, w′0, x′0, pseudo-random chaotic sequences U, V, W, X are generated through an iterative process, and the number of iterations is set to t+MN×3;

[0078] In order to eliminate the transient effects in the initial iteration stage, a truncation strategy is adopted to discard the first t elements of the sequence; then, the elements in the sequence U are equally divided into three sub-vectors, each of which contains M×N elements, as shown in the following formula:

[0079] U1=[U(1),U(2),...,U(MN)]

[0080] U2=[U(MN+1), U(MN+2),..., U(2MN)]

[0081] U3=[U(2MN+1), U(2MN+2),..., U(3MN)]

[0082] Based on the DNA coding framework, combined with the sequence generated by chaos theory, each color channel of the face image is independently encoded; the DNA coding rule is selected according to the preset selection rule, and each DNA base is arranged according to the array size of 1×4MN after being selected:

[0083]

[0084] DNA_Encode_Rule represents a DNA encoding operation.

[0085] Preferably, S5 includes:

[0086] Using DNA coding rules, perform XOR operations on the three color channels of R, G, and B of the face image respectively;

[0087] Using the method described in S4, two chaotic sequences V and W are generated, which are converted into the starting value sets start_RGB and times_RGB used to guide the XOR operation, as shown in the following formula:

[0088] start_RGB=(V×10 12 )mod 8

[0089] times_RGB=(W×10 12 )mod 8

[0090] start_RGB and times_RGB are decomposed into three sub-arrays, each with a length of MN, corresponding to the three color channels R, G, and B respectively:

[0091] start_R=[start_RGB(1),...,start_RGB(MN)]

[0092] start_G=[start_RGB(MN+1),...,start_RGB(2MN)]

[0093] start_B=[start_RGB(2MN+1),...,start_RGB(3MN)]

[0094] times_R=[times_RGB(1),..., times_RGB(MN)]

[0095] times_G=[times_RGB(MN+1),..., times_RGB(2MN)]

[0096] times_B=[times_RGB(2MN+1),..., times_RGB(3MN)]

[0097] The obtained subarray is used for the replacement and encoding process of the pixel values ​​of the three color channels R, G, and B. In this process, the pixel value of the R channel of the original image will be converted according to the DNA encoding rule determined by the sequence U generated in the Chen chaotic map, combined with the starting point start_R and the number of iterations times_R generated by the chaotic sequence; this process involves converting the four DNA bases of each pixel value Encoding is performed according to a specific permutation logic to generate encrypted vectors of three color channels: R′_DNA, G′_DNA, and B′_DNA.

[0098] Preferably, S6 includes:

[0099] A pseudo-random array X consisting of 3MN elements is used as the key input of the decoding process;

[0100] By randomly sampling the preset DNA rule set, the decoding rules obtained by random sampling are used in the following DNA-Decode-Rule operation to realize the decoding of the three color channels of R, G, and B. The pseudo-random array is generated by the encoding stage and continues to be used in the decoding process to maintain the consistency of the data conversion process;

[0101] Divide the vector X into three arrays according to the following formula, as shown below:

[0102] X1=[X(1),X(2),...,X(MN)]

[0103] X2=[X(MN+1),X(MN+2),...,X(2MN)]

[0104] X3=[X(2MN+1),X(2MN+2),...,X(3MN)]

[0105] Decoding process:

[0106] Cryptogram_R=DNA_Decode_Rule(R′_DNA,X1)

[0107] Cryptogram_G=DNA_Decode_Rule(G′_DNA,X2)

[0108] Cryptogram_B=DNA_Decode_Rule(B′_DNA,X3)

[0109] DNA-Decode-Rule represents the DNA decoding operation, and finally the images corresponding to the three color channels after decoding are merged to obtain the encrypted face image I'. Cryptogram_R, Cryptogram_G and Cryptogram_B represent the images obtained by decoding in the three color channels of R, G and B respectively.

[0110] Beneficial effects:

[0111] The present invention solves the challenges of existing chaotic encryption technology in terms of security, system complexity and resistance to known attacks: (1) In order to enhance security and resist chosen plaintext attacks, the present invention dynamically adjusts the initial conditions and control parameters of chaotic tent mapping, piecewise linear chaotic mapping, Lorenz chaotic system and Chen chaotic system. This dynamic parameter selection mechanism effectively expands the key space and improves the security and reliability of the algorithm. (2) The chaotic system and DNA cryptography strategy designed by the present invention achieve improved security without significantly increasing the computing cost. Through the iteration of chaotic sequences and pixel-level confusion of DNA encoding, the present invention significantly improves the complexity of the encryption process while maintaining the efficiency of the algorithm. (3) The present invention specifically considers the resistance to specific attacks such as salt and pepper noise. Through the nonlinear characteristics of the chaotic system and the randomness of DNA encoding, the defense mechanism against known attack patterns is enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0112] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0113] Figure 1 It is a schematic diagram of the face image data encryption method based on chaotic tent mapping of the present invention.

[0114] Figure 2 is the original Lena image.

[0115] Figure 3 Lena image for face recognition.

[0116] Figure 4 Encrypt Lena image for face.

[0117] Figure 5 Decrypt Lena images for faces.

[0118] Figure 6 This is the face decryption image after introducing key perturbation.

[0119] Figure 7 It is the histogram of the three color channels of R, G, and B of the standard Lena image.

[0120] Figure 8 These are the corresponding three color channel histograms of the encrypted Lena image.

[0121] Fig. 9 Schematic diagram of the correlation between adjacent pixels in the three color channels of the original image.

[0122] Fig.10 Schematic diagram of the correlation between adjacent pixels in the three color channels of the encrypted image.

[0123] Fig.11a Schematic diagram of the first method of destroying an encrypted image.

[0124] Fig.11b According to Fig.11a The encrypted image is destroyed by decrypting the image obtained.

[0125] Fig.11c Schematic diagram of the second method of destroying the encrypted image.

[0126] Fig.11d In accordance with Fig.11c The encrypted image is destroyed by decrypting the image obtained.

[0127] Fig.12a The image is obtained by performing a cropping operation on the encrypted image.

[0128] Figure 12b For Fig.12a The image obtained by decrypting the image in .

[0129] Fig.13 The image obtained by decrypting the image with salt and pepper noise added.

