An improved cross-coupled mapping lattice-based complementary embedded image encryption method

Abstract

The application provides a complementary embedding image encryption method based on an improved cross-coupled mapping lattice, which comprises the following steps: establishing an improved sine cross-coupled mapping lattice model; based on a plaintext image, generating a secret key by SHA-384 and an original image, and calculating model parameters and initial values according to the secret key; detecting the plaintext image, identifying and obtaining airport coordinates, randomly selecting a coordinate point, and obtaining a visual confusion image through a complementary embedding algorithm; iterating the established sine cross-coupled mapping lattice model to obtain a matrix, converting the matrix into a one-bit array, taking the last rxc data to obtain b; distributing an initial matrix and iterating to an appropriate size to obtain a fractal disorder matrix; selecting the first r rows and the first c columns to form a one-dimensional array, sorting the one-dimensional array to obtain an index array; performing synchronous dynamic scrambling and diffusion operations on three channels R, G and B in a color image respectively to obtain a sub-encrypted image corresponding to each channel. The sub-encrypted images are assembled into a final encrypted image C.

Description

Technical Field

[0001] This invention relates to the field of image encryption technology, and more particularly to a complementary embedding image encryption method based on an improved cross-coupled mapping lattice. Background Technology

[0002] With the development of information transmission technology, digital images, as a form of information transmission, are widely used in military, commercial, medical, and social fields. To protect image information, image security has become a hot research topic for many scholars. Commonly used image protection methods include data hiding, image hashing, image steganography, and image encryption. Image encryption mainly hides information by changing pixels with salient features into random pixels. This encryption method is easy to implement and has high scalability. Especially with the development of chaos theory, chaos-based image encryption algorithms have become one of the research hotspots.

[0003] Spatiotemporal chaotic systems simultaneously generate pseudo-random sequences in both time and space. Therefore, compared to lower-order chaotic systems, they exhibit more complex dynamic behaviors and chaotic properties. Kaneko first proposed the Coupled Lattice Model (CML), pioneering the spatiotemporal chaotic model. Meherzi et al. proposed a synchronous and unpredictable spatiotemporal chaotic system (SCCML). Zhou et al. combined PWLCML and sin hybrid spatiotemporal chaotic systems to propose a two-dimensional hybrid coupled PS mapping (2DMCPM). Liu et al. introduced a tent mapping, proposing a coupled chaotic mapping lattice called (CML-UD). However, the aforementioned spatiotemporal chaotic systems lack dynamic lattice linkages; the spatial iteration pattern remains constant with the time series; the parameter range is narrow, and they cannot fully meet cryptographic requirements.

[0004] Image encryption strategies are crucial to the security of encrypted images, and many scholars have proposed different encryption strategies. Neto et al. proposed an encryption method for multi-parameter fractional-theoretical transforms, in which the image is encrypted by constructing a number-theoretical transform feature basis with selected parameters; Kang et al. proposed a realism-preserving multi-parameter fractional Hartley transform, in which the preprocessed image is transferred to the transform domain and further encrypted by a non-adjacent coupled mapping grid system; Xian et al. defined a biparameter fractal sorting matrix with iteration, self-similarity, and better periodicity, and combined it with a logical chaotic system for image encryption. However, these strategies apply to the entire image and perform the same encryption operation on important and unimportant regions, lacking unique protection for important information. Wang et al. proposed a private image encryption algorithm that ensures the security of important information by doubly encrypting the identity of important facial information.

[0005] In summary, existing image encryption methods have the following problems:

[0006] Existing spatiotemporal chaotic systems lack dynamic lattice connections, and the spatial iteration mode remains unchanged with the time series. The parameter range is narrow and cannot fully meet cryptographic requirements, posing a threat to the security of image encryption.

