Seal authenticity distinguishing system based on artificial intelligence

Abstract

The invention relates to the technical field of information security, in particular to a seal authenticity identification system based on artificial intelligence, which comprises a target data acquisition module used for acquiring a target seal image and image illumination data, a standard data acquisition module used for acquiring standard seal sample data and standard texture feature data, an image data processing module used for processing the image data, and a seal authenticity identification module used for identifying the seal authenticity. The system comprises a gray scale analysis module used for carrying out gray scale analysis on a target seal image, adjusting the gray scale analysis process of the target seal image and processing the target seal image, a first feature extraction module used for extracting texture features and seal features of the target seal image, and a second feature extraction module used for extracting the texture features and the seal features of the target seal image. The extraction module is used for extracting standard texture features and standard seal features, and the authenticity distinguishing module is used for distinguishing authenticity of a target seal image. The accuracy of authenticity identification of the dark-red enameled pottery seal is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of information security technology, and in particular to a seal authenticity identification system based on artificial intelligence. Background Art

[0002] With the development of social economy, the importance of seals in various legal documents has become increasingly prominent, and the counterfeiting of seals has also occurred from time to time. The traditional seal identification method relies on expert experience, which is inefficient and prone to errors. Therefore, it is of great significance to develop an efficient and accurate seal authenticity identification system.

[0003] Chinese patent publication number CN113705330A discloses a seal authenticity identification method and system, which specifically includes the following steps: obtaining the original seal pattern and the seal pattern to be compared; performing a positive operation on the seal pattern to be compared; using a preset sampling area for local comparison screening; taking special points from the seal pattern, and using the connection of the special points for comparison operations. The invention uses a preset sampling area for local comparison screening and takes special points from the seal pattern, and uses the connection of the special points for comparison operations to perform multiple screening and comparisons on the seal pattern to be compared from multiple angles, making the scheme of the present application more accurate and mature than the existing seal recognition technology; it can be seen that the method for distinguishing the authenticity of the seal in the invention only screens the seal graphic pattern, and does not analyze the influencing factors and material texture of the seal image, and there is a problem of inaccurate seal authenticity identification. Summary of the invention

[0004] The purpose of the present invention is to provide a seal authenticity identification system based on artificial intelligence to solve at least one of the problems existing in the prior art.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A target data acquisition module is used to acquire a target seal image and image illumination data;

[0007] A standard data acquisition module is used to acquire standard seal sample data and standard texture feature data;

[0008] An image data processing module is used to perform grayscale analysis on the target seal image, and adjust the grayscale analysis process of the target seal image according to the grayscale values ​​of the coordinate points of each coordinate system of the target seal image, and optimize the grayscale adjustment process of the target seal image according to the image illumination data. The image data processing module is also used to process the target seal image according to the grayscale analysis result of the target seal image;

[0009] A first feature extraction module is used to extract texture features and seal features of the target seal image according to the processing result of the target seal image;

[0010] A second feature extraction module is used to extract standard texture features and standard seal features according to the standard seal sample data and the standard texture feature data;

[0011] The authenticity identification module is used to identify the authenticity of the target seal image according to the texture features of the target seal image, the seal features of the target seal image, the standard texture features and the standard seal features.

[0012] Further, the image data processing module is provided with a grayscale analysis unit, the grayscale analysis unit establishes a plane rectangular coordinate system of the target seal image based on the pixel coordinates in the target seal image, and projects the pixel coordinates into coordinate system coordinates, the grayscale analysis unit is also used to record the grayscale value of each coordinate system coordinate point as g(x,y), wherein x is the abscissa of the coordinate system coordinate, y is the ordinate of the coordinate system coordinate, and g(x,y) is the grayscale value of the coordinate system coordinate (x,y);

[0013] The grayscale analysis unit performs grayscale analysis on the grayscale value g(x,y) of each coordinate point in the coordinate system: if g(x,y)<G×η, the grayscale analysis unit determines that the grayscale value of the coordinate point in the coordinate system is a high grayscale value; if g(x,y)≥G×η, the grayscale analysis unit determines that the grayscale value of the coordinate point in the coordinate system is a background grayscale value;

[0014] Where G is the average gray value of the coordinate system point, and η is the background area ratio of the sample image;

[0015] The grayscale analysis unit performs grayscale processing on the target seal image according to the grayscale analysis result: the grayscale value of the coordinate point of the coordinate system whose grayscale analysis result is the background grayscale value is set to 255.

[0016] Furthermore, the image data processing module is further provided with a contrast adjustment unit, which calculates the gray value variation coefficient σ according to the gray value g(x, y) of each coordinate point of the target seal image:

[0017] σ={[g(x1,y1)-E] 2 +[g(x1,y2)-E] 2 +...+[g(xN1,yN2)-E] 2} / (N1

[0018] ×N2);

[0019] E=[g(x1,y1)+g(x1,y2)+...+g(xN1,yN2)] / (N1×N2);

[0020] Among them, g(x1,y1) is the grayscale value of the coordinate point of the coordinate system with the horizontal coordinate x1 and the vertical coordinate y1, g(x1,y2) is the grayscale value of the coordinate point of the coordinate system with the horizontal coordinate x1 and the vertical coordinate y2, g(xN1,yN2) is the grayscale value of the coordinate point of the coordinate system with the horizontal coordinate xN1 and the vertical coordinate yN2, N1 is the number of horizontal pixels of the target seal image, and N2 is the number of vertical pixels of the target seal image;

[0021] The contrast adjustment unit adjusts the grayscale analysis process of the target seal image according to the grayscale value variation coefficient σ: if σ≥T, the contrast adjustment unit does not adjust the grayscale analysis process of the target seal image; if σ<T, the contrast adjustment unit adjusts the background area ratio η of the sample image to η', and sets η'=exp[(T-σ) / T];

[0022] Where T is the preset coefficient of variation.