[0130] Fig.14 A schematic diagram of the detailed encryption process of the present invention. DETAILED DESCRIPTION

[0131] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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 are within the scope of protection of the present invention.

[0132] like Figure 1 The present invention provides a face image data encryption method based on chaotic tent mapping, comprising:

[0133] S1, set the initial state of the chaotic system;

[0134] S2, receives the face image and initialization parameters and generates an initial seed;

[0135] S3, scramble and diffuse the face image to obtain the encrypted images R′, G′, B′ of the three channels of R, G, and B;

[0136] S4, DNA encoding is performed on R′, G′, and B′ respectively to obtain vectors R_DNA, G_DNA, and B_DNA;

[0137] S5, confuse R_DNA, R_DNA, and R_DNA respectively to obtain encrypted vectors R′_DNA, G′_DNA, and B′_DNA;

[0138] S6, decode and merge R′_DNA, G′_DNA, and B′_DNA to obtain an encrypted face image.

[0139] The present invention proposes a color face image encryption technology based on chaos theory, which realizes a high-security encryption effect in a single-round encryption process, and effectively solves the balance problem between efficiency and security in traditional algorithms. The present invention first synthesizes the three RGB channels into a one-dimensional data stream, and then sorts them using the sequence generated by chaotic tent mapping and piecewise linear chaotic mapping to break the correlation between channels. The sorted data stream is decomposed into three independent sub-streams, and the Lorenz chaotic sequence is further applied to the sub-stream of each color channel for fine sorting. Finally, combined with the Chen chaotic sequence, the present invention adopts a DNA coding strategy to encode and dynamically confuse the pixels of each channel, thereby further improving the encryption security of the image at the pixel level.

[0140] Preferably, S1 includes:

[0141] The chaotic tent mapping system, the piecewise linear chaotic mapping system, the Lorenz chaotic mapping system and the Chen chaotic mapping system are selected as the chaotic systems for encrypting the face images;

[0142] Initialize the above four types of chaotic systems respectively:

[0143] Initialize the chaos tent mapping system, its basic form is:

[0144]

[0145] x represents a state variable in the mapping, representing the state of the chaotic tent mapping system at a certain moment. nrepresents the state of the chaotic tent mapping system in the nth iteration, f represents a constant, and s represents the threshold parameter in the chaotic tent mapping system;

[0146] Wherein, f=0.8, s=0.5, and the starting value of the chaotic sequence x0=0.1 are set. For each pixel value pixels[i-1] in the pixel value array, its corresponding chaotic sequence value x[i-1] is calculated, and the chaotic sequence value x[i-1] is used to perform an XOR operation with the pixel value pixels[i-1] to obtain the pixel value encrypted_arrary[i-1] after chaotic tent mapping encryption; the next value x[i] of the chaotic sequence is updated according to the value of x[i-1] to ensure the dynamic and unpredictable nature of the sequence; this process is repeated on each element of the pixel value array, thereby realizing the chaotic tent mapping encryption of the entire image to be encrypted;

[0147] Initialize the piecewise linear chaotic mapping system, and its iteration formula is:

[0148]

[0149] p0 represents the threshold parameter in the piecewise linear chaotic mapping system;

[0150] Through the above iterative formula, a chaotic sequence a is generated i , the length of the sequence is 3×w×h, where w and h represent the width and height of the image to be encrypted respectively;

[0151] The chaotic sequence a i Combined with the pixel value list pixels after the first tent mapping scrambling, a tuple containing chaotic values ​​and corresponding pixel values ​​is formed, and according to the chaotic value a i Sort them; after sorting, reorganize the pixel values ​​to obtain a second scrambled pixel value list pixels; divide the encrypted pixel value list according to the three color channels of RGB to obtain a pixel value list of the three color channels of RGB;

[0152] Initialize the Lorentz chaotic mapping system, which is defined by the following three nonlinear differential equations:

[0153]

[0154] The variable y represents the intensity of the convective motion in the fluid, which is used to quantify the amplitude difference between the rising and falling airflows in the air pool. The variable z represents the temperature gradient in the fluid, which is used to reflect the temperature difference between the rising fluid and the sinking fluid. The user q describes the degree of deviation of the temperature inside the air pool relative to the average temperature of the environment. This deviation is a key factor affecting the dynamic behavior of the system. The parameters f, g, and r are constants representing the physical properties of the airflow.

[0155] Given the lack of a closed - form analytical expression for the nonlinear system under investigation, the present invention employs an iterative method for its numerical simulation. Through the iterative process, the numerical solution of the system is obtained, revealing that under specific parameter selections, namely when the control parameter \(0 < r < 1\), the system exhibits stable fixed - point behavior. As \(r\) increases, when the parameter satisfies the condition \(1 < r < 24\), the system enters a periodic oscillation mode, showing stable periodic solutions. Further increasing the value of \(r\) beyond 24 and under specific parameter settings \(f = 10\), \(g=\frac{8}{3}\), the system dynamics transform into a chaotic state, where its behavior no longer呈现出可预测的周期性模式,而是表现出复杂的、对初始条件高度敏感的混沌特性。

[0156] Initialize the initial states \(y_0\), \(z_0\), \(q_0\) of the Lorenz chaotic mapping system, call the Lorenz model function to generate chaotic sequences \(Y\), \(Z\), \(Q\). To remove the initial transient effect, discard the first 100 values of each sequence, and then use the chaotic sequences to sort the pixel values of the RGB three channels of the image to be encrypted, enhancing the randomness and unpredictability of encryption;

[0157] Initialize the Chen chaotic mapping system, which is defined by the following four nonlinear differential equations:

[0158]

[0159] where \(a\), \(b\), \(c\), \(d\), \(k\) are system parameters, and \(u\), \(v\), \(w\), \(x\) are system state variables; when the parameters \(a = 36\), \(b = 3\), \(c = 28\), \(d = 16\) and the value range of the parameter \(k\) is \(-0.7\leq k\leq0.7\), the Chen chaotic system exhibits typical chaotic dynamics behavior and has the ability to generate multiple chaotic sequences; within this parameter interval, the Chen chaotic mapping system can generate four independent chaotic sequences. Select the parameter \(k = 0.2\) to construct the Chen chaotic sequence to achieve specific dynamic characteristics and controllability;

[0160] Adopt the fourth - order Runge–Kutta numerical method to perform an iterative numerical solution process for the system of equations of the nonlinear dynamics system composed of the above four nonlinear differential equations; under the given initial conditions and parameter settings, generate four chaotic sequences \(U\), \(V\), \(W\), \(X\) with different dynamic characteristics. To remove the initial transient effect, discard the first 100 values of each sequence.