[0007] Most existing image encryption schemes operate on the entire image, performing the same encryption operation on important and unimportant areas, lacking unique protection for important information. Summary of the Invention

[0008] To address the aforementioned technical problems, this invention provides a complementary embedding image encryption method based on an improved cross-coupled mapping lattice. The invention establishes an improved sinusoidal cross-coupled mapping lattice (ISCCML) to enhance cryptographic features, including: establishing a fractal disorder matrix (FDM) for scrambling image positions; and proposing a complementary embedding encryption strategy. This strategy first identifies airport regions, then replaces the best similar regions, and embeds the airport image into random positions using a complementary embedding algorithm, thereby generating a visually confusing image to protect important information.

[0009] The technical means employed in this invention are as follows:

[0010] A complementary embedding image encryption method based on an improved cross-coupled mapping lattice includes:

[0011] An improved sinusoidal cross-coupled mapping lattice model is established;

[0012] Detect the plaintext image P, identify and obtain the airport coordinates (x1, y1, w, h), randomly select a coordinate point (x2, y2) and obtain the visually confusing image p_com through the complementarity embedding algorithm;

[0013] Based on the plaintext image P, a key K is generated from SHA-384 and the original image, and model parameters and initial values ​​are calculated based on the key K.

[0014] The improved sinusoidal cross-coupled mapping lattice model is iterated to obtain matrix B. Matrix B is then converted into a one-dimensional array and the last r×c data points are taken to obtain b.

[0015] Assign initial matrix A 1 Iterate to an appropriate size to obtain a fractal disordered matrix Am; select the first r rows and the first c columns to form a one-dimensional array, and sort the one-dimensional array to obtain an index array s;

[0016] Synchronous dynamic scrambling and diffusion operations are performed on the three channels R, G and B in the color image to obtain sub-encrypted images R_c, G_c and B_c;

[0017] The sub-encrypted images R_c, G_c, and B_c are assembled into the final encrypted image C.

[0018] Furthermore, the establishment of the improved sinusoidal cross-coupled mapping lattice model specifically involves:

[0019]

[0020] Among them, the control parameters μ and Within the range [1.132, +∞); for even cells, x n+1 Depending on the non-adjacent lattices of the previous iteration, the boundary conditions are p = L when p = 0 and q = L when q = 0; for odd-numbered lattices, the dynamic coupling coefficient and boundary conditions are i – 1 = L when i = 1 and i + 1 = 1 when I = L; in the above equation, h(x) represents the improved one-dimensional sinusoidal chaotic mapping, x represents the chaotic sequence of the mapping, μ represents the parameters of the mapping, mod1 represents the modulo 1 operation, i, p, and q represent different lattices, j and k are positive integers, modL represents the modulo L operation, x n+1 (i) represents the spatiotemporal chaotic sequence generated by the i-th cell, x n (i) represents the value of the i-th cell when the current time series is n, e represents the parameters of the model, and x n (p) represents the value of the p-th cell when the current time series is n, x n (q) represents the value of the q-th cell when the current time series is n, e n This represents the dynamic parameters of the model, x. n+1 (i-1) represents the value of the (i-1)th cell when the current time series is n+1, x n+1 (i+1) represents the value of the (i+1)th cell when the current time series is n+1.

[0021] Further, the process of detecting the plaintext image P, identifying and obtaining the airport coordinates (x1, y1, w, h), randomly selecting a coordinate point (x2, y2), and obtaining a visually confusing image p_com through a complementary embedding algorithm specifically includes:

[0022] Identify line segments in the image, estimate airport location using vision-oriented saliency (VOS) and knowledge-oriented saliency (KOS), and locate airport outline using SOACM.

[0023] After locating the airport's position information, complementary regions are found by comparing the 2-norm of the airport and sub-blocks. The optimal region is then transplanted to the airport region for visual blurring, and the airport image is embedded into random positions within the image.

