CN110751041A - Certificate authenticity verification method, system, computer equipment and readable storage medium - Google Patents
Certificate authenticity verification method, system, computer equipment and readable storage medium Download PDFInfo
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Abstract
The embodiment of the invention provides a certificate authenticity verification method, which comprises the following steps: acquiring an original certificate image, and identifying the certificate type of the original certificate image; extracting a plurality of pieces of characteristic information from the original certificate image based on the certificate type, analyzing the plurality of pieces of characteristic information to obtain a plurality of pieces of target characteristic information, and verifying the plurality of pieces of target characteristic information to generate a plurality of verification results; and generating a verification conclusion form according to the plurality of verification results. The embodiment of the invention greatly improves the accuracy of certificate authenticity verification. The embodiment of the invention also provides a certificate authenticity verification system, computer equipment and a computer readable storage medium.
Description
Technical Field
The embodiment of the invention relates to the field of big data, in particular to a certificate authenticity verification method, a certificate authenticity verification system, a certificate authenticity verification computer device and a computer readable storage medium.
Background
At present, when people perform online operation or transact business in the fields of finance, government affairs, medical treatment and the like online, certificate authenticity verification is often required to be performed on personnel, and the online certificate authenticity verification depends on remote electronic certificate authenticity verification.
In a traditional online transaction service, an online remote electronic certificate authenticity verification method generally depends on comparing certificate information of a user on an acquired original certificate image with corresponding user certificate information in a user identity database by a worker to determine authenticity of a certificate. However, the conventional online remote authentication method for electronic certificates has the following defects: 1. the labor cost is high; 2. the accuracy rate of certificate authenticity verification is low.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method, a system, a computer device, and a computer-readable storage medium for verifying authenticity of a certificate, which are used to solve the problem of low accuracy in performing authenticity verification of a certificate by an online remote electronic certificate authenticity verification method.
The embodiment of the invention solves the technical problems through the following technical scheme:
a method of verifying authenticity of a document, comprising:
acquiring an original certificate image, and identifying the certificate type of the original certificate image;
extracting a plurality of pieces of characteristic information from the original certificate image based on the certificate type, analyzing the plurality of pieces of characteristic information to obtain a plurality of pieces of target characteristic information, and verifying the plurality of pieces of target characteristic information to generate a plurality of verification results;
and generating a verification conclusion form according to the plurality of verification results.
Further, the plurality of feature information comprises a plurality of feature vector sequences and a plurality of image areas; the step of extracting a plurality of pieces of feature information from the original certificate image based on the certificate type, analyzing the plurality of pieces of feature information to obtain a plurality of pieces of target feature information, and verifying the plurality of pieces of target feature information to generate a plurality of verification results includes:
positioning the original certificate image to obtain a plurality of characteristic vector sequences and a plurality of position identifications corresponding to the plurality of characteristic vector sequences;
extracting a plurality of character feature vector sequences from the plurality of feature vector sequences;
based on the certificate type, performing identification operation on the character feature vector sequences to obtain a plurality of target characters corresponding to the character feature vector sequences;
acquiring a plurality of preset text content rules corresponding to the certificate type based on the certificate type and a plurality of position identifications;
and checking the target characters based on the certificate type and the character content rules to generate a character checking result.
Further, the original certificate image comprises a certificate front side shooting image and at least two certificate side shooting images;
the step of extracting a plurality of pieces of feature information from the original certificate image based on the certificate type, analyzing the plurality of pieces of feature information to obtain a plurality of pieces of target feature information, and verifying the plurality of pieces of target feature information to generate a plurality of verification results includes:
extracting a person image area and a standard person image area of a certificate holder photo from the image shot on the front side of the certificate according to a preset anti-counterfeit label;
matching the portrait area of the bearer photo with a standard portrait area to obtain a face verification result; when the matching of the portrait area of the bearer photo and the standard portrait area is consistent, judging that the face verification result is successful; and when the matching of the portrait area of the bearer photo and the standard portrait area is inconsistent, judging that the face verification result is verification failure.
Further, the step of extracting a plurality of feature information from the original certificate image based on the certificate type, analyzing the plurality of feature information to obtain a plurality of target feature information, and verifying the plurality of target feature information to generate a plurality of verification results further includes:
extracting a target rainbow printing area from the photographed image on the front side of the certificate according to a preset anti-counterfeit label; acquiring a first similarity between the overall color gradient style of the target rainbow printing area and the overall color gradient style of the standard certificate;
comparing the first similarity with a preset first threshold value to obtain an overall color gradient style verification result: when the first similarity is higher than the first threshold value, judging that the overall color gradient style verification result is successful; and when the first similarity is lower than the first threshold, judging that the overall color gradient style verification result is verification failure.
Further, the step of extracting a plurality of feature information from the original certificate image based on the certificate type, analyzing the plurality of feature information to obtain a plurality of target feature information, and verifying the plurality of target feature information to generate a plurality of verification results further includes:
extracting a target diversified pattern background area from at least two certificate side face shot images according to a preset anti-counterfeiting label;
acquiring a second similarity between the background area of the target diversified pattern and the background area of the diversified pattern of the standard certificate;
comparing the second similarity with a preset second threshold value to obtain a diversified pattern background verification result: when the second similarity is higher than the second threshold, judging that the verification of the background of the diversified patterns is successful; and when the second similarity is lower than the second threshold, judging that the background verification result of the diversified patterns is verification failure.