[0161] It should be noted that in the translation of the first paragraph, the part "不再呈现出可预测的周期性模式,而是表现出复杂的、对初始条件高度敏感的混沌特性" is not accurately translated because the original Chinese expression is not clear. It is guessed that it may be "no longer presents a predictable periodic pattern, but shows complex and highly sensitive chaotic characteristics to initial conditions". If there are more specific requirements or corrections for this part, please let me know.The chaotic sequence generated by Chen's chaotic system is used to select the subsequent DNA coding rules. According to the value of the U sequence, the corresponding DNA coding strategy is matched, and the three color channels of the image, red (R), green (G), and blue (B), are precisely coded in turn. The V and W sequences are used to generate the starting position and the number of iterations for the subsequent DNA coding rule selection. Eight different DNA coding rules are applied to encrypt each pixel value multiple times to enhance the complexity and security of encryption.

[0162] Preferably, S2 includes:

[0163] Receive an original color face image I, and a series of parameter sets for initializing and controlling chaotic systems, including a0, p0, u0, v0, w0, x0, y0, z0, q0, and four initialized chaotic systems Tent, PWLCM, Lorenz and Chen;

[0164] Split the message digest generated by SHA-256 into eight equal-sized sub-blocks, denoted as m j , where j = 1, 2, ... 8, each sub-block consists of seven hexadecimal digits, and the hexadecimal values ​​are converted into decimal floating point numbers according to the following formula, and their value range is in the interval [0, 0.0156):

[0165] m j =hex2dec(m1,...,m8) / 2 34

[0166] The second group is directly converted to floating point values ​​in the range (0, 0.0156):

[0167] d=hex2dec(d) / 2 38

[0168]

[0169] a′0 and p′0 represent the initial seeds of the newly generated piecewise linear chaotic mapping system;

[0170] Assuming that the seed values ​​of the Chen chaotic system are u0, v0, w0, x0, the following formula is used to generate a new initial seed:

[0171]

[0172] For the Lorenz chaotic system, three seeds are required, which are calculated as follows:

[0173]

[0174] The above SHA-256 generates a 64-bit message digest, which is divided into nine sub-blocks. The first eight sub-blocks are of equal size, each with a seven-digit hexadecimal number, denoted as m. j , the remaining sub-block is an eight-digit hexadecimal number, denoted as d;

[0175] CK is the generated public key, as shown below:

[0176] CK=a0+p0+u0+v0+w0+x0+y0+z0+q0mod 1.

[0177] Preferably, S3 includes:

[0178] The images of the three color channels of the face image, R, G, and B, are combined and flattened into a one-dimensional array of size 1×3MN, and an XOR operation is performed with the chaotic sequence T generated by the chaotic tent mapping to achieve preliminary encryption of the pixel values:

[0179]

[0180] The chaotic sequence A is obtained by iterating PWLCM 3MN times. The pixel values ​​of I are arranged as follows according to the chaotic sequence A to form I′:

[0181] A={a i ,a i+1 ,...,a 3MN}

[0182] [valueA,indexA]=order(A)

[0183] I′=I(indexA)

[0184] order indicates a sorting operation; [valueA, indexA] indicates an index-value pair, valueA is an element in the chaotic sequence, and indexA is the index corresponding to this element in the pixel array; I() indicates a reference to the original pixel array;

[0185] Divide I′ into the following three 1×MN one-dimensional arrays R1, G1, B1 for the third permutation:

[0186] R1=[I′(1),I′(2),...,I′(MN)]

[0187] G1=[I′(MN+1),I′(MN+2),...,I′(2MN)]

[0188] B1=[I′(2MN+1),I′(2MN+2),...,I′(3MN)]

[0189] A pseudo-random number generation strategy based on the initial seeds y′0, z′0, q′0 is used to construct three chaotic sequences Y, Z, Q of length t+MN; these sequences are used to perform chaotic permutation and diffusion operations on the three RGB pixel arrays of the face image; in order to eliminate the transient effect of the initial sequence, a truncation strategy is adopted to discard the first t values ​​in the sequence; then, the remaining sequences are sorted to achieve a specific pixel value permutation pattern, as shown in the following formula:

[0190] [valueY,indexY]=order(Y)

[0191] [valueZ,indexZ]=order(Z)

[0192] [valueQ,indexQ]=order(Q)

[0193] In the proposed image encryption framework, the sequences Y, Z, Q generated by chaos theory are sorted to obtain the corresponding index sets indexY, indexZ, indexQ; using these index values, the pixel value elements of the three color channels R, G, and B of the image are subjected to index-based rearrangement operations; this operation follows a specific permutation rule to ensure the remapping of pixel values, thereby generating a new permutation image, as follows:

[0194]

[0195] Among them, f R , f G , f B They represent the rearrangement functions applied to the three color channels R, G, and B respectively. The function replaces the original pixel values ​​according to the index generated by the chaotic sequence to obtain the encrypted images R′, G′, and B′.

[0196] Preferably, S4 includes:

[0197] The encryption process of the three color channels of the face image is converted using four specific DNA coding rules to achieve binary-based pixel value representation;

[0198] The specific DNA coding rules are as follows:

[0199] 00: rule[0]

[0200] 01: rule[1]

[0201] 10: rule[2]

[0202] 11: rule[3]

[0203] 00, 01, 10, 11 represent 4 groups of 2-bit binary numbers extracted after the decimal pixel value is converted to an 8-bit binary number; the 8-bit binary number is intercepted with 2 bits as the interception interval to obtain 4 groups of 2-bit binary numbers;

[0204] For example, for the 8-bit binary number 11100011, the four groups of binary numbers after truncation are: 11, 10, 00, 11.

