A method for information camouflage and restoration of terrain lines

Abstract

The present invention provides a method for information camouflage and restoration of terrain lines. The camouflage method includes: Step 1: For the real DEM data H, extract n real terrain lines, perform spatial representation on the n real terrain lines to obtain the coefficient matrix of the n real terrain lines; Step 2: Scramble the coefficient matrix, perform spatial representation on the scrambled coefficient matrix to obtain the corresponding n camouflaged terrain lines; Step 3: Determine the influence area of the terrain feature points according to the n camouflaged terrain lines, and camouflage the influence area; Step 4: Divide the real DEM data H into blocks, and then embed the elements of the camouflage parameter matrix of the n real terrain lines into the corresponding data sub-blocks according to the constructed synchronization function to obtain the camouflaged DEM data; the camouflage parameter matrix refers to the matrix composed of the scrambled coefficients of each real terrain line and the position coordinates of the corresponding head and tail points after camouflage.

Description

Technical Field

[0001] The present invention relates to the technical field of geographic data protection, and particularly to a method for information camouflage and restoration of terrain lines. Background Art

[0002] In big geographic data, DEM data of most regions need to be shared, and only high-precision data of some key regions need to be protected, that is, points, lines, and planes that are particularly significant for morphological description of the terrain surface are protected, and they constitute the main terrain features of DEM. Terrain lines are the most important type of linear terrain feature, and the most important ones are ridge lines and valley lines. By camouflaging some important terrain features, important information can be protected from being leaked, and the normal use of data in other regions can also be ensured. Summary of the Invention

[0003] In order to protect the security of important DEM data and ensure that important geographic information is not leaked, the present invention provides a method for information camouflage and restoration of terrain lines.

[0004] On the one hand, the present invention provides a method for information camouflage of terrain lines, including:

[0005] Step 1: For the real DEM data H, extract n real terrain lines, perform spatial representation on the n real terrain lines to obtain a coefficient matrix of the n real terrain lines;

[0006] Step 2: Scramble the coefficient matrix, perform spatial representation on the scrambled coefficient matrix to obtain the corresponding n camouflaged terrain lines;

[0007] Step 3: Determine the influence area of terrain feature points according to the n camouflaged terrain lines, and camouflage the influence area;

[0008] Step 4: Divide the real DEM data H into blocks, and then embed the elements of the camouflage parameter matrix of the n real terrain lines into the corresponding data sub-blocks according to the constructed synchronization function to obtain camouflaged DEM data; the camouflage parameter matrix refers to a matrix composed of the scrambled coefficients of each real terrain line and the position coordinates of the corresponding head and tail points after camouflage.

[0009] Further, step 1 specifically includes: performing spatial representation on each terrain line to be camouflaged to obtain the coefficient of the terrain line to be camouflaged; using the coefficient of each terrain line to be camouflaged as a row or a column of the coefficient matrix, so as to obtain a coefficient matrix composed of the coefficients of the n terrain lines to be camouflaged;

[0010] Wherein, the spatial representation process of each terrain line to be camouflaged specifically includes:

[0011] The ground line is represented by the intersection line of the ground surface feature plane passing through the feature points on the ground line and the vertical plane where the projection curve formed by all the feature points lies;

[0012] Select a quadratic curve to fit the ground surface feature plane passing through the feature points on the ground line to obtain the first set of fitting coefficients; select a cubic curve to fit the vertical plane where the projection curve formed by all the feature points lies to obtain the second set of fitting coefficients;

[0013] The first set of fitting coefficients and the second set of fitting coefficients together constitute the coefficients of this ground line to be camouflaged.

[0014] Furthermore, the method further includes: determining whether the fitting results of the ground surface feature plane passing through the feature points on the ground line and the fitting results of the vertical plane where the projection curve formed by all the feature points lies are available, specifically including:

[0015] Calculate the error corresponding to the data point P i ,y i ,z i ) with the position coordinates (x i ) according to formula (3):

[0016]

[0017] where v 1i represents the error corresponding to the quadratic curve, and v 2i represents the error corresponding to the cubic curve;

[0018] Calculate the sum of residuals and and obtain the corresponding first set of fitting coefficients A = {a 0 ,a 1 ,a 2 ,a 3 ,a 4 ,a 5}, the second set of fitting coefficients B = {b 0 ,b 1 ,b 2 ,b 3}, and m represents the number of terrain feature points;

[0019] Normalize the sum of residuals V 1 and V 2 according to formula (4) to obtain the corresponding normalized values and

[0020]

[0021] If and Then the first set of fitting coefficients A = {a 0 , a 1 , a 2 , a 3 , a 4 , a 5} obtained by the current fitting and the second set of fitting coefficients B = {b 0 , b 1 , b 2 , b 3} are available; otherwise, refitting is performed. Among them, D 1 and D 2 are set thresholds.

[0022] Furthermore, step 3 specifically includes:

[0023] Set the influence area of the terrain feature point O at position (a, b) in the elevation matrix as the spatial range D with a grid size of (2d + 1) × (2d + 1); where d is the number of grid cells of the influence radius.

[0024] Calculate the influence coefficient k ij of the terrain feature point O on the grid point at position (i, j) within the spatial range D according to formula (5). The influence coefficients of the terrain feature point O on all grid points within the spatial range D form the spatial influence coefficient matrix of the terrain feature point O.

[0025]

[0026] According to the elevation difference h ab before and after the disguise of the terrain feature point O, the influence coefficient k ij and the true elevation value H ij of the grid point at position (i, j), calculate the new elevation H ij ' of the grid point at position (i, j) after disguise according to formula (6) to achieve the disguise of the current grid point.