Further, the step of extracting a plurality of feature information from the original certificate image based on the certificate type, analyzing the plurality of feature information to obtain a plurality of target feature information, and verifying the plurality of target feature information to generate a plurality of verification results further includes:
extracting a plurality of target optical color-changing ink triangular areas and a plurality of target hologram areas with wave and three-dimensional effects from at least two certificate side face shot images according to a preset anti-counterfeit label;
acquiring a third similarity between the triangular area of the target optical color-changing ink and the triangular area of the optical color-changing ink of the standard certificate and a fourth similarity between the hologram area with the wave and three-dimensional effect of the target and the hologram area with the wave and three-dimensional effect of the standard certificate;
comparing the third similarity with a preset third threshold, and comparing the fourth similarity with a preset fourth threshold to obtain a background pattern verification result: when the third similarity is higher than the third threshold, and the fourth similarity is higher than the fourth threshold; judging that the background pattern verification result is successful; when the third similarity is lower than the third threshold and/or the fourth similarity is lower than the fourth threshold; the background pattern verification result is judged to be verification failure.
Further, the step of extracting a plurality of feature information from the original certificate image based on the certificate type, analyzing the plurality of feature information to obtain a plurality of target feature information, and verifying the plurality of target feature information to generate a plurality of verification results further includes:
extracting a target micro-text printing area and a target transparent window area from the image shot on the front side of the certificate according to a preset anti-counterfeit label, wherein the target transparent window area comprises personal data of a user;
amplifying the target miniature character printing area to obtain an amplified target miniature character printing area;
acquiring a fifth similarity between the target micro-text printing area and the micro-text printing area of the standard certificate and a sixth similarity between the target transparent window area and the corresponding user personal data in the database corresponding to the certificate type;
comparing the fifth similarity with a preset fifth threshold, and comparing the sixth similarity with a preset sixth threshold to obtain a content verification result: when the fifth similarity is higher than the fifth threshold and the sixth similarity is higher than the sixth threshold, determining that the content verification result is successful; and when the fifth similarity is lower than the fifth threshold and/or the sixth similarity is lower than the sixth threshold, determining that the content verification result is verification failure.
In order to achieve the above object, an embodiment of the present invention further provides a certificate authenticity verification system, including:
the acquisition module is used for acquiring an original certificate image and identifying the certificate type of the original certificate image;
the processing module extracts a plurality of pieces of characteristic information from the original certificate image based on the certificate type, analyzes the plurality of pieces of characteristic information to obtain a plurality of pieces of target characteristic information, and verifies the plurality of pieces of target characteristic information to generate a plurality of verification results;
and the result generation module is used for generating a verification conclusion form according to the plurality of verification results.
In order to achieve the above object, an embodiment of the present invention further provides a computer device, where the computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the certificate authenticity verification method when executing the computer program.
In order to achieve the above object, an embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored, where the computer program is executable by at least one processor, so as to cause the at least one processor to execute the steps of the certificate authenticity verification method described above.
According to the certificate authenticity verification method, the certificate authenticity verification system, the computer equipment and the computer readable storage medium, provided by the embodiment of the invention, a plurality of pieces of characteristic information are extracted from an original certificate image by identifying the certificate type of the original certificate image, the plurality of pieces of characteristic information are analyzed to obtain a plurality of pieces of target characteristic information, and the plurality of pieces of target characteristic information are verified to generate a plurality of verification results; and the authenticity of the certificate is judged according to a plurality of check results, so that the authenticity verification accuracy of the certificate is greatly improved, and the speed of identity verification in online business transaction in the fields of finance, government affairs, medical treatment and the like is increased.
The invention is described in detail below with reference to the drawings and specific examples, but the invention is not limited thereto.
Drawings
FIG. 1 is a flowchart illustrating a method for verifying authenticity of a document according to an embodiment of the present invention;
FIG. 2 is a schematic flowchart of step S20 in FIG. 1;
FIG. 3 is a detailed flowchart of step S20 in FIG. 1;
FIG. 4 is a schematic diagram of a specific process of training a face comparison model according to a first embodiment of the present invention;
FIG. 5 is a detailed flowchart of step S20 in FIG. 1;
FIG. 6 is a detailed flowchart of step S20 in FIG. 1;
FIG. 7 is a flowchart illustrating the step S20 in FIG. 1;
FIG. 8 is a flowchart illustrating the step S20 in FIG. 1;
FIG. 9 is a block diagram of a second embodiment of the document authentication system;
FIG. 10 is a diagram of a hardware structure of a third embodiment of the computer apparatus according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Technical solutions between various embodiments may be combined with each other, but must be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope of the present invention.
Example one
Referring to fig. 1, a flowchart of steps of a method for verifying authenticity of a certificate according to an embodiment of the present invention is shown. It is to be understood that the flow charts in the embodiments of the present method are not intended to limit the order in which the steps are performed. The following description is given by taking a computer device as an execution subject, specifically as follows:
step S10, acquiring an original certificate image, and identifying a certificate type of the original certificate image.
The certificate type is a type corresponding to an original certificate image, and can be a second-generation resident identification card, a hong Kong and Macau pass, a passport and the like.
Specifically, the computer device acquires an original certificate image sent by a user terminal, wherein the certificate type corresponding to the original certificate image can be the certificate type determined when the user terminal uploads the original certificate image; the certificate type corresponding to the original certificate image can also be a certificate type of the original certificate image which is identified by computer equipment through an image identification technology so as to be matched with the original certificate image according to the corresponding format.
Step S20, based on the certificate type, extracting a plurality of pieces of feature information from the original certificate image, analyzing the plurality of pieces of feature information to obtain a plurality of pieces of target feature information, and verifying the plurality of pieces of target feature information to generate a plurality of verification results.
Specifically, the feature information includes a plurality of feature vector sequences and a plurality of image regions; the target feature information comprises corresponding character feature vector sequences and image area data.
In an exemplary embodiment, referring to fig. 2, step S20 may further include:
step S200A, positioning the original certificate image to obtain a plurality of feature vector sequences and a plurality of position identifiers corresponding to the feature vector sequences.
