CN111898538A - Certificate authentication method and device, electronic equipment and storage medium - Google Patents
Certificate authentication method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention relates to an artificial intelligence technology, and discloses a certificate authenticity identification method, which comprises the following steps: acquiring a multi-image combination or a video frame sequence of the counterfeit identification points in the certificate to obtain an image set to be counterfeit identified; identifying each picture in the image set to be identified by using a certificate identification model trained in advance to obtain a plurality of prediction results; and performing data fusion on the plurality of prediction results to obtain a result probability value, and obtaining the counterfeit identification result of the counterfeit identification point according to the result probability value. The invention also relates to a block chain technology, and the multi-image combination or the video frame sequence of the certificate counterfeit point can be stored in the block chain. The invention also discloses a certificate authenticity identifying device, electronic equipment and a computer readable storage medium. The invention can improve the accuracy of certificate authentication.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a certificate authenticity identification method and device, electronic equipment and a computer readable storage medium.
Background
With the development of computers and the research of computer vision theory, especially the appearance of deep learning and artificial intelligence, the computers can imitate human vision perception systems conveniently and accurately to identify the certificates.
At present, the existing certificate counterfeit identification mode can be carried out through methods such as image processing and traditional machine learning, but counterfeit identification is mostly carried out through images or single images, and a plurality of counterfeit identification points in the certificate can be comprehensively identified only by conditions such as different angles, different visual ranges or different illumination, so that the accuracy of counterfeit identification results is low.
Disclosure of Invention
The invention provides a certificate authenticity identifying method, a certificate authenticity identifying device, electronic equipment and a computer readable storage medium, and mainly aims to provide a certificate authenticity identifying method capable of improving authenticity identification accuracy.
In order to achieve the above object, the present invention provides a certificate authentication method, which comprises:
acquiring a multi-image combination or a video frame sequence of the counterfeit identification points in the certificate to obtain an image set to be counterfeit identified;
identifying each picture in the image set to be identified by using a certificate identification model trained in advance to obtain a plurality of prediction results;
and performing data fusion on the plurality of prediction results to obtain a result probability value, and obtaining the counterfeit identification result of the counterfeit identification point according to the result probability value.
Optionally, the authenticating each picture in the image set to be authenticated by using a pre-trained certificate authentication model includes:
extracting features in the picture by utilizing the convolution layer and the pooling layer of the certificate counterfeit identification model;
and calculating the characteristics by utilizing the activation layer of the certificate authenticity identification model to obtain the prediction result of the picture.
Optionally, the set of images to be authenticated includes: the method comprises the following steps that a true counterfeit identification point image and a forged counterfeit identification point image are identified, each image in the to-be-identified image set is identified by using a pre-trained certificate identification model, and before a plurality of prediction results are obtained, the method further comprises the following steps:
collecting the true and false distinguishing point image, and inputting the true and false distinguishing point image into the certificate false distinguishing model for feature extraction to obtain true feature data;
collecting the fake counterfeit identification point image, and inputting the fake counterfeit identification point image into the certificate counterfeit identification model for feature extraction to obtain fake feature data;
analyzing the difference characteristics of the true characteristic data and the forged characteristic data;
and improving the convolution layer and the pooling layer of the certificate authenticity identification model according to the difference characteristics. Optionally, the performing data fusion on the multiple prediction results to obtain a result probability value includes:
sequencing the plurality of prediction results to obtain a sequencing result set;
screening the sequencing result set to obtain an effective result set;
and merging the data in the effective result set by using a preset merging algorithm to obtain a result probability value.
Optionally, the screening the ranked result set includes:
determining a distribution interval of the data in the sequencing result set;
and deleting the data which do not belong to the distribution interval from the sequencing result set.
Optionally, the obtaining the counterfeit result of the counterfeit point according to the result probability value includes:
comparing the result probability value with a preset confidence threshold;
when the result probability value is greater than or equal to the confidence coefficient threshold value, obtaining a true authenticity distinguishing result of the certificate corresponding to the authenticity distinguishing point;
and when the result probability value is smaller than the confidence coefficient threshold value, obtaining the counterfeit discrimination result that the certificate corresponding to the counterfeit discrimination point is false.