[0024] Furthermore, for the plaintext image P, a key K is generated from SHA-384 and the original image, and model parameters and initial values ​​are calculated based on K. Specifically, it includes:

[0025] Given a plaintext image P of size r×c, generate a key K from the SHA-384 and the original image;

[0026] Calculate the following model parameters and initial values ​​based on the generated key K:

[0027]

[0028] In the above formula, avg_P represents the average value of image P, sum(P) represents the sum of pixel values ​​of image P, r represents the number of rows, and c represents the number of columns;

[0029] B = dec2bin(avg_P)

[0030] In the above formula, dec2bin(avg_P) means converting avg_P to binary B;

[0031]

[0032] In the above formula, K i B1-B8 represent the i-th bit of the key K, and B bits represent the B bits. This indicates an XOR operation. K1-K8 represent the 1st to 8th bits of the key K. x1 represents the x-coordinate of the airport, and x2 represents the x-coordinate of the random embedding position.

[0033]

[0034] In the above formula, K 21 -K 28 This represents the 21st to 28th bits of the key K, where y1 represents the ordinate of the airport and y2 represents the ordinate of the random embedding position.

[0035]

[0036] In the above formula, K 41 -K 48 This represents bits 41-48 of the key K, and w represents the width of the airport.

[0037]

[0038]

[0039] In the above formula, K 141 -K 148 This represents bits 141-148 of the key K, and h represents the altitude of the airport.

[0040] Let m = 1, and execute the following formula x m (0), let m = m + 1, and then execute the following formula xm (0), formula x m (0) was executed 8 times, formula x m (0) is as follows:

[0041]

[0042] In the above formula, Represents the system parameters, x m-1 This represents the value of the (m-1)th iteration;

[0043]

[0044] In the above formula, This represents the value at position (1,1) of the initial fractal disordered matrix A;

[0045]

[0046] In the above formula, This represents the value at position (1,2) of the initial fractal disordered matrix A;

[0047]

[0048] In the above formula, This represents the value at position (2,1) of the initial fractal disordered matrix A;

[0049]

[0050] In the above formula, This represents the value at position (2,2) of the initial fractal disordered matrix A;

[0051]

[0052] Furthermore, the iterative process of the established improved sinusoidal cross-coupled mapping lattice model to obtain matrix B, converting matrix B into a one-dimensional array and taking the last r×c data to obtain b, specifically includes:

[0053] Set lattice = 8, iterate ISCCML(w×h)+(r×c) / 8 times to obtain matrix B;

[0054] Convert B to a one-dimensional array and select the last r×c data as b.

[0055] Furthermore, the step of performing synchronous dynamic scrambling and diffusion operations on the three channels R, G, and B in the color image to obtain sub-encrypted images R_c, G_c, and B_c specifically includes:

[0056] A synchronous dynamic scrambling and diffusion operation is performed on channel R of the color image to obtain the sub-encrypted image R_c, as follows:

[0057]

[0058] A synchronous dynamic scrambling and diffusion operation is performed on channel G in the color image to obtain the sub-encrypted image G_c, as follows:

[0059]

[0060] A synchronous dynamic scrambling and diffusion operation is performed on channel B of the color image to obtain a sub-encrypted image B_c, as follows:

[0061]

[0062] Compared with the prior art, the present invention has the following advantages:

[0063] 1. The complementary embedding image encryption method based on the improved cross-coupled mapping lattice provided by the present invention, by establishing an improved cross-coupled mapping lattice, changes the dynamic system, expands the parameter range, and changes the coupling mode and model structure to weaken the correlation between adjacent lattices in the phase space, reduces the chaotic window period, and is more in line with cryptographic characteristics.

[0064] 2. The complementary embedding image encryption method based on the improved cross-coupled mapping lattice provided by the present invention detects the image, identifies the important information of the image, and provides unique protection for the important information.

[0065] 3. The complementary embedding image encryption method based on the improved cross-coupled mapping lattice provided by this invention utilizes the features of remote sensing images to find the region with the best similarity to airport information, transplants the region to the airport location and embeds the airport information into a random location, thereby generating a complementary embedding image with visual confusion, effectively protecting the security of important information.