The position identification refers to an identification corresponding to the characteristic vector sequence, and the corresponding characteristic vector sequence can be found through the position identification; for example, the position identifier of a certain hong Kong identity card number is 1, and the feature vector sequence can be positioned as a hong Kong identity card number area through the position identifier 1; the position identification of the standard portrait area (namely the area of the big portrait on the hong Kong identity card) of a certain hong Kong identity card is 2, and the characteristic vector sequence can be positioned as the standard portrait area through the position identification 2.
Illustratively, the original document image can be positioned and analyzed by adopting a CNN-BLSTM model.
The CNN (Convolutional Neural Networks, hereinafter abbreviated as CNN) model is a model for extracting image features. The BLSTM (Bi-directional long-short term memory, hereinafter abbreviated BLSTM) model is a model for processing sequence data. The text verification is completed by seamlessly integrating the CNN model into the BLSTM model.
Specifically, the CNN-BLSTM model includes an input layer, a convolutional layer, a pooling layer, a fully-connected layer, a BLSTM layer, an output layer, and the like, where the BLSTM layer is composed of two separate LSTM layers.
Inputting the original certificate image into an input layer of a CNN-BLSTM model; the convolution layer performs convolution operation on the original certificate image of the input layer to obtain a plurality of convolution characteristic graphs; the pooling layer performs pooling operation on the plurality of convolution characteristic graphs to obtain a plurality of convolution characteristic graphs after dimensionality reduction; the full connection layer converts a plurality of convolution characteristics in the plurality of convolution characteristic graphs after dimension reduction into a plurality of corresponding characteristic vector sequences; the BLSTM layer carries out prediction operation on each frame of the plurality of characteristic vector sequences to obtain position coordinate relations corresponding to the plurality of characteristic vector sequences; based on the position coordinate relations and the preset rule, the output layer outputs a plurality of characteristic vector sequences and position identifications corresponding to the characteristic vector sequences.
Step S200B, extracting a plurality of character feature vector sequences from the plurality of feature vector sequences.
Step S200C, performing an identification operation on the plurality of character feature vector sequences based on the certificate type to obtain a plurality of target characters corresponding to the plurality of character feature vector sequences.
Specifically, the target characters may include chinese, numeric, and english, etc.
Step S200D, based on the certificate type and the location identifiers, obtaining a plurality of preset text rules corresponding to the certificate type.
The preset text content rule refers to a preset rule for verifying the target text corresponding to each certificate type.
In the embodiment of the invention, the plurality of text content rules can be codes written in advance according to certificate standard rules, and the corresponding target text is verified by executing the codes. A credential standard rule is a state-made rule regarding the credential standard.
Specifically, a database corresponding to each certificate type is stored in the computer device, and the database contains text content rules corresponding to the certificates of all users and standard texts related to the text content rules. The computer equipment searches a corresponding database according to the certificate type, acquires the database corresponding to the certificate type, acquires corresponding text content rules according to the position identifications, and acquires corresponding target texts according to the text content rules.
In an exemplary embodiment, when the type of the certificate is hong Kong identity card, a database corresponding to the hong Kong identity card is searched, wherein the database corresponding to the hong Kong identity card comprises a text content rule which is written in advance according to the standard rule of the certificate and is used for verifying the hong Kong identity card. The hong Kong identity card number comprises three parts: one or two English letters; six numbers; a number in parentheses or any of numbers 0 to 9 in parentheses or a letter a in parentheses. Wherein, the number or letter A in the bracket is a checking digital code used for checking whether the former number is correct.
According to the content rules of hong Kong ID card, the "one or two English letters" part of hong Kong ID card can be represented by a number, i.e. A is represented by 1, B is represented by 2.. Z is represented by 26, then the number represented by the "one or two English letters" part is multiplied by 8, the first number of the six numbers is multiplied by 7, the second number of the six numbers is multiplied by 6, and so on, the sixth number of the six numbers is multiplied by 2, then all the products are added to obtain a number, and then the number is divided by 11 to obtain the remainder. If the number is divided completely, the checking number in the bracket is 0; if the remainder is 1, the checking number in the brackets is A; if the remainder is 2-10, the difference of the remainder is subtracted by 11, and the check number in the parenthesis is obtained.
Furthermore, according to the difference between the type and the format of the certificate, the text content rules corresponding to different positions of the certificate are different. In an exemplary embodiment, the hong kong id card includes areas such as a chinese code, a chinese name, an issue date, a label, a first issue date, an english name, a birth date, a gender, and an id, and each area corresponds to a location identifier, and a corresponding text rule can be found in a database corresponding to the hong kong id card through the location identifier. For example, in the database corresponding to the hong Kong ID card, the text content rule corresponding to the Chinese code area can be found through the position identification of the Chinese code.
Step S200E, based on the certificate type and the text content rules, verifying the target texts to generate a text verification result.
Specifically, in the above example, the corresponding standard characters are obtained according to the character content rules, and then the target characters are matched with the standard characters based on the certificate type to generate the character verification result, where the character verification result may be a verification success or a verification failure.
When any target character is unsuccessfully matched with the corresponding standard character, judging that the character verification result is verification failure; and when all the target characters are successfully matched with the corresponding standard characters, judging that the character verification result is successful.
In the embodiment of the present invention, based on the certificate type, a plurality of image areas may be extracted from the original certificate image according to a plurality of preset anti-counterfeit labels, and the plurality of image areas are analyzed to obtain a plurality of image area data, and then the plurality of image area data are verified to generate a plurality of image verification results.
The multiple image verification results may be verification success or verification failure.