Optionally, before the pre-trained certificate counterfeit identification model is used to counterfeit each picture in the image set to be counterfeit, and a plurality of prediction results are obtained, the method further includes:
generating effective sample data and a standard result corresponding to the effective sample data;
inputting the effective sample data into a certificate authenticity identification model for authenticity identification to obtain a training result;
calculating loss values of the training results and the standard results by using a preset loss function to obtain loss values;
when the loss value is larger than or equal to a preset loss threshold value, adjusting parameters of the certificate counterfeit identification model, and carrying out counterfeit identification again to obtain a training result;
and when the loss value is smaller than the loss threshold value, obtaining the trained certificate authentication model.
In order to solve the above problems, the present invention also provides a certificate authentication apparatus, comprising:
the image acquisition module is used for acquiring a multi-image combination or a video frame sequence of the counterfeit identification points in the certificate to obtain an image set to be counterfeit identified;
the model counterfeit identification module is used for identifying each picture in the to-be-authenticated image set by using a pre-trained certificate counterfeit identification model to obtain a plurality of prediction results;
and the false distinguishing result output module is used for carrying out data fusion on the plurality of prediction results to obtain a result probability value and obtaining the false distinguishing result of the false distinguishing point according to the result probability value.
In order to solve the above problem, the present invention also provides an electronic device, including:
a memory storing at least one instruction; and
and the processor executes the instructions stored in the memory to realize the certificate authentication method.
In order to solve the above problem, the present invention further provides a computer-readable storage medium, in which at least one instruction is stored, and the at least one instruction is executed by a processor in an electronic device to implement the certificate authentication method.
The embodiment of the invention obtains the multi-image combination or the video frame sequence of the counterfeit identifying points in the certificate to obtain the image set to be counterfeit identified, can enlarge the presenting coverage range of the counterfeit identifying points through the multi-image combination and the video frame sequence and improve the utilization rate of the counterfeit identifying point data; identifying each picture in the image set to be identified by using a pre-trained certificate identification model to obtain a plurality of prediction results, and identifying by using the certificate identification model to improve the accuracy and precision of identification; and performing data fusion on the plurality of prediction results to obtain a result probability value, obtaining the counterfeit identification result of the counterfeit identification point according to the result probability value, and reducing deviation errors and extreme value interference by performing data fusion on the plurality of prediction results, thereby ensuring the accuracy of the counterfeit identification result. Therefore, the certificate authenticity identification method, the certificate authenticity identification device and the computer readable storage medium can achieve the purpose of improving the certificate authenticity identification accuracy.
Drawings
FIG. 1 is a schematic flow chart illustrating a method for authenticating a certificate according to an embodiment of the present invention;
fig. 2 is a schematic flowchart of a picture detection method according to an embodiment of the present invention;
fig. 3 is a schematic flow chart of a method for obtaining a result probability value according to an embodiment of the present invention;
FIG. 4 is a block diagram of a certificate authenticating device according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of an internal structure of an electronic device implementing a certificate authentication method according to an embodiment of the present invention;
the implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The execution main body of the certificate authentication method provided by the embodiment of the application includes but is not limited to at least one of the electronic devices, such as a server and a terminal, which can be configured to execute the method provided by the embodiment of the application. In other words, the certificate authentication method may be performed by software or hardware installed in the terminal device or the server device, and the software may be a block chain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.
The core of the invention is that the computer vision is utilized to carry out comprehensive counterfeit identification judgment on counterfeit identification points in the certificate in different vision ranges or under different illumination conditions and the like, so as to obtain the counterfeit identification result of the certificate.
The counterfeit identification point refers to an anti-counterfeiting mark on the certificate, such as an anti-counterfeiting mark on an identity certificate.
A document may include a plurality of authentication points, the authentication points being of a type including visually perceptible authentication points, tactilely perceptible authentication points, and other types of perceptible authentication points. The visual perception counterfeit distinguishing point is a counterfeit distinguishing point for visually distinguishing authenticity, for example, images of the counterfeit distinguishing point can have color replacement along with different viewing angles, and show waves and three-dimensional effects. The tactile perception counterfeit distinguishing point is a counterfeit distinguishing point for identifying authenticity through tactile sense, and if a finger touches the counterfeit distinguishing point, the counterfeit distinguishing point can have a convex feeling; the other types of perception and counterfeit discrimination points refer to counterfeit discrimination points for discriminating authenticity through other modes except vision and touch, for example, micro characters at the counterfeit discrimination points can be recognized through instruments, ultraviolet ray optometry and magnifying glasses. The embodiment of the invention is suitable for the counterfeit discrimination judgment of the visual perception counterfeit discrimination point.