[0027] H ij ' = H ij + k ij ·h ab (6).

[0028] Furthermore, step 4 specifically includes:

[0029] Set the true DEM data H to be divided into n 1 × n 2 blocks, and set the total number of elements N of the disguise parameter matrix. Then, the construction of the synchronization function f(s, t) representing the correspondence between the data sub-block at position (s, t) and any element in the disguise parameter matrix should follow the following three principles simultaneously:

[0030] A1: The number of data sub - blocks should be much larger than the number of elements in the camouflage parameter matrix;

[0031] A2: The value range of f(s,t) should include [1,N];

[0032] A3: For any element in the camouflage parameter matrix, there should be at least one data sub - block (i,j) corresponding to it, that is, f(s,t) must be surjective, 1 ≤ s ≤ n 1 , 1 ≤ t ≤ n 2 .

[0033] Furthermore, step 4 specifically includes:

[0034] Step B1: Generate the embedding information sequence, specifically including: convert the element of the camouflage parameter matrix to be embedded into an integer, and then calculate the binary number W = {w l |l = 1,2,3…k}; where, determine the value of k according to the longest binary number among all elements of the camouflage parameter matrix, and fill 0 in front of the binary numbers of other values to form the k - bit embedding information sequence corresponding to the current element of the camouflage parameter matrix to be embedded;

[0035] Step B2: Select carrier coefficients, specifically including: according to the calculation result of the synchronization function, perform a DWT transform on the data sub - block corresponding to the current element of the camouflage parameter matrix, and select the largest k data from the low - frequency coefficients as carrier coefficients according to the corresponding relationship;

[0036] It should be noted that when several low - frequency coefficients are equal in magnitude, select the low - frequency coefficients with the earlier positions.

[0037] Step B3: Embed the k - bit embedding information sequence to form a wavelet coefficient set; specifically including: when the l - th embedding information w l in the sequence is 1, calculate the even - multiple value of the closest K 0 to b, and use this even - multiple value of K 0 to replace b; when the l - th embedding information w l in the sequence is 0, calculate the odd - multiple value of the closest K 0 to b, and use this odd - multiple value of K 0 to replace b; where, b is the carrier coefficient and K 0 is the base embedding information;

[0038] Step B4: Perform an inverse transform on the formed wavelet coefficient set to obtain the final camouflaged data sub - block. After all elements of the camouflage parameter matrix are embedded, synthesize all the camouflaged data sub - blocks to obtain the final camouflaged DEM data.

[0039] On the other hand, the present invention provides a method for recovering the information of the terrain line, which is applied to the above-mentioned information camouflage method of the terrain line. The method includes:

[0040] Step 1: Extract the camouflage parameter matrix of the terrain line from the camouflaged DEM data according to the hidden key;

[0041] Step 2: Decompose the camouflage parameter matrix into the set of the start and end point coordinate positions of the terrain line and the camouflage coefficient matrix, and perform inverse scrambling on the camouflage coefficient matrix by using the scrambling key to obtain the real coefficient matrix of the terrain line;

[0042] Step 3: Recover the real DEM data according to the real coefficient matrix of the terrain line and the coordinate positions of the start and end points of the terrain line.

[0043] Further, Step 1 specifically includes:

[0044] Step 1.1: Divide the camouflaged DEM data into blocks, establish a one-to-many correspondence between each data sub-block and the elements of the camouflage parameter matrix according to the constructed synchronization function, and form a set of candidate data sub-blocks corresponding to the elements of the camouflage parameter matrix;

[0045] Step 1.2: Calculate and compare the average slopes of the data sub-blocks in the set of candidate data sub-blocks corresponding to the elements of the camouflage parameter matrix, and determine the one-to-one correspondence between the data sub-blocks and the elements of the camouflage parameter matrix;

[0046] Step 1.3: Perform a DWT transform on the data sub-block corresponding to each element of the camouflage parameter matrix once, select the largest k coefficients in the low-frequency coefficients to extract the embedded information contained in the data sub-block. After extracting all the embedded information, convert the information sequence composed of all the embedded information into a decimal number, and this decimal number is the element of the camouflage parameter matrix extracted;

[0047]

[0048] where, d l represents the wavelet coefficient, w l represents the l-th embedded information hidden in the data sub-block, l = 1, 2, 3... k.

[0049] Further, Step 3 specifically includes:

[0050] Step 3.1: Determine the grid points passed by the terrain line according to the two spatial surfaces formed by the coefficients of each terrain line and their corresponding start and end point coordinate positions;

[0051] Step 3.2: Calculate the elevation value of each grid point according to the coordinate value of the grid point, and obtain the elevation change value;

[0052] Step 3.3: Calculate the influence coefficient k of the grid points with the position of (i, j) within the spatial range D of (2d + 1) × (2d + 1), where d represents the number of grids of the influence radius. ij , d represents the number of grids of the influence radius;

[0053] Step 3.4: Taking the feature points on the terrain line as the center, restore their original elevations according to the current elevation values of the grid points themselves and their influence coefficients.

[0054] Advantages of the present invention:

[0055] (1) The present invention performs spatial representation on multiple terrain lines extracted from the original DEM data, and then scrambles the values of each coefficient in the obtained coefficient matrix; and when scrambling the coefficient matrix formed by the spatial fitting of the terrain lines, the data volume is very small, and the scrambling speed can be almost ignored.