Specifically, different certificate types are pre-configured with preset anti-counterfeit labels. In an exemplary embodiment, taking hong kong id card as an example, the plurality of preset anti-counterfeit labels include: standard portrait of ID card (big portrait area), photo of bearer, rainbow printing, diversified pattern background, optical color-changing ink triangle, hologram with wave and stereo effect, micro text printing, transparent window, etc.
In a database corresponding to the hong Kong identity card, a plurality of image areas of the identity card of each user are provided with preset anti-counterfeit labels. The computer equipment searches a corresponding database according to the certificate type, acquires the database corresponding to the certificate type, acquires a plurality of anti-counterfeiting labels from the database, and acquires image areas corresponding to successful matching according to the matching of the anti-counterfeiting labels and the image areas of the original certificate image. For example, the anti-counterfeit label is an identity card standard portrait, the anti-counterfeit label is matched with each image area in the original certificate image, if the matching of a certain image area and the identity card standard portrait area with the identity card standard portrait label in the database corresponding to the hong Kong identity card is successful, the image area is matched with the identity card standard portrait label, and the image area is extracted from the original certificate image.
In an embodiment of the invention, the raw document images acquired by the computer device include a document front side captured image and at least two document side captured images. The certificate front side shot image comprises a certificate front side image and a certificate back side image which are shot perpendicularly to the certificate; the image shooting of the side face of the certificate comprises the certificate front image and the certificate back image which are shot obliquely at a preset angle by taking the certificate front image as a reference, wherein the preset angle can be 20-50 degrees.
In an exemplary embodiment, referring to fig. 3, step S20 may further include:
and step S210, extracting a certificate holder photo portrait area and a standard portrait area from the certificate front face photographed image according to a preset anti-counterfeit label.
Specifically, according to the corresponding standard portrait (large portrait area) of the identity card and the anti-counterfeiting label of the certificate holder photo, the front face of the certificate is cut according to the size of a standard rectangle, and the cut front face of the certificate comprises the portrait area of the certificate holder photo and the standard portrait area.
Step S211, matching the image area of the bearer photo with a standard image area to obtain a face verification result: when the matching of the portrait area of the bearer photo and the standard portrait area is consistent, judging that the face verification result is successful; and when the matching of the portrait area of the bearer photo and the standard portrait area is inconsistent, judging that the face verification result is verification failure.
Specifically, a face comparison model is adopted to match a face region of a bearer photo with a standard face region; the face comparison model may be a trained first neural network model.
Referring to fig. 4, the embodiment of the present invention further includes a step of training the first neural network model.
Step S2111, initializing a plurality of parameters of a pre-configured first neural network model;
step S2112, obtaining a plurality of sample data of a plurality of users from a database corresponding to the hong Kong identity card, wherein the plurality of sample data comprise standard portrait areas and bearer photo portrait areas of the plurality of users;
step S2113, training the first neural network model through a plurality of sample data, and continuously adjusting a plurality of parameters to obtain a face comparison model.
In an exemplary embodiment, referring to fig. 5, step S20 may further include:
and S220, extracting a target rainbow printing area from the image shot on the front side of the certificate according to a preset anti-counterfeiting label.
Specifically, the front face of the certificate is cut according to the size of a standard rectangle according to a corresponding rainbow printing anti-counterfeit label, and the cut front face of the certificate contains a target rainbow printing area.
Step S221, a first similarity between the whole color gradient style of the target rainbow printing area and the whole color gradient style of the standard certificate is obtained.
Specifically, the cut document front side shooting image containing the target rainbow printing area is input into a second neural network model, so that the first similarity between the whole color gradient style of the target rainbow printing area and the standard color gradient style of the standard document is output through the second neural network model.
The embodiment of the invention also comprises a training process of the second neural network model:
acquiring a plurality of compliant hong Kong identity card pictures and non-compliant hong Kong identity card pictures from a database corresponding to the hong Kong identity card; inputting the plurality of compliant hong Kong identity card pictures (namely standard hong Kong identity card pictures) and non-compliant hong Kong identity card pictures into a second neural network model, and continuously training and classifying the plurality of compliant hong Kong identity card pictures and non-compliant hong Kong identity card pictures through the second neural network model until the compliant hong Kong identity card pictures are classified into the classification classes of the positive sample pictures and the non-compliant hong Kong identity card pictures are classified into the classification classes of the negative sample pictures. The trained second neural network model stores the characteristics of the standard color gradient style of the rainbow printing area of the compliant hong Kong identity card picture.
Step S222, comparing the first similarity with a preset first threshold value to obtain an overall color gradient style verification result; when the first similarity is higher than the first threshold value, judging that the overall color gradient style verification result is successful; and when the first similarity is lower than the first threshold, judging that the overall color gradient style verification result is verification failure.
In an exemplary embodiment, referring to fig. 6, step S20 may further include:
and step S230, extracting a background area of the target diversified pattern from at least two side shot images of the certificate according to a preset anti-counterfeiting label.
Specifically, according to the corresponding diversified pattern background anti-counterfeit label, the certificate side face shot image is cut according to the size of a standard rectangle, and the cut certificate side face shot image comprises a target rainbow printing area.
Step S231, obtaining a second similarity between the background area of the target diversified pattern and the background area of the diversified pattern of the standard certificate.
Specifically, a plurality of cut certificate side shooting images containing the target diversified pattern background area are input into a first convolution neural network model, and second similarity of the target diversified pattern background area and the diversified pattern background area of the standard certificate is output through the first convolution neural network model.
The first convolutional neural network model comprises 12 convolutional layers, 2 pooling layers, 2 full-link layers, a sigmod activation function and the like.
Furthermore, a plurality of certificate side shot images which are cut after being cut and comprise the background area of the target diversified pattern are input into the first convolution neural network model, and a second similarity in a [0, 1] interval is output by performing convolution operation, pooling operation, dimension reduction operation and the like through 12 convolution layers.