[0023] Furthermore, the image data processing module is also provided with a light optimization unit, and the light optimization unit is used to optimize the grayscale adjustment process of the target seal image according to the image illumination data;

[0024] The light optimization unit calculates the light index α according to the image light data;

[0025] The light optimization unit compares the illumination index α with each preset illumination index, and optimizes the grayscale adjustment process of the target seal image according to the comparison result: when α<A1, the light optimization unit determines that the illumination index of the target seal image is low, and optimizes the preset variation coefficient T to T', and sets T'=T×exp{-(A1-α) / A1}; when A1≤α<A2, the light optimization unit determines that the illumination index of the target seal image is normal and does not perform optimization; when α≥A2, the light optimization unit determines that the illumination index of the target seal image is abnormal, and optimizes the preset variation coefficient T to T", and sets T"=T×(A2-α) / A2; the light optimization unit optimizes the contrast of illumination conditions of different target seal images by optimizing the grayscale adjustment process of the target seal image, thereby improving the accuracy of the adjustment process;

[0026] Wherein, A1 is a first preset illumination index, A2 is a second preset illumination index, and A1<A2.

[0027] Furthermore, the first feature extraction module is provided with a gradient analysis unit, and the gradient analysis unit analyzes the gradient of the target seal image according to the processing result of the target seal image;

[0028] The gradient analysis unit clusters the coordinate points of the target seal image after grayscale processing according to the grayscale value g(x,y) to obtain clusters of each coordinate point;

[0029] The gradient analysis unit uses the average grayscale value of the coordinate system coordinate points contained in each coordinate point cluster as the marked grayscale value of each coordinate point cluster, recorded as D(i), and sets D(i)=[G(i,1)+G(i,2)+...+G(i,J(i))] / J(i), wherein i=1,2...M, M is the number of coordinate point clusters, G(i,1) is the grayscale value of the first coordinate system coordinate point in the i-th coordinate cluster, G(i,2) is the grayscale value of the second coordinate system coordinate point in the i-th coordinate cluster, G(i,J(i)) is the grayscale value of the J(i)-th coordinate system coordinate point in the i-th coordinate cluster, and J(i) is the number of coordinate system coordinate points in the i-th coordinate cluster;

[0030] The gradient analysis unit sorts each coordinate point cluster in descending order according to the marked gray value D(i), and classifies each coordinate point cluster according to the sorting result: the gradient analysis unit classifies the first 20% of the sorting result into seal pixel clusters, and classifies the coordinate point clusters outside the first 20% of the sorting result into texture pixel clusters.

[0031] Furthermore, the first feature extraction module is further provided with a texture feature analysis unit, and the texture feature analysis unit analyzes the texture feature of the target seal image according to the gradient analysis result of the target seal image;

[0032] The texture feature analysis unit regards the continuous coordinate system coordinates in the texture pixel cluster as a texture graph;

[0033] The texture feature analysis unit counts the number P of texture patterns in the target seal image, and calculates the texture density ρ, setting ρ=P / S;

[0034] Where S is the target stamp area;

[0035] The texture feature analysis unit divides the target seal image into equidistant rectangles to obtain rectangular divided areas, and counts the number of texture graphics p(k) in each rectangular divided area;

[0036] The texture feature analysis unit calculates the texture roughness γ according to the number of texture patterns p(k) in each rectangular divided area;

[0037] The texture feature analysis unit uses the texture roughness γ and the texture density ρ as the texture feature analysis results of the target seal image.

[0038] Furthermore, the first feature extraction module is further provided with a seal feature analysis unit, and the seal feature analysis unit analyzes the seal feature of the target seal image according to the gradient analysis result of the target seal image;

[0039] The seal feature analysis unit generates a seal shape feature vector group H according to the coordinate system coordinate points in the seal pixel clustering cluster;

[0040] The seal feature analysis unit uses the seal shape feature vector group H as the seal feature analysis result of the target seal image.

[0041] Furthermore, the authenticity identification module is provided with a shape feature matching unit, and the shape feature matching unit is used to perform shape feature matching between the seal features of the target seal image and the standard seal features;

[0042] The shape feature matching unit calculates the offset coefficient β between the seal shape feature vector group H and the standard seal shape feature vector group h, and sets β=[|H(1)-h(1)|+|H(2)-h(2)|+...+|H(V)-h(V)|] / V;

[0043] Wherein, H(1) is the first component vector in the seal shape feature vector group H, h(1) is the first component vector in the standard seal shape feature vector group h, H(2) is the second component vector in the seal shape feature vector group H, h(2) is the second component vector in the standard seal shape feature vector group h, H(V) is the Vth component vector in the seal shape feature vector group H, h(V) is the Vth component vector in the standard seal shape feature vector group h, and V is the number of component vectors in the seal shape feature vector group H;

[0044] The shape feature matching unit compares the offset coefficient β with the preset offset value R, and analyzes the shape matching state according to the comparison result, wherein:

[0045] When β<R, the shape matching unit determines that the shape matching state is a low-fitting match;

[0046] When β≥R, the shape matching unit determines that the shape matching state is a normal fitting match.