[0056] (2) Since the terrain features of the DEM data do not exist in isolation, camouflaging the information of the feature points on the terrain line will affect other grid points in the surrounding area. Therefore, the present invention further synchronously camouflages the influence area of the terrain feature points on the terrain line, thereby further enhancing the data confusion and ensuring the security of the DEM data.

[0057] (3) In the present invention, the camouflage parameter matrix is an essential element for restoring the camouflaged data. It needs to be transmitted to the data receiver together with the camouflaged DEM data. By hiding it in the camouflaged DEM data for transmission, the difficulty of transmitting the key data can be greatly reduced, and the security of the algorithm is enhanced. Description of the drawings

[0058] Figure 1 It is a schematic flowchart of a method for information camouflage of terrain lines provided by an embodiment of the present invention;

[0059] Figure 2 It is a schematic diagram of the surface fitting result of the projection line of the terrain line on the XOY plane provided by an embodiment of the present invention;

[0060] Figure 3 It is a schematic diagram for comparing the camouflage before and after the influence area of the terrain feature points provided by an embodiment of the present invention;

[0061] Figure 4 It is a schematic diagram for comparing the three-dimensional display of the original data and the restored data in the surrounding area of the starting point of terrain line 1 provided by an embodiment of the present invention. Detailed implementation manners

[0062] To make the objectives, technical solutions and advantages of the present invention clearer, the following will clearly describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0063] The terrain line is a line segment composed of several terrain feature points. Camouflaging the terrain line essentially aims to protect information by changing the manifestation form of the terrain line and its surrounding strip areas. The purpose of terrain line camouflage is to protect the linear terrain features with special significance and the surrounding areas on the premise of keeping the terrain undulation in other areas basically unchanged.

[0064] Embodiment 1

[0065] As Figure 1 shown, the embodiment of the present invention provides an information camouflage method for terrain lines, including the following steps:

[0066] S101: For the real DEM data H, extract n real terrain lines, perform spatial representation on the n real terrain lines, and obtain the coefficient matrix of the n real terrain lines;

[0067] Specifically, after determining the terrain line to be camouflaged, it is necessary to perform reasonable spatial representation on it. The terrain line is a spatial curve distributed in a three-dimensional environment, and the basis for fitting is to select two reasonable spatial surfaces. According to the principle of surface fitting, the selected surface does not require passing through all the terrain feature points on the terrain line completely, but should be able to reflect the overall change trend of the data and be as close to these points as possible. Considering the need to obtain an accurate mathematical expression, combined with the requirements of DEM information camouflage and the data organization form, the present invention selects the surface of the ground feature passing through the feature points on the terrain line and the intersection line of the vertical plane where the projection curve formed by all feature points is located for terrain line representation, that is

[0068]

[0069] Normally, the higher the order of the fitting surface is selected, the more accurately it can represent the actual terrain undulation characteristics and be closer to the real spatial surface, but the computational cost and execution efficiency consumption are also greater. According to the general expression law of terrain features, the terrain surface where the terrain line is located is usually relatively smooth, and there will be no sudden jumps between data. Therefore, in the embodiment of the present invention, a quadratic surface (such as in Equation (2)) is selected to fit the surface of the ground feature passing through the feature points on the terrain line, and the first set of fitting coefficients (such as a in Equation (2)) is obtained i(i=0,1,2,3,4,5)); select a cubic curve (such as in formula (2) ) fits the vertical plane where the projection curve formed by all feature points is located, and obtains the second set of fitting coefficients (b j (j=0,1,2,3));

[0070]

[0071] in, It represents the spatial surface that passes through the curve and is perpendicular to the XOY plane.

[0072] In order to ensure the maximum accuracy of the surface, the least square method is used for spatial surface fitting. Assuming there are m terrain feature points, 2m error equations can be formed. i The corresponding error equation is

[0073]

[0074] Calculate the sum of residuals and Solve the first set of fitting coefficients A = {a 0 ,a 1 ,a 2 ,a 3 ,a 4 ,a 5} and the second set of fitting coefficients B = {b 0 ,b 1 ,b 2 ,b 3}.

[0075] Generally speaking, the accuracy of curve fitting is much higher than that of surface fitting, so V 1 >V 2 In order to make all calculated V 1 and V 2 To be comparable, the naturalization process is as follows

[0076]

[0077] Distribute the fitting error on each feature point. Set the threshold D 1 and D 2 ,if and This indicates that both fitting surfaces are within a reasonable range. The first set of fitting coefficients obtained by the current fitting is A = {a 0 ,a 1 ,a 2 ,a 3 ,a 4 ,a 5}, the second set of fitting coefficients B = {b 0 , b 1 , b 2 , b 3}, is available; otherwise, it indicates that the fitting surface and the actual terrain differ greatly and cannot accurately express the characteristics of the terrain line, and the fitting result is discarded.

[0078] It should be noted that using a quadratic surface and a cubic curve is just a method of fitting and a surface. According to the actual situation, different spatial fitting methods can be selected to represent the terrain line, and different coefficient sets can be obtained.

[0079] Take the coefficients of each terrain line to be camouflaged as a row or a column of the coefficient matrix, so as to obtain a coefficient matrix composed of the coefficients of n terrain lines to be camouflaged.

[0080] For example, take the coefficients of each terrain line to be camouflaged as a row of the coefficient matrix, and obtain a coefficient matrix composed of the coefficients of n terrain lines to be camouflaged, as shown in the following matrix C.