When the first convolution neural network model is trained, the output value of the positive sample certificate picture is marked to be 1, the output value of the negative sample certificate picture is marked to be 0, the result of the network fitting trend is that the positive sample approaches to 1, and the negative sample approaches to 0, so that the output value output by the first convolution neural network model can be considered to be in accordance with the second similarity of the positive sample certificate picture.
Step S232, comparing the second similarity with a preset second threshold value to obtain a diversified pattern background check result; when the second similarity is higher than the second threshold, judging that the verification of the background of the diversified patterns is successful; and when the second similarity is lower than the second threshold, judging that the background verification result of the diversified patterns is verification failure.
In an exemplary embodiment, referring to fig. 7, step S20 may further include:
and S240, extracting a plurality of target optical color-changing ink triangular areas and a plurality of target hologram areas with wave and three-dimensional effects from at least two certificate side face shot images according to a preset anti-counterfeit label.
Specifically, according to the corresponding optical color-changing ink triangular anti-counterfeiting label and the hologram anti-counterfeiting label with the wave and three-dimensional effects, at least two certificate side face shot images are cut according to the size of a standard rectangle, and the cut certificate side face shot images comprise a target optical color-changing ink triangular area and a target hologram area with the wave and three-dimensional effects.
Furthermore, the number of the side face shot images of the certificate is two, the position of the optical color-changing ink triangular area is positioned based on the optical color-changing ink triangular anti-counterfeiting label, and the target optical color-changing ink triangular area is extracted from the two side face shot images of the certificate; the target optically variable ink triangular area includes a first optically variable ink triangular area and a second optically variable ink triangular area.
In the embodiment of the invention, as the hologram area with the wave and three-dimensional effects needs to be judged by different shooting angles, the compliant hong Kong ID card pictures and the non-compliant hong Kong ID card pictures shot at different angles are obtained from the database corresponding to the hong Kong ID card; inputting the plurality of compliant hong Kong identity card pictures (namely standard hong Kong identity card pictures) and non-compliant hong Kong identity card pictures into a second convolutional neural network model for training, and synthesizing judgment results corresponding to the hong Kong identity card pictures at different shooting angles to obtain a final judgment result. Therefore, the trained second convolutional neural network model stores the characteristics of the holograms with the wave and three-dimensional effects of the standard certificates with different angles.
Step S241, acquiring a third similarity between the triangular area of the target optical color-changing ink and the triangular area of the optical color-changing ink of the standard certificate and a fourth similarity between the hologram area with the wave and three-dimensional effect of the target and the hologram area with the wave and three-dimensional effect of the standard certificate.
Specifically, the first optical color-changing ink triangular area and the second optical color-changing ink triangular area are input into a third neural network model, and a third similarity between the target optical color-changing ink triangular area and the optical color-changing ink triangular area of the standard certificate is output through the third neural network model.
Specifically, the hologram areas with the wave and the three-dimensional effect of the plurality of targets are input into a second convolution neural network model, so that a fourth similarity between the hologram area with the wave and the three-dimensional effect of the targets and the hologram area with the wave and the three-dimensional effect of the standard certificate is output through the second convolution neural network model.
Step S242, comparing the third similarity with a preset third threshold, and comparing the fourth similarity with a preset fourth threshold to obtain a background pattern verification result; when the third similarity is higher than the third threshold, and the fourth similarity is higher than the fourth threshold; judging that the background pattern verification result is successful; when the third similarity is lower than the third threshold and/or the fourth similarity is lower than the fourth threshold; the background pattern verification result is judged to be verification failure.
In an exemplary embodiment, referring to fig. 8, step S20 may further include:
and step S250, extracting a target micro text printing area and a target transparent window area from the image shot on the front side of the certificate according to a preset anti-counterfeit label, wherein the target transparent window area comprises personal data of a user.
Specifically, the anti-counterfeiting label and the transparent window anti-counterfeiting label are printed according to corresponding miniature characters, the image shot on the front side of the certificate is cut according to the size of a standard rectangle, and the cut image shot on the side surface of the certificate comprises a target miniature character printing area and a target transparent window area.
And step S251, performing amplification operation on the target miniature character printing area to obtain an amplified target miniature character printing area.
Step S252, acquiring a fifth similarity between the target micro-text printing area and the micro-text printing area of the standard certificate and a sixth similarity between the target transparent window area and the corresponding user personal data in the database corresponding to the certificate type.
Specifically, the amplified target micro-text printing area is input into the fourth neural network model, so that the fifth similarity between the target micro-text printing area and the micro-text printing area of the standard certificate is output through the fourth neural network model.
And inputting the target transparent window area into a fifth neural network model so as to output a sixth similarity between the target transparent window area and the corresponding personal data of the user in the database corresponding to the hong Kong identity card through the fifth neural network model.
Step S253, comparing the fifth similarity with a preset fifth threshold, and comparing the sixth similarity with a preset sixth threshold to obtain a content verification result; when the fifth similarity is higher than the fifth threshold and the sixth similarity is higher than the sixth threshold, determining that the content verification result is successful; and when the fifth similarity is lower than the fifth threshold and/or the sixth similarity is lower than the sixth threshold, determining that the verification result of the content pattern is verification failure.
And step S30, generating a verification conclusion form according to the plurality of verification results.
Specifically, when the character check result, the face check result, the color-changing ink check result, the overall color gradient style check result, the diversified pattern background check result, the background pattern check result and the content check result are all successfully checked, the certificate corresponding to the original certificate image is a compliance certificate; and generating a verification conclusion form according to the final verification result, and filling conclusion data of the certificate corresponding to the original certificate image as a compliant certificate into the verification conclusion form.