[0066] 4. The complementary embedding image encryption method based on the improved cross-coupled mapping lattice provided by this invention establishes a fractal disordered matrix, which effectively improves the scrambling degree of pixel positions due to its inherent disorder.

[0067] 5. The complementary embedding image encryption method based on the improved cross-coupled mapping lattice provided by this invention has better dynamic behavior in the designed model while ensuring the security of privacy information. It has important value for application in engineering fields such as image encryption. Moreover, the model is beneficial for the demonstration and teaching of chaotic phenomena.

[0068] In summary, this invention proposes a complementary embedded image encryption strategy for critical airport information. In remote sensing images, topographical similarity features near the airport are utilized to generate a visually confusing image to conceal airport information while embedding it within the image. For the encryption process, FDM is used for synchronous scrambling during the diffusion stage. In the proposed strategy, airport information is embedded at random locations, generating a visually confusing complementary embedded image. This image hides the airport's location and shape before encryption, ensuring the security of critical information while avoiding double encryption.

[0069] Based on the above reasons, this invention can be widely applied in fields such as image encryption. Attached Figure Description

[0070] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0071] Figure 1 This is a flowchart of the present invention.

[0072] Figure 2 This is an application scenario provided by an embodiment of the present invention.

[0073] Figure 3 The Kolmogorov-Sinai entropy analysis diagram provided for embodiments of the present invention.

[0074] Figure 3 In Chinese: (a) h in CCML; (b) hu in CCML; (c) h in ISCCML; (d) hu in ISCCML;

[0075] Figure 4 The bifurcation diagrams of CCML and ISCCML under different parameters provided in the embodiments of the present invention.

[0076] Figure 4 In the diagram: (a) Bifurcation plot of the CCML system with parameter e = 0.0067; (b) Bifurcation plot of the CCML system with parameter e = 0.87; (c) Bifurcation plot of the ISCCML system with parameter e = 0.0067; (d) Bifurcation plot of the ISCCML system with parameter e = 0.87; (e) Bifurcation plot of the ISCCML system in the interval [0, 3.5] with parameter e = 0.017; (f) Bifurcation plot of the ISCCML system in the interval [4, 10] with parameter e = 0.91.

[0077] Figure 5 This is a diagram illustrating the airport positioning process provided in an embodiment of the present invention.

[0078] Figure 5 In the middle: (a) detected line segments; (b) binary saliency map; (c) location of airport;

[0079] Figure 6 Examples of complementary embedding algorithms provided in embodiments of the present invention.

[0080] Figure 7 The simulation results are shown in the figure provided for the embodiments of the present invention. Detailed Implementation

[0081] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0082] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the present invention or its application or use. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0083] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0084] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of the invention. It should also be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following figures denote similar items; therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0085] In the description of this invention, it should be understood that the orientation or positional relationship indicated by directional terms such as "front, back, up, down, left, right", "horizontal, vertical, horizontal" and "top, bottom" is generally based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing this invention and simplifying the description. Unless otherwise stated, these directional terms do not indicate or imply that the device or element referred to must have a specific orientation or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on the scope of protection of this invention. The directional terms "inner" and "outer" refer to the inner and outer contours relative to the outline of each component itself.

[0086] For ease of description, spatial relative terms such as "above," "over," "on the upper surface of," "above," etc., are used herein to describe the spatial positional relationship of a device or feature as shown in the figures to other devices or features. It should be understood that spatial relative terms are intended to encompass different orientations in use or operation besides the orientation of the device as described in the figures. For example, if the device in the figures is inverted, a device described as "above" or "above" other devices or structures would subsequently be positioned as "below" or "under" other devices or structures. Thus, the exemplary term "above" can include both "above" and "below." The device may also be positioned in other different ways (rotated 90 degrees or in other orientations), and the spatial relative descriptions used herein will be interpreted accordingly.

[0087] Furthermore, it should be noted that the use of terms such as "first" and "second" to define components is merely for the purpose of distinguishing the corresponding components. Unless otherwise stated, the above terms have no special meaning and therefore should not be construed as limiting the scope of protection of this invention.