[0205] rule is the DNA coding rule determined by the sequence U generated in Chen chaotic mapping;

[0206] Using Chen hyperchaotic system, combined with initial key vectors u′0, v′0, w′0, x′0, pseudo-random chaotic sequences U, V, W, X are generated through an iterative process, and the number of iterations is set to t+MN×3;

[0207] In order to eliminate the transient effects in the initial iteration stage, a truncation strategy is adopted to discard the first t elements of the sequence; then, the elements in the sequence U are equally divided into three sub-vectors, each of which contains M×N elements, as shown in the following formula:

[0208] U1=[U(1),U(2),...,U(MN)]

[0209] U2=[U(MN+1), U(MN+2),..., U(2MN)]

[0210] U3=[U(2MN+1), U(2MN+2),..., U(3MN)]

[0211] Based on the DNA coding framework, combined with the sequence generated by chaos theory, each color channel of the face image is independently encoded; the DNA coding rule is selected according to the preset selection rule, and each DNA base is arranged according to the array size of 1×4MN after being selected:

[0212]

[0213] DNA_Encode_Rule represents a DNA encoding operation.

[0214] Specifically, the preset selection rule is to select the corresponding DNA encoding rule according to the chaotic system parameter interval. The encoding rules and decoding rules corresponding to different parameter intervals are shown in Table 1:

[0215] Table 1 Encoding rules and decoding rules corresponding to different hybrid parameter intervals

[0216] Chaotic system parameter range Coding Rules Decoding rules 0.001-0.05，0.20-0.25，0.40-0.45，0.50-0.55，0.95-0.99 AGCT GTAC 0.05-0.10，0.30-0.35，0.60-0.65，0.070-0.75，0.85-0.90 ACGT TGCA 0.10-0.015，0.35-0.40，0.55-0.60，0.65-0.70，0.80-0.85 GATC CTAG 0.15-0.20，0.25-0.30，0.45-0.50，0.75-0.80，0.90-0.95 CATG TCGA

[0217] When the chaotic system parameters fall within the corresponding interval, select the corresponding encoding rule and decoding rule on the right side of the table.

[0218] Preferably, S5 includes:

[0219] Using DNA coding rules, perform XOR operations on the three color channels of R, G, and B of the face image respectively;

[0220] This process relies on two key deterministic random number sequences: one is a sequence based on chaos theory, which is used to guide the selection of DNA coding rules; the other sequence, also derived from a chaotic system, is used to determine the iteration cycle of the XOR operation. The synergy of these two sequences creates a dynamic encryption environment for each channel of the image;

[0221] Using the method described in S4, two chaotic sequences V and W are generated, which are converted into the starting value sets start_RGB and times_RGB used to guide the XOR operation, as shown in the following formula:

[0222] start_RGB=(V×10 12 )mod 8

[0223] times_RGB=(W×10 12 )mod 8

[0224] start_RGB and times_RGB are decomposed into three sub-arrays, each with a length of MN, corresponding to the three color channels R, G, and B respectively:

[0225] start_R=[start_RGB(1),...,start_RGB(MN)]

[0226] start_G=[start_RGB(MN+1),...,start_RGB(2MN)]

[0227] start_B=[start_RGB(2MN+1),...,start_RGB(3MN)]

[0228] times_R=[times_RGB(1),..., times_RGB(MN)]

[0229] times_G=[times_RGB(MN+1),..., times_RGB(2MN)]

[0230] times_B=[times_RGB(2MN+1),..., times_RGB(3MN)]

[0231] These subarrays are then applied to the permutation and encoding process of the three channel pixels as described in the following pseudo code:

[0232] for i=1to MN

[0233]

[0234] s=start_R(i)

[0235] for k=1to times_R(i)

[0236]

[0237] s=(s+1)mod 8

[0238] end

[0239] RED(i)=pixel

[0240] end

[0241] The iterative process described in the above pseudo code will be repeated MN times.

[0242] The obtained subarray is used for the replacement and encoding process of the pixel values ​​of the three color channels R, G, and B. In this process, the pixel value of the R channel of the original image will be converted according to the DNA encoding rule determined by the sequence U generated in the Chen chaotic map, combined with the starting point start_R and the number of iterations times_R generated by the chaotic sequence; this process involves converting the four DNA bases of each pixel value Encoding is performed according to a specific permutation logic to generate encrypted vectors of three color channels: R′_DNA, G′_DNA, and B′_DNA.

[0243] Preferably, S6 includes:

[0244] A pseudo-random array X consisting of 3MN elements is used as the key input of the decoding process;

[0245] By randomly sampling the preset DNA rule set, the decoding rules obtained by random sampling are used in the following DNA-Decode-Rule operation to realize the decoding of the three color channels of R, G, and B. The pseudo-random array is generated by the encoding stage and continues to be used in the decoding process to maintain the consistency of the data conversion process;

[0246] Specifically, the preset DNA rule set includes the four decoding rules in Table 1.

[0247] Divide the vector X into three arrays according to the following formula, as shown below:

[0248] X1=[X(1),X(2),...,X(MN)]

[0249] X2=[X(MN+1),X(MN+2),...,X(2MN)]

[0250] X3=[X(2MN+1),X(2MN+2),...,X(3MN)]

[0251] Decoding process:

[0252] Cryptogram_R=DNA_Decode_Rule(R′_DNA,X1)

[0253] Cryptogram_G=DNA_Decode_Rule(G′_DNA,X2)

[0254] Cryptogram_B=DNA_Decode_Rule(B′_DNA,X3)

[0255] DNA-Decode-Rule represents the DNA decoding operation, and finally the images corresponding to the three color channels after decoding are merged to obtain the encrypted face image I'. Cryptogram_R, Cryptogram_G and Cryptogram_B represent the images obtained by decoding in the three color channels of R, G and B respectively.

[0256] Fig.14 A schematic diagram of the detailed encryption process of the present invention.

[0257] The process of conducting safety testing on the present invention is as follows:

[0258] The initial values ​​of the piecewise linear chaotic map setting are: a0 = 0.123456789010 and p0 = 0.234578900. The Chen system configuration is: u0 = 0.3456789012, v0 = 0.245789012, w0 = 0.4567890124, x0 = 0.567890124. The Lorenz system is given the initial state: y0 = 0.678901246, z0 = 0.7890123456, q0 = 0.6890123450. The control parameters of the Tent chaotic system are a_tent = 0.5, b_tent = 0.5. The control parameters of the Chen system are a = 36, b = 3, c = 28, d = 16, k = 0.2. The control parameters of the Loren system are: f = 10, g = 8 / 3, r = 28. The dataset uses Lena_512*512 and CelebA-HQ_512*512 face image datasets, and uses the pre-trained facial and feature recognition models in the OpenCV library to perform a high-precision recognition process on the submitted image data, and the recognition results are clearly displayed in color images. The results are shown in Figure 2. Figure 2-Figure 5 As shown, Figure 2 is the original Lena image; Figure 3 To recognize Lena images for faces; Figure 4 Encrypt Lena images for faces; Figure 5 Decrypt the Lena image for the face. According to the algorithm simulation scheme, the original image can be restored to a large extent. The local facial attributes and face data of the original image have been encrypted, and the decrypted information of the original face image can be seen from the visual effect. The designed algorithm effectively protects the face image data.