When any one of the character check result, the face check result, the color-changing ink check result, the overall color gradient style check result, the diversified pattern background check result, the background pattern check result and the content check result is a check failure, the certificate corresponding to the original certificate image is an unconventional certificate; and generating a verification conclusion form according to the final verification result, and filling conclusion data of the certificate which corresponds to the original certificate image and is an unqualified certificate into the verification conclusion form.
In an exemplary embodiment, the original document image is stored in a database corresponding to the hong kong identity card according to the final verification result, and the original document image is classified.
Further, the truth-checking conclusion form is returned to the user side.
Example two
Referring still to FIG. 9, a schematic diagram of the program modules of the document authentication system of the present invention is shown. In embodiments of the present invention, the document authentication system 20 may include or be divided into one or more program modules, which are stored in a storage medium and executed by one or more processors to implement the present invention and implement the above-described document authentication method. The program module referred to in the embodiments of the present invention refers to a series of computer program instruction segments capable of performing specific functions, and is more suitable for describing the execution process of the certificate authenticity verification system 20 in the storage medium than the program itself. The following description will specifically describe the functions of the program modules of the embodiments of the present invention:
the acquisition module 200 is used for acquiring an original certificate image and identifying the certificate type of the original certificate image.
The processing module 210 is configured to extract a plurality of pieces of feature information from the original certificate image based on the certificate type, analyze the plurality of pieces of feature information to obtain a plurality of pieces of target feature information, and verify the plurality of pieces of target feature information to generate a plurality of verification results.
Further, the plurality of feature information comprises a plurality of feature vector sequences and a plurality of image areas; the target feature information comprises corresponding character feature vector sequences and image area data. The processing module 210 is further configured to:
positioning the original certificate image to obtain a plurality of characteristic vector sequences and a plurality of position identifications corresponding to the plurality of characteristic vector sequences; extracting a plurality of character feature vector sequences from the plurality of feature vector sequences; based on the certificate type, performing identification operation on the character feature vector sequences to obtain a plurality of target characters corresponding to the character feature vector sequences; acquiring a plurality of preset text content rules corresponding to the certificate type based on the certificate type and a plurality of position identifications; and verifying the target characters based on the certificate types and the character content rules to generate a character verification result.
Further, the processing module 210 is further configured to:
extracting a person image area and a standard person image area of a certificate holder photo from the image shot on the front side of the certificate according to a preset anti-counterfeit label; matching the portrait area of the bearer photo with a standard portrait area to obtain a face verification result; when the matching of the portrait area of the bearer photo and the standard portrait area is consistent, judging that the face verification result is successful; and when the matching of the portrait area of the bearer photo and the standard portrait area is inconsistent, judging that the face verification result is verification failure.
Extracting a target rainbow printing area from the photographed image on the front side of the certificate according to a preset anti-counterfeit label; acquiring a first similarity between the overall color gradient style of the target rainbow printing area and the overall color gradient style of the standard certificate; comparing the first similarity with a preset first threshold value to obtain an overall color gradient style verification result: when the first similarity is higher than the first threshold value, judging that the overall color gradient style verification result is successful; and when the first similarity is lower than the first threshold, judging that the overall color gradient style verification result is verification failure.
Extracting a target diversified pattern background area from at least two certificate side face shot images according to a preset anti-counterfeiting label; acquiring a second similarity between the background area of the target diversified pattern and the background area of the diversified pattern of the standard certificate; comparing the second similarity with a preset second threshold value to obtain a diversified pattern background verification result: when the second similarity is higher than the second threshold, judging that the verification of the background of the diversified patterns is successful; and when the second similarity is lower than the second threshold, judging that the background verification result of the diversified patterns is verification failure.
Extracting a plurality of target optical color-changing ink triangular areas and a plurality of target hologram areas with wave and three-dimensional effects from at least two certificate side face shot images according to a preset anti-counterfeit label; acquiring a third similarity between the triangular area of the target optical color-changing ink and the triangular area of the optical color-changing ink of the standard certificate and a fourth similarity between the hologram area with the wave and three-dimensional effect of the target and the hologram area with the wave and three-dimensional effect of the standard certificate; comparing the third similarity with a preset third threshold, and comparing the fourth similarity with a preset fourth threshold to obtain a background pattern verification result: when the third similarity is higher than the third threshold, and the fourth similarity is higher than the fourth threshold; judging that the background pattern verification result is successful; when the third similarity is lower than the third threshold and/or the fourth similarity is lower than the fourth threshold; the background pattern verification result is judged to be verification failure.
Extracting a target micro-text printing area and a target transparent window area from the image shot on the front side of the certificate according to a preset anti-counterfeit label, wherein the target transparent window area comprises personal data of a user; amplifying the target miniature character printing area to obtain an amplified target miniature character printing area; acquiring a fifth similarity between the target micro-text printing area and the micro-text printing area of the standard certificate and a sixth similarity between the target transparent window area and the corresponding user personal data in the database corresponding to the certificate type; comparing the fifth similarity with a preset fifth threshold, and comparing the sixth similarity with a preset sixth threshold to obtain a content verification result: when the fifth similarity is higher than the fifth threshold and the sixth similarity is higher than the sixth threshold, determining that the content verification result is successful; and when the fifth similarity is lower than the fifth threshold and/or the sixth similarity is lower than the sixth threshold, determining that the content verification result is verification failure.
And the result generating module 220 is configured to generate a verification conclusion form according to the text verification result and the plurality of image verification results.
