A tamper detection method for land type map patch data

Abstract

The application is suitable for the technical field of geographic information security, and provides a tampering detection method for land class map patch data, which comprises the following steps: extracting unique identification codes of all map patches from the land class map patch data to be detected, and performing identification code comparison on the land class map patch data to be detected; when the identification code comparison is passed, obtaining attribute field signatures of the land class map patch data to be detected, and performing attribute signature comparison on the land class map patch data to be detected; when the attribute signature comparison is passed, obtaining geometric invariant signatures of the land class map patch data to be detected, and performing geometric shape detection on the land class map patch data to be detected; when the geometric shape detection is passed, obtaining grid encoding signatures of the land class map patch data to be detected, performing grid encoding detection on the land class map patch data to be detected, and outputting a tampering detection result. The application can effectively reduce the false positive rate without modifying the original data.

Description

Technical Field

[0001] This application belongs to the field of geographic information security technology, and in particular relates to a method for detecting tampering of land parcel data. Background Technology

[0002] Land parcel data is a core outcome of land surveys, and its authenticity and integrity are crucial for natural resource management, land spatial planning, and farmland protection. During distribution, sharing, and application, this data often undergoes various legal technical processing steps, including file format conversion, coordinate system projection transformation, and topological tolerance processing. How to verify whether the geometric shape and attribute information have been tampered with while allowing these legal operations is a key issue in ensuring the security of land parcel data.

[0003] Existing tamper detection schemes for land parcel data mainly fall into the following two categories: (I) Tampering Detection Method Based on File Hash Existing methods typically employ cryptographic hash algorithms to verify the entire data file or geometric binary sequence. The basic idea is to calculate a hash value for the original data and save it as a "digital fingerprint." During detection, the hash value of the data to be checked is recalculated, and the two are compared to determine whether the data has been tampered with.

[0004] The drawback of this type of scheme is that legitimate operations such as file format conversion, coordinate projection transformation, and topology tolerance processing can cause changes in the underlying binary representation or coordinate values, resulting in drastic changes in the hash value. Legitimate operations may be falsely reported as tampering, which limits the application of data in actual business.

[0005] (II) Tampering Detection Method Based on Digital Watermarking Another type of method uses digital watermarking technology to detect tampering by embedding fragile or semi-fragile watermark information into vector map data. The basic idea is to embed authentication information into a specific location of the data coordinates (such as modifying the least significant bit of the coordinates), and then extract the watermark during detection and compare it with the original watermark to determine whether the data has been tampered with.

[0006] The disadvantages of this type of scheme are: (1) the watermark information needs to be embedded in the coordinate data, which causes irreversible modification to the original data; (2) geometric operations such as projection transformation, coordinate translation and rotation may change the coordinate values, making it difficult to extract the watermark information correctly.

[0007] It is evident that current methods for detecting land parcel data tampering suffer from high false alarm rates and the need to modify the original data. Summary of the Invention

[0008] This application provides a method for detecting tampering of land parcel data, which can solve the problems of high false alarm rate and the need to modify the original data in detecting tampering of land parcel data.

[0009] This application provides a method for detecting tampering with land parcel data, including: Extract the unique identifier code of all land parcels from the land parcel data to be detected, and compare the identifier codes of the land parcel data to be detected based on the extracted unique identifier codes; When comparing the land parcel data to be detected using the identification code, the attribute field signature of the land parcel data to be detected is obtained, and the attribute signature comparison of the land parcel data to be detected is performed based on the attribute field signature; the attribute field signature is used as a digital fingerprint to describe the key attribute information of the land parcel in the land parcel data to be detected. When comparing the attribute signatures of the land parcels to be detected, the geometric invariant signature of the land parcels to be detected is obtained, and the geometric morphology of the land parcels to be detected is detected based on the geometric invariant signature; the geometric invariant signature is used to describe the shape features of the land parcels in the land parcels to be detected. When the land parcel data to be detected undergoes geometric morphology detection, the grid-coded signature of the land parcel data to be detected is obtained. Based on the grid-coded signature, the land parcel data to be detected is subjected to grid-coded detection, and the tampering detection result is output. The tampering detection result is used to indicate whether the land parcel data to be detected has been tampered with, and the grid-coded signature is used to describe the relative spatial location of the land parcel in the land parcel data to be detected within the village-level administrative region to which it belongs.

[0010] Optionally, the signature library pre-stores a set of original unique identifiers corresponding to the land cover patch data to be detected. The set of original unique identifiers includes the original unique identifier of each patch in the land cover patch data to be detected.

[0011] Optionally, the identifier code of the land parcel data to be detected is compared based on the extracted unique identifier code, including: All extracted unique identifiers are used as the set of unique identifiers to be inspected; Perform a difference operation between the set of unique identifiers to be checked and the original set of unique identifiers to obtain the difference set; If the difference set is empty, then the land parcel data to be detected is determined to be compared using the identification code; If the difference set is non-empty, the tampered patches in the land use patch data to be detected are determined according to the difference set, and the tampering detection result is output to indicate that the land use patch data to be detected has been tampered with. The tampering detection result contains the original unique identifier of the tampered patch.