[0047] Furthermore, the authenticity identification module is also provided with a gradient feature matching unit, which is used to perform gradient feature matching on the seal features of the target seal image and the standard seal features: if (ρ-ρ')×(γ-γ') / (ρ'×γ')<L, the gradient matching unit determines that the gradient features match; if (ρ-ρ')×(γ-γ') / (ρ'×γ')≥L, the gradient matching unit determines that the gradient features do not match.

[0048] Furthermore, the authenticity identification module is also provided with an output unit, and the output unit is used to identify the authenticity of the target seal image according to the shape feature matching result and the gradient feature matching result: if the shape matching state is a normal fitting match and a gradient feature match, the output unit determines that the authenticity identification result of the target image is true; if the shape matching state is a low fitting match and the gradient feature does not match, the output unit determines that the authenticity identification result of the target image is false; if the shape matching state is a normal fitting match and the gradient feature does not match, the output unit determines that the authenticity identification result of the target image is a material process mismatch; if the shape matching state is a low fitting match and the gradient feature matches, the output unit determines that the authenticity identification result of the target image is false;

[0049] The output unit outputs the authenticity identification result to the user.

[0050] Compared with the prior art, the beneficial effects of the present invention lie in that the target data acquisition module and the standard data acquisition module acquire the data information required for the present embodiment, thereby improving the accuracy and completeness of data information acquisition; the image data processing module processes the acquired target image, thereby improving the accuracy of image data processing; the first feature extraction module performs feature extraction on the target image, thereby improving the accuracy of feature extraction on the target seal image; the second feature extraction module performs feature extraction on the standard seal sample image, thereby improving the accuracy of extracting standard texture features and standard seal features; the authenticity identification module performs authenticity identification on the target seal image, thereby improving the accuracy of authenticity identification of purple clay teapot seals. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0052] Figure 1 Schematic diagram of the structure of the seal authenticity identification system based on artificial intelligence in this embodiment.

[0053] Figure 2 Schematic diagram of the structure of the image data processing module in this embodiment.

[0054] Figure 3 Schematic diagram of the structure of the first feature extraction module in this embodiment.

[0055] Figure 4 Schematic diagram of the structure of the authenticity identification module of this embodiment. DETAILED DESCRIPTION

[0056] In order to more clearly illustrate the present invention, the present invention is further described below in conjunction with preferred embodiments and accompanying drawings. Similar components in the accompanying drawings are represented by the same reference numerals. It should be understood by those skilled in the art that the content specifically described below is illustrative rather than restrictive, and should not be used to limit the scope of protection of the present invention.

[0057] It should be noted that, although the terms first, second, third, etc. may be used to describe in the embodiments of the present application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, without departing from the scope of the embodiments of the present application, the first may also be referred to as the second, and similarly, the second may also be referred to as the first.

[0058] Specifically, the artificial intelligence-based seal authenticity identification system described in this embodiment is applied to the authenticity identification of purple clay teapot seals; the purple clay teapot seal described in this embodiment is specifically a wide seal on the bottom of a purple clay teapot; this embodiment improves the accuracy of authenticity identification of purple clay teapot seals by extracting shape features and texture features of the standard seal sample image and the target seal image of the purple clay teapot seal image, and performing authenticity identification based on the extraction results.

[0059] See also Figure 1 As shown, it is a structural schematic diagram of the seal authenticity identification system based on artificial intelligence in this embodiment, including:

[0060] A target data acquisition module is used to acquire a target seal image and image illumination data;

[0061] The standard data acquisition module is used to acquire standard seal sample data and standard texture feature data.

[0062] Specifically, the target seal image is the seal image data that needs to be verified for authenticity; the standard seal sample data is the standard seal sample image, which is a mirror image of the original seal scan image; the standard texture feature data is the standard texture image of the purple clay teapot; and the image illumination data is the shooting illumination intensity of the target seal image.

[0063] It can be understood that the standard seal image is a rectangular grayscale image data after binarization, that is, the grayscale value of the seal content is 0, and the grayscale value of the non-seal content is 255; the target seal image is a rectangular grayscale image with a pixel grayscale value in the interval [0,255]; this embodiment does not specifically limit the method for obtaining the target seal image and the standard seal sample data, and those skilled in the art can freely set it as long as the requirements for obtaining the target seal image and the standard seal sample data are met, such as obtaining the standard seal sample data through user interactive input, and obtaining the target seal image by taking pictures with a high-definition camera.