[0081]

[0082] S102: Scramble the coefficient matrix, perform a spatial representation on the scrambled coefficient matrix, and obtain the corresponding n camouflaged terrain lines;

[0083] Specifically, by changing the values of the coefficients in the coefficient matrix, the purpose of information camouflage can be achieved. Moreover, by changing the magnitudes of the coefficients at various positions of different terrain lines, the camouflage processing of n terrain lines can be completed at one time, and the best camouflage effect can be achieved. There are mainly 3 types of camouflage processing methods for the coefficient matrix, namely position scrambling, value transformation, and their combination methods. In the embodiments of the present invention, the position scrambling method is selected for camouflage. When scrambling the coefficient matrix formed by the spatial fitting of the terrain line, the data volume is very small, and the scrambling speed can be almost ignored.

[0084] Use the terrain line coefficients in the scrambled coefficient matrix to replace the original coefficients of the spatial fitting curve. According to the geographical spatial position (x, y), the new elevation at each spatial point can be calculated to complete the information camouflage of the terrain line and obtain the corresponding n camouflaged terrain lines. At the same time, for the need of data restoration, record the scrambled coefficient matrix and the head and tail point positions of the terrain line, and transmit them to the authorized receiving party through a secure channel.

[0085] It should be noted that the security of the scrambling algorithm should be the key concern, and a more complex but highly secure algorithm can be selected according to the actual situation. Considering that the coefficients at different levels vary greatly, the result obtained from the spatial calculation after scrambling far exceeds the reasonable range. When taking the coefficients of each ground feature line to be camouflaged as a row of the coefficient matrix, it can be considered to perform scrambling only among the elements in the same column of the coefficient matrix; when taking the coefficients of each ground feature line to be camouflaged as a column of the coefficient matrix, it can be considered to perform scrambling only among the elements in the same row of the coefficient matrix.

[0086] S103: Determine the influence area of the terrain feature points according to n camouflaged ground feature lines, and camouflage the influence area;

[0087] Specifically, the terrain features of DEM data do not exist in isolation. Camouflaging the information of the feature points on the ground feature lines will affect other grid points in the surrounding area. The influence area of the terrain feature points refers to the associated area composed of all grid points that may be affected after the elevation value of the feature points changes. Due to the existence of spatial autocorrelation, in order to meet the requirement of data confusion, it is necessary to synchronously camouflage the influence area of the terrain feature points on the ground feature lines. The basic principle of performing information camouflage on the influence area is to change the elevation values of other grid points according to the elevation change of the terrain feature points before and after camouflage and the influence ability of the point on the surrounding area. The key is the quantification of the influence ability of the feature point change on the surrounding grid points.

[0088] As an implementable manner, set the influence area of the terrain feature point O at position (a, b) in the elevation matrix as the spatial range D with a grid size of (2d + 1) × (2d + 1); where d is the number of grid cells of the influence radius;

[0089] Calculate the influence coefficient k of the terrain feature point O on the grid point at position (i, j) within the spatial range D according to formula (5) ij , and the influence coefficients of the terrain feature point O on all grid points within the spatial range D form the spatial influence coefficient matrix of the terrain feature point O;

[0090]

[0091] It should be noted that the change in the elevation value of point O affects other grid points within the entire D area. The farther the distance, the smaller the influence. To ensure the seamless connection between D and the surrounding environment, the influence coefficient of this feature point on the outermost edge of D and the grid points adjacent to other data should be 0. To ensure that the data can be restored, if a certain point in the influence area is also a terrain feature point, no processing is performed, that is, the influence coefficient is directly assigned 0.

[0092] According to the elevation difference h of the terrain feature point O before and after camouflage ab , influence coefficient k ijand the true elevation value H of the grid point at position (i, j) ij , calculate the new elevation H after camouflage of the grid point at position (i, j) according to formula (6) ij ', to achieve the camouflage of the current grid point;

[0093] H ij ' = H ij + k ij ·h ab (6).

[0094] It should be noted that when the grid point is within the influence range of multiple terrain feature points, only the nearest camouflage process is considered.

[0095] S104: Divide the true DEM data H into blocks, and then embed the elements of the camouflage parameter matrix of n true terrain lines into the corresponding data sub-blocks according to the constructed synchronization function to obtain the camouflaged DEM data; the camouflage parameter matrix refers to the matrix composed of the scrambled coefficients of each true terrain line and the position coordinates of the corresponding head and tail points after camouflage.

[0096] Specifically, it can be seen from the above steps that there are 14 parameters such as the 4 coordinate values of the head and tail points corresponding to a single terrain line after camouflage and 10 fitting coefficients. When n terrain lines are camouflaged, they jointly form a matrix to be protected, called the camouflage parameter matrix. The camouflage parameter matrix is the basis for restoration and needs to be protected with emphasis. Its data volume is much smaller than the DEM data, but it is still not conducive to direct transmission. According to the idea of information hiding, embedding it in the camouflaged DEM data can greatly reduce the difficulty of transmission.

[0097] Traditional information hiding methods generally embed the information to be hidden into the data sub-blocks in the order of block appearance. The method is simple and it is easy for attackers to find the rules. If the corresponding relationship depending on the appearance order is broken and the matrix elements are seemingly randomly embedded into the data sub-blocks, the security of the algorithm will be greatly improved. The key is to construct the corresponding relationship between the data sub-blocks and the elements in the parameter matrix and establish a synchronization function between the two.

[0098] As an implementable manner, the embodiments of the present invention construct a synchronization function according to the following principles;

[0099] Set the true DEM data H to be divided into n 1 × n 2 blocks, and set the total number of elements N of the camouflage parameter matrix to be 14n. Then, the construction of the synchronization function f(s, t) representing the corresponding relationship between the data sub-block at position (s, t) and any element in the camouflage parameter matrix should simultaneously follow the following three principles:

[0100] A1: The number of data sub - blocks should be much larger than the number of elements in the camouflage parameter matrix; this is beneficial to increasing the screening range for selecting embedded sub - blocks by matrix elements.

