Visual security image encryption and decryption method

Abstract

The embodiment of the invention provides a visual security image encryption and decryption method, and belongs to the technical field of communication, and the method specifically comprises the steps: 1, generating a measurement matrix through a chaotic system, compressing a plaintext privacy image according to the measurement matrix, and obtaining a compression matrix; step 2, performing bit plane reordering and diffusion on the compression matrix to generate ciphertext data; 3, obtaining a corner area and a non-corner area of the carrier image by using a corner detection algorithm, and embedding the ciphertext data with different capacities into different areas of the carrier image to obtain a visual security image; and step 4, performing inverse encryption operation on the visual security image to obtain a decrypted plaintext privacy image. According to the scheme of the invention, the hyperchaos characteristic and the deep fusion of the image characteristics are introduced, the complex parameter adjustment process is simplified, and the adaptability, the calculation efficiency and the safety of the algorithm are remarkably improved.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of communication technologies, and in particular, to a method for encrypting and decrypting visual security images. Background Art

[0002] The non-linear characteristics of chaotic systems are very suitable for the requirements of data information protection. Chaotic image encryption technology is one of the key methods to solve image security problems. With the continuous development of chaotic image encryption technology, further masking the encrypted image as a visually meaningful image can further enhance the confidentiality of the algorithm. At present, some ordinary chaotic systems may have the possibility of being cracked due to insufficiently complex dynamics. In addition, many image encryption algorithms can achieve high-quality encryption only through multiple rounds of scrambling-diffusion operations. Secondly, existing visual security image encryption algorithms do not fully consider the characteristics of the carrier image and adopt simple or fixed methods for image masking, and there is still room for improvement in the imperceptibility performance of the generated visual security encrypted images. These disadvantages limit their effectiveness in information security applications that require complexity and efficiency.

[0003] It can be seen that there is an urgent need for a visual security image encryption and decryption method with encryption and decryption security, efficiency, and adaptability. Summary of the Invention

[0004] In view of this, embodiments of the present disclosure provide a method for encrypting and decrypting visual security images, which at least partially solves the problems of poor encryption and decryption security, efficiency, and adaptability in the prior art.

[0005] Embodiments of the present disclosure provide a method for encrypting and decrypting visual security images, including:

[0006] Step 1, generating a measurement matrix through a chaotic system, and compressing the plaintext privacy image accordingly to obtain a compression matrix;

[0007] Step 2, performing bit-plane reordering and diffusion on the compression matrix to generate ciphertext data;

[0008] Step 3, obtaining the corner region and non-corner region of the carrier image by using a corner detection algorithm, and embedding ciphertext data of different capacities into different regions of the carrier image accordingly to obtain a visual security image;

[0009] Step 4, performing an inverse encryption operation on the visual security image to obtain the decrypted plaintext privacy image.

[0010] According to a specific implementation manner of the embodiments of the present disclosure, the specific steps of Step 1 include:

[0011] Step 1.1, calculating the average value I mean and the standard deviation I sd, take the fractional part x0 of the average value I mean as the initial value of the hyperchaotic system, and the integer value L of I sd as the step size for selecting the random sequence in the bit-plane reordering, where the average value I a has the expression mean is

[0012]

[0013] x0 = I mean mod 1;

[0014] The standard deviation I sd has the expression

[0015]

[0016] where N and M are the sizes of the plaintext privacy image, and x i represents the gray value of the i-th pixel;

[0017] Step 1.2, set the initial state of the hyperchaotic system as {x0, p0, y0, p1}, and use the fourth-fifth order Runge-Kutta method to numerically solve the trajectory of the hyperchaotic system to obtain four random chaotic sequences X, Y, W, Z

[0018]

[0019] where x i , y i , z i , w i respectively represent the state values of the corresponding trajectory points in each step of the numerical solution process of the hyperchaotic system;