[0088] like Figure 1As shown, this invention provides a complementary embedding image encryption method based on an improved cross-coupled mapping lattice, comprising:

[0089] S1. Establish an improved sinusoidal cross-coupled mapping lattice model;

[0090] S2. Detect the plaintext image P, identify and obtain the airport coordinates (x1, y1, w, h), randomly select a coordinate point (x2, y2) and obtain the visually confusing image p_com through the complementary embedding algorithm;

[0091] S3. Based on the plaintext image P, generate a key K from the SHA-384 and the original image, and calculate the model parameters and initial values ​​based on the key K.

[0092] S4. Iterate the established improved sinusoidal cross-coupled mapping lattice model to obtain matrix B. Convert matrix B into a one-dimensional array and take the last r×c data to obtain b.

[0093] S5. Allocate the initial matrix A 1 Iterate to an appropriate size to obtain a fractal disordered matrix Am; select the first r rows and the first c columns to form a one-dimensional array, and sort the one-dimensional array to obtain an index array s;

[0094] S6. Perform synchronous dynamic scrambling and diffusion operations on the three channels R, G and B in the color image respectively to obtain sub-encrypted images R_c, G_c and B_c.

[0095] S7. Assemble the sub-encrypted images R_c, G_c and B_c into the final encrypted image C.

[0096] In a specific implementation, as a preferred embodiment of the present invention, step S1 involves establishing an improved sinusoidal cross-coupled mapping lattice model, specifically as follows:

[0097]

[0098] Among them, the control parameters μ and Within the range [1.132, +∞); for even cells, x n+1 Depending on the non-adjacent lattices of the previous iteration, the boundary conditions are p = L when p = 0 and q = L when q = 0; for odd-numbered lattices, the dynamic coupling coefficient and boundary conditions are i – 1 = L when i = 1 and i + 1 = 1 when I = L; in the above equation, h(x) represents the improved one-dimensional sinusoidal chaotic mapping, x represents the chaotic sequence of the mapping, μ represents the parameters of the mapping, mod1 represents the modulo 1 operation, i, p, and q represent different lattices, j and k are positive integers, modL represents the modulo L operation, x n+1 (i) represents the spatiotemporal chaotic sequence generated by the i-th cell, x n(i) represents the value of the i-th cell when the current time series is n, e represents the parameters of the model, and x n (p) represents the value of the p-th cell when the current time series is n, x n (q) represents the value of the q-th cell when the current time series is n, e n This represents the dynamic parameters of the model, x. n+1 (i-1) represents the value of the (i-1)th cell when the current time series is n+1, x n+1 (i+1) represents the value of the (i+1)th cell when the current time series is n+1.

[0099] In this model, the Logistic map and e are replaced by an improved one-dimensional sinusoidal map (I1DS), ensuring that ISCCML has a more stable chaotic state. At the structural level, changing the iterative dependency value of even-numbered lattices to non-adjacent lattices improves the local lattice chaos of CCML, uniformly dispersing energy between lattices and enhancing the security of cryptographic applications. The chaotic characteristics of ISCCML are analyzed experimentally and theoretically, as follows:

[0100] Figure 3 These are the test results from the KSE (Kolmogorov-Sinai entropy). Figure 3 In (a) and 3(b), some parameter pairs (μ, e) have values ​​of h (μ ≤ 3.84 and e ≤ 0.15 or e ≥ 0.05 and μ ≤ 3.26) and hu tending towards 0, which means that the current lattice exhibits weak or no chaos. Specifically, in CCML, the average value of h is 0.110796 (see...). Figure 3 (a) Only 4.01% of the parameter pairs cause the lattice to exhibit chaotic behavior (see [reference]). Figure 3 (b)). And in Figure 3 In (c)-(d), for ISCCML, the average value of h reaches 5.4656, and 99.86% of the parameter pairs cause lattice chaos. For example... Figure 2 As shown, the sender differs from traditional encryption schemes. First, the airport region is identified, and then the best-matching image is searched for replacement. Simultaneously, the airport region is embedded at a random location to generate a visually confusing image. Finally, the proposed encryption algorithm is used for channel encryption and transmission. The receiver extracts and reconstructs the airport region after decrypting the image. Therefore, the analysis of KSE shows that introducing a non-adjacent crossover model and dynamic system h(x) into ISCCML not only compensates for the lattice defects of CCML and expands the chaotic range, but also significantly improves the chaotic performance.