Fig. 1 is a schematic flow chart of a certificate authentication method according to an embodiment of the present invention. In this embodiment, the certificate authentication method includes:
and S1, acquiring a multi-image combination or a video frame sequence of the counterfeit point in the certificate to obtain a to-be-authenticated image set.
As described above, when the counterfeit detection point in the certificate is at different viewing angles, the image may have color replacement, or show wave and stereoscopic effect, so that the embodiment of the present invention obtains a plurality of pictures or video segments of the counterfeit detection point taken from different angles to obtain the multi-picture combination or video frame sequence. Therefore, in the embodiment of the invention, the multi-image combination is a plurality of images of the same counterfeit identification point at different angles, and the plurality of images correspond to the plurality of shooting angles one by one. The video frame sequence is obtained by converting a video with a fake-distinguishing point change effect into a video frame.
Optionally, before acquiring the video frame sequence of the counterfeit-distinguishing points in the certificate, the embodiment of the present invention further needs to analyze the video key frames of the video, and since the change of the images between adjacent frames of the video is not large, and if each frame is analyzed, redundancy exists, an image method for extracting a key frame I frame, or a frame skipping extraction (for example, 10 frames per interval, 20 frames per interval, etc.) method, etc. may be adopted to analyze the key frames of the video, so as to obtain the video frame sequence.
In detail, the multi-graph combination or the video frame sequence may be obtained from a preset database, and in order to further ensure the privacy and security of the certificate information, the multi-graph combination or the video frame sequence of the certificate authentication point may also be obtained from a preset blockchain node.
And S2, carrying out counterfeit discrimination on each picture in the image set to be counterfeit by using a pre-trained certificate counterfeit discrimination model to obtain a plurality of prediction results.
Preferably, the certificate authentication model in the embodiment of the present invention may be a Deep Neural Network (DNN) model for performing image recognition, classification, and the like, and the DNN model includes an input layer, a convolution layer, a pooling layer, an activation layer, and an output layer. The input layer is used for receiving data; the convolutional layer is used for preliminarily extracting features from the data; the pooling layer is used for extracting main features from the data; the activation layer is used for performing prediction identification on the features; the output layer is used for outputting a prediction recognition result.
In detail, referring to fig. 2, the detecting each picture in the to-be-authenticated image set by using the pre-trained certificate authentication model includes:
s20, extracting the features in the picture by using the convolution layer and the pooling layer of the certificate counterfeit identification model;
and S21, calculating the characteristics by using the activation layer of the certificate authentication model to obtain the prediction result of the picture.
Wherein the prediction result is a probability value that the counterfeit point in the picture is true.
In an optional embodiment of the present invention, the set of images to be authenticated includes: in order to improve the accuracy of feature extraction of the certificate authenticity model, the method further comprises, before extracting features in the picture by using the certificate authenticity model convolution layer and the pooling layer:
collecting the true and false distinguishing point image, and inputting the true and false distinguishing point image into the certificate false distinguishing model for feature extraction to obtain true feature data;
collecting the fake counterfeit identification point image, and inputting the fake counterfeit identification point image into the certificate counterfeit identification model for feature extraction to obtain fake feature data;
analyzing the difference characteristics of the true characteristic data and the forged characteristic data;
and improving the convolution layer and the pooling layer of the certificate authenticity identification model according to the difference characteristics.
The fake identification points are fake identification points which are forged by means of tampering, copying, splicing, shielding and the like.
Preferably, by enhancing the feature expression of the certificate counterfeit identification model, the accuracy of the certificate counterfeit identification model in feature extraction can be improved.
Optionally, before detecting each image in the to-be-authenticated image set by using a pre-trained certificate authentication model, the method further includes training the certificate authentication model, where the training process of the certificate authentication model includes:
generating effective sample data and a standard result corresponding to the effective sample data;
inputting the effective sample data into a certificate authenticity identification model for authenticity identification to obtain a training result;
calculating loss values of the training results and the standard results by using a preset loss function to obtain loss values;
when the loss value is larger than or equal to a preset loss threshold value, adjusting parameters of the certificate counterfeit identification model, and carrying out counterfeit identification again to obtain a training result;
and when the loss value is smaller than the loss threshold value, obtaining the trained certificate authentication model.