[0012] Optionally, obtain the attribute field signatures of the land parcel data to be detected, including... Obtain the key attribute fields of each land parcel in the land parcel data to be detected; The key attribute fields of all land parcels in the land parcel data to be detected are sorted according to the preset sorting rules to obtain the attribute strings of the land parcel data to be detected. The attribute string is calculated using a hash algorithm, and the calculation result is used as the attribute field signature of the land parcel data to be detected.

[0013] Optionally, the signature database also pre-stores the original attribute field signatures corresponding to the land cover patch data to be detected; attribute signature comparison is performed on the land cover patch data to be detected based on the attribute field signatures, including: Compare the attribute field signature with the original attribute field signature; If the attribute field signature matches the original attribute field signature, then the land parcel data to be detected is determined to have passed the attribute signature comparison. If the attribute field signature and the original attribute field signature are inconsistent, the tampered land parcels in the land parcel data to be detected are determined based on the comparison result of the attribute field signature and the original attribute field signature, and the tampering detection result is output to indicate that the land parcel data to be detected has been tampered with. The tampering detection result contains the original unique identifier of the tampered land parcel.

[0014] Optionally, obtain the geometric invariant signature of the land parcel data to be detected, including: For each land parcel in the data to be detected, the area of ​​the parcel and the area of ​​the minimum convex hull of the parcel are obtained, and the ratio of the area of ​​the parcel to the area of ​​the minimum convex hull is used as the normalized area ratio of the parcel. For each land parcel in the data to be detected, the perimeter of the parcel is calculated, and the ratio of the square of the perimeter to the area of ​​the parcel is used as the shape compactness of the parcel. According to the preset sorting rules, the normalized area ratio and shape compactness of each land parcel in the land parcel data to be detected are sorted to obtain the geometric string of the land parcel data to be detected. A hash algorithm is used to calculate the geometric string, and the calculation result is used as the geometric invariant signature of the land parcel data to be detected.

[0015] Optionally, the signature library also pre-stores the original geometric invariant signatures corresponding to the land cover patch data to be detected; geometric morphology detection is performed on the land cover patch data to be detected based on the geometric invariant signatures, including: Compare the geometric invariant signature with the original geometric invariant signature; If the geometric invariant signature is consistent with the original geometric invariant signature, then the land parcel data to be detected is determined to have passed the geometric morphology detection. If the geometric invariant signature and the original geometric invariant signature are inconsistent, the tampered patches in the land use patch data to be detected are determined based on the comparison result between the geometric invariant signature and the original geometric invariant signature, and the tampering detection result is output to indicate that the land use patch data to be detected has been tampered with. The tampering detection result contains the original unique identifier of the tampered patch.

[0016] Optionally, obtain the grid-coded signature of the land parcel data to be detected, including: For each village-level administrative region in the area corresponding to the land parcel data to be detected, the centroids of all parcels corresponding to that village-level administrative region are extracted from the land parcel data to be detected. The minimum bounding rectangle of that village-level administrative region is used as the root node, and the minimum bounding rectangle is recursively divided into grids using a quadtree until the number of centroids in the divided grid cells is 1 or 0. The divided grid cells are then encoded according to a preset encoding rule to obtain the grid codes of all parcels corresponding to that village-level administrative region. The grid codes of all land parcels in the land parcel data to be detected are sorted according to the preset sorting rules to obtain the grid codes of the land parcel data to be detected. A hash algorithm is used to calculate the grid code of the land parcel data to be detected, and the calculation result is used as the grid code signature of the land parcel data to be detected.

[0017] Optionally, after encoding the divided grid cells according to preset encoding rules, the tamper detection method further includes: If the number of centroids in the resulting grid cell is 0, then discard that grid cell.

[0018] Optionally, the signature library also pre-stores the original grid-coded signatures corresponding to the land cover patch data to be detected; grid-coded detection of the land cover patch data to be detected is performed based on the grid-coded signatures, including: Compare the lattice-coded signature with the original lattice-coded signature; If the grid-coded signature matches the original grid-coded signature, the tamper detection result is output to indicate that the land parcel data to be detected has not been tampered with; If the grid-coded signature and the original grid-coded signature are inconsistent, the tampered patches in the land use patch data to be detected are determined based on the comparison results of the grid-coded signature and the original grid-coded signature, and the tampering detection result is output to indicate that the land use patch data to be detected has been tampered with. The tampering detection result contains the original unique identifier of the tampered patch.

[0019] The above-mentioned solution in this application has the following beneficial effects: In the embodiments of this application, the tampering detection result of the land use patch data is obtained by comparing the unique identifier of each patch in the patch data to be detected. After the identifier comparison is passed, attribute signature comparison is performed based on attribute field signature, geometric morphology detection is performed based on geometric invariant signature, and grid code detection is performed based on grid code signature. From the tampering detection process of this application, it can be seen that the tampering detection method provided by this application does not require modification of the original data. At the same time, since the unique identifier, attribute field signature, geometric invariant signature, and grid code signature will not change due to legitimate operations such as file format conversion, coordinate projection transformation, and topology tolerance processing, this application can effectively reduce the false alarm rate.