[0101] In this embodiment, the bifurcation diagram is drawn using the 68th grid. For CCML, in Figure 4In (a), it can be observed that periodic windows and weak chaos occur at 3.57 ≤ μ ≤ 4. When e = 0.87, the chaotic range extends to 4.291 ≤ μ ≤ 4.946 (see...). Figure 4 (b)). The chaotic range of CCML changes dynamically with parameter e, which is not conducive to practical applications. For ISCCML, model modification and dynamic system selection enhance the chaotic properties, increase the chaotic range, and reduce the periodic window (see...). Figure 4 (c)-(f)).

[0102] In a specific implementation, as a preferred embodiment of the present invention, step S2 involves detecting the plaintext image P, identifying and obtaining the airport coordinates (x1, y1, w, h), randomly selecting a coordinate point (x2, y2), and obtaining the visually confusing image p_com through a complementary embedding algorithm. Specifically, this includes:

[0103] The process involves identifying line segments in an image, estimating airport locations using vision-oriented saliency (VOS) and knowledge-oriented saliency (KOS), and locating the airport outline using SOACM. This process is as follows: Figure 5 As shown.

[0104] After locating the airport's position information, complementary regions are found by comparing the 2-norm of the airport and sub-blocks. The optimal region is then transplanted to the airport area for visual blurring, embedding the airport image into a random location within the image. In this embodiment, a complementary embedding algorithm is used to implement this process, as illustrated in the table below. Figure 6 This is a use case description of the algorithm.

[0105]

[0106] In a preferred embodiment of the present invention, in step S3, for the plaintext image P, a key K is generated from SHA-384 and the original image, and model parameters and initial values ​​are calculated based on K. Specifically, it includes:

[0107] Given a plaintext image P of size r×c, generate a key K from the SHA-384 and the original image;

[0108] Calculate the following model parameters and initial values ​​based on the generated key K:

[0109]

[0110] In the above formula, avg_P represents the average value of image P, sum(P) represents the sum of pixel values ​​of image P, r represents the number of rows, and c represents the number of columns;

[0111] B = dec2bin(avg_P)

[0112] In the above formula, dec2bin(avg_P) means converting avg_P to binary B;

[0113]

[0114] In the above formula, K i B1-B8 represent the i-th bit of the key K, and B bits represent the B bits. This indicates an XOR operation. K1-K8 represent the 1st to 8th bits of the key K. x1 represents the x-coordinate of the airport, and x2 represents the x-coordinate of the random embedding position.

[0115]

[0116] In the above formula, K 21 -K 28 This represents the 21st to 28th bits of the key K, where y1 represents the ordinate of the airport and y2 represents the ordinate of the random embedding position.

[0117]

[0118] In the above formula, K 41 -K 48 This represents bits 41-48 of the key K, and w represents the width of the airport.

[0119]

[0120] In the above formula, K 141 -K 148 This represents bits 141-148 of the key K, and h represents the altitude of the airport.