[0064] The image data processing module is used to perform grayscale analysis on the target seal image, and adjust the grayscale analysis process of the target seal image according to the grayscale values ​​of the coordinate points of each coordinate system of the target seal image, and optimize the grayscale adjustment process of the target seal image according to the image illumination data. The image data processing module is also used to process the target seal image according to the grayscale analysis result of the target seal image. The image data processing module is connected to the target data acquisition module; please refer to Figure 2 As shown, the image data processing module includes:

[0065] A grayscale analysis unit is used to perform grayscale analysis on the target seal image and perform grayscale processing on the target seal image according to the contrast analysis result;

[0066] The grayscale analysis unit establishes a plane rectangular coordinate system of the target seal image based on the pixel coordinates in the target seal image, and projects the pixel coordinates into coordinate system coordinates. The grayscale analysis unit is also used to record the grayscale value of each coordinate system coordinate point as g(x, y), wherein x is the abscissa of the coordinate system coordinate, y is the ordinate of the coordinate system coordinate, and g(x, y) is the grayscale value of the coordinate system coordinate (x, y);

[0067] The grayscale analysis unit performs grayscale analysis on the grayscale value g(x,y) of each coordinate point in the coordinate system: if g(x,y)<G×η, the grayscale analysis unit determines that the grayscale value of the coordinate point in the coordinate system is a high grayscale value; if g(x,y)≥G×η, the grayscale analysis unit determines that the grayscale value of the coordinate point in the coordinate system is a background grayscale value; wherein G is the average grayscale value of the coordinate point in the coordinate system, and η is the background area ratio of the sample image; the grayscale analysis unit classifies the coordinate points into background pixels and pixels that can be used as a basis for authenticity identification, thereby improving the efficiency of image processing;

[0068] The grayscale analysis unit performs grayscale processing on the target seal image according to the grayscale analysis result: the grayscale value of the coordinate point of the coordinate system whose grayscale analysis result is the background grayscale value is set to 255; the grayscale analysis unit performs grayscale processing on the target seal image to remove the interference of ordinary background pixels on subsequent feature extraction, thereby improving the accuracy of feature analysis of the target image.

[0069] Specifically, this embodiment does not make any specific limitation on the value of the background area ratio η of the sample image. Those skilled in the art can set it freely as long as the value requirement of the background area ratio η of the sample image is met. In this embodiment, the background area ratio of the sample image is 0.73; at the same time, this embodiment does not make any specific limitation on the process of "establishing a plane rectangular coordinate system of the target seal image based on the coordinates of the pixel points in the target seal image" of the grayscale analysis unit. For example, the plane rectangular coordinate system of the target seal image can be established with the upper left starting point of the image as the origin of the coordinate system, the horizontal axis positive direction from left to right, and the vertical axis positive direction from top to bottom.

[0070] Please continue reading Figure 2 As shown, the image data processing module is further provided with a contrast adjustment unit, and the contrast adjustment unit is used to calculate the gray value variation coefficient σ according to the gray value g(x, y) of each coordinate system coordinate point of the target seal image:

[0071] σ={[g(x1,y1)-E] 2 +[g(x1,y2)-E] 2 +...+[g(xN1,yN2)-E] 2} / (N1×N2);

[0072] E=[g(x1,y1)+g(x1,y2)+...+g(xN1,yN2)] / (N1×N2);

[0073] Wherein, g(x1,y1) is the gray value of the coordinate point of the coordinate system with the abscissa x1 and the ordinate y1, g(x1,y2) is the gray value of the coordinate point of the coordinate system with the abscissa x1 and the ordinate y2, g(xN1,yN2) is the gray value of the coordinate point of the coordinate system with the abscissa xN1 and the ordinate yN2, N1 is the number of horizontal pixels of the target seal image, and N2 is the number of vertical pixels of the target seal image; the contrast adjustment unit numerically calculates the contrast of the target seal image by calculating the gray value variation coefficient σ, so as to provide a numerical basis for the subsequent adjustment process, so that the adjustment process is more accurate;

[0074] The contrast adjustment unit adjusts the grayscale analysis process of the target seal image according to the grayscale value variation coefficient σ: if σ≥T, the contrast adjustment unit does not adjust the grayscale analysis process of the target seal image; if σ<T, the contrast adjustment unit adjusts the background area ratio η of the sample image to η', and sets η'=exp[(T-σ) / T]; wherein T is a preset variation coefficient; the contrast adjustment unit adjusts the grayscale analysis process of the target seal image, amplifies the judgment process of the grayscale analysis of the low-contrast target seal image, and improves the accuracy of the grayscale analysis.

[0075] Specifically, this embodiment does not impose any specific limitation on the value of the preset coefficient of variation T, and those skilled in the art may freely set it as long as the value requirement of the preset coefficient of variation T is met. For example, the preset coefficient of variation T may be set to 15%.

[0076] Please continue reading Figure 2 As shown, the image data processing module is further provided with a light optimization unit, and the light optimization unit is used to optimize the grayscale adjustment process of the target seal image according to the image illumination data;

[0077] The light optimization unit calculates the light index α according to the image light data. The calculation formula of the light index α is as follows:

[0078] α=ln{1+|wW| / W|};

[0079] Wherein, w is the illumination intensity when the target seal image is photographed, and W is the preset image illumination intensity; the light optimization unit optimizes the reflection and light saturation of the acquired target seal image by calculating the illumination index α, thereby improving the accuracy of adjusting the grayscale analysis process of the target seal image;

[0080] The light optimization unit compares the illumination index α with each preset illumination index, and optimizes the grayscale adjustment process of the target seal image according to the comparison result: when α<A1, the light optimization unit determines that the illumination index of the target seal image is low, and optimizes the preset variation coefficient T to T', and sets T'=T×exp{-(A1-α) / A1}; when A1≤α<A2, the light optimization unit determines that the illumination index of the target seal image is normal and does not perform optimization; when α≥A2, the light optimization unit determines that the illumination index of the target seal image is abnormal, and optimizes the preset variation coefficient T to T", and sets T"=T×(A2-α) / A2; wherein A1 is the first preset illumination index, A2 is the second preset illumination index, and A1<A2; the light optimization unit optimizes the contrast of illumination conditions of different target seal images by optimizing the grayscale adjustment process of the target seal image, thereby improving the accuracy of the adjustment process.

