CN116486121A - Image verification method, device, medium and equipment - Google Patents

Image verification method, device, medium and equipment Download PDF

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CN116486121A
CN116486121A CN202310413549.2A CN202310413549A CN116486121A CN 116486121 A CN116486121 A CN 116486121A CN 202310413549 A CN202310413549 A CN 202310413549A CN 116486121 A CN116486121 A CN 116486121A
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image
verified
verification
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intelligent
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朱召卫
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Ping An Bank Co Ltd
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Ping An Bank Co Ltd
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    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
    • G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
    • G—PHYSICS
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    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/03—Credit; Loans; Processing thereof
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    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
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Abstract

The embodiment of the application provides an image verification method, an image verification device, a medium and image verification equipment, wherein the method comprises the following steps: acquiring an image to be verified; judging whether a target preset image matched with the image to be verified exists in a preset list or not according to the image to be verified; if the target preset image matched with the image to be verified exists, the intelligent verification operation is skipped, and the image to be verified is uploaded to the manual verification system for manual verification operation. By utilizing the embodiment of the application, partial data in the auditing data before being sent to the auditing system is screened out to be subjected to preliminary verification by the intelligent quality inspection system preferentially, the data before being sent to the intelligent quality inspection system is judged, the data existing in the preset list is determined to be a type which is easy to be misjudged by the intelligent quality inspection system, the type of data is skipped over the intelligent quality inspection system to be directly audited by manpower, and the error reason is determined by manual audit. The verification passing rate of the data is improved, and the pressure of a verification system is also relieved.

Description

Image verification method, device, medium and equipment
Technical Field
The present disclosure relates to the field of electronic communications technologies, and in particular, to an image verification method, apparatus, medium, and device.
Background
The auditing system comprises the work of examining and approving the data provided by the credit card applicant, the work of conducting background investigation on the credit condition of the credit card applicant and the like. After the credit card application is completed, the data provided by the credit card applicant is required to be checked, the corresponding card number is generated and the credit card is manufactured after the checking is passed, and finally the card is sent to the hand of the credit card applicant. If the verification is not passed in the middle, the credit card applicant supplementary information is required, and then the auditing system is used for auditing again, so that the pressure is intensively transmitted to the auditing system, the auditing system is excessively stressed, and the data approval efficiency is further affected. In order to solve the above problems, at present, there is a method of assisting in data auditing work by means of artificial intelligence, for example, an identity card image uploaded by a credit card applicant is input into an artificial intelligence system, the identity card image is automatically compared with a real image, and whether the data submitted by the credit card applicant is qualified is confirmed according to the comparison result. However, since the artificial intelligence is not perfect at present, the accuracy of the percentage cannot be ensured, and thus, the situation of misjudgment also exists, and the accuracy of data auditing is affected.
Disclosure of Invention
The embodiment of the application provides an image verification method, an image verification device, a medium and image verification equipment, wherein the image verification method is utilized to screen out partial data of audit data before being sent to an audit system, and the partial data is subjected to preliminary verification by an intelligent quality inspection system, so that audit pressure of the audit system is reduced, the data before being sent to the intelligent quality inspection system is judged, the data existing in a preset list is determined to be a type which is easy to be misjudged by the intelligent quality inspection system, the type of data is skipped over the intelligent quality inspection system and is directly sent to a manual quality inspection system, the audit is performed manually, and an error reason is determined through manual audit. The verification passing rate of the data is improved, and the pressure of a verification system is also relieved.
An aspect of an embodiment of the present application provides an image verification method, including:
acquiring an image to be verified;
judging whether a target preset image matched with the image to be verified exists in a preset list or not according to the image to be verified, wherein the preset image contained in the preset list is an image with the error rate exceeding a preset threshold value after intelligent verification operation is performed by an intelligent verification system;
If the target preset image matched with the image to be verified exists, skipping the intelligent verification operation, and uploading the image to be verified to a manual verification system for manual verification operation.