Further, the embodiment of the present invention performs difference calculation on the training result and a preset standard result by using the loss function as follows to obtain a difference value:
wherein,
is the training result; y is the standard result; alpha represents an error factor which is a preset constant; n is the total amount of data of the sample data.
And S3, performing data fusion on the plurality of prediction results to obtain a result probability value, and obtaining the counterfeit discrimination result of the counterfeit discrimination point according to the result probability value.
In detail, referring to fig. 3, the performing data fusion on the multiple prediction results to obtain a result probability value includes:
s30, sequencing the plurality of prediction results to obtain a sequencing result set;
s31, screening the sequencing result set to obtain an effective result set;
and S32, merging the data in the effective result set by using a preset merging algorithm to obtain a result probability value.
Wherein the screening the ranked result set comprises:
determining a distribution interval of the data in the sequencing result set;
and deleting the data which do not belong to the distribution interval from the sequencing result set.
The preset merging algorithm in the embodiment of the invention comprises the following steps:
wherein P is the result probability value, n is the total number of data in the valid result set, yiIs the ith data of the valid result set and y' is the average of all the data in the valid result set.
Preferably, in the embodiment of the present invention, the result probability value is determined according to a preset confidence threshold of the counterfeit identification point, so as to obtain the counterfeit identification result of the certificate corresponding to the original image set to be counterfeit identified.
In detail, the obtaining the counterfeit result of the counterfeit point according to the result probability value includes:
comparing the result probability value with a preset confidence threshold;
when the result probability value is greater than or equal to the confidence coefficient threshold value, obtaining a true authenticity distinguishing result of the certificate corresponding to the authenticity distinguishing point;
and when the result probability value is smaller than the confidence coefficient threshold value, obtaining the counterfeit discrimination result that the certificate corresponding to the counterfeit discrimination point is false.
The certificate authentication method and the certificate authentication device can authenticate the certificate through the certificate authentication model, and the certificate authentication model adopts a multi-image combination or video frame sequence to carry out comprehensive authentication, so that the accuracy of an authentication result is improved, and the error rate is reduced.
The embodiment of the invention obtains the multi-image combination or the video frame sequence of the counterfeit identifying points in the certificate to obtain the image set to be counterfeit identified, can enlarge the presenting coverage range of the counterfeit identifying points through the multi-image combination and the video frame sequence and improve the utilization rate of the counterfeit identifying point data; identifying each picture in the image set to be identified by using a pre-trained certificate identification model to obtain a plurality of prediction results, and identifying by using the certificate identification model to improve the accuracy and precision of identification; and performing data fusion on the plurality of prediction results to obtain a result probability value, obtaining the counterfeit identification result of the counterfeit identification point according to the result probability value, and reducing deviation errors and extreme value interference by performing data fusion on the plurality of prediction results, thereby ensuring the accuracy of the counterfeit identification result. Therefore, the certificate authenticity identification method, the certificate authenticity identification device and the computer readable storage medium can achieve the purpose of improving the certificate authenticity identification accuracy.
FIG. 4 is a functional block diagram of the certificate authentication device according to the present invention.
The certificate authentication apparatus 100 according to the present invention may be installed in an electronic device. According to the realized functions, the certificate authentication device can comprise an image acquisition module 101, a model authentication module 102 and an authentication result output module 103. A module according to the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the image acquisition module 101 is configured to acquire a multi-image combination or a video frame sequence of the counterfeit detection points in the certificate to obtain an image set to be counterfeit.
When some counterfeit identifying points in the certificate are at different viewing angles, the images have color replacement or display wave and three-dimensional effects, so that the embodiment of the invention acquires a plurality of pictures or video segments of the counterfeit identifying points shot at different angles to obtain the multi-picture combination or the video frame sequence. Therefore, in the embodiment of the invention, the multi-image combination is a plurality of images of the same counterfeit identification point at different angles, and the plurality of images correspond to the plurality of shooting angles one by one. The video frame sequence is obtained by converting a video with a fake-distinguishing point change effect into a video frame.