EXAMPLE III
Fig. 10 is a schematic diagram of a hardware architecture of a computer device according to a third embodiment of the present invention. In the embodiment of the present invention, the computer device 2 is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction. The computer device 2 may be a rack server, a blade server, a tower server or a rack server (including an independent server or a server cluster composed of a plurality of servers), and the like. As shown in fig. 10, the computer device 2 includes, but is not limited to, at least a memory 21, a processor 22, a network interface 23, and a certificate authenticity verification system 20, which are communicatively connected to each other via a system bus. Wherein:
in an embodiment of the present invention, the memory 21 includes at least one type of computer-readable storage medium, which includes a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. In some embodiments, the storage 21 may be an internal storage unit of the computer device 2, such as a hard disk or a memory of the computer device 2. In other embodiments, the memory 21 may also be an external storage device of the computer device 2, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like provided on the computer device 2. Of course, the memory 21 may also comprise both internal and external memory units of the computer device 2. In the embodiment of the present invention, the memory 21 is generally used to store an operating system installed in the computer device 2 and various types of application software, such as the program codes of the certificate authenticity verification system 20 in the second embodiment. Further, the memory 21 may also be used to temporarily store various types of data that have been output or are to be output.
The network interface 23 may comprise a wireless network interface or a wired network interface, and the network interface 23 is generally used for establishing communication connection between the computer device 2 and other electronic apparatuses. For example, the network interface 23 is used to connect the computer device 2 to an external terminal through a network, establish a data transmission channel and a communication connection between the computer device 2 and the external terminal, and the like. The network may be a wireless or wired network such as an Intranet (Intranet), the Internet (Internet), a Global System of Mobile communication (GSM), Wideband Code Division Multiple Access (WCDMA), a 4G network, a 5G network, Bluetooth (Bluetooth), Wi-Fi, and the like.
It is noted that fig. 10 only shows the computer device 2 with components 20-23, but it is to be understood that not all of the shown components are required to be implemented, and that more or less components may be implemented instead.
In an embodiment of the present invention, the certificate authenticity verification system 20 stored in the memory 21 can be further divided into one or more program modules, and the one or more program modules are stored in the memory 21 and executed by one or more processors (in an embodiment of the present invention, the processor 22) to complete the present invention.
For example, fig. 9 shows a schematic diagram of program modules for implementing the second embodiment of the certificate based authenticity verification system 20, in which the certificate based authenticity verification system 20 may be divided into an acquisition module 200, a processing module 210, and a result generation module 220. The program modules referred to in the present invention refer to a series of computer program instruction segments capable of performing specific functions, and are more suitable than programs for describing the execution process of the certificate authenticity verification system 20 in the computer device 2. The specific functions of the program modules 200 and 220 have been described in detail in the second embodiment, and are not described herein again.
Example four
Embodiments of the present invention also provide a computer-readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application store, etc., on which a computer program is stored, which when executed by a processor implements a corresponding function. The computer-readable storage medium of the embodiment of the present invention is used for storing a certificate authenticity verification system 20, and when being executed by a processor, the certificate authenticity verification method of the first embodiment is implemented.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A method for verifying authenticity of a certificate, comprising:
acquiring an original certificate image, and identifying the certificate type of the original certificate image;
extracting a plurality of pieces of characteristic information from the original certificate image based on the certificate type, analyzing the plurality of pieces of characteristic information to obtain a plurality of pieces of target characteristic information, and verifying the plurality of pieces of target characteristic information to generate a plurality of verification results;
and generating a verification conclusion form according to the plurality of verification results.
2. The method of claim 1, wherein the plurality of feature information includes a plurality of feature vector sequences and a plurality of image areas;
the step of extracting a plurality of pieces of feature information from the original certificate image based on the certificate type, analyzing the plurality of pieces of feature information to obtain a plurality of pieces of target feature information, and verifying the plurality of pieces of target feature information to generate a plurality of verification results includes:
positioning the original certificate image to obtain a plurality of characteristic vector sequences and a plurality of position identifications corresponding to the plurality of characteristic vector sequences;
extracting a plurality of character feature vector sequences from the plurality of feature vector sequences;
based on the certificate type, performing identification operation on the character feature vector sequences to obtain a plurality of target characters corresponding to the character feature vector sequences;
acquiring a plurality of preset text content rules corresponding to the certificate type based on the certificate type and a plurality of position identifications;
and checking the target characters based on the certificate type and the character content rules to generate a character checking result.
3. The method of claim 2, wherein the original document image comprises a front side image and at least two side images of the document;
the step of extracting a plurality of pieces of feature information from the original certificate image based on the certificate type, analyzing the plurality of pieces of feature information to obtain a plurality of pieces of target feature information, and verifying the plurality of pieces of target feature information to generate a plurality of verification results includes:
extracting a person image area and a standard person image area of a certificate holder photo from the image shot on the front side of the certificate according to a preset anti-counterfeit label;
matching the portrait area of the bearer photo with a standard portrait area to obtain a face verification result; when the matching of the portrait area of the bearer photo and the standard portrait area is consistent, judging that the face verification result is successful; and when the matching of the portrait area of the bearer photo and the standard portrait area is inconsistent, judging that the face verification result is verification failure.
4. The method of claim 3, wherein the step of extracting a plurality of feature information from the original document image based on the document type, analyzing the feature information to obtain a plurality of target feature information, and checking the target feature information to generate a plurality of checking results further comprises:
extracting a target rainbow printing area from the photographed image on the front side of the certificate according to a preset anti-counterfeit label;
acquiring a first similarity between the overall color gradient style of the target rainbow printing area and the overall color gradient style of the standard certificate;
comparing the first similarity with a preset first threshold value to obtain an overall color gradient style verification result: when the first similarity is higher than the first threshold value, judging that the overall color gradient style verification result is successful; and when the first similarity is lower than the first threshold, judging that the overall color gradient style verification result is verification failure.