[0020] Other beneficial effects of this application will be described in detail in the following detailed description section. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 A flowchart illustrating a method for detecting tampering of land parcel data provided in an embodiment of this application. Detailed Implementation

[0023] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0024] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0025] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0026] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0027] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0028] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0029] To address the issues of high false alarm rates and the need to modify original data in current land use parcel data tampering detection methods, this application provides a tampering detection method for land use parcel data. This method compares the unique identifier of each parcel in the land use parcel data to the identifier code of the parcel to be detected. After the identifier code comparison, the method sequentially performs attribute signature comparison based on attribute field signatures, geometric morphology detection based on geometric invariant signatures, and grid code detection based on grid code signatures to obtain the tampering detection result of the land use parcel data. From the tampering detection process of this application, it can be seen that the tampering detection method provided by this application does not require modification of the original data. Furthermore, since the unique identifier, attribute field signature, geometric invariant signature, and grid code signature do not change due to legitimate operations such as file format conversion, coordinate projection transformation, and topology tolerance processing, this application can effectively reduce the false alarm rate.

[0030] The method for detecting tampering of land parcel data provided in this application will be illustrated below with reference to specific embodiments.

[0031] like Figure 1 As shown in the embodiments of this application, the method for detecting tampering of land parcel data includes the following steps: Step 11: Extract the unique identifier code of all land parcels from the land parcel data to be detected, and compare the identifier codes of the land parcel data to be detected based on the extracted unique identifier codes.

[0032] In related technologies, land use map data for relevant areas is generated periodically and stored in a database. When this data is needed (such as for land spatial planning, land use approval, etc.), it is necessary to first check whether the data has been tampered with to avoid affecting the results. The land use map data to be checked mentioned above is the land use map data that needs to be checked for tampering.

[0033] It is understandable that when storing land use parcel data, each parcel carries a corresponding unique identifier (BSM). Specifically, the signature library pre-stores a set of original unique identifiers corresponding to the land use parcel data to be detected. This set of original unique identifiers includes the original unique identifier of each parcel in the land use parcel data to be detected. For example, a specified field of the parcel can be used as the unique identifier of the parcel.

[0034] When it is necessary to detect tampering in the land parcel data to be inspected, a specified field of the parcel can be extracted and used as the unique identifier of the parcel.

[0035] In some embodiments of this application, the process of comparing the identifier codes of the land parcel data to be detected is as follows: all the extracted unique identifier codes are used as the set of unique identifier codes to be detected; Perform a difference operation between the set of unique identifiers to be inspected and the original set of unique identifiers to obtain the difference set. If the difference set is empty, it is determined that the land parcel data to be inspected has passed the identifier comparison. If the difference set is not empty, the tampered land parcels in the land parcel data to be inspected are determined according to the difference set, and the tampering detection result is output to indicate that the land parcel data to be inspected has been tampered with. The tampering detection result contains the original unique identifier of the tampered land parcel.

[0036] If the difference set is empty, it indicates that the land cover patch data to be detected has not been added, deleted, or tampered with. If the difference set is not empty, it indicates that the land cover patch data to be detected has been added or deleted. When the difference set is not empty, the unique identifiers that are different between the set of unique identifiers to be detected and the original unique identifier set can be determined first through the difference set. Then, the patches corresponding to the original unique identifiers that exist in the original unique identifier set but do not exist in the set of unique identifiers to be detected are taken as the tampered patches, and the tampering detection result is output to indicate that the land cover patch data to be detected has been tampered with. In practical applications, the location (i.e., the tampered patch) can be determined in GIS by "querying features by attribute".

[0037] Step 12: When the land parcel data to be detected is compared with the identification code, obtain the attribute field signature of the land parcel data to be detected, and perform attribute signature comparison on the land parcel data to be detected based on the attribute field signature.

[0038] The aforementioned attribute field signature is used to describe the digital fingerprint of key attribute information of the land parcels in the land parcel data to be detected.

[0039] In some embodiments of this application, the specific implementation of obtaining the attribute field signature of the land parcel data to be detected in step 12 above includes steps 12.1 to 12.3: Step 12.1: Obtain the key attribute fields of each land parcel in the land parcel data to be detected.

[0040] The aforementioned key attribute fields include, but are not limited to: Unique Identifier (BSM), Land Category Code (DLBM), Location Unit Code (ZLDWDM), and Patch Area (TBMJ). Generally, key attribute fields are specified fields within a patch (different key attribute fields correspond to different specified fields). Therefore, during tampering detection, each key attribute field can be obtained by extracting the specified fields from the patch.

[0041] Step 12.2: Sort the key attribute fields of all land parcels in the land parcel data to be detected according to the preset sorting rules to obtain the attribute strings of the land parcel data to be detected.