[0121] Let m = 1, and execute the following formula x m (0), let m = m + 1, and then execute the following formula x m (0), formula x m (0) was executed 8 times, formula x m (0) is as follows:

[0122]

[0123] In the above formula, Represents the system parameters, x m-1 This represents the value of the (m-1)th iteration;

[0124]

[0125] In the above formula, This represents the value at position (1,1) of the initial fractal disordered matrix A;

[0126]

[0127] In the above formula, This represents the value at position (1,2) of the initial fractal disordered matrix A;

[0128]

[0129] In the above formula, This represents the value at position (2,1) of the initial fractal disordered matrix A;

[0130]

[0131] In the above formula, This represents the value at position (2,2) of the initial fractal disordered matrix A;

[0132]

[0133] In a specific implementation, as a preferred embodiment of the present invention, step S4 involves iterating the established improved sinusoidal cross-coupled mapping lattice model to obtain matrix B, converting matrix B into a one-dimensional array, and taking the last r×c data to obtain b. Specifically, this includes:

[0134] Set lattice = 8, iterate ISCCML(w×h)+(r×c) / 8 times to obtain matrix B;

[0135] Convert B to a one-dimensional array and select the last r×c data as b.

[0136] In a specific implementation, as a preferred embodiment of the present invention, in step S5, the initial matrix A is allocated. 1 Iterate to an appropriate size to obtain the fractal disordered matrix Am; select the first r rows and the first c columns to form a one-dimensional array, and sort the one-dimensional array to obtain the index array s; specifically including:

[0137] Establish a fractal disordered matrix. In this embodiment, a 2nd-order square matrix is ​​used as an example. Construct a fractal disordered matrix A * The iterative method is as follows: The iteration steps are as follows:

[0138] Step 1: It is a 4th order square matrix, consisting of A 1 Iteration.

[0139]

[0140] In the above formula, A i,j This represents the sub-block located at (i, j) in A.

[0141] Step 2: Obtain the 4th order fractal disordered matrix A using the following equation. 2* :

[0142]

[0143] A 2* (x,y)=A 2 (i,j)(i=1,2and j=1,2).

[0144] Step 3: Derive the initial m through iteration n Fractal disordered matrix generated by a square matrix The derivation process is as follows:

[0145]

[0146]

[0147]

[0148]

[0149] In a specific implementation, as a preferred embodiment of the present invention, step S6 involves performing synchronous dynamic scrambling and diffusion operations on the three channels R, G, and B in the color image to obtain sub-encrypted images R_c, G_c, and B_c, specifically including:

[0150] A synchronous dynamic scrambling and diffusion operation is performed on channel R of the color image to obtain the sub-encrypted image R_c, as follows:

[0151]

[0152] A synchronous dynamic scrambling and diffusion operation is performed on channel G in the color image to obtain the sub-encrypted image G_c, as follows:

[0153]

[0154] A synchronous dynamic scrambling and diffusion operation is performed on channel B of the color image to obtain a sub-encrypted image B_c, as follows:

[0155]

[0156] Finally, the sub-encrypted images R_c, G_c, and B_c are assembled into the final encrypted image C. Simulation results are as follows: Figure 7 As shown.