In the image verification method according to the embodiment of the present application, the determining whether a target preset image matched with the image to be verified exists in the preset list includes:
inputting the image to be verified into a trained image type recognition model to perform image type recognition operation, so as to obtain an image type corresponding to the image to be verified;
and judging whether target preset images of the same image category exist in a preset list or not.
In the image verification method according to the embodiment of the present application, before the inputting the image to be verified into the trained image type recognition model for performing the image type recognition operation, the method further includes:
acquiring a training sample of an image type recognition model to be trained, wherein the training sample comprises image data provided with a label;
extracting features of image data in the training sample through the image type recognition model to be trained to obtain an image feature vector corresponding to the image data;
Identifying the image category of the image data in the training sample based on the image feature vector through the image type identification model to be trained, and obtaining an identification result of the image data;
and adjusting parameters of the image type recognition model to be trained based on the recognition result and the label of the image data to obtain the pre-trained image type recognition model.
In the image verification method according to the embodiment of the present application, the method further includes:
if the target preset image matched with the image to be verified does not exist, the image to be verified is sent to the intelligent verification system for intelligent verification operation;
judging whether the image to be verified is matched with the target expected image or not, and obtaining a judging result;
and if the judging result is that the matching is consistent, determining that the image to be verified passes verification.
In the image verification method according to the embodiment of the present application, after the image to be verified is sent to the intelligent verification system to perform intelligent verification operation, the method further includes:
and if the judging result is inconsistent, uploading the image to be verified to a manual verification system for manual verification operation.
In the image verification method according to the embodiment of the present application, after the uploading the image to be verified to the manual verification system for performing the manual verification operation, the method further includes:
obtaining a verification result according to the manual verification operation;
if the verification result is that the verification is not passed, generating reminding information containing abnormal matters in the image to be verified;
and sending the reminding information to a client of the requestor corresponding to the image to be verified.
In the image verification method described in the embodiment of the present application, the reminding information includes one or more of a short message, a mail and a phone.
Accordingly, another aspect of the embodiments of the present application further provides an image verification apparatus, where the image verification apparatus includes:
the image acquisition module is used for acquiring an image to be verified;
the image judging module is used for judging whether a target preset image matched with the image to be verified exists in a preset list or not according to the image to be verified, wherein the preset image contained in the preset list is an image with the error rate exceeding a preset threshold value after intelligent verification operation is performed by the intelligent verification system;
and the first execution module is used for skipping the intelligent verification operation if a target preset image matched with the image to be verified exists, and uploading the image to be verified to the manual verification system for manual verification operation.
Accordingly, another aspect of embodiments of the present application provides a storage medium storing a plurality of instructions adapted to be loaded by a processor to perform the image verification method as described above.
Accordingly, another aspect of the embodiments of the present application further provides a terminal device, including a processor and a memory, where the memory stores a plurality of instructions, and the processor loads the instructions to perform the image verification method as described above.
The embodiment of the application provides an image verification method, an image verification device, a medium and equipment, wherein the method comprises the steps of obtaining an image to be verified; judging whether a target preset image matched with the image to be verified exists in a preset list or not according to the image to be verified, wherein the preset image contained in the preset list is an image with the error rate exceeding a preset threshold value after intelligent verification operation is performed by an intelligent verification system; if the target preset image matched with the image to be verified exists, skipping the intelligent verification operation, and uploading the image to be verified to a manual verification system for manual verification operation. By utilizing the image verification method provided by the embodiment of the application, part of the auditing data before being sent to the auditing system is screened out and is subjected to preliminary verification by the intelligent quality inspection system preferentially, the auditing pressure of the auditing system is reduced, the data before being sent to the intelligent quality inspection system is judged, the data existing in the preset list is determined to be a type which is easy to be misjudged by the intelligent quality inspection system, the type of data is skipped over the intelligent quality inspection system and is directly sent to the manual quality inspection system, the auditing is performed manually, and the error reason is determined by the manual auditing. The verification passing rate of the data is improved, and the pressure of a verification system is also relieved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained from these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of an image verification method according to an embodiment of the present application.
Fig. 2 is a schematic structural diagram of an image verification device according to an embodiment of the present application.