Optionally, before acquiring the video frame sequence of the counterfeit-distinguishing points in the certificate, the embodiment of the present invention further needs to analyze the video key frames of the video, and since the change of the images between adjacent frames of the video is not large, and if each frame is analyzed, redundancy exists, an image method for extracting a key frame I frame, or a frame skipping extraction (for example, 10 frames per interval, 20 frames per interval, etc.) method, etc. may be adopted to analyze the key frames of the video, so as to obtain the video frame sequence.
In detail, the multi-graph combination or the video frame sequence may be obtained from a preset database, and in order to further ensure the privacy and security of the certificate information, the multi-graph combination or the video frame sequence of the certificate authentication point may also be obtained from a preset blockchain node.
The model counterfeit identifying module 102 is configured to use a pre-trained certificate counterfeit identifying model to identify each image in the image set to be counterfeit, so as to obtain a plurality of prediction results.
Preferably, the certificate authentication model in the embodiment of the present invention may be a Deep Neural Network (DNN) model for performing image recognition, classification, and the like, and the DNN model includes an input layer, a convolution layer, a pooling layer, an activation layer, and an output layer. The input layer is used for receiving data; the convolutional layer is used for preliminarily extracting features from the data; the pooling layer is used for extracting main features from the data; the activation layer is used for performing prediction identification on the features; the output layer is used for outputting a prediction recognition result.
In detail, when each picture in the to-be-authenticated image set is detected by using a pre-trained certificate authentication model, the model authentication module 102 specifically performs the following operations:
extracting features in the picture by utilizing the convolution layer and the pooling layer of the certificate counterfeit identification model;
and calculating the characteristics by utilizing the activation layer of the certificate authenticity identification model to obtain the prediction result of the picture.
Wherein the prediction result is a probability value that the counterfeit point in the picture is true.
In an optional embodiment of the present invention, the set of images to be authenticated includes: in order to improve the accuracy of feature extraction of the certificate authenticity model, before extracting features in the picture by using the certificate authenticity model convolution layer and pooling layer, the model authenticity identifying module 102 is further configured to:
collecting the true and false distinguishing point image, and inputting the true and false distinguishing point image into the certificate false distinguishing model for feature extraction to obtain true feature data;
collecting the fake counterfeit identification point image, and inputting the fake counterfeit identification point image into the certificate counterfeit identification model for feature extraction to obtain fake feature data;
analyzing the difference characteristics of the true characteristic data and the forged characteristic data;
and improving the convolution layer and the pooling layer of the certificate authenticity identification model according to the difference characteristics.
The fake identification points are fake identification points which are forged by means of tampering, copying, splicing, shielding and the like.
Preferably, by enhancing the feature expression of the certificate counterfeit identification model, the accuracy of the certificate counterfeit identification model in feature extraction can be improved.
Optionally, before detecting each image in the to-be-authenticated image set by using a pre-trained certificate authentication model, the invention may further include training the certificate authentication model, where the certificate authentication model is configured to:
generating effective sample data and a standard result corresponding to the effective sample data;
inputting the effective sample data into a certificate authenticity identification model for authenticity identification to obtain a training result;
calculating loss values of the training results and the standard results by using a preset loss function to obtain loss values;
when the loss value is larger than or equal to a preset loss threshold value, adjusting parameters of the certificate counterfeit identification model, and carrying out counterfeit identification again to obtain a training result;
and when the loss value is smaller than the loss threshold value, obtaining the trained certificate authentication model.
Further, the embodiment of the present invention performs difference calculation on the training result and a preset standard result by using the loss function as follows to obtain a difference value:
wherein,
is the training result; y is the standard result; alpha represents an error factor which is a preset constant; n is the total amount of data of the sample data.
The counterfeit discrimination result output module 103 is configured to perform data fusion on the multiple prediction results to obtain a result probability value, and obtain a counterfeit discrimination result of the counterfeit discrimination point according to the result probability value.
In detail, after performing data fusion on the multiple prediction results to obtain a result probability value, the counterfeit identification result output module 103 specifically performs the following operations:
sequencing the plurality of prediction results to obtain a sequencing result set;
screening the sequencing result set to obtain an effective result set;
and merging the data in the effective result set by using a preset merging algorithm to obtain a result probability value.