[0157] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A complementary embedding image encryption method based on an improved cross-coupled mapping lattice, characterized in that, include: An improved sinusoidal cross-coupled mapping lattice model is established, specifically as follows: Among them, control parameters μ and φ In scope Inside; for even-numbered cells, x n+1 The boundary conditions depend on the non-adjacent cells from the previous iteration. p = 0 p = L , q = 0 q = L For odd-numbered lattices, the dynamic coupling coefficient and boundary conditions are: i =1 i -1 = L , I = L hour i +1 = 1; In the above formula, This represents an improved one-dimensional sinusoidal chaotic mapping. This represents a chaotic sequence of maps. The parameters representing this mapping, This indicates the modulo 1 operation. , and To represent different cells, j and k It is a positive integer. Indicates modulo L The operation, Indicates the first i The spatiotemporal chaotic sequence generated by each grid This indicates that the current time series is n Time i The value of each cell. This represents the parameters of the model. This indicates that the current time series is n Time p The value of each cell. This indicates that the current time series is n Time q The value of each cell. This represents the dynamic parameters of the model. This indicates that the current time series is n +1 hour i The value of one cell. This indicates that the current time series is n +1 hour i +1 cell value; Detect plaintext images P Identify and obtain airport coordinates ( x 1, y 1, w , h ), randomly select a coordinate point ( x 2, y 2) Obtain visually confusing images through complementary embedding algorithms. p _ com ; Based on plaintext images P Composed of SHA-384 and plaintext images P Generate key K And according to the key K Calculate model parameters and initial values Specifically, it includes: Input size is r×c plaintext images P Composed of SHA-384 and plaintext images P Generate key K ; Based on the generated key K Calculate the following model parameters and initial values: In the above formula, Representing an image P The average value, Representing an image P The sum of pixel values, Indicates the row number. Indicates the column number; In the above formula, 2 Indicates will Convert to binary ; In the above formula, Indicates the key K The i 1 bit - Indicated Bit, This indicates the XOR operation. - Indicates the key K The first 8 bits, Represents the x-coordinate of the airport. Represents the x-coordinate of the random embedding position; In the above formula, - Indicates the key K Bits 21-28 Represents the ordinate of the airport. Represents the ordinate of the random embedding position; In the above formula, - Indicates the key K Bits 41-48 Indicates the width of the airport; In the above formula, - Indicates the key K Bits 141-148 Indicates the altitude of the airport; Let m=1, and execute the following formula. Let m = m + 1, and then execute the following formula. ,formula The formula was executed 8 times. as follows: In the above formula, Represents system parameters, Indicates the first m Value of the first iteration; In the above formula, Represents the initial fractal disordered matrix A The value at position (1, 1); In the above formula, Represents the initial fractal disordered matrix A The value at position (1, 2); In the above formula, Represents the initial fractal disordered matrix A The value at position (2, 1); In the above formula, Represents the initial fractal disordered matrix A The value at position (2, 2); The improved sinusoidal cross-coupled mapping lattice model is iterated to obtain the matrix. B , matrix B Convert to a one-dimensional array and take the last element. r×c Data obtained b ; Assigning initial matrix A 1 Iterate to an appropriate size to obtain a fractal disordered matrix. Am Before selection r line and front c The columns form a one-dimensional array, and the one-dimensional array is sorted to obtain an index array. s ; Synchronous dynamic scrambling and diffusion operations are performed on the three channels R, G, and B of the color image to obtain a sub-encrypted image. R_ c , G_c and B_c Specifically, it includes: A synchronous dynamic scrambling and diffusion operation is performed on channel R of the color image to obtain a sub-encrypted image. R_c ,as follows: A synchronous dynamic scrambling and diffusion operation is performed on channel G of the color image to obtain a sub-encrypted image. G_c ,as follows: A synchronous dynamic scrambling and diffusion operation is performed on channel B of the color image to obtain a sub-encrypted image. B_c ,as follows: Encrypt sub-images R_c , G_c and B_c Assemble into the final encrypted image C .

2. The image encryption method based on the improved cross-coupled mapping lattice with complementary embedding as described in claim 1, characterized in that, The detected plaintext image P Identify and obtain airport coordinates ( x 1, y 1, w , h ), randomly select a coordinate point ( x 2, y 2) Obtain visually confusing images through complementary embedding algorithms. p _ com Specifically, it includes: Identify line segments in the image, estimate airport location using vision-oriented saliency (VOS) and knowledge-oriented saliency (KOS), and locate airport outline using SOACM. After locating the airport's position information, complementary regions are found by comparing the 2-norm of the airport and sub-blocks. The optimal region is then transplanted to the airport region for visual blurring, and the airport image is embedded into random positions within the image.

3. The image encryption method based on the improved cross-coupled mapping lattice with complementary embedding as described in claim 1, characterized in that, The improved sinusoidal cross-coupled mapping lattice model is iterated to obtain the matrix. B , will matrix B Convert to a one-dimensional array and take the last element. r×c Data obtained b Specifically, it includes: Set lattice = 8, iterate ISCCML (w×h) + (r×c) / 8 times to obtain matrix B; Convert B to a one-dimensional array and select the last r×c data as b.

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