Fig. 3 is another schematic structural diagram of an image verification apparatus according to an embodiment of the present application.
Fig. 4 is a schematic structural diagram of a terminal device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by a person skilled in the art without any inventive effort, are intended to be within the scope of the present application based on the embodiments herein.
It should be noted that the scheme is mainly applicable to the data auditing link in the credit card approval process, and the following is a simple introduction made to the background of the scheme:
the technical problem of how to improve the data auditing passing rate and simultaneously reduce the pressure of an auditing system is mainly solved. It will be appreciated that auditing systems include approval of the information provided by the credit card applicant, background investigation of the credit card applicant's credit. After the credit card application is completed, the data provided by the credit card applicant is required to be checked, the corresponding card number is generated and the credit card is manufactured after the checking is passed, and finally the card is sent to the hand of the credit card applicant. If the verification is not passed in the middle, the credit card applicant supplementary information is required, and then the auditing system is used for auditing again, so that the pressure is intensively transmitted to the auditing system, the auditing system is excessively stressed, and the data approval efficiency is further affected. In order to solve the above problems, at present, there is a method of assisting in data auditing work by means of artificial intelligence, for example, an identity card image uploaded by a credit card applicant is input into an artificial intelligence system, the identity card image is automatically compared with a real image, and whether the data submitted by the credit card applicant is qualified is confirmed according to the comparison result. However, since the artificial intelligence is not perfect at present, the accuracy of the percentage cannot be ensured, and thus, the situation of misjudgment also exists, and the accuracy of data auditing is affected.
In order to solve the above technical problems, an embodiment of the present application provides an image verification method. By utilizing the image verification method provided by the embodiment of the application, part of the auditing data before being sent to the auditing system is screened out and is subjected to preliminary verification by the intelligent quality inspection system preferentially, the auditing pressure of the auditing system is reduced, the data before being sent to the intelligent quality inspection system is judged, the data existing in the preset list is determined to be a type which is easy to be misjudged by the intelligent quality inspection system, the type of data is skipped over the intelligent quality inspection system and is directly sent to the manual quality inspection system, the auditing is performed manually, and the error reason is determined by the manual auditing. The verification passing rate of the data is improved, and the pressure of a verification system is also relieved.
Referring to fig. 1, fig. 1 is a flowchart of an image verification method according to an embodiment of the present application. The image verification method is applied to the terminal equipment. Optionally, the terminal device is a terminal or a server. Optionally, the server is an independent physical server, or a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs (Content Delivery Network, content delivery networks), basic cloud computing services such as big data and artificial intelligence platforms, and the like. Optionally, the terminal is a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, a smart voice interaction device, a smart home appliance, a vehicle-mounted terminal, and the like, but is not limited thereto.
In an embodiment, the method may comprise the steps of:
step 101, obtaining an image to be verified.
The terminal equipment receives the data uploaded by the credit card applicant and screens out the image to be verified from the data uploaded by the credit card applicant. The image to be verified refers to an image which can be used for determining whether the image passes verification or not through a similarity comparison operation in the data submitted by the credit card applicant, such as an identity card, a photo and the like.
Step 102, judging whether a target preset image matched with the image to be verified exists in a preset list according to the image to be verified, wherein the preset image contained in the preset list is an image with the error rate exceeding a preset threshold value after intelligent verification operation is performed by an intelligent verification system.
The preset images included in the preset list are images with error rate exceeding a preset threshold value after intelligent verification operation is performed by the intelligent verification system, and the images are likely to be difficult to be recognized by the intelligent system, so that recognition errors occur. For example, some images with denser text paragraphs or smaller fonts exist, and the intelligent quality inspection system can easily misrecognize part of the text into other text, or can not directly recognize the text to cause recognition errors. Compared with the mode of automatic identification through the intelligent quality inspection system, the verification operation of the image is more suitable for manual identification operation, so that whether a target preset image matched with the image to be verified exists in a preset list or not is judged before the image to be verified is input into the intelligent quality inspection system, and whether the image to be verified is continuously sent to the intelligent quality inspection system is determined according to a judging result.