[0020] Step 1.3, construct the wavelet transform matrix Ψ, sparsify the plaintext privacy image PI with size M×N, and after threshold detection with threshold TS, set the values less than the threshold in the wavelet transform matrix to zero to obtain the matrix P1

[0021]

[0022] P spare = Ψ·PI·Ψ T

[0023] where P spare represents, T represents the sparse matrix, T represents the transpose;

[0024] Step 1.4: Concatenate the random sequences X and Y into sequence XY, then sort XY in ascending order to obtain sequence XY′, and finally divide sequence XY′ into two equal parts to get the sequences AN1 and AN2 for Arnold scrambling;

[0025] Step 1.5: Use sequences AN1 and AN2 to perform Arnold matrix scrambling on matrix P1 to obtain matrix P2

[0026]

[0027] Step 1.6: Set the step size d, and use piecewise linear mapping to construct the measurement matrix Φ ∈ R with the initial state {x′0, p′0, y′0, p1′} CN×M

[0028] where CN = CR × N, and CR is the compression ratio;

[0029] Step 1.7: Compress matrix P2 through the measurement matrix to obtain matrix P3

[0030] P3 = Φ × P2;

[0031] Step 1.8: Quantize matrix P3 to obtain the compressed matrix P4

[0032]

[0033] According to a specific implementation manner of the embodiments of the present disclosure, step 2 specifically includes:

[0034] Step 2.1: Determine the labels of the bit planes in the bit plane reordering according to the chaotic random sequence Z. Using Z1(L a ) as the first element, select eight random numbers in sequence Z1 with a step size of L a θ = {λ1, λ2,..., λ8} = {Z1(i1), Z2(i2),..., Z8(i8)}

[0035] where i

[0036] i k = mod(10 × k × L a , length(Z1))

[0037] LABLE = mod(θ, 3);

[0038] Step 2.2: Distribute LABLE to the eight bit planes of the compressed matrix P4 in order, and then perform the bit plane reordering method according to a preset method to obtain matrix P5;

[0039] Step 2.3: Construct a new chaotic sequence W′ = {w1, w2,..., w for sequence W with a step size of d′ = 4CN×M}, and then use the sequence W' to diffuse the matrix P5 according to a preset formula to obtain the ciphertext data P6

[0040] P6 = mod(P5(i,j) + W'(i,j), 256).

[0041] According to a specific implementation manner of the embodiment of the present disclosure, step 3 specifically includes:

[0042] Step 3.1, perform SURF corner detection on the carrier image HI, and divide the carrier image HI into a corner region and a non-corner region;

[0043] Step 3.2, embed the data of the preset capacity of the ciphertext data P6 into the four least significant bits of the corner region, and embed the remaining data of the ciphertext data P6 into the two least significant bits of the non-corner region to obtain the visually secure encrypted image SI.

[0044] According to a specific implementation manner of the embodiment of the present disclosure, step 4 specifically includes:

[0045] Step 4.1, use the SURF algorithm to perform corner detection on the carrier image HI, and then extract data of different capacities from different regions to form the ciphertext data P6;

[0046] Step 4.2, with the initial state {x0, p0, y0, p1}, use the fourth and fifth order Runge-Kutta method to numerically solve the trajectory of the hyperchaotic system to obtain four random chaotic sequences X, Y, W, Z;

[0047] Step 4.3, construct a new chaotic sequence W' = {w1, w2,..., w CN×M} with a step size d' = 4 for the sequence W, and then perform inverse diffusion on the matrix P6 to obtain the matrix P5

[0048] P5 = mod(P6(i,j) - W'(i,j), 256);

[0049] Step 4.4, determine the label of the bit plane in the bit plane reordering according to the chaotic random sequence Z, use Z1(L a ) as the first element, select eight random numbers in the sequence Z1 with a step size of L a to obtain LABLE, and then perform inverse bit plane reordering on the matrix P5 to obtain the compressed matrix P4;

[0050] Step 4.5, use piecewise linear mapping to construct the measurement matrix Φ ∈ R with the initial state {x'0, p'0, y'0, p1'} CN ×M ;

[0051] Step 4.6, perform inverse quantization on the compression matrix P4 to obtain the matrix P3

[0052]

[0053] where max and min are the maximum and minimum values of the compression matrix P4 respectively;

[0054] Step 4.7, use the smooth l0 norm algorithm to reconstruct the matrix P3 to obtain the matrix P2;

[0055] Step 4.8, use the random sequences X and Y to perform inverse Arnold matrix scrambling and inverse wavelet transform on the matrix P2 to obtain the decrypted plaintext privacy image PI.