Wherein the screening the ranked result set comprises:
determining a distribution interval of the data in the sequencing result set;
and deleting the data which do not belong to the distribution interval from the sequencing result set.
The preset merging algorithm in the embodiment of the invention comprises the following steps:
where P is the result probability value, n is the total number of data in the valid result set, y _ i is the ith data in the valid result set, and y ^' is the average of all the data in the valid result set.
Preferably, in the embodiment of the present invention, the result probability value is determined according to a preset confidence threshold of the counterfeit identification point, so as to obtain the counterfeit identification result of the certificate corresponding to the original image set to be counterfeit identified.
In detail, when the counterfeit result of the counterfeit detection point is obtained according to the result probability value, the counterfeit detection result output module specifically executes the following operations:
comparing the result probability value with a preset confidence threshold;
when the result probability value is greater than or equal to the confidence coefficient threshold value, obtaining a true authenticity distinguishing result of the certificate corresponding to the authenticity distinguishing point;
and when the result probability value is smaller than the confidence coefficient threshold value, obtaining the counterfeit discrimination result that the certificate corresponding to the counterfeit discrimination point is false.
The certificate authentication method and the certificate authentication device can authenticate the certificate through the certificate authentication model, and the certificate authentication model adopts a multi-image combination or video frame sequence to carry out comprehensive authentication, so that the accuracy of an authentication result is improved, and the error rate is reduced.
Fig. 5 is a schematic structural diagram of an electronic device for implementing the certificate authentication method according to the present invention.
The electronic device 1 may comprise a processor 10, a memory 11 and a bus, and may further comprise a computer program, such as a certificate authentication program 12, stored in the memory 11 and operable on the processor 10.
The memory 11 includes at least one type of readable storage medium, which includes flash memory, removable hard disk, multimedia card, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only to store application software installed in the electronic device 1 and various types of data, such as a code of the certificate authentication program 12, but also to temporarily store data that has been output or is to be output.
The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the whole electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device 1 by running or executing programs or modules (e.g., executing a certificate authentication program, etc.) stored in the memory 11 and calling data stored in the memory 11.
The bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
Fig. 5 only shows an electronic device with components, and it will be understood by a person skilled in the art that the structure shown in fig. 5 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or a combination of certain components, or a different arrangement of components.
For example, although not shown, the electronic device 1 may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so as to implement functions of charge management, discharge management, power consumption management, and the like through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device 1 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
Further, the electronic device 1 may further include a network interface, and optionally, the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a bluetooth interface, etc.), which are generally used for establishing a communication connection between the electronic device 1 and other electronic devices.
Optionally, the electronic device 1 may further comprise a user interface, which may be a Display (Display), an input unit (such as a Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable for displaying information processed in the electronic device 1 and for displaying a visualized user interface, among other things.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The certificate authenticity verification program 12 stored in the memory 11 of the electronic device 1 is a combination of instructions that, when executed in the processor 10, enable:
acquiring a multi-image combination or a video frame sequence of the counterfeit identification points in the certificate to obtain an image set to be counterfeit identified;
identifying each picture in the image set to be identified by using a certificate identification model trained in advance to obtain a plurality of prediction results;
and performing data fusion on the plurality of prediction results to obtain a result probability value, and obtaining the counterfeit identification result of the counterfeit identification point according to the result probability value.
Further, the integrated modules/units of the electronic device 1, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. The computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any accompanying claims should not be construed as limiting the claim concerned.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A method of authenticating a document, the method comprising:
acquiring a multi-image combination or a video frame sequence of the counterfeit identification points in the certificate to obtain an image set to be counterfeit identified;
identifying each picture in the image set to be identified by using a certificate identification model trained in advance to obtain a plurality of prediction results;
and performing data fusion on the plurality of prediction results to obtain a result probability value, and obtaining the counterfeit identification result of the counterfeit identification point according to the result probability value.
2. The method of claim 1, wherein the authenticating each image in the set of images to be authenticated using a pre-trained certificate authentication model comprises:
extracting features in the picture by utilizing the convolution layer and the pooling layer of the certificate counterfeit identification model;
and calculating the characteristics by utilizing the activation layer of the certificate authenticity identification model to obtain the prediction result of the picture.