[0259] Key space analysis:

[0260] The precision of the 9 floating-point parameters in the key set γ0 = [a′0, p′0, u′0, v′0, w′0, x′0, y′0, z′0, q′0] is 10 16 , so the total key space size is Modern high performance computers check that the key space size is 2 128 All possible keys require about 10 21 The key space of the present invention is much larger than the minimum requirement for resisting brute force attacks. 128 , so the present invention can resist any form of violent attack.

[0261] Key sensitivity analysis:

[0262] Figure 6 We show that we can introduce very small perturbations (k[0] = k[0] + 10) on different components of the key to evaluate the sensitivity of the encryption algorithm to key changes.-16 , k[1]=k[1]+10 -16 ...k[8]=k[8]+10 -16 ). It can be seen in the displayed image sequence that even a very small adjustment of the key parameters will result in a significant difference between the decrypted image and the original plaintext image. This difference indicates that any attempt to deviate from the correct key will cause the decryption result to deviate seriously from the expectation, making it impossible to recover or extract any valuable information from it. This result not only demonstrates the effectiveness of the present invention in key protection, but also shows the good performance of the encryption algorithm designed by the present invention in resisting key guessing, because any slight change to the key will prevent the attacker from obtaining a valid decryption result.

[0263] Ciphertext distribution analysis:

[0264] When constructing an image encryption algorithm that is resistant to statistical analysis, it is crucial to ensure that the ciphertext distribution of the encrypted image is uniform. In addition, in order to improve security, the histogram distribution of the encrypted image should be significantly different from the histogram of the original image in terms of statistical characteristics, thereby effectively preventing attacks based on frequency analysis. The histogram of the Lena image is used to illustrate the ciphertext distribution analysis of the present invention. Figure 7 The histogram distribution of the RGB color channels of the standard Lena image is shown. Figure 8 The corresponding color channel histogram of the encrypted Lena image is presented. Through comparative analysis, it can be observed that there is no similarity between the histogram distribution of the encrypted image and the histogram distribution of the original image, and the pixel value distribution histogram of the encrypted image is more uniform than that of the original image, thus verifying the obfuscation effect of the encryption algorithm used.

[0265] Adjacent pixel correlation analysis:

[0266] 3000 pairs of adjacent pixels are randomly selected from the horizontal, vertical and diagonal directions to implement pixel-level correlation measurement and conduct a detailed quantitative analysis of the correlation between the original image and the encrypted image. Fig. 9 and Fig.10As shown, the correlation distribution diagram between the original image and the encrypted image reveals a significant change in statistical characteristics: after applying the encryption technology proposed in the present invention, the statistical correlation between image pixels is significantly weakened, close to the ideal state of no correlation. In addition, Table 2 lists in detail the correlation coefficients between adjacent pixel values ​​of the original image and the encrypted image. It is clearly observed that the encryption algorithm proposed in this chapter is close to the theoretical minimum value (i.e., 0) in terms of correlation coefficient, indicating that it exhibits excellent randomness in statistical characteristics. This result strongly confirms the excellent performance of the present invention in resisting statistical analysis attacks. Therefore, it can be concluded that the image encryption algorithm designed by the present invention has a high degree of security and can significantly reduce the possibility of successful attacks launched through statistical dependencies between pixels.

[0267] Table 2 Correlation coefficients of adjacent pixels of original image and encrypted image

[0268]

[0269] Information entropy analysis

[0270] Information entropy is an important indicator to measure the randomness of information. It evaluates the uncertainty of an image by calculating the pixel distribution of each gray level in each color channel. Ideally, if the pixel value distribution is closer to a uniform distribution, the higher the entropy value of the image, indicating that the image has higher randomness and better resistance to statistical attacks. For a color image with 256 intensity values, the ideal entropy value of its red, green, and blue color channels should be 8 bits. Being closer to the ideal entropy value means a more uniform distribution, which makes it more difficult to predict or attack through statistical analysis. The present invention performs information entropy analysis on a color Lena image with a size of 512×512 pixels to quantify the randomness of the image. The obtained information entropy values ​​are recorded in Table 3. It can be seen that the image encryption method used in the present invention effectively increases the randomness of the image, thereby improving its security and making it more difficult to be threatened by statistical attacks.

[0271] Table 3 Encrypted image information entropy of the present invention

[0272] Information Entropy The present invention R Channel 7.9989 G Channel 7.9990 B Channel 7.9988

[0273] Stochastic Analysis

[0274] Gray Value Difference (GVD) is a statistical measure of randomness by comparing the original image and the encrypted image. Within the GVD calculation framework, if the pixel distribution of the two images is exactly the same, the GVD value reaches the minimum value of 0; conversely, if there is a difference in the pixel distribution of the two images, the GVD value increases to the maximum value of 1, thereby reflecting the difference between the images. In this study, the GVD score of the standard test image Lena and its encrypted version was evaluated. Table 4 shows the GVD scores of the Lena image and its encrypted image in each channel. As can be seen from Table 4, the GVD score of the encrypted image in each channel is close to 1. This result shows that there is a significant visual difference between the original image and the encrypted image, thereby improving the security of the encrypted image.

[0275] Table 4 GVD score

[0276] GVD The present invention R Channel 0.9864 G Channel 0.9753 B Channel 0.9769

[0277] Differential attack analysis

[0278] In the field of cryptography, Eli and Biham proposed two widely recognized test methods, namely Non-Overlapping Pixel Change Rate (NPCR) and Unified Average Changing Intensity (UACI). These two key indicators are used to quantitatively evaluate the effectiveness of the proposed encryption algorithm in defending differential attacks. These two indicators are calculated by comparing two ciphertext images with only a single-bit difference, and the ideal values ​​are NPCR = 99.9604% and UACI = 33.4635%, which respectively represent the highest performance standards of encrypted images in resisting differential attacks. The present invention selects the Lena standard test image for encryption, and after encryption, randomly selects a pixel and changes its 1 bit, thereby generating a comparison image with a 1-bit difference to simulate the minimum difference that an attacker may exploit. These images are then encrypted using the encryption algorithm proposed in the present invention. According to the experimental data shown in Table 5, the image encryption algorithm proposed in the present invention has achieved a level close to the theoretical optimal value in both NPCR and UACI evaluation indicators, thereby verifying its advantage in resisting differential attacks.