[0101] A2: The value range of f(s,t) should include [1,N]; it should be noted that the more the value range of f(s,t) converges to [1,N], the better the stability of the synchronization function.

[0102] A3: Any element in the camouflage parameter matrix should have at least one corresponding data sub - block (i,j), that is, f(s,t) must be surjective, 1 ≤ s ≤ n 1 , 1 ≤ t ≤ n 2 , so as to ensure that each matrix element can find its corresponding data block.

[0103] It can be seen that the synchronization function in the embodiment of the present invention is easy to construct, and by adjusting the coefficients in the synchronization function, different corresponding relationships can be formed, greatly increasing the security of the algorithm. Since the designed synchronization function is a many - to - one relationship, one matrix element may correspond to multiple data sub - blocks.

[0104] It should be noted that data changes in flat areas are more likely to attract attention. Therefore, when embedding information, data sub - blocks with a larger average slope should be selected as much as possible for embedding camouflage elements.

[0105] After constructing the synchronization function, the next step is to embed the elements of the camouflage parameter matrix into the corresponding data sub - blocks. Compared with image data, DEM data represents actual elevation information and has higher requirements for data accuracy. The embedding of information should have as little impact on the original data as possible, and at the same time, blind extraction of information should be achievable. As an implementable way, the embodiment of the present invention adopts the idea of discrete wavelet transform and the optimal neighborhood method: first, determine the data sub - block corresponding to the element of the camouflage parameter matrix, convert it to the transform domain using DWT, and then hide the camouflage parameter matrix in its corresponding low - frequency coefficient. The specific method is as follows:

[0106] Step B1: Generate an embedded information sequence, specifically including: converting the element of the camouflage parameter matrix to be embedded into an integer and then calculating the binary number W = {w l |l = 1,2,3…k}; where, the value of k is determined according to the longest binary number among all elements of the camouflage parameter matrix, and other values are filled with 0 in front of their binary numbers to form the k - bit embedded information sequence corresponding to the current element of the camouflage parameter matrix to be embedded.

[0107] Step B2: Select the carrier coefficients, specifically including: According to the calculation result of the synchronization function, perform a DWT transformation on the data sub-block corresponding to the current element of the camouflage parameter matrix, and select the largest k data in the low-frequency coefficients as the carrier coefficients according to the corresponding relationship;

[0108] Step B3: Embed the k-bit information sequence to be embedded to form a wavelet coefficient set; specifically including: When the l-th bit of the sequence embeds the information w l is 1, calculate the even multiple value of K 0 nearest to b, and use this even multiple value of K 0 to replace b; when the l-th bit of the sequence embeds the information w l is 0, calculate the odd multiple value of K 0 nearest to b, and use this odd multiple value of K 0 to replace b; where b is the carrier coefficient and K 0 is the base embedding information;

[0109] Step B4: Perform an inverse transformation on the formed wavelet coefficient set to obtain the final camouflaged data sub-block. After all elements of the camouflage parameter matrix are embedded, synthesize all the camouflaged data sub-blocks to obtain the final camouflaged DEM data.

[0110] The camouflage parameter matrix is an essential element for restoring camouflaged data. It needs to be transmitted to the data receiver together with the camouflaged DEM data. Hiding it in the camouflaged DEM data for transmission can greatly reduce the difficulty of transmitting key data and enhance the security of the algorithm.

[0111] Embodiment 2

[0112] Corresponding to the information camouflage method of a terrain line in the above embodiment, the embodiment of the present invention provides an information restoration method for a terrain line. The restoration of DEM data and camouflage are inverse operations of each other. The information restoration method includes the following steps:

[0113] S201: Extract the camouflage parameter matrix of the terrain line from the camouflaged DEM data according to the hidden key; specifically including the following sub-steps:

[0114] S2011: Divide the camouflaged DEM data into blocks, establish a one-to-many correspondence between each data sub-block and the elements of the camouflage parameter matrix according to the constructed synchronization function, and form a candidate data sub-block set corresponding to the elements of the camouflage parameter matrix;

[0115] S2012: Calculate and compare the average slopes of the data sub-blocks in the candidate data sub-block set corresponding to the elements of the camouflage parameter matrix, and determine the one-to-one correspondence between the data sub-blocks and the elements of the camouflage parameter matrix;

[0116] S2013: Perform a DWT transformation on each data sub-block corresponding to the elements of the camouflage parameter matrix. Select the k largest coefficients in the low-frequency coefficients to extract the embedded information contained in the data sub-block. After extracting all the embedded information, convert the information sequence composed of all the embedded information into a decimal number, and this decimal number is the camouflage parameter matrix element extracted;

[0117]

[0118] where, d l represents the wavelet coefficient, and w l represents the l-th embedded information hidden in the data sub-block, where l = 1, 2, 3... k.

[0119] Finally, all the extracted camouflage parameter matrix elements form the camouflage parameter matrix.