[0042] The aforementioned preset sorting rule can be as follows: after sorting all key attribute fields in lexicographical order by field name, concatenate them into an attribute string using a fixed separator. Specifically, according to the order of the land parcels in the list of land parcels to be detected, sort all the unique identifier codes of all parcels into a unique identifier code sequence {BSM}, sort all the land parcel code sequences into a land parcel code sequence {DLBM}, sort all the land parcel location unit codes into a land parcel location unit code sequence {ZLDWDM}, and sort all the land parcel areas into a land parcel area sequence {TBMJ}. Then, concatenate the unique identifier code sequence {BSM}, land parcel code sequence {DLBM}, land parcel location unit code sequence {ZLDWDM}, and land parcel area sequence {TBMJ} into the attribute string {BSM}|{DLBM}|{ZLDWDM}|{TBMJ}.

[0043] Step 12.3: Calculate the attribute string using a hash algorithm, and use the calculation result as the attribute field signature of the land parcel data to be detected.

[0044] Specifically, after obtaining the attribute string of the land parcel data to be detected, a hash algorithm (such as the SHA-256 hash algorithm) can be used to calculate the attribute string, and then the calculation result is used as the attribute field signature of the land parcel data to be detected.

[0045] In some embodiments of this application, the signature library also pre-stores the original attribute field signatures corresponding to the land cover patch data to be detected. It should be noted that the process of obtaining the original attribute field signature is the same as the process of obtaining the attribute field signature described above, the only difference being that the original attribute field signature is obtained based on the land cover patch data when the land cover patch data (i.e., the land cover patch data to which the original unique identifier set belongs) is initially generated.

[0046] In some embodiments of this application, the specific implementation of the attribute signature comparison of the land parcel data to be detected based on the attribute field signature in step 12 above is as follows: The attribute field signature is compared with the original attribute field signature. If the attribute field signature and the original attribute field signature are consistent, the land parcel data to be detected is determined to have passed the attribute signature comparison. If the attribute field signature and the original attribute field signature are inconsistent, the tampered land parcels in the land parcel data to be detected are determined based on the comparison result of the attribute field signature and the original attribute field signature, and the tampering detection result is output to indicate that the land parcel data to be detected has been tampered with. The tampering detection result contains the original unique identifier of the tampered land parcel.

[0047] If the attribute field signature matches the original attribute field signature, it indicates that the land parcel data to be detected has not been tampered with. If the attribute field signature does not match the original attribute field signature, it indicates that the land parcel data to be detected has been tampered with. When attribute tampering exists, the tampered parcels can be identified based on the order of the parcels when the attribute field signature was generated, and the tampering detection result indicating that the land parcel data to be detected has been tampered with can be output.

[0048] Step 13: When the land parcel data to be detected is compared with the attribute signature, the geometric invariant signature of the land parcel data to be detected is obtained, and the geometric morphology of the land parcel data to be detected is detected based on the geometric invariant signature.

[0049] The aforementioned geometric invariant signature is used to describe the shape characteristics of patches in the land parcel data to be detected.

[0050] In some embodiments of this application, the specific implementation of obtaining the geometric invariant signature of the land parcel data to be detected in step 13 above includes steps 13.1 to 13.4: Step 13.1: For each patch in the land cover data to be detected, obtain the area of ​​the patch and the area of ​​the minimum convex hull of the patch, and use the ratio of the area of ​​the patch to the area of ​​the minimum convex hull as the normalized area ratio of the patch.

[0051] It is understandable that the area of ​​a patch (i.e., the aforementioned patch area) is a specified field of the patch, so the area of ​​the patch can be obtained by reading the specified field. The minimum convex hull refers to the smallest convex polygon that encloses all the vertices of a single patch. Specifically, it can be obtained by the following algorithm steps: (1) Obtain the coordinate set of all vertices of the patch polygon; (2) Select the point with the smallest x-coordinate from the vertex set as the starting point; (3) Sort the remaining vertices according to the polar angle order; (4) Use the monotonic chain algorithm or Graham scan algorithm to construct the boundary of the convex hull in sequence, gradually remove the points that cause the boundary to be concave, and finally obtain the minimum convex polygon that encloses all vertices. In practical applications, the minimum convex hull and its area of ​​a patch can be directly obtained through the convex hull calculation function of the GIS platform. For example, in ArcPy, the minimum convex hull of a patch can be obtained by using the convexHull() method of the geometric object, and then its area can be obtained through the area attribute. convexHull() is a function used to calculate the minimum convex hull of a two-dimensional point set. When calculating the ratio of the area of ​​a patch (in square meters) to the area of ​​the smallest convex hull, the result is retained to 6 decimal places. The calculated normalized area ratio (NAR) eliminates the overall scaling effect and only reflects the degree of filling of the shape. It is not sensitive to projection transformation or scale changes.

[0052] Step 13.2: For each patch in the land cover data to be detected, calculate the perimeter of the patch and use the ratio of the square of the perimeter to the area of ​​the patch as the shape compactness of the patch.

[0053] Specifically, a feature can be viewed as the boundary of a polygon composed of several line segments connected end to end, with its perimeter equal to the sum of the lengths of all boundary line segments of the polygon. For a feature with n vertices... , , The perimeter of the polygon formed by the pattern is... The calculation formula is: ; in, The coordinates of the first vertex. The coordinates of the second vertex. For the first The coordinates of each vertex.