[0056] The visual security image encryption and decryption scheme in the embodiments of the present disclosure includes: Step 1, generate a measurement matrix through a chaotic system, and accordingly compress the plaintext privacy image to obtain a compression matrix; Step 2, perform bit-plane reordering and diffusion on the compression matrix to generate ciphertext data; Step 3, use the corner detection algorithm to obtain the corner region and non-corner region of the carrier image, and accordingly embed ciphertext data of different capacities into different regions of the carrier image to obtain a visual security image; Step 4, perform inverse encryption operation on the visual security image to obtain the decrypted plaintext privacy image.

[0057] The beneficial effects of the embodiments of the present disclosure are as follows: Through the solution of the present disclosure, the introduction of hyperchaotic characteristics and the deep fusion of image features simplify the complex parameter adjustment process, significantly improve the adaptability and computational efficiency of the algorithm. It realizes the adaptive embedding of image features, generates a visual security image with high imperceptibility, so that the ciphertext image is visually protected while minimally damaging the original pixel statistical distribution of the carrier image. Without increasing the system resource occupancy, the present invention realizes the high-efficiency encryption of image data while ensuring a low error rate in the decryption process. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present disclosure. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0059] Figure 1 It is a schematic flowchart of a visual security image encryption and decryption method provided by the embodiments of the present disclosure;

[0060] Figure 2 It is a schematic flowchart of a bit-plane reordering provided by the embodiments of the present disclosure;

[0061] Figure 3 A flowchart showing the encryption process provided by an embodiment of the present disclosure;

[0062] Figure 4 A flowchart showing the decryption process provided by an embodiment of the present disclosure. Detailed implementation manners

[0063] The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0064] The following uses specific specific examples to illustrate the implementation manners of the present disclosure. Those skilled in the art can easily understand other advantages and effects of the present disclosure from the content disclosed in this specification. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all the embodiments. The present disclosure can also be implemented or applied through other different specific implementation manners. The details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present disclosure without making creative efforts belong to the scope of protection of the present disclosure.

[0065] It should be noted that the following describes various aspects of the embodiments within the scope of the appended claims. It should be obvious that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is illustrative only. Based on the present disclosure, those skilled in the art should understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects described herein can be used to implement the device and / or practice the method. In addition, this device and / or this method can be implemented using other structures and / or functions in addition to one or more of the aspects described herein.

[0066] It should also be noted that the diagrams provided in the following embodiments only schematically illustrate the basic concept of the present disclosure. The diagrams only show the components related to the present disclosure, rather than being drawn according to the number, shape, and size of the components in actual implementation. The type, quantity, and ratio of each component in its actual implementation can be an arbitrary change, and the component layout type may also be more complex.

[0067] In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.

[0068] Embodiments of the present disclosure provide a visual security image encryption and decryption method, which can be applied to the image encryption and decryption process in the Internet security scenario.

[0069] See Figure 1 , which is a schematic flowchart of a visual security image encryption and decryption method provided by an embodiment of the present disclosure. As Figure 1 shown, the method mainly includes the following steps:

[0070] Step 1, generate a measurement matrix through a chaotic system, and compress the plaintext privacy image accordingly to obtain a compressed matrix;

[0071] In image encryption algorithms, scrambling is usually performed at the pixel level and combined with multiple rounds of diffusion operations to achieve encryption. For grayscale images, their pixels are composed of eight bits. If confusion operations are performed on the bit planes, not only can the pixel scrambling effect be achieved, but also the diffusion function can be achieved. Compared with pixel-level scrambling and diffusion, bit-plane reordering can effectively improve the efficiency of image encryption algorithms. The present invention proposes a bit-plane reordering technique based on chaotic sequences.