[0120] S202: Decompose the camouflage parameter matrix into the set of the start and end point coordinate positions of the terrain lines and the camouflage coefficient matrix, and use the scrambling key to perform inverse scrambling on the camouflage coefficient matrix to obtain the true coefficient matrix of the terrain lines;

[0121] S203: Restore the true DEM data based on the true coefficient matrix of the terrain lines and the start and end point coordinates of the terrain lines; specifically, it includes the following sub-steps:

[0122] S2031: Determine the grid points passed by each terrain line according to the two spatial surfaces formed by the coefficients of each terrain line and their corresponding start and end point coordinate positions;

[0123] S2032: Calculate the elevation value of each grid point according to the coordinate values of the grid points to obtain the elevation change value;

[0124] S2033: Calculate the influence coefficient k ij of the grid point with the position of (i, j) in the (2d + 1) × (2d + 1) spatial range D, where d is the number of grid cells of the influence radius;

[0125] S2034: Taking the characteristic points on the terrain line as the center, restore the original elevation according to the current elevation value of the grid point itself and its influence coefficient;

[0126] Finally, for any terrain line, execute the above steps S2031 to S2034 until all the terrain lines in the true coefficient matrix are calculated, that is, the true DEM data is restored.

[0127] It can be understood that due to the loss of some precision in spatial fitting, there will be a certain error between the restored DEM data and the original data. As long as the fitting precision is reasonably controlled, it will not affect the normal use of the data. However, it is very difficult to effectively control the error of spatial fitting. Especially for the terrain lines composed of more terrain feature points, the spatial fitting effect often fails to reach the expected effect, resulting in a large error and affecting the camouflage effect. At this time, the method of dividing the long terrain line into multiple segments can be used to reduce the influence of fitting error. At the same time, for information camouflage with high precision requirements, the data residuals in the fitting process can be transmitted to the data receiver as auxiliary data together, which can greatly improve the accuracy of data restoration.

[0128] In order to verify the effectiveness of the information camouflage and restoration method of the terrain line provided by the present invention, the following experimental data are also provided by the present invention.

[0129] Select the DEM of some areas in Songshan area as experimental data, with a resolution of 90m, a maximum elevation of 1485m, and a minimum elevation of 85m. Use the method of the present invention to camouflage 4 terrain lines (2 ridge lines and 2 valley lines) among them.

[0130] I. Analysis of camouflage effect

[0131] (1) Fitting effect of terrain line

[0132] Select 4 terrain lines among them and use the method of hyperbolic surface intersection in formula (2) for fitting. Among them, the fitting results of the projection lines of the terrain lines are as Figure 2 shown.

[0133] Combined with the fitting results of the surface of the earth, record the coordinates of the start and end points of the 4 terrain lines and the obtained coefficient set as shown in Table 1.

[0134] Table 1 Spatial fitting coefficients of 4 selected terrain lines

[0135]

[0136]

[0137] In Table 1, V 1m and V 2m represent the maximum residuals of fitting. It can be found that the value of is much smaller than Assume that each V value in Table 1 meets the requirements.

[0138] (2) Influence on the camouflage effect of the area

[0139] Scramble the formed matrix C. Since the data in different columns of the coefficient matrix vary greatly while the data in the same column vary little, to ensure the rationality of the scrambling result, the method of shifting is used to permute the data within the same column of the coefficient matrix. Taking the topographic feature points on each ground line as the center, a 21×21 spatial range is selected as the influence area D of a single feature point, and the spatial influence coefficient matrix is calculated. According to the change in the elevation of the feature points on the ground line after the scrambling transformation and their original values, the camouflage result of this influence area can be obtained. Taking the topographic feature point (359, 608) as an example, the result is as Figure 3 shown.

[0140] Compare Figure 3 (a) and Figure 3 (b), it can be found that the contour lines in the area around this topographic feature point have changed significantly before and after camouflage, indicating that the expected camouflage effect has been achieved and the topographic features in this area have been changed; at the same time, compare Figure 3 (c) and Figure 3 (d) for the elevation grid change map of this area. The elevation change in this area is radial: the topographic feature point at the very center changes the most, and the influence becomes smaller and the elevation change also becomes smaller as the distance increases. The data at the edge of the area hardly changes, ensuring the continuity of the elevation change between this area and the adjacent areas and avoiding semantic fragmentation during data connection.

[0141] II. Data Difference Analysis

[0142] The difference between the topographic representations of two different types of DEM data is called the topographic difference degree, including two types: the camouflage difference degree and the restoration accuracy. The topographic difference degree between the camouflage data and the original data is also called the camouflage difference degree, which represents the difference between the camouflage data and the original real terrain and is an important indicator for judging whether important elevation information is completely hidden and reasonably protected. The restoration accuracy represents the difference between the restored data and the original data and is an important indicator for judging the usability of the data. The topographic difference degree can be represented by directly calculating the elevation difference between the corresponding camouflages of the two data, or by calculating the slope and aspect differences at the corresponding positions.

[0143] (1) Camouflage Difference Degree Analysis

[0144] According to the calculation method of the topographic difference degree of the camouflage data, the elevation differences and slope and aspect changes before and after camouflage of 4 ground lines and their influence areas are calculated respectively using the direct calculation method and the indirect calculation method, and the results are shown in Table 2.

[0145] Table 2 Elevation Difference Conditions before and after Camouflage

[0146]

[0147] In Table 2, the maximum differences in elevation among the four terrain line influence areas all come from the terrain feature points, and the minimum differences are all 0, coming from the edges of the influence areas. This is determined by the calculation method of the spatial autocorrelation coefficient. The transformation of grid points closer to the feature points becomes larger, and the change of grid points farther away becomes smaller until it is 0. All three indicators show that the terrain feature changes in the influence areas around the terrain lines before and after camouflage are obvious, meeting the requirements of DEM information camouflage for terrain difference. At the same time, based on the experimental data, it can also be obtained that the slope and aspect change violently around the terrain feature points, and the slope and aspect values of the grid points around the influence areas change relatively slowly, which is also determined by the characteristics of spatial autocorrelation.