[0054] This involves calculating and summing the Euclidean distances between adjacent vertices, and finally adding the distance from the last vertex to the first vertex (closed edge). In practical applications, the perimeter of a polygon can be directly obtained through the geometric calculation function of a GIS platform. For example, in ArcGIS, the perimeter of a polygon feature can be obtained through the "Computational Geometry" dialog box or the "Add Geometric Attribute" tool; in Geodatabase, the value of the !shape.length! field can also be read directly.

[0055] When calculating the ratio of the square of the perimeter (in meters) to the area of ​​the patch, the result is rounded to 6 decimal places. The calculated shape compactness (SC) comprehensively reflects the complexity and compactness of the shape and remains stable for translation, rotation and linear scaling.

[0056] Step 13.3: Sort the normalized area ratio and shape compactness of each land parcel in the land parcel data to be detected according to the preset sorting rules to obtain the geometric string of the land parcel data to be detected.

[0057] The aforementioned preset sorting rule can be as follows: according to the order of the patches in the patch list of the land parcel data to be detected, sort all the normalized area ratios of the patches into a normalized area ratio sequence {NAR}, sort all the patch shape compactness into a shape compactness sequence {SC}, and then concatenate the normalized area ratio sequence {NAR} and the shape compactness sequence {SC} into a geometric string {NAR}|{SC}.

[0058] Step 13.4: Calculate the geometric string using a hash algorithm, and use the calculation result as the geometric invariant signature of the land parcel data to be detected.

[0059] Specifically, after obtaining the geometric string of the land parcel data to be detected, a hash algorithm (such as the SHA-256 hash algorithm) can be used to calculate the geometric string, and the calculation result is then used as the geometric invariant signature of the land parcel data to be detected. It is worth mentioning that this geometric invariant signature remains stable against legal operations such as format conversion and projection transformation, while being sensitive to substantial modifications to the shape of the parcel, such as boundary point movement and boundary deformation.

[0060] In some embodiments of this application, the signature library also pre-stores the original geometric invariant signatures corresponding to the land cover patch data to be detected. It should be noted that the process of obtaining the original geometric invariant signature is the same as the process of obtaining the geometric invariant signature described above, the only difference being that the original geometric invariant signature is obtained based on the land cover patch data when the land cover patch data (i.e., the land cover patch data to which the original unique identifier set belongs) is initially generated.

[0061] In some embodiments of this application, the specific implementation of the geometric morphology detection of the land use patch data to be detected based on the geometric invariant signature in step 13 above is as follows: the geometric invariant signature and the original geometric invariant signature are compared; if the geometric invariant signature and the original geometric invariant signature are consistent, it is determined that the land use patch data to be detected has passed the geometric morphology detection; if the geometric invariant signature and the original geometric invariant signature are inconsistent, the tampered patches in the land use patch data to be detected are determined according to the comparison result of the geometric invariant signature and the original geometric invariant signature, and the tampering detection result is output to indicate that the land use patch data to be detected has been tampered with, and the tampering detection result contains the original unique identifier of the tampered patch.

[0062] If the geometric invariant signature is consistent with the original geometric invariant signature, it indicates that the geometric shape of the land cover patch data to be detected has not changed. If the geometric invariant signature is inconsistent with the original geometric invariant signature, it indicates that the geometric shape of the land cover patch data to be detected has been tampered with. When geometric shape tampering occurs, the tampered patch can be identified based on the order of each patch when generating the geometric invariant signature, and the tampering detection result indicating that the land cover patch data to be detected has been tampered with can be output.

[0063] Step 14: When the land parcel data to be detected is subjected to geometric morphology detection, obtain the grid code signature of the land parcel data to be detected, perform grid code detection on the land parcel data to be detected based on the grid code signature, and output the tampering detection result.

[0064] The aforementioned tampering detection results are used to indicate whether the land parcel data to be detected has been tampered with, and the grid-coded signature is used to describe the relative spatial location of the land parcels in the land parcel data to be detected within their respective village-level administrative regions.

[0065] In some embodiments of this application, the specific implementation of obtaining the grid-coded signature of the land cover patch data to be detected in step 14 above includes steps 14.1 to 14.3: Step 14.1: For each village-level administrative region in the area corresponding to the land cover patch data to be detected, extract the centroids of all patches corresponding to that village-level administrative region from the land cover patch data to be detected. Using the smallest bounding rectangle of that village-level administrative region as the root node, use the quadtree recursive partitioning method to divide the smallest bounding rectangle into grids until the number of centroids in the divided grid cells is 1 or 0. Then, encode the divided grid cells according to the preset encoding rules to obtain the grid codes of all patches corresponding to that village-level administrative region.