[0081] Specifically, this embodiment does not impose any specific restrictions on the values ​​of the first preset light index A1 and the second preset light index A2. Technical personnel in this field can set them freely as long as the value requirements of the first preset light index A1 and the second preset light index A2 are met. For example, the first preset light index A1 can be set to 0.2, and the second preset light index A2 can be set to 0.4.

[0082] Please continue reading Figure 1As shown, the seal authenticity identification system based on artificial intelligence is also provided with a first feature extraction module, which is used to extract the texture features and seal features of the target seal image according to the processing result of the target seal image, and the first feature extraction module is connected to the image data processing module; please refer to Figure 3 As shown, the first feature extraction module includes a gradient analysis unit, which is used to analyze the gradient of the target seal image according to the processing result of the target seal image;

[0083] The gradient analysis unit clusters the coordinate points of the coordinate system of the target seal image after grayscale processing with the grayscale value g(x,y) to obtain clusters of each coordinate point; the gradient analysis unit clusters the processed target image so as to use the subsequent sorting result as the gradient representation of the target seal image, thereby improving the accuracy of feature extraction of the target seal image;

[0084] The gradient analysis unit uses the average grayscale value of the coordinate system coordinate points contained in each coordinate point cluster as the marked grayscale value of each coordinate point cluster, recorded as D(i), and sets D(i)=[G(i,1)+G(i,2)+...+G(i,J(i))] / J(i), wherein i=1,2...M, M is the number of coordinate point clusters, G(i,1) is the grayscale value of the first coordinate system coordinate point in the i-th coordinate cluster, G(i,2) is the grayscale value of the second coordinate system coordinate point in the i-th coordinate cluster, G(i,J(i)) is the grayscale value of the J(i)-th coordinate system coordinate point in the i-th coordinate cluster, and J(i) is the number of coordinate system coordinate points in the i-th coordinate cluster;

[0085] The gradient analysis unit sorts each coordinate point cluster in descending order according to the marked gray value D(i), and classifies each coordinate point cluster according to the sorting result: the gradient analysis unit classifies the first 20% of the sorting result into seal pixel clusters, and classifies the coordinate point clusters outside the first 20% of the sorting result into texture pixel clusters; the gradient analysis unit classifies the clustering results to distinguish between texture graphics and seal images, which is convenient for subsequent texture feature extraction and seal feature extraction.

[0086] Specifically, in this embodiment, "the first 20% of the sorting results" specifically refers to the coordinate point clusters within the sorting interval [0, 20%].

[0087] Please continue reading Figure 3 As shown, the first feature extraction module also includes a texture feature analysis unit, which is used to analyze the texture feature of the target seal image according to the gradient analysis result of the target seal image, and the texture feature analysis unit is connected to the gradient analysis unit;

[0088] The texture feature analysis unit regards the continuous coordinate system coordinates in the texture pixel cluster as a texture graph;

[0089] The texture feature analysis unit counts the number P of texture patterns in the target seal image and calculates the texture density ρ, setting ρ=P / S, where S is the target seal area; the texture feature analysis unit calculates the texture density and uses it as part of the texture feature, so that the seal authenticity identification adds the identification content of the purple clay teapot texture, thereby improving the accuracy of authenticity identification;

[0090] The texture feature analysis unit divides the target seal image into equidistant rectangles to obtain each rectangular divided area, and counts the number of texture graphics p(k) in each rectangular divided area;

[0091] The texture feature analysis unit calculates the texture roughness γ according to the number of texture patterns p(k) in each rectangular divided area, defines the texture roughness γ as an index representing the uniform distribution state of the texture in the target seal image, and sets the texture roughness γ = {[p(1)-EP] 2 +[p(2)-EP] 2 +...+[p(K)-EP] 2} / EP, EP=[Σp(k)] / K; wherein p(1) is the number of texture graphics in the first divided area, p(2) is the number of texture graphics in the second divided area, p(K) is the number of texture graphics in the Kth divided area, p(k) is the number of texture graphics in the kth divided area, k is a mathematical index, and K is the number of rectangular divided areas;

[0092] The texture feature analysis unit uses the texture roughness γ and the texture density ρ as the texture feature analysis results of the target seal image.

[0093] Specifically, the process of "dividing the target seal image into equidistant rectangles" in this embodiment is to divide the target seal image into equidistant rectangles with similar rectangles of the target seal image. In this embodiment, the ratio of the similar rectangles to the original target seal image is 1:4. Those skilled in the art can also set it freely. If more accurate texture features are needed, the ratio of the similar rectangles to the original target seal image can be set to 1:6, etc.