3. The method of claim 2, wherein the set of images to be authenticated comprises: the method comprises the following steps that a true counterfeit identification point image and a forged counterfeit identification point image are identified, each image in the to-be-identified image set is identified by using a pre-trained certificate identification model, and before a plurality of prediction results are obtained, the method further comprises the following steps:
collecting the true and false distinguishing point image, and inputting the true and false distinguishing point image into the certificate false distinguishing model for feature extraction to obtain true feature data;
collecting the fake counterfeit identification point image, and inputting the fake counterfeit identification point image into the certificate counterfeit identification model for feature extraction to obtain fake feature data;
analyzing the difference characteristics of the true characteristic data and the forged characteristic data;
and improving the convolution layer and the pooling layer of the certificate authenticity identification model according to the difference characteristics.
4. The method of claim 1, wherein the fusing the plurality of predicted results to obtain a result probability value comprises:
sequencing the plurality of prediction results to obtain a sequencing result set;
screening the sequencing result set to obtain an effective result set;
and merging the data in the effective result set by using a preset merging algorithm to obtain a result probability value.
5. The method of claim 4, wherein the screening the ranked result set comprises:
determining a distribution interval of the data in the sequencing result set;
and deleting the data which do not belong to the distribution interval from the sequencing result set.
6. The method of claim 1, wherein obtaining the authenticity of the authenticity feature according to the result probability value comprises:
comparing the result probability value with a preset confidence threshold;
when the result probability value is greater than or equal to the confidence coefficient threshold value, obtaining a true authenticity distinguishing result of the certificate corresponding to the authenticity distinguishing point;
and when the result probability value is smaller than the confidence coefficient threshold value, obtaining the counterfeit discrimination result that the certificate corresponding to the counterfeit discrimination point is false.
7. The method according to any one of claims 1 to 6, wherein before the steps of using a pre-trained certificate authentication model to authenticate each image in the image set to be authenticated and obtaining a plurality of prediction results, the method further comprises:
generating effective sample data and a standard result corresponding to the effective sample data;
inputting the effective sample data into a certificate authenticity identification model for authenticity identification to obtain a training result;
calculating loss values of the training results and the standard results by using a preset loss function to obtain loss values;
when the loss value is larger than or equal to a preset loss threshold value, adjusting parameters of the certificate counterfeit identification model, and carrying out counterfeit identification again to obtain a training result;
and when the loss value is smaller than the loss threshold value, obtaining the trained certificate authentication model.
8. A certificate authentication apparatus, comprising:
the image acquisition module is used for acquiring a multi-image combination or a video frame sequence of the counterfeit identification points in the certificate to obtain an image set to be counterfeit identified;
the model counterfeit identification module is used for identifying each picture in the to-be-authenticated image set by using a pre-trained certificate counterfeit identification model to obtain a plurality of prediction results;
and the false distinguishing result output module is used for carrying out data fusion on the plurality of prediction results to obtain a result probability value and obtaining the false distinguishing result of the false distinguishing point according to the result probability value.
9. An electronic device, characterized in that the electronic device comprises:
a memory storing at least one instruction; and
a processor executing instructions stored in the memory to perform a method of authenticating a document as claimed in any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements a method of authenticating a document according to any one of claims 1 to 7.
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| CN202010753977.6A CN111898538B (en) | 2020-07-30 | 2020-07-30 | Certificate authentication method and device, electronic equipment and storage medium |
| PCT/CN2020/122052 WO2021151313A1 (en) | 2020-07-30 | 2020-10-20 | Method and apparatus for document forgery detection, electronic device, and storage medium |
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| CN202010753977.6A CN111898538B (en) | 2020-07-30 | 2020-07-30 | Certificate authentication method and device, electronic equipment and storage medium |
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| CN113240043A (en) * | 2021-06-01 | 2021-08-10 | 平安科技（深圳）有限公司 | False identification method, device and equipment based on multi-picture difference and storage medium |
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| CN114648814A (en) * | 2022-02-25 | 2022-06-21 | 北京百度网讯科技有限公司 | Face living body detection method, training method, device, equipment and medium of model |
| CN114495176B (en) * | 2022-03-30 | 2022-12-06 | 北京字节跳动网络技术有限公司 | Tissue image identification method and device, readable medium and electronic equipment |
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| CN111898538B (en) | 2024-04-12 |
| WO2021151313A1 (en) | 2021-08-05 |
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