[0148] (2) Analysis of restoration accuracy

[0149] Restore the camouflaged data according to the data restoration method. Taking the first feature point of terrain line 1 and its surrounding area as an example, the three-dimensional displays of the original data and the restored data can be obtained as Figure 4 shown.

[0150] Through comparative analysis Figure 4 of the two groups of data in

[0151] it can be found that although there is a certain error between the restored data and the original data at the same point, there is almost no difference in expressing the terrain features, and it can fully reflect the real terrain expressed by the original data. In this method, the error of data restoration has two parts: the error caused by data fitting and the error generated by the extraction of the coefficient matrix. Among them, the fitting error is the main error source, and the lossless hiding technology can reduce or even avoid the appearance of the second error. Restore the four camouflaged areas of the experimental data, and the error results are shown in Table 3.

[0152]

[0153]

[0154] Since there are overlapping parts among the influence areas of grid points, the number of grid points in the influence area is not equal to the sum of the products of each feature point on the terrain line and its influence points. Analyzing Table 3, the following conclusions can be obtained: (1) The error of the feature points on the terrain line is greater than that of the general grid points. This is because the expression obtained by data fitting cannot completely pass through each feature point, resulting in errors; while the camouflage basis of other grid points mainly comes from the change values before and after the feature points and the local influence coefficients, and their accurate values can be obtained through calculation; (2) Combining the values of V 1 and V 2 in Table 1, it can be found that the smaller the V value, the smaller the error of the terrain line and its influence area, indicating that the fitting accuracy is an important indicator affecting the data restoration accuracy.

[0155] It is also found that the camouflaged data obtained by curve fitting will cause relatively large errors during data restoration. Although it does not affect the general use of the data, it is not applicable to situations with special requirements for data accuracy. If it is corrected by transmitting the fitting error and the fitting error is transmitted to the legitimate user through a secure channel together, the restored data can be corrected, and the error results are shown in Table 4.

[0156] Table 4 Error analysis of the restoration of the combined residuals in 4 regions of the experimental data (unit: m)

[0157]

[0158] Comparing Table 3 and analyzing Table 4, the following conclusions can be obtained: The accuracy of restoration by combining the fitting residuals is much higher than that obtained by direct restoration, and the terrain feature points can be almost completely restored. This is because the data in Table 4 eliminates the most important fitting errors in Table 3, greatly optimizing the data restoration accuracy. In a sense, this is also a method for multi-scale restoration of DEM data with different access rights.

[0159] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An information camouflage method for terrain lines, characterized in that, it includes: Step 1: For the real DEM data H, extract n real terrain lines, perform spatial representation on the n real terrain lines, and obtain the coefficient matrix of the n real terrain lines; Step 2: Scramble the coefficient matrix, perform spatial representation on the scrambled coefficient matrix, and obtain the corresponding n camouflaged terrain lines; Step 3: Determine the influence area of the terrain feature points according to the n camouflaged terrain lines, and camouflage the influence area; specifically including: Set the influence area of the terrain feature point O at position (a, b) in the elevation matrix as the spatial range D with a grid size of (2d + 1)×(2d + 1); where d is the number of grid cells of the influence radius; Calculate the influence coefficient \(k\) of the terrain feature point \(O\) on the grid point at position \((i, j)\) within the spatial range \(D\) according to formula (5). ij The influence coefficients of the terrain feature point \(O\) on all grid points within the spatial range \(D\) form the spatial influence coefficient matrix of the terrain feature point \(O\). According to the elevation difference h of the terrain feature point O before and after camouflage ab , influence coefficient k ij , and the true elevation value H of the grid point at position (i, j) ij , the new elevation H ij ' of the grid point at position (i, j) after camouflage is calculated according to formula (6) to achieve the camouflage of the current grid point; H ij ' = H ij + k ij · h ab (6); Step 4: Divide the real DEM data H into blocks, and then embed the elements of the camouflage parameter matrix of the n real terrain lines into the corresponding data sub-blocks according to the constructed synchronization function to obtain the camouflaged DEM data; the camouflage parameter matrix refers to the matrix composed of the scrambled coefficients of each real terrain line and the position coordinates of the corresponding head and tail points after camouflage; specifically including: Set the real DEM data H to be divided into n 1 ×n 2 blocks, and set the total number N of elements in the camouflage parameter matrix. Then, the construction of the synchronization function f(s, t) used to represent the correspondence between the data sub-block at position (s, t) and any element in the camouflage parameter matrix should follow the following three principles simultaneously: A1: The number of data sub-blocks should be much larger than the number of elements in the camouflage parameter matrix; A2: The value range of f(s, t) should include [1, N]; A3: Any element in the camouflage parameter matrix should have at least one corresponding data sub-block (i, j), that is, f(s, t) must be surjective, where 1 ≤ s ≤ n 1 , 1 ≤ t ≤ n 2 ; Step B1: Generate an embedded information sequence, specifically including: converting the elements of the camouflage parameter matrix to be embedded into integers and then calculating the binary number W = {w l | l = 1, 2, 3…k}; where the value of k is determined according to the longest binary number among all the elements of the camouflage parameter matrix, and other values are padded with 0s in front of their binary numbers to form a k-bit embedded information sequence corresponding to the current element of the camouflage parameter matrix to be embedded; Step B2: Select the carrier coefficients, specifically including: According to the calculation result of the synchronization function, perform a DWT transform on the data sub-block corresponding to the current camouflage parameter matrix element, and select the largest k data in the low-frequency coefficients as the carrier coefficients according to the corresponding relationship; when several low-frequency coefficients are equal in size, select the low-frequency coefficients with the earlier position; Step B3: Embed the k-bit information sequence to be embedded to form a wavelet coefficient set; specifically including: when the l-th bit of the sequence embeds information w l is 1, calculate the even multiple value of K 0 nearest to b, and use the even multiple value of this K 0 to replace b; when the l-th bit of the sequence embeds information w l is 0, calculate the odd multiple value of K 0 nearest to b, and use the odd multiple value of this K 0 to replace b; where b is the carrier coefficient and K 0 is the base embedding information. Step B4: Perform inverse transformation on the formed wavelet coefficient set to obtain the final camouflaged data sub-block. After all the elements of the camouflage parameter matrix are embedded, synthesize all the camouflaged data sub-blocks to obtain the final camouflaged DEM data.