[0094] Please continue reading Figure 3 As shown, the first feature extraction module also includes a shape feature analysis unit, which is used to analyze the seal features of the target seal image according to the gradient analysis result of the target seal image, and the shape feature analysis unit is connected to the gradient analysis unit;

[0095] The seal feature analysis unit generates a seal shape feature vector group H according to the coordinate system coordinate points in the seal pixel clustering cluster;

[0096] The seal feature analysis unit uses the seal shape feature vector group H as the seal feature analysis result of the target seal image; the seal feature analysis unit generates a vector group that can represent the seal shape and uses it as the seal feature, thereby improving the accuracy of the seal feature analysis.

[0097] Specifically, the process of "generating a seal shape feature vector group based on the coordinate system coordinate points within the seal pixel clustering cluster" in this embodiment is to perform vector calculation on the coordinate system coordinate points within the continuous seal pixel clustering cluster. For example, if the coordinate system coordinate point a (c, d) within the seal pixel clustering cluster is continuous with the coordinate system coordinate point b (n, m) within the seal pixel clustering cluster, then the vector calculation process is: z = (nc, md), where z is the vector calculation result, and the seal shape feature vector group is the collection of the results of vector calculation on the coordinate system coordinate points within all continuous seal pixel clustering clusters.

[0098] Please continue reading Figure 1 As shown, the seal authenticity identification system based on artificial intelligence also includes a second feature extraction module, which is used to extract standard seal features and standard texture features according to the standard seal sample image and standard texture feature data to obtain standard seal features h and standard texture features; the standard texture features include standard roughness γ' and standard texture density ρ';

[0099] The second feature extraction module stores the extraction results of the standard seal features and the standard texture features.

[0100] Specifically, the process of "extracting standard seal features and standard texture features based on standard seal sample images and standard texture feature data" in this embodiment is the same as the implementation process and data structure of "extracting texture features and seal features of the target seal image" in this embodiment, and this embodiment will not be repeated here.

[0101] Please continue reading Figure 1 As shown, the seal authenticity identification system based on artificial intelligence also includes an authenticity identification module, which is used to identify the authenticity of the target seal image according to the texture features of the target seal image, the seal features of the target seal image, the standard texture features and the standard seal features, and output the authenticity identification result to the user. The authenticity identification module is connected to the first feature extraction module and the second feature extraction module; please refer to Figure 4 As shown, the authenticity identification module includes a shape feature matching unit, which is used to perform shape feature matching between the texture features of the target seal image and the standard texture features;

[0102] The shape feature matching unit calculates the offset coefficient β between the seal shape feature vector group H and the standard seal shape feature vector group h, and sets β=[|H(1)-h(1)|+|H(2)-h(2)|+...+|H(V)-h(V)|] / V;

[0103] Wherein, H(1) is the first component vector in the seal shape feature vector group H, h(1) is the first component vector in the standard seal shape feature vector group h, H(2) is the second component vector in the seal shape feature vector group H, h(2) is the second component vector in the standard seal shape feature vector group h, H(V) is the Vth component vector in the seal shape feature vector group H, h(V) is the Vth component vector in the standard seal shape feature vector group h, and V is the number of component vectors in the seal shape feature vector group H;

[0104] The shape feature matching unit compares the offset coefficient β with the preset offset value R, and analyzes the shape matching state according to the comparison result, wherein:

[0105] When β<R, the shape matching unit determines that the shape matching state is a low-fitting match;

[0106] When β≥R, the shape matching unit determines that the shape matching state is a normal fitting match.

[0107] Specifically, this embodiment does not impose any specific limitation on the value of the preset offset value R, and those skilled in the art may freely set it as long as the value requirement of the preset offset value R is met. For example, the preset offset value R may be set to 2.

[0108] Please continue reading Figure 4 As shown, the authenticity identification module is further provided with a gradient feature matching unit, which is used to perform gradient feature matching on the seal features of the target seal image and the standard seal features: if (ρ-ρ')×(γ-γ') / (ρ'×γ')<L, the gradient matching unit determines that the gradient features match; if (ρ-ρ')×(γ-γ') / (ρ'×γ')≥L, the gradient matching unit determines that the gradient features do not match; wherein L is a preset comprehensive matching index; the gradient feature matching unit improves the accuracy of authenticity identification by performing matching analysis on the gradient features to determine the texture gradient matching result.

[0109] Specifically, this embodiment does not specifically limit the value of the preset comprehensive matching index L, and those skilled in the art can freely set it as long as the value requirement of the preset comprehensive matching index L is met. For example, the preset comprehensive matching index L can be set to 0.4.

[0110] Please continue reading Figure 4As shown, the authenticity identification module is further provided with an output unit, which is used to identify the authenticity of the target seal image according to the shape feature matching result and the gradient feature matching result, and output the authenticity identification result to the user, and the output unit is connected to the gradient feature matching unit and the shape feature matching unit;

[0111] The output unit distinguishes the authenticity of the target seal image according to the shape feature matching result and the gradient feature matching result: if the shape matching state is a normal fitting match and a gradient feature match, the output unit determines that the authenticity distinction result of the target image is true; if the shape matching state is a low fitting match and the gradient feature does not match, the output unit determines that the authenticity distinction result of the target image is false; if the shape matching state is a normal fitting match and the gradient feature does not match, the output unit determines that the authenticity distinction result of the target image is a material process mismatch; if the shape matching state is a low fitting match and the gradient feature matches, the output unit determines that the authenticity distinction result of the target image is false; the output unit determines the authenticity result of the target seal image by jointly analyzing the shape matching result and the gradient matching result, thereby improving the accuracy of authenticity distinction;

[0112] The output unit outputs the authenticity identification result to the user.