5. The method of claim 4, wherein the step of extracting a plurality of feature information from the original document image based on the document type, analyzing the feature information to obtain a plurality of target feature information, and checking the target feature information to generate a plurality of checking results further comprises:
extracting a target diversified pattern background area from at least two certificate side face shot images according to a preset anti-counterfeiting label;
acquiring a second similarity between the background area of the target diversified pattern and the background area of the diversified pattern of the standard certificate;
comparing the second similarity with a preset second threshold value to obtain a diversified pattern background verification result: when the second similarity is higher than the second threshold, judging that the verification of the background of the diversified patterns is successful; and when the second similarity is lower than the second threshold, judging that the background verification result of the diversified patterns is verification failure.
6. The method of claim 5, wherein the step of extracting a plurality of feature information from the original document image based on the document type, analyzing the feature information to obtain a plurality of target feature information, and checking the target feature information to generate a plurality of checking results further comprises:
extracting a plurality of target optical color-changing ink triangular areas and a plurality of target hologram areas with wave and three-dimensional effects from at least two certificate side face shot images according to a preset anti-counterfeit label;
acquiring a third similarity between the triangular area of the target optical color-changing ink and the triangular area of the optical color-changing ink of the standard certificate and a fourth similarity between the hologram area with the wave and three-dimensional effect of the target and the hologram area with the wave and three-dimensional effect of the standard certificate;
comparing the third similarity with a preset third threshold, and comparing the fourth similarity with a preset fourth threshold to obtain a background pattern verification result: when the third similarity is higher than the third threshold, and the fourth similarity is higher than the fourth threshold; judging that the background pattern verification result is successful; when the third similarity is lower than the third threshold and/or the fourth similarity is lower than the fourth threshold; the background pattern verification result is judged to be verification failure.
7. The method of claim 6, wherein the step of extracting a plurality of feature information from the original document image based on the document type, analyzing the feature information to obtain a plurality of target feature information, and checking the target feature information to generate a plurality of checking results further comprises:
extracting a target micro-text printing area and a target transparent window area from the image shot on the front side of the certificate according to a preset anti-counterfeit label, wherein the target transparent window area comprises personal data of a user;
amplifying the target miniature character printing area to obtain an amplified target miniature character printing area;
acquiring a fifth similarity between the target micro-text printing area and the micro-text printing area of the standard certificate and a sixth similarity between the target transparent window area and the corresponding user personal data in the database corresponding to the certificate type;
comparing the fifth similarity with a preset fifth threshold, and comparing the sixth similarity with a preset sixth threshold to obtain a content verification result: when the fifth similarity is higher than the fifth threshold and the sixth similarity is higher than the sixth threshold, determining that the content verification result is successful; and when the fifth similarity is lower than the fifth threshold and/or the sixth similarity is lower than the sixth threshold, determining that the content verification result is verification failure.
8. A system for verifying authenticity of a document, comprising:
the acquisition module is used for acquiring an original certificate image and identifying the certificate type of the original certificate image;
the processing module extracts a plurality of pieces of characteristic information from the original certificate image based on the certificate type, analyzes the plurality of pieces of characteristic information to obtain a plurality of pieces of target characteristic information, and verifies the plurality of pieces of target characteristic information to generate a plurality of verification results;
and the result generation module is used for generating a verification conclusion form according to the plurality of verification results.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the computer program implements the steps of the method of verifying the authenticity of a document as claimed in any one of claims 1 to 7.
10. A computer-readable storage medium, having stored thereon a computer program executable by at least one processor to cause the at least one processor to perform the steps of the method of verifying authenticity of a document as claimed in any one of claims 1 to 7.
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| PCT/CN2019/117551 WO2021051554A1 (en) | 2019-09-19 | 2019-11-12 | Certificate authenticity verification method and system, and computer device and readable storage medium |
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| CN201910885149.5A CN110751041A (en) | 2019-09-19 | 2019-09-19 | Certificate authenticity verification method, system, computer equipment and readable storage medium |
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| CN111914769B (en) * | 2020-08-06 | 2024-01-26 | 腾讯科技（深圳）有限公司 | User validity determination method, device, computer readable storage medium and equipment |
| CN112132812A (en) * | 2020-09-24 | 2020-12-25 | 平安科技（深圳）有限公司 | Certificate checking method and device, electronic equipment and medium |
| CN112132812B (en) * | 2020-09-24 | 2023-06-30 | 平安科技（深圳）有限公司 | Certificate verification method and device, electronic equipment and medium |
| CN112215225A (en) * | 2020-10-22 | 2021-01-12 | 北京通付盾人工智能技术有限公司 | KYC certificate verification method based on computer vision technology |
| CN112215225B (en) * | 2020-10-22 | 2024-03-15 | 北京通付盾人工智能技术有限公司 | KYC certificate verification method based on computer vision technology |
| CN113705486B (en) * | 2021-08-31 | 2023-11-10 | 支付宝(杭州)信息技术有限公司 | Method and device for detecting authenticity of certificate |
| CN113705486A (en) * | 2021-08-31 | 2021-11-26 | 支付宝(杭州)信息技术有限公司 | Method and device for detecting authenticity of certificate |
| CN113837287A (en) * | 2021-09-26 | 2021-12-24 | 平安科技（深圳）有限公司 | Certificate abnormal information identification method, device, equipment and medium |
| CN113837287B (en) * | 2021-09-26 | 2023-08-29 | 平安科技（深圳）有限公司 | Certificate abnormal information identification method, device, equipment and medium |
| CN116597259A (en) * | 2023-05-26 | 2023-08-15 | 广州欢聚马克网络信息有限公司 | Site information verification method and device, equipment, medium and product thereof |
| CN116597259B (en) * | 2023-05-26 | 2023-12-05 | 广州欢聚马克网络信息有限公司 | Site information verification method and device, equipment, medium and product thereof |
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