[0072] Taking Figure 2 as an example to introduce the bit-plane reordering based on chaotic sequences. First, divide each bit plane of the image into 2×2 blocks. Then, use a chaotic system to generate a random sequence with chaotic characteristics and extract eigenvalues from the plaintext privacy image. According to the eigenvalues, select eight random numbers in the chaotic sequence. Next, assign these eight random numbers to the eight bit planes of the image, and label each bit plane with "00", "01", "10", or "11". Finally, execute different reordering rules according to different labels. The specific sorting rules are as follows:

[0073] When the label is "00", sort by row within the block of the bit plane and sort by row between blocks;

[0074] When the label is "01", sort by row within the block of the bit plane and sort by column between blocks;

[0075] When the label is "10", sort by column within the block of the bit plane and sort by row between blocks;

[0076] When the label is "11", sort by column within the block of the bit plane and sort by column between blocks;

[0077] Specifically in implementation, based on the above bit-plane reordering technique, the encryption flowchart of the system proposed by the present invention is as Figure 3 shown.

[0078] The encryption process can be divided into two main stages: the private data encryption stage and the visually secure encrypted image generation stage. Specifically, in the private information encryption stage, first, a chaotic measurement matrix is generated through a chaotic system, and then the plaintext private information is compressed and sensed using this matrix. Then, the compressed data is subjected to bit-plane reordering and diffusion to generate ciphertext data. In the visually secure encrypted image generation stage, first, the corner points of the carrier image are obtained using a corner detection algorithm. Then, according to the situation of corner points or non-corner points, ciphertext data of different capacities is embedded into different regions of the carrier image. Finally, a visually secure image is obtained.

[0079] In the encryption scheme proposed by the present invention, it is assumed that the plaintext private image is: The carrier image is: The visually secure encrypted image is: The decrypted image is: The encryption process will be introduced in detail below.

[0080] Step 1: Some initial parameters of the hyperchaotic system are associated with the plaintext private image information. First, the average value I mean and the standard deviation I sd of the plaintext private image PI are calculated. The fractional part x0 of I mean is used as the initial value of the four-dimensional hyperchaotic system. The integer value L sd of I a is regarded as the step size for selecting a random sequence in the bit-plane reordering. The specific process is as follows:

[0081]

[0082] x0 = I mean mod1(3)

[0083] where N and M are the dimensions of the image, and x i represents the gray value of the i-th pixel.

[0084] Step 2: Set the initial state as {x0, p0, y0, p1}, and numerically solve the trajectory of the Lorenz hyperchaotic system using the fourth and fifth-order Runge-Kutta method to obtain four random chaotic sequences X, Y, W, and Z. As shown in Equation (4):

[0085]

[0086] Step 3: Construct a wavelet transform matrix Ψ, sparsify the plaintext private image PI with dimensions M×N, and after detection by a threshold TS, the values in the matrix less than the threshold are set to zero to obtain a matrix P1. This process can be described as:

[0087] P spare = Ψ·PI·ΨT (5)

[0088]

[0089] Step 4: Concatenate the random sequences X and Y into the sequence XY. Then, sort XY in ascending order to obtain the sequence XY'. Finally, bisect the sequence XY' to obtain the sequences AN1 and AN2 for Arnold scrambling.

[0090] Step 5: Perform Arnold matrix scrambling on the matrix P1 using the sequences AN1 and AN2 to obtain the matrix P2, as shown in Equation (7):

[0091]

[0092] Step 6: Construct a chaotic measurement matrix with a step size of d. Use a piecewise linear chaotic system to construct the measurement matrix Φ ∈ R CN×M . where CN = CR × N, and CR is the compression ratio.