[0113] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A seal authenticity identification system based on artificial intelligence, characterized in that: include: A target data acquisition module is used to acquire a target seal image and image illumination data; A standard data acquisition module is used to acquire standard seal sample data and standard texture feature data; An image data processing module is used to perform grayscale analysis on the target seal image, and adjust the grayscale analysis process of the target seal image according to the grayscale values ​​of the coordinate points of each coordinate system of the target seal image, and optimize the grayscale adjustment process of the target seal image according to the image illumination data. The image data processing module is also used to process the target seal image according to the grayscale analysis result of the target seal image; A first feature extraction module is used to extract texture features and seal features of the target seal image according to the processing result of the target seal image; A second feature extraction module is used to extract standard texture features and standard seal features according to the standard seal sample data and the standard texture feature data; The authenticity identification module is used to identify the authenticity of the target seal image according to the texture features of the target seal image, the seal features of the target seal image, the standard texture features and the standard seal features.

2. The seal authenticity identification system based on artificial intelligence according to claim 1 is characterized in that: The image data processing module is provided with a grayscale analysis unit, which establishes a plane rectangular coordinate system of the target seal image based on the pixel coordinates in the target seal image, and projects the pixel coordinates into coordinate system coordinates. The grayscale analysis unit is also used to record the grayscale value of each coordinate system coordinate point as g(x, y), wherein x is the abscissa of the coordinate system coordinate, y is the ordinate of the coordinate system coordinate, and g(x, y) is the grayscale value of the coordinate system coordinate (x, y); The grayscale analysis unit performs grayscale analysis on the grayscale value g(x,y) of each coordinate point in the coordinate system: if g(x,y)<G×η, the grayscale analysis unit determines that the grayscale value of the coordinate point in the coordinate system is a high grayscale value; if g(x,y)≥G×η, the grayscale analysis unit determines that the grayscale value of the coordinate point in the coordinate system is a background grayscale value; Where G is the average gray value of the coordinate system coordinate point, and η is the background area ratio of the sample image; The grayscale analysis unit performs grayscale processing on the target seal image according to the grayscale analysis result: the grayscale value of the coordinate point of the coordinate system whose grayscale analysis result is the background grayscale value is set to 255.

3. The seal authenticity identification system based on artificial intelligence according to claim 2 is characterized in that: The image data processing module is further provided with a contrast adjustment unit, which calculates the gray value variation coefficient σ according to the gray value g(x, y) of each coordinate point of the target seal image: σ6{[g(x1,y1)-E] 2 +[g(x1,y2)-E] 2 +...+[g(xN1,yN2)-E] 2 } / (N1 ×N2)； E=[g(x1,y1)+g(x1,y2)+...+g(xN1,yN2)] / (N1×N2); Among them, g(x1,y1) is the grayscale value of the coordinate point of the coordinate system with the horizontal coordinate x1 and the vertical coordinate y1, g(x1,y2) is the grayscale value of the coordinate point of the coordinate system with the horizontal coordinate x1 and the vertical coordinate y2, g(xN1,yN2) is the grayscale value of the coordinate point of the coordinate system with the horizontal coordinate xN1 and the vertical coordinate yN2, N1 is the number of horizontal pixels of the target seal image, and N2 is the number of vertical pixels of the target seal image; The contrast adjustment unit adjusts the grayscale analysis process of the target seal image according to the grayscale value variation coefficient σ: if σ≥T, the contrast adjustment unit does not adjust the grayscale analysis process of the target seal image; if σ<T, the contrast adjustment unit adjusts the background area ratio η of the sample image to η', and sets η'=exp[(T-σ) / T]; Where T is the preset coefficient of variation.

4. The seal authenticity identification system based on artificial intelligence according to claim 3 is characterized in that: The image data processing module is also provided with a light optimization unit, and the light optimization unit is used to optimize the grayscale adjustment process of the target seal image according to the image illumination data; The light optimization unit calculates the light index α according to the image light data; The light optimization unit compares the illumination index α with each preset illumination index, and optimizes the grayscale adjustment process of the target seal image according to the comparison result: when α<A1, the light optimization unit determines that the illumination index of the target seal image is low, and optimizes the preset variation coefficient T to T', and sets T'=T×exp{-(A1-α) / A1}; when A1≤α<A2, the light optimization unit determines that the illumination index of the target seal image is normal and does not perform optimization; when α≥A2, the light optimization unit determines that the illumination index of the target seal image is abnormal, and optimizes the preset variation coefficient T to T", and sets T"=T×(A2-α) / A2; the light optimization unit optimizes the contrast of illumination conditions of different target seal images by optimizing the grayscale adjustment process of the target seal image, thereby improving the accuracy of the adjustment process; Wherein, A1 is a first preset illumination index, A2 is a second preset illumination index, and A1<A2.