2. An information camouflage method for terrain lines according to claim 1, characterized in that, Step 1 specifically includes: Perform spatial representation on each terrain line to be camouflaged to obtain the coefficient of this terrain line to be camouflaged; Use the coefficient of each terrain line to be camouflaged as a row or a column of the coefficient matrix, so as to obtain the coefficient matrix composed of the coefficients of n terrain lines to be camouflaged; Among them, the spatial representation process of each terrain line to be camouflaged specifically includes: Use the intersection line of the surface feature plane passing through the feature points on the terrain line and the vertical plane where the projection curve formed by all feature points is located to represent the terrain line; Select a quadratic surface to fit the surface feature plane passing through the feature points on the terrain line to obtain the first set of fitting coefficients; Select a cubic curve to fit the vertical plane where the projection curve formed by all feature points is located to obtain the second set of fitting coefficients; The first set of fitting coefficients and the second set of fitting coefficients together form the coefficient of this terrain line to be camouflaged.

3. An information camouflage method for terrain lines according to claim 2, characterized in that, it further includes: Judge whether the fitting results of the surface feature plane passing through the feature points on the terrain line and the vertical plane where the projection curve formed by all feature points is located are available, specifically including: Calculate the error corresponding to the data point P i , y i , z i ) with the position coordinates (x i as follows: Among them, v 1i represents the error corresponding to the quadratic surface, and v 2i represents the error corresponding to the cubic curve; The number A = {a 0 , a 1 , a 2 , a 3 , a 4 , a 5}, the second set of fitting coefficients B = {b 0 , b 1 , b 2 , b 3}, where m represents the number of terrain feature points; For the sum of residuals V 1 and V 2 perform normalization processing according to formula (4) to obtain the corresponding normalized values and If and then the first set of fitting coefficients A = {a 0 , a 1 , a 2 , a 3 , a 4 , a 5} obtained by the current fitting and the second set of fitting coefficients B = {b 0 , b 1 , b 2 , b 3} are available; otherwise, refitting is performed again. Among them, D 1 and D 2 are set thresholds.

4. A method for information recovery of terrain lines, applied to the information camouflage method of a terrain line according to any one of claims 1 to 3, characterized in that, the method includes: Step 1: Extract the camouflage parameter matrix of the terrain line from the camouflaged DEM data according to the hidden key; Step 2: Decompose the camouflage parameter matrix into the set of coordinate positions of the start and end points of the terrain line and the camouflage coefficient matrix, and perform inverse scrambling on the camouflage coefficient matrix using the scrambling key to obtain the true coefficient matrix of the terrain line; Step 3: Restore the true DEM data according to the true coefficient matrix of the terrain line and the coordinate positions of the start and end points of the terrain line.

5. The method for information recovery of a terrain line according to claim 4, characterized in that, Step 1 specifically includes: Step 1.1: Divide the camouflaged DEM data into blocks, and establish a one-to-many correspondence between each data sub-block and the elements of the camouflage parameter matrix according to the constructed synchronization function, forming a candidate data sub-block set corresponding to the elements of the camouflage parameter matrix; Step 1.2: Calculate and compare the average slopes of the data sub-blocks in the candidate data sub-block set corresponding to the elements of the camouflage parameter matrix, and determine the one-to-one correspondence between the data sub-blocks and the elements of the camouflage parameter matrix; Step 1.3: Perform a DWT transform on the data sub-block corresponding to each element of the camouflage parameter matrix once, select the largest k coefficients in the low-frequency coefficients to extract the embedded information contained in the data sub-block, and after extracting all the embedded information, convert the information sequence composed of all the embedded information into a decimal number, and this decimal number is the element of the camouflage parameter matrix extracted; Among them, d l represents the wavelet coefficient, w l represents the l-th embedded information hidden in the data sub-block, where l = 1, 2, 3... k.

6. The method for information recovery of a terrain line according to claim 4, characterized in that, Step 3 specifically includes: Step 3.1: Determine the grid points passed by the terrain line according to the two spatial surfaces formed by the coefficients of each terrain line and their corresponding start and end point coordinate positions; Step 3.2: Calculate the elevation value of each grid point according to the coordinate value of the grid point to obtain the elevation change value; Step 3.3: Calculate the influence coefficient k of the grid points with the position of (i, j) among the feature point pairs within the spatial range D of (2d + 1) × (2d + 1), where d is the number of grid cells of the influence radius. ij , where d is the number of grid cells of the influence radius. Step 3.4: Centered on the characteristic points on the terrain line, restore the original elevation according to the current elevation value of the grid point itself and its influence coefficient.

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