[0093] Step 7: Compress the matrix P2 using the measurement matrix to obtain the matrix P3. As shown in Equation (8).

[0094] P3 = Φ × P2 (8)

[0095] Step 8: Quantize the matrix P3 to obtain the matrix P4, specifically as follows:

[0096]

[0097] Step 2, perform bit-plane reordering and diffusion on the compressed matrix to generate ciphertext data;

[0098] Specifically, when implemented, the chaotic random sequence Z is used to determine the label of the bit plane in the bit-plane reordering. Using Z1(L a ) as the first element, select eight random numbers in the sequence Z1 with a step size of L a . Specifically as follows:

[0099] θ = {λ1, λ2,..., λ8} = {Z1(i1), Z2(i2),..., Z8(i8)} (10)

[0100] i k = mod(10 × k × L a , length(Z1)) (11)

[0101] LABLE = mod(θ, 3) (12)

[0102] The LABLE is distributed to the eight bit - planes of matrix P4 in sequence, and then the bit - plane re - ordering method is carried out according to the method in Section 4.2.1 to obtain matrix P5.

[0103] A new chaotic sequence W′={w1, w2, …, w CN×M} is constructed for the sequence W with a step size d′ = 4. Then, the matrix P5 is diffused according to formula (13) using the sequence W′ to obtain the ciphertext data P6.

[0104] P6 = mod(P5(i, j)+W′(i, j), 256) (13)

[0105] Step 3: Use the corner detection algorithm to obtain the corner region and non - corner region of the carrier image, and accordingly embed ciphertext data of different capacities into different regions of the carrier image to obtain a visually secure image;

[0106] Specifically, when implementing, the SURF corner detection can be performed on the carrier image HI, so that HI is divided into a corner region and a non - corner region. Then, a part of the data of the ciphertext data P6 is embedded into the four least significant bits of the corner region; the remaining data of the ciphertext data P6 is embedded into the two least significant bits of the non - corner region. Finally, the visually secure encrypted image SI is obtained.

[0107] Step 4: Perform the inverse encryption operation on the visually secure image to obtain the decrypted plaintext privacy image.

[0108] Specifically, considering that the visually secure image encryption algorithm proposed in this embodiment of the present disclosure belongs to the symmetric encryption mechanism. Therefore, the decryption process is the inverse process of the encryption process. That is, using the corresponding key to perform the inverse process of the encryption process on the visually secure encrypted image can obtain the required privacy image. Figure 4 The flowchart of the decryption algorithm is presented.

[0109] First, use the corner detection algorithm to extract the ciphertext data. Then, perform inverse diffusion and inverse bit - plane re - ordering on the ciphertext data. Next, use the smooth l0 - norm algorithm to reconstruct the compressed ciphertext data. Finally, perform inverse Arnold scrambling to obtain the plaintext privacy image. In addition, in order to be able to correctly decrypt the visually secure image, some necessary keys need to be transmitted to the decryption end, including: I mean , I sd , CR, {x0, p0, y0, p1}, {x0′, p0′, y0′, p1′}, d. The detailed decryption steps are as follows.

[0110] Step 1: Use the SURF algorithm to perform corner detection on the carrier image HI. Then, extract data of different capacities from different regions to obtain the ciphertext data P6.

[0111] Step 2: With the initial state {x0, p0, y0, p1}, numerically solve the trajectory of the Lorenz hyperchaotic system using the fourth and fifth-order Runge-Kutta method to obtain four random chaotic sequences X, Y, W, and Z.

[0112] Step 3: Construct a new chaotic sequence W′={w1, w2, …, w CN×M} with a step size d′ = 4 for the sequence W. Then perform inverse diffusion on the matrix P6. Specifically, as shown in formula (14).

[0113] P5 = mod(P6(i,j) - W′(i,j), 256) (14)

[0114] Step 4: Perform the same operation on the sequence Z and the encryption process step 9 to obtain LABLE, and then perform inverse bit-plane reordering on P5 to obtain P4.