[0279] Table 5 Evaluation index values ​​of the encryption algorithm of the present invention against differential attacks

[0280]

[0281] Analysis of cropping attacks

[0282] Cropping attack, as a typical attack method, simulates the partial information destruction behavior that an attacker may perform after obtaining the encrypted image. It aims to evaluate the recovery ability of the encryption algorithm when the image information is incomplete. Two sets of experiments are designed to evaluate the robustness and anti-attack ability of the encryption algorithm.

[0283] In the first set of analyses, Fig.11a As shown in the figure, the encrypted face image is first randomly removed from the red channel with an area of ​​80×80 pixels, the green channel with an area of ​​50×80 pixels, and the whole channel with an area of ​​60×50 pixels, simulating the possible information destruction of a specific color channel of the image by an attacker. The removal operation is implemented by setting the pixel value of the selected area to 0, thereby simulating the loss of information. Fig.11a The encrypted image is decrypted by destroying it in this way. Fig.11b shown.

[0284] like Fig.11c As shown in Figure 2, the second time, the image is randomly removed from the red channel with an area of ​​80×120 pixels, the green channel with an area of ​​270×80 pixels, and the whole channel with an area of ​​60×180 pixels. Fig.11c The encrypted image is decrypted by destroying it in this way. Fig.11d shown.

[0285] like Fig.12a As shown in Figure 2, the second set of analysis further increased the intensity of the attack by performing large-scale cropping operations on the encrypted image, removing all pixel values ​​of 1 / 16, 1 / 8, 1 / 4, and 1 / 2 of the total image area to simulate more radical information destruction behaviors that the attacker may carry out. The experimental results are shown in Figure 2. Figure 12b shown.

[0286] exist Fig.12a In the figure, the first row from left to right respectively shows schematic diagrams of images obtained after removing all pixel values ​​of 1 / 16, 1 / 8, 1 / 4 and 1 / 2 of the total area of ​​an encrypted image;

[0287] exist Fig.12a In the figure, the second row from left to right respectively shows schematic diagrams of images obtained after removing all pixel values ​​of 1 / 16, 1 / 8, 1 / 4 and 1 / 2 of the total area of ​​another encrypted image;

[0288] exist Figure 12b In the first row, from left to right, the Fig.12a Schematic diagram of images obtained by decrypting the images in the first row from left to right;

[0289] exist Figure 12b In the second row, from left to right, the Fig.12aSchematic diagram of the images obtained by decrypting the images in the second row from left to right.

[0290] The experimental results show that under smaller cropping ratios and intensities, the decrypted image can maintain high clarity and details, indicating that the encryption algorithm of the present invention resists cropping attacks to a certain extent. However, as the cropping ratio and intensity increase, the quality of the decrypted image gradually decreases, and some details begin to be lost, indicating that the security of the encryption algorithm may be affected when facing larger-scale cropping attacks. However, even in the face of larger-scale cropping attacks, the method proposed in the present invention can still recover most of the key information from the remaining encrypted image, and can identify the main content and structural features in the restored image. This result further confirms the robustness of the encryption algorithm of the present invention, indicating that the present invention can minimize information loss and maintain the main visual features and structural integrity of the image when dealing with unauthorized cropping of encrypted images, especially in the face of malicious attacks and accidental damage, to ensure the integrity and availability of image data.

[0291] Noise attack analysis

[0292] In order to evaluate the system's ability to resist noise attacks, this experiment mixed different levels of salt and pepper noise into the encrypted image to simulate the channel noise in real-world communications and observe the impact of noise density changes on the quality of the decrypted image. Four different levels of salt and pepper noise density, 0.005, 0.05, 0.1 and 0.5, were selected in the analysis to process the encrypted image. Subsequently, the decryption algorithm proposed in this invention was used to restore the noisy image. The results are shown in Figure 2. Fig.13 As shown in the figure, as the noise density increases from left to right, the clarity of the decrypted image gradually decreases, and a certain degree of blurring occurs. However, even under unfavorable conditions with a noise density of up to 50%, the encryption framework can still maintain the identifiability of the core content and structural features of the image, indicating that the encryption system of the present invention exhibits significant fault tolerance and robustness in the face of high noise interference, thereby verifying its effectiveness in enhancing image security.

[0293] In order to further quantify the comparison, the Peak Signal-to-Noise Ratio (PSNR) is used as the evaluation index, and the PSNR test is performed on the decrypted images under different noise densities. Table 6 shows the PSNR results of the encryption algorithm of the present invention. The analysis shows that the encryption algorithm of the present invention exhibits a high PSNR value at each noise density level, thereby verifying the superior performance of the encryption algorithm proposed in the present invention in resisting salt and pepper noise attacks.

[0294] Table 6 Evaluation index values ​​of the encryption algorithm of the present invention against noise attacks

[0295] PSNR(dB) The present invention 0.005 31.5973 0.05 21.6589 0.1 18.7934 0.5 12.6397

[0296] The present invention solves the challenges of existing chaotic encryption technology in terms of security, system complexity and resistance to known attacks: (1) In order to enhance security and resist chosen plaintext attacks, the present invention dynamically adjusts the initial conditions and control parameters of chaotic tent mapping, piecewise linear chaotic mapping, Lorenz chaotic system and Chen chaotic system. This dynamic parameter selection mechanism effectively expands the key space and improves the security and reliability of the algorithm. (2) The chaotic system and DNA cryptography strategy designed by the present invention achieve improved security without significantly increasing the computing cost. Through the iteration of chaotic sequences and pixel-level confusion of DNA encoding, the present invention significantly improves the complexity of the encryption process while maintaining the efficiency of the algorithm. (3) The present invention specifically considers the resistance to specific attacks such as salt and pepper noise. Through the nonlinear characteristics of the chaotic system and the randomness of DNA encoding, the defense mechanism against known attack patterns is enhanced.