[0066] In some embodiments of this application, the polygon can be viewed as a planar geometric object composed of several discrete points, and its centroid coordinates can be calculated by weighted averaging of the coordinates of each vertex of the polygon. For any polygon, its centroid coordinates... The calculation formula is: ; ; in, and These are the coordinates of adjacent vertices of the polygon. The number of vertices. This represents the area of ​​the polygon. In practical applications, the centroid of the polygon can be directly obtained through the geometric calculation function of the GIS platform. For example, using ArcPy's SHAPE@XY token can directly return the centroid coordinates of the polygon feature.

[0067] It should be noted that the grid division was performed using the smallest bounding rectangle of the village-level administrative region as the root node, and a quadtree was used for recursive division. Each time, the current grid was evenly divided into four sub-grids, and the number of centroids in each sub-grid was counted. If the number of centroids in a sub-grid was greater than 1, it was further subdivided; if it was equal to 0, it was discarded; and if it was equal to 1, the process stopped.

[0068] Each time the quadtree is partitioned, the current grid is evenly divided into four subgrids (i.e., the top-left grid cell, the top-right grid cell, the bottom-left grid cell, and the bottom-right grid cell). The default encoding rule is as follows: the four quadrants are encoded as follows: the top-left grid cell is encoded as 00, the top-right grid cell as 01, the bottom-left grid cell as 10, and the bottom-right grid cell as 11. That is, the top-left grid cell is encoded as 00, the top-right grid cell as 01, the bottom-left grid cell as 10, and the bottom-right grid cell as 11.

[0069] The tamper detection method, after encoding the divided grid cells according to preset encoding rules, also includes the following step of discarding grid cells: if the number of centroids in the divided grid cell is 0, then the grid cell is discarded. That is, grid cells with a centroid count of 0 are discarded, and finally, the number of centroids in each grid cell is 1. Each grid cell corresponds to a patch, and the encoding of the grid cell can be directly used as the grid encoding of the corresponding patch.

[0070] Step 14.2: Sort the grid codes of all land parcels in the land parcel data to be detected according to the preset sorting rules to obtain the grid codes of the land parcel data to be detected.

[0071] The above-mentioned preset sorting rule is as follows: the grid codes of all land cover patches can be sorted according to the order of the patches in the patch list of the land cover patch data to be detected, and all sorted grid codes are used as the grid codes of the land cover patch data to be detected.

[0072] Step 14.3: Calculate the grid code of the land parcel data to be detected using a hash algorithm, and use the calculation result as the grid code signature of the land parcel data to be detected.

[0073] Specifically, after obtaining the grid code of the land parcel data to be detected, a hash algorithm (such as the SHA-256 hash algorithm) can be used to calculate the grid code, and the calculation result is then used as the grid code signature of the land parcel data to be detected. It is worth mentioning that this grid code signature uniquely reflects the relative spatial location of the parcel within its village-level unit (i.e., village-level administrative region), remains stable to coordinate system transformations and data format migrations, and is sensitive to position interchanges or movements.

[0074] In some embodiments of this application, the signature library also pre-stores the original grid-coded signatures corresponding to the land cover patch data to be detected. It should be noted that the process of obtaining the original grid-coded signature is the same as the process of obtaining the grid-coded signature described above, the only difference being that the original grid-coded signature is obtained based on the land cover patch data when the land cover patch data (i.e., the land cover patch data to which the original unique identifier set belongs) is initially generated.

[0075] In some embodiments of this application, the specific implementation of step 14 above, which involves performing grid code detection on the land parcel data to be detected based on the grid code signature and outputting the tampering detection result, is as follows: the grid code signature is compared with the original grid code signature; if the grid code signature and the original grid code signature are consistent, a tampering detection result indicating that the land parcel data to be detected has not been tampered with is output; if the grid code signature and the original grid code signature are inconsistent, the tampered parcels in the land parcel data to be detected are determined according to the comparison result between the grid code signature and the original grid code signature, and a tampering detection result indicating that the land parcel data to be detected has been tampered with is output, wherein the tampering detection result contains the original unique identifier of the tampered parcel.

[0076] If the grid code signature matches the original grid code signature, it indicates that the land parcel data to be detected has not been tampered with. If the grid code signature does not match the original grid code signature, it indicates that the land parcel data to be detected has been tampered with. When tampering occurs, the tampered parcels can be identified based on the order of the parcels when the grid code signature was generated, and a tampering detection result indicating that the land parcel data to be detected has been tampered with can be output.

[0077] In practical applications, tamper detection results can be output through a tamper detection report. In addition to the original unique identifier of the tampered patch, the tamper detection report also includes the tampering type, such as adding / deleting / modifying attributes / changing shape / changing position, etc.

[0078] The following example illustrates the method for detecting tampering of land parcel data provided in this application, using specific experiments as examples.

[0079] This experiment is explained using signature generation, anti-legal operation experiment, and tamper detection experiment as the main steps.

[0080] (1) Signature generation Using the map data from the Third National Land Survey of a certain county as a sample, ArcPy was used to read the vector data, extract the geometric shape and attribute information of each map patch, and calculate the attribute field signature, geometric invariant signature, and grid code signature in sequence. The original unique identifier set (BSM), attribute field signature, geometric invariant signature, and grid code signature were stored in a signature library (relational database or JSON file).