[0115] Step 5: With the initial values {x′0, p′0, y′0, p′1}, construct a measurement matrix Φ ∈ R CN×M .

[0116] Step 6: Perform inverse quantization on the matrix P4 to obtain the matrix P3. Specifically, as follows:

[0117]

[0118] where max and min are the maximum and minimum values of the P4 matrix, respectively.

[0119] Step 7: Reconstruct P3 using the smoothed l0 norm algorithm to obtain the matrix P2.

[0120] Step 8: Finally, perform inverse Arnold matrix scrambling and inverse wavelet transform on the matrix P2 using the random sequences X and Y to obtain the final plaintext privacy image PI.

[0121] So far, the encryption and decryption process involved in the present invention is completed.

[0122] Traditional visual image encryption systems have problems such as high algorithm complexity, large consumption of computing resources, and insufficient adaptability to dynamic environments during the information protection process, making it difficult to meet the actual needs of modern information security applications. The visual security image encryption method based on bit-plane reordering and image corner features proposed in the present invention. This method can significantly enhance security in the encrypted transmission of private images. The system can be widely applied in the field of information security technology, such as military image confidential transmission, encrypted video conferencing systems, intelligent monitoring privacy protection and other scenarios. By providing higher security and privacy guarantees for data transmission, this technology can effectively prevent the theft and tampering of data during transmission. In addition, this system also has important value in fields such as medical image protection and intelligent device privacy management, and can achieve dynamic encryption and decryption of sensitive data in a variety of complex environments, meeting the diverse needs of modern information security.

[0123] The visual security image encryption and decryption method provided in this embodiment simplifies the complex parameter adjustment process by introducing hyperchaotic characteristics and deep fusion of image features, and significantly improves the adaptability and computational efficiency of the algorithm. It realizes the adaptive embedding of image features, generates a visually secure image with high imperceptibility, so that the encrypted image minimally damages the original pixel statistical distribution of the carrier image while obtaining visual protection. Without increasing the system resource occupancy, the present invention realizes high-efficiency encryption of image data while ensuring a low error rate in the decryption process. The proposed technology of the present invention provides an innovative solution for achieving more efficient and secure image processing and transmission, and has broad application prospects and practical value.

[0124] It should be understood that each part of the present disclosure can be implemented by hardware, software, firmware or a combination thereof.

[0125] The above are only specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present disclosure should be covered by the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be subject to the protection scope of the claims.

Claims

1. A visual security image encryption and decryption method, characterized in that: include: Step 1: Generate a measurement matrix through a chaotic system, and compress the plaintext privacy image based on it to obtain a compression matrix; Step 2, reordering and diffusing the bit planes of the compression matrix to generate ciphertext data; Step 3, using a corner detection algorithm to obtain corner areas and non-corner areas of the carrier image, and accordingly embedding ciphertext data of different capacities into different areas of the carrier image to obtain a visually secure image; Step 4: Perform an inverse encryption operation on the visual security image to obtain a decrypted plaintext privacy image.