[0297] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A face image data encryption method based on chaotic tent mapping, characterized in that: include: S1, set the initial state of the chaotic system; S2, receives the face image and initialization parameters and generates an initial seed; S3, scramble and diffuse the face image to obtain the encrypted images R′, G′, B′ of the three channels of R, G, and B; S4, DNA encoding is performed on R′, G′, and B′ respectively to obtain vectors R_DNA, G_DNA, and B_DNA; S5, confuse R_DNA, R_DNA, and R_DNA respectively to obtain encrypted vectors R′_DNA, G′_DNA, and B′_DNA; S6, decode and merge R′_DNA, G′_DNA, and B′_DNA to obtain an encrypted face image.

2. The method for encrypting facial image data based on chaotic tent mapping according to claim 1 is characterized in that: S1 includes: The chaotic tent mapping system, the piecewise linear chaotic mapping system, the Lorenz chaotic mapping system and the Chen chaotic mapping system are selected as the chaotic systems for encrypting the face images; Initialize the above four types of chaotic systems respectively: Initialize the chaos tent mapping system, its basic form is: x represents a state variable in the mapping, representing the state of the chaotic tent mapping system at a certain moment. n represents the state of the chaotic tent mapping system in the nth iteration, f represents a constant, and s represents the threshold parameter in the chaotic tent mapping system; Wherein, f=0.8, s=0.5, and the starting value of the chaotic sequence x0=0.1 are set. For each pixel value pixels[i-1] in the pixel value array, its corresponding chaotic sequence value x[i-1] is calculated, and the chaotic sequence value x[i-1] is used to perform an XOR operation with the pixel value pixels[i-1] to obtain the pixel value encrypted_arrary[i-1] after chaotic tent mapping encryption; the next value x[i] of the chaotic sequence is updated according to the value of x[i-1] to ensure the dynamic and unpredictable nature of the sequence; this process is repeated on each element of the pixel value array, thereby realizing the chaotic tent mapping encryption of the entire image to be encrypted; Initialize the piecewise linear chaotic mapping system, and its iteration formula is: p0 represents the threshold parameter in the piecewise linear chaotic mapping system; Through the above iterative formula, a chaotic sequence a is generated i , the length of the sequence is 3×w×h, where w and h represent the width and height of the image to be encrypted respectively; The chaotic sequence a i Combined with the pixel value list pixels after the first tent mapping scrambling, a tuple containing chaotic values ​​and corresponding pixel values ​​is formed, and according to the chaotic value a i Sort them; after sorting, reorganize the pixel values ​​to obtain a second scrambled pixel value list pixels; divide the encrypted pixel value list according to the three color channels of RGB to obtain a pixel value list of the three color channels of RGB; Initialize the Lorentz chaotic mapping system, which is defined by the following three nonlinear differential equations: The variable y represents the intensity of the convective motion in the fluid, which is used to quantify the difference in amplitude between the rising and falling air currents in the air pool. The variable z represents the temperature gradient in the fluid, which is used to reflect the temperature difference between the rising fluid and the sinking fluid. The user describes the degree of deviation of the temperature inside the air pool relative to the average temperature of the environment. The parameters f, g, and r are constants representing the physical properties of the airflow. Initialize the initial state y0, z0, q0 of the Lorentz chaotic mapping system, call the Lorentz model function, generate the chaotic sequence Y, Z, Q, discard the first 100 values ​​of each sequence to remove the initial transient effect, and then use the chaotic sequence to sort the pixel values ​​of the RGB channels of the image to be encrypted to enhance the randomness and unpredictability of encryption; Initialize the Chen chaotic map system, which is defined by the following four nonlinear differential equations: Among them, a, b, c, d, k are system parameters, u, v, w, x are state variables of the system; when parameters a=36, b=3, c=28, d=16 and the value range of parameter k is -0.7≤k≤0.7, the Chen chaotic system exhibits typical chaotic dynamic behavior and has the ability to generate multiple chaotic sequences; within this parameter range, the Chen chaotic mapping system can generate four independent chaotic sequences, and the parameter k=0.2 is selected to construct the Chen chaotic sequence to achieve specific dynamic characteristics and controllability; The fourth-order Runge–Kutta numerical method is used to perform an iterative numerical solution process on the nonlinear dynamic system equations consisting of the above four nonlinear differential equations; under given initial conditions and parameter settings, four chaotic sequences U, V, W, X with different dynamic characteristics are generated. In order to remove the initial transient effect, the first 100 values ​​of each sequence are discarded.

3. The method for encrypting facial image data based on chaotic tent mapping according to claim 1, characterized in that S2 include: Receive an original color face image I, and a series of parameter sets for initializing and controlling chaotic systems, including a0, p0, u0, v0, w0, x0, y0, z0, q0, and four initialized chaotic systems Tent, PWLCM, Lorenz and Chen; Split the message digest generated by SHA-256 into eight equal-sized sub-blocks, denoted as m j , where j = 1, 2, ... 8, each sub-block consists of seven hexadecimal digits, and the hexadecimal values ​​are converted into decimal floating point numbers according to the following formula, and their value range is in the interval [0, 0.0156): m j =hex2dec(m1,...,m8) / 2 34 The second group is directly converted to floating point values ​​in the range (0, 0.0156): d=hex2dec(d) / 2 38 a′0 and p′0 represent the initial seeds of the newly generated piecewise linear chaotic mapping system; Assuming that the seed values ​​of the Chen chaotic system are u0, v0, w0, x0, the following formula is used to generate a new initial seed: For the Lorenz chaotic system, three seeds are required, which are calculated as follows: The above SHA-256 generates a 64-bit message digest, which is divided into nine sub-blocks. The first eight sub-blocks are of equal size, each with a seven-digit hexadecimal number, denoted as m. j , the remaining sub-block is an eight-digit hexadecimal number, denoted as d; CK is the generated public key, as shown below: CK=a0+p0+u0+v0+w0+x0+y0+z0+q0mod 1.