[0081] (2) Experiment on resistance to legal operation Using the map data from the Third National Land Survey of a certain county as a sample, the following legal operations were performed respectively: (1) Convert GDB format to Shapefile format; (2) Project the Gauss-Kruger projection (central meridian 114°) into CGCS2000 geographic coordinates; (3) Apply a topology tolerance of 0.01 meters for node capture processing.

[0082] Experimental results show that after the above legal operations, the signature hash value of the map patch remains completely consistent, and no false alarms are generated.

[0083] (3) Tampering with the detection experiment Simulate the following four types of tampering behavior: (1) Addition, deletion and alteration: Randomly delete map features and simultaneously add fake map features; (2) Attribute tampering: Randomly select a plot and change its land use code from "arable land (011)" to "urban residential land (0701)" while keeping the geometry unchanged; (3) Geometric alteration: Randomly select a patch and move its boundary points; (4) Position alteration: The positions of identical shapes of patches are swapped.

[0084] Experimental results show that: additions, deletions, and tampering can be accurately located through full comparison of the BSM list; attribute-tampered patches are accurately located due to changes in the signature hash caused by changes in land type coding; geometrically tampered patches are accurately located due to changes in geometric invariants (NAR / SC); and position-swap tampered patches are accurately located due to changes in grid coding.

[0085] In summary, the land parcel data tampering detection method provided in this application has the following advantages: (i) Resist legitimate disturbances and avoid false alarms The geometric invariants (normalized area ratio, shape compactness) and adaptive mesh encoding of this application remain stable for legitimate operations such as format conversion, projection transformation, and topological tolerance, and will not trigger false alarms due to changes in the underlying binary representation. Compared with file hash-based methods, this solution is usable in real-world business scenarios.

[0086] (ii) Sensitive to actual tampering and able to pinpoint the location When there are actual modifications to the boundaries of a map patch (moving boundary points, changing shape), merging or splitting of map patches, tampering with attributes (such as malicious modification of land cover codes), or swapping of locations, the geometric invariants or grid codes will change immediately, and the signature will change accordingly. By comparing the content signatures of each map patch, each tampered map patch and its tampering type (addition / deletion / attribute modification / location modification / shape modification) can be accurately identified.

[0087] (iii) No need to modify the original data, zero intrusion The entire signature calculation process does not write any information to the original data, making it a zero-intrusion scheme that meets the stringent requirements for data integrity from authoritative data management bodies such as land surveys. Compared to methods based on digital watermarking, this scheme does not sacrifice data accuracy.

[0088] (iv) A unique coding method for the relative spatial location of map features was constructed. This application establishes a spatial location code for each map patch within a village-level administrative unit, uniquely corresponding to its geometric centroid. This code does not rely on any external coordinate reference; it is determined solely by the relative spatial order of the map patches within the village-level area, thus remaining unchanged under any coordinate system transformation, projection transformation, or data format migration. Furthermore, this code possesses spatial exclusivity; any two map patches in different locations correspond to different grid codes, effectively resisting covert tampering behaviors such as map patch position swapping, and providing a stable and reliable spatial dimensional basis for tamper detection.

[0089] The above description is the preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principles described in this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for detecting tampering with land parcel data, characterized in that, include: Extract the unique identifier code of all land parcels from the land parcel data to be detected, and compare the identifier codes of the land parcel data to be detected based on the extracted unique identifier codes; When the land parcel data to be detected is compared with the identification code, the attribute field signature of the land parcel data to be detected is obtained, and the attribute signature comparison of the land parcel data to be detected is performed based on the attribute field signature; the attribute field signature is used as a digital fingerprint to describe the key attribute information of the land parcel in the land parcel data to be detected. When the land parcel data to be detected is compared with the attribute signature, the geometric invariant signature of the land parcel data to be detected is obtained, and the geometric morphology of the land parcel data to be detected is detected based on the geometric invariant signature; the geometric invariant signature is used to describe the shape features of the land parcels in the land parcel data to be detected. When the land parcel data to be detected undergoes geometric morphology detection, a grid-coded signature of the land parcel data to be detected is obtained. Based on the grid-coded signature, grid-coded detection is performed on the land parcel data to be detected, and a tampering detection result is output. The tampering detection result is used to indicate whether the land parcel data to be detected has been tampered with, and the grid-coded signature is used to describe the relative spatial location of the land parcels in the land parcel data to be detected within their respective village-level administrative regions.

2. The method for detecting tampering of land parcel data according to claim 1, characterized in that, The signature library pre-stores a set of original unique identifiers corresponding to the land cover patch data to be detected. The set of original unique identifiers includes the original unique identifier of each patch in the land cover patch data to be detected.

3. The method for detecting tampering of land parcel data according to claim 2, characterized in that, The step of comparing the identifier codes of the land parcels to be detected based on the extracted unique identifier codes includes: All extracted unique identifiers are used as the set of unique identifiers to be inspected; Perform a difference operation between the set of unique identifiers to be detected and the original set of unique identifiers to obtain the difference set; If the difference set is empty, then it is determined that the land parcel data to be detected is compared by the identification code; If the difference set is non-empty, the tampered patches in the land use patch data to be detected are determined according to the difference set, and a tampering detection result indicating that the land use patch data to be detected has been tampered is output. The tampering detection result contains the original unique identifier of the tampered patch.