5. The seal authenticity identification system based on artificial intelligence according to claim 4 is characterized in that: The first feature extraction module is provided with a gradient analysis unit, and the gradient analysis unit analyzes the gradient of the target seal image according to the processing result of the target seal image; The gradient analysis unit clusters the coordinate points of the target seal image after grayscale processing according to the grayscale value g(x,y) to obtain clusters of each coordinate point; The gradient analysis unit uses the average grayscale value of the coordinate system coordinate points contained in each coordinate point cluster as the marked grayscale value of each coordinate point cluster, recorded as D(i), and sets D(i)=[G(i,1)+G(i,2)+...+G(i,J(i))] / J(i), wherein i=1,2...M, M is the number of coordinate point clusters, G(i,1) is the grayscale value of the first coordinate system coordinate point in the i-th coordinate cluster, G(i,2) is the grayscale value of the second coordinate system coordinate point in the i-th coordinate cluster, G(i,J(i)) is the grayscale value of the J(i)-th coordinate system coordinate point in the i-th coordinate cluster, and J(i) is the number of coordinate system coordinate points in the i-th coordinate cluster; The gradient analysis unit sorts each coordinate point cluster in descending order according to the marked gray value D(i), and classifies each coordinate point cluster according to the sorting result: the gradient analysis unit classifies the first 20% of the sorting result into seal pixel clusters, and classifies the coordinate point clusters outside the first 20% of the sorting result into texture pixel clusters.

6. The seal authenticity identification system based on artificial intelligence according to claim 5 is characterized in that: The first feature extraction module is further provided with a texture feature analysis unit, and the texture feature analysis unit analyzes the texture feature of the target seal image according to the gradient analysis result of the target seal image; The texture feature analysis unit regards the continuous coordinate system coordinates in the texture pixel cluster as a texture graph; The texture feature analysis unit counts the number P of texture patterns in the target seal image, and calculates the texture density ρ, setting ρ=P / S; Where S is the target stamp area; The texture feature analysis unit divides the target seal image into equidistant rectangles to obtain rectangular divided areas, and counts the number of texture graphics p(k) in each rectangular divided area; The texture feature analysis unit calculates the texture roughness γ according to the number of texture patterns p(k) in each rectangular divided area; The texture feature analysis unit uses the texture roughness γ and the texture density ρ as the texture feature analysis results of the target seal image.

7. The seal authenticity identification system based on artificial intelligence according to claim 6 is characterized in that: The first feature extraction module is further provided with a seal feature analysis unit, and the seal feature analysis unit analyzes the seal features of the target seal image according to the gradient analysis result of the target seal image; The seal feature analysis unit generates a seal shape feature vector group H according to the coordinate system coordinate points in the seal pixel clustering cluster; The seal feature analysis unit uses the seal shape feature vector group H as the seal feature analysis result of the target seal image.

8. The seal authenticity identification system based on artificial intelligence according to claim 7 is characterized in that: The authenticity identification module is provided with a shape feature matching unit, and the shape feature matching unit is used to perform shape feature matching between the seal feature of the target seal image and the standard seal feature; The shape feature matching unit calculates the offset coefficient β between the seal shape feature vector group H and the standard seal shape feature vector group h, and sets β=[|H(1)-h(1)|+|H(2)-h(2)|+...+|H(V)-h(V)|] / V; Wherein, H(1) is the first component vector in the seal shape feature vector group H, h(1) is the first component vector in the standard seal shape feature vector group h, H(2) is the second component vector in the seal shape feature vector group H, h(2) is the second component vector in the standard seal shape feature vector group h, H(V) is the Vth component vector in the seal shape feature vector group H, h(V) is the Vth component vector in the standard seal shape feature vector group h, and V is the number of component vectors in the seal shape feature vector group H; The shape feature matching unit compares the offset coefficient β with the preset offset value R, and analyzes the shape matching state according to the comparison result, wherein: When β<R, the shape matching unit determines that the shape matching state is a low-fitting match; When β≥R, the shape matching unit determines that the shape matching state is a normal fitting match.

9. The seal authenticity identification system based on artificial intelligence according to claim 8 is characterized in that: The authenticity identification module is also provided with a gradient feature matching unit, which is used to perform gradient feature matching on the seal features of the target seal image and the standard seal features: if (ρ-ρ')×(γ-γ') / (ρ'×γ')<L, the gradient matching unit determines that the gradient features match; if (ρ-ρ')×(γ-γ') / (ρ'×γ')≥L, the gradient matching unit determines that the gradient features do not match.

10. The seal authenticity identification system based on artificial intelligence according to claim 9 is characterized in that: The authenticity identification module is further provided with an output unit, and the output unit is used to identify the authenticity of the target seal image according to the shape feature matching result and the gradient feature matching result: if the shape matching state is a normal fitting match and a gradient feature match, the output unit determines that the authenticity identification result of the target image is true; if the shape matching state is a low fitting match and the gradient feature does not match, the output unit determines that the authenticity identification result of the target image is false; if the shape matching state is a normal fitting match and the gradient feature does not match, the output unit determines that the authenticity identification result of the target image is a material process mismatch; if the shape matching state is a low fitting match and the gradient feature matches, the output unit determines that the authenticity identification result of the target image is false; The output unit outputs the authenticity identification result to the user.

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