4. The method for encrypting facial image data based on chaotic tent mapping according to claim 1 is characterized in that: S3 includes: The images of the three color channels of the face image, R, G, and B, are combined and flattened into a one-dimensional array of size 1×3MN, and an XOR operation is performed with the chaotic sequence T generated by the chaotic tent mapping to achieve preliminary encryption of the pixel values: The chaotic sequence A is obtained by iterating PWLCM 3MN times. The pixel values ​​of I are arranged as follows according to the chaotic sequence A to form I′: A={a i ,a i+1 ,...,a 3MN } [valueA,indexA]=order(A) I′=I(indexA) order indicates a sorting operation; [valueA, indexA] indicates an index-value pair, valueA is an element in the chaotic sequence, and indexA is the index corresponding to this element in the pixel array; I() indicates a reference to the original pixel array; Divide I′ into the following three 1×MN one-dimensional arrays R1, G1, B1 for the third permutation: R1=[I′(1),I′(2),...,I′(MN)] G1=[I′(MN+1),I′(MN+2),...,I′(2MN)] B1=[I′(2MN+1),I′(2MN+2),...,I′(3MN)] A pseudo-random number generation strategy based on the initial seeds y′0, z′0, q′0 is used to construct three chaotic sequences Y, Z, Q of length t+MN; these sequences are used to perform chaotic permutation and diffusion operations on the three RGB pixel arrays of the face image; in order to eliminate the transient effect of the initial sequence, a truncation strategy is adopted to discard the first t values ​​in the sequence; then, the remaining sequences are sorted to achieve a specific pixel value permutation pattern, as shown in the following formula: [valueY,indexY]=order(Y) [valueZ,indexZ]=order(Z) [valueQ,indexQ]=order(Q) In the proposed image encryption framework, the sequences Y, Z, Q generated by chaos theory are sorted to obtain the corresponding index sets indexY, indexZ, indexQ; using these index values, the pixel value elements of the three color channels R, G, and B of the image are subjected to index-based rearrangement operations; this operation follows a specific permutation rule to ensure the remapping of pixel values, thereby generating a new permutation image, as follows: Among them, f R , f G , f B They represent the rearrangement functions applied to the three color channels R, G, and B respectively. The function replaces the original pixel values ​​according to the index generated by the chaotic sequence to obtain the encrypted images R′, G′, and B′.

5. The method for encrypting facial image data based on chaotic tent mapping according to claim 1, characterized in that S4 include: The encryption process of the three color channels of the face image is converted using four specific DNA coding rules to achieve binary-based pixel value representation; The specific DNA coding rules are as follows: 00: rule[0] 01: rule[1] 10: rule[2] 11: rule[3] 00, 01, 10, 11 represent 4 groups of 2-bit binary numbers extracted after the decimal pixel value is converted to an 8-bit binary number; the 8-bit binary number is intercepted with 2 bits as the interception interval to obtain 4 groups of 2-bit binary numbers; rule is the DNA coding rule determined by the sequence U generated in Chen chaotic mapping; Using Chen hyperchaotic system, combined with initial key vectors u′0, v′0, w′0, x′0, pseudo-random chaotic sequences U, V, W, X are generated through an iterative process, and the number of iterations is set to t+MN×3; In order to eliminate the transient effects in the initial iteration stage, a truncation strategy is adopted to discard the first t elements of the sequence; then, the elements in the sequence U are equally divided into three sub-vectors, each of which contains M×N elements, as shown in the following formula: U1=[U(1),U(2),...,U(MN)] U2=[U(MN+1), U(MN+2),..., U(2MN)] U3=[U(2MN+1), U(2MN+2),..., U(3MN)] Based on the DNA coding framework, combined with the sequence generated by chaos theory, each color channel of the face image is independently encoded; the DNA coding rule is selected according to the preset selection rule, and each DNA base is arranged according to the array size of 1×4MN after being selected: DNA_Encode_Rule represents a DNA encoding operation.

6. The method for encrypting facial image data based on chaotic tent mapping according to claim 1, characterized in that S5 include: Using DNA coding rules, the XOR operation is performed on the three color channels of the face image, R, G, and B. Using the method described in S4, two chaotic sequences V and W are generated, which are converted into the starting value sets start_RGB and times_RGB used to guide the XOR operation, as shown in the following formula: start_RGB=(V×10 12 )mod 8 times_RGB=(W×10 12 )mod 8 start_RGB and times_RGB are decomposed into three sub-arrays, each with a length of MN, corresponding to the three color channels of R, G, and B: start_R=[start_RGB(1),...,start_RGB(MN)] start_G=[start_RGB(MN+1),...,start_RGB(2MN)] start_B=[start_RGB(2MN+1),...,start_RGB(3MN)] times_R=[times_RGB(1),..., times_RGB(MN)] times_G=[times_RGB(MN+1),..., times_RGB(2MN)] times_B=[times_RGB(2MN+1),..., times_RGB(3MN)] The obtained subarray is used for the replacement and encoding process of the pixel values ​​of the three color channels R, G, and B. In this process, the pixel value of the R channel of the original image will be converted according to the DNA encoding rule determined by the sequence U generated in the Chen chaotic map, combined with the starting point start_R and the number of iterations times_R generated by the chaotic sequence; this process involves converting the four DNA bases of each pixel value Encoding is performed according to a specific permutation logic to generate encrypted vectors of three color channels: R′_DNA, G′_DNA, and B′_DNA.

7. The method for encrypting facial image data based on chaotic tent mapping according to claim 1 is characterized in that: S6 includes: A pseudo-random array X consisting of 3MN elements is used as the key input of the decoding process; By randomly sampling the preset DNA rule set, the decoding rules obtained by random sampling are used in the following DNA-Decode-Rule operation to realize the decoding of the three color channels of R, G, and B. The pseudo-random array is generated by the encoding stage and continues to be used in the decoding process to maintain the consistency of the data conversion process; Divide the vector X into three arrays according to the following formula, as shown below: X1=[X(1),X(2),...,X(MN)] X2=[X(MN+1),X(MN+2),...,X(2MN)] X3=[X(2MN+1),X(2MN+2),...,X(3MN)] Decoding process: Cryptogram_R=DNA_Decode_Rule(R′_DNA,X1) Cryptogram_G=DNA_Decode_Rule(G′_DNA,X2) Cryptogram_B=DNA_Decode_Rule(B′_DNA,X3) DNA-Decode-Rule represents the DNA decoding operation, and finally the images corresponding to the three color channels after decoding are merged to obtain the encrypted face image I'. Cryptogram_R, Cryptogram_G and Cryptogram_B represent the images obtained by decoding in the three color channels of R, G and B respectively.

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