2. The method according to claim 1, characterized in that The step 1 specifically includes: Step 1.1, calculate the average value I of the plaintext privacy image PI mean and standard deviation I sd , the average value I mean The fractional part x0 is used as the initial value of the hyperchaotic system. sd The integer value L a As the step size of selecting a random sequence in the bit plane reordering, wherein the average value I mean The expression is x0=I mean mod1; The standard deviation I sd The expression is Among them, N and M are the sizes of the plaintext privacy image, x i Represents the gray value of the i-th pixel; Step 1.2, set the initial state of the hyperchaotic system to {x0, p0, y0, p1}, use the fourth- and fifth-order Runge-Kutta methods to numerically solve the trajectory of the hyperchaotic system, and obtain four random chaotic sequences X, Y, W, Z Among them, x i ,y i , z i , w i They represent the state values ​​of the corresponding trajectory points of the hyperchaotic system in each step of the numerical solution process; Step 1.3, construct the wavelet transform matrix Ψ, sparse the plaintext privacy image PI of size M×N, and after threshold TS detection, set the values ​​in the wavelet transform matrix that are less than the threshold to zero, and obtain the matrix P1 P spare =Ψ·PI·Ψ T Among them, P spare represents a sparse matrix, T represents transpose; Step 1.4, concatenate the random sequences X and Y into a sequence XY, then sort XY in ascending order to obtain a sequence XY′, and finally divide the sequence XY′ into two halves to obtain sequences AN1 and AN2 for Arnold scrambling; Step 1.5, use sequences AN1 and AN2 to perform Arnold matrix scrambling on matrix P1 to obtain matrix P2 Step 1.6, set the step size d, and use the piecewise linear mapping to construct the measurement matrix Φ∈R with the initial state {x′0, p′0, y′0, p1′} CN×M Among them, CN = CR × N, CR is the compression ratio; Step 1.7, compress the matrix P2 through the measurement matrix to obtain the matrix P3 P3 = Φ × P2; Step 1.8, quantize the matrix P3 to obtain the compressed matrix P4 3. The method according to claim 2, characterized in that The step 2 specifically includes: Step 2.1, determine the labels of the bit planes in the bit plane reordering according to the chaotic random sequence Z, and use Z1(L a ) is the first element, according to L a Select eight random numbers in sequence Z1 for step size i k =mod(10×k×L a ,length(Z1)) Step 2.2, distribute LABLE to the eight bit planes of the compression matrix P4 in sequence, and then perform the bit plane reordering method according to a preset method to obtain the matrix P5; Step 2.3: construct a new chaotic sequence W′={w1,w2,...,w CN×M }, and then use the sequence W′ to diffuse the matrix P5 according to the preset formula to obtain the ciphertext data P6 P6=mod(P5(i,j)+W′(i,j),256).

4. The method according to claim 3, characterized in that The step 3 specifically includes: Step 3.1, perform SURF corner point detection on the carrier image HI, and divide the carrier image HI into corner point areas and non-corner point areas; Step 3.2, embed the preset capacity of the ciphertext data P6 into the four least significant bits of the corner point area, and embed the remaining data of the ciphertext data P6 into the two least significant bits of the non-corner point area to obtain the visually secure encrypted image SI.

5. The method according to claim 4, characterized in that The step 4 specifically includes: Step 4.1, use the SURF algorithm to detect corners of the carrier image HI, and then extract data of different capacities from different regions to form ciphertext data P6; Step 4.2, with the initial state as {x0, p0, y0, p1}, the trajectory of the hyperchaotic system is numerically solved using the fourth- and fifth-order Runge-Kutta methods to obtain four random chaotic sequences X, Y, W, and Z; Step 4.3: construct a new chaotic sequence W′={w1,w2,...,w CN×M }, then perform inverse diffusion on the matrix P6 to obtain the matrix P5 P5=mod(P6(i,j)-W′(i,j),256); Step 4.4, determine the labels of the bit planes in the bit plane reordering according to the chaotic random sequence Z, and use Z1(L a ) is the first element, according to L a Select eight random numbers in sequence Z1 for the step size to obtain LABLE, and then perform inverse bit plane reordering on matrix P5 to obtain compressed matrix P4; Step 4.5: Use piecewise linear mapping to construct the measurement matrix Φ∈R with the initial state {x′0, p′0, y′0, p1′} CN×M ; Step 4.6, perform inverse quantization on the compressed matrix P4 to obtain the matrix P3 Among them, max and min are the maximum and minimum values ​​of the compressed matrix P4 respectively; Step 4.7, reconstruct the matrix P3 using the smoothed l0 norm algorithm to obtain the matrix P2; Step 4.8, use the random sequences X and Y to perform inverse Arnold matrix scrambling and inverse wavelet transform on the matrix P2 to obtain the decrypted plaintext privacy image PI.

Images