4. The method for detecting tampering of land parcel data according to claim 2, characterized in that, The acquisition of the attribute field signature of the land parcel data to be detected includes Obtain the key attribute fields of each land parcel in the land parcel data to be detected; The key attribute fields of all land parcels in the land parcel data to be detected are sorted according to the preset sorting rules to obtain the attribute string of the land parcel data to be detected. The attribute string is calculated using a hash algorithm, and the calculation result is used as the attribute field signature of the land parcel data to be detected.

5. The method for detecting tampering of land parcel data according to claim 4, characterized in that, The signature library also pre-stores the original attribute field signatures corresponding to the land parcel data to be detected; the attribute signature comparison of the land parcel data to be detected based on the attribute field signatures includes: The signature of the attribute field is compared with the signature of the original attribute field; If the attribute field signature is consistent with the original attribute field signature, then the land parcel data to be detected is determined to have passed the attribute signature comparison. If the attribute field signature and the original attribute field signature are inconsistent, the tampered patches in the land use patch data to be detected are determined based on the comparison result of the attribute field signature and the original attribute field signature, and a tampering detection result is output to indicate that the land use patch data to be detected has been tampered with. The tampering detection result contains the original unique identifier of the tampered patch.

6. The method for detecting tampering of land parcel data according to claim 2, characterized in that, The step of obtaining the geometric invariant signature of the land parcel data to be detected includes: For each patch in the land parcel data to be detected, the area of ​​the patch and the area of ​​the minimum convex hull of the patch are obtained respectively, and the ratio of the area of ​​the patch to the area of ​​the minimum convex hull is used as the normalized area ratio of the patch. For each patch in the land parcel data to be detected, the perimeter of the patch is calculated, and the ratio of the square of the perimeter to the area of ​​the patch is used as the shape compactness of the patch. According to the preset sorting rules, the normalized area ratio and shape compactness of each land parcel in the land parcel data to be detected are sorted to obtain the geometric string of the land parcel data to be detected. The geometric string is calculated using a hash algorithm, and the calculation result is used as the geometric invariant signature of the land parcel data to be detected.

7. The method for detecting tampering of land parcel data according to claim 6, characterized in that, The signature library also pre-stores the original geometric invariant signatures corresponding to the land parcel data to be detected; the geometric morphology detection of the land parcel data to be detected based on the geometric invariant signatures includes: The geometric invariant signature is compared with the original geometric invariant signature; If the geometric invariant signature is consistent with the original geometric invariant signature, then the land parcel data to be detected is determined to have passed the geometric morphology detection. If the geometric invariant signature and the original geometric invariant signature are inconsistent, the tampered patches in the land use patch data to be detected are determined based on the comparison result between the geometric invariant signature and the original geometric invariant signature, and a tampering detection result is output to indicate that the land use patch data to be detected has been tampered with. The tampering detection result contains the original unique identifier of the tampered patch.

8. The method for detecting tampering of land parcel data according to claim 2, characterized in that, The step of obtaining the grid-encoded signature of the land parcel data to be detected includes: For each village-level administrative region in the area corresponding to the land parcel data to be detected, the centroids of all parcels corresponding to that village-level administrative region are extracted from the land parcel data to be detected. Taking the smallest bounding rectangle of that village-level administrative region as the root node, the smallest bounding rectangle is divided into grids using a quadtree recursive partitioning method until the number of centroids in the divided grid cells is 1 or 0. The divided grid cells are then encoded according to a preset encoding rule to obtain the grid codes of all parcels corresponding to that village-level administrative region. The grid codes of all land parcels in the land parcel data to be detected are sorted according to a preset sorting rule to obtain the grid codes of the land parcel data to be detected. The grid code of the land parcel data to be detected is calculated using a hash algorithm, and the calculation result is used as the grid code signature of the land parcel data to be detected.

9. The method for detecting tampering of land parcel data according to claim 8, characterized in that, After the step of encoding the divided grid cells according to the preset encoding rules, the tamper detection method further includes: If the number of centroids in the resulting grid cell is 0, then discard that grid cell.

10. The method for detecting tampering of land parcel data according to claim 8, characterized in that, The signature library also pre-stores the original grid-coded signatures corresponding to the land parcel data to be detected; the grid-coded detection of the land parcel data to be detected based on the grid-coded signatures includes: The mesh-coded signature is compared with the original mesh-coded signature; If the grid-coded signature is consistent with the original grid-coded signature, a tampering detection result is output to indicate that the land parcel data to be detected has not been tampered with; If the grid-coded signature and the original grid-coded signature are inconsistent, the tampered patches in the land use patch data to be detected are determined based on the comparison result of the grid-coded signature and the original grid-coded signature, and a tampering detection result is output to indicate that the land use patch data to be detected has been tampered with. The tampering detection result contains the original unique identifier of the tampered patch.

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