Step 103, if a target preset image matched with the image to be verified exists, skipping the intelligent verification operation, and uploading the image to be verified to a manual verification system for manual verification operation.
In this embodiment, if there is a target preset image matching with the image to be verified, it is indicated that such image is not suitable for automatic identification by using the intelligent quality inspection system, in order to reduce the error rate, the intelligent verification operation needs to be skipped for such image, the image to be verified is directly input into the trained image type identification model to perform the image type identification operation, the image type corresponding to the image to be verified is obtained, and whether there is a target preset image of the same image type in the preset list is determined.
The image type recognition model can be obtained based on the training of a neural convolution network, and the specific training process comprises the following steps:
acquiring a training sample of an image type recognition model to be trained, wherein the training sample comprises image data provided with a label; extracting features of image data in a training sample through an image type recognition model to be trained to obtain an image feature vector corresponding to the image data; identifying the image category of the image data in the training sample based on the image feature vector through an image type identification model to be trained, and obtaining an identification result of the image data; based on the identification result and the label of the image data, adjusting parameters of the image type identification model to be trained to obtain a pre-trained image type identification model.
In some embodiments, if there is no target preset image matching with the image to be verified, it is indicated that the image to be verified does not currently have a problem that is not suitable for automatic recognition by the intelligent quality inspection system, so that the image to be verified can be directly sent to the intelligent verification system to perform intelligent verification operation, whether the image to be verified matches with the target expected image is judged to be consistent, a judgment result is obtained, and if the judgment result is consistent, it is determined that the image to be verified passes verification. If the judging result is that the matching is inconsistent, uploading the image to be verified to the manual verification system for manual verification operation.
In some embodiments, in order to facilitate the credit card applicant to learn that the reasons of the audit failure exist in the submitted data, the scheme further generates reminding information containing abnormal matters in the to-be-verified image by a verification result obtained according to the manual verification operation, and sends the reminding information to a client of a requester corresponding to the to-be-verified image if the verification result is not passed. The reminding information comprises one or more of short messages, mails and telephones, and is not limited herein.
Any combination of the above optional solutions may be adopted to form an optional embodiment of the present application, which is not described herein in detail.
In particular, the present application is not limited by the order of execution of the steps described, and certain steps may be performed in other orders or concurrently without conflict.
As can be seen from the above, the image verification method provided by the embodiment of the present application obtains the image to be verified; judging whether a target preset image matched with the image to be verified exists in a preset list or not according to the image to be verified, wherein the preset image contained in the preset list is an image with the error rate exceeding a preset threshold value after intelligent verification operation is performed by an intelligent verification system; if the target preset image matched with the image to be verified exists, skipping the intelligent verification operation, and uploading the image to be verified to a manual verification system for manual verification operation. By utilizing the image verification method provided by the embodiment of the application, part of the auditing data before being sent to the auditing system is screened out and is subjected to preliminary verification by the intelligent quality inspection system preferentially, the auditing pressure of the auditing system is reduced, the data before being sent to the intelligent quality inspection system is judged, the data existing in the preset list is determined to be a type which is easy to be misjudged by the intelligent quality inspection system, the type of data is skipped over the intelligent quality inspection system and is directly sent to the manual quality inspection system, the auditing is performed manually, and the error reason is determined by the manual auditing. The verification passing rate of the data is improved, and the pressure of a verification system is also relieved.
The embodiment of the application also provides an image verification device which can be integrated in the terminal equipment.
Referring to fig. 2, fig. 2 is a schematic structural diagram of an image verification device according to an embodiment of the present application. The image authentication apparatus 30 may include:
an image acquisition module 31 for acquiring an image to be verified;
the image judging module 32 is configured to judge, according to the image to be verified, whether a target preset image matched with the image to be verified exists in a preset list, where the preset image included in the preset list is an image with an error rate exceeding a preset threshold value after the intelligent verification operation is performed by the intelligent verification system;
the first execution module 33 is configured to skip the intelligent verification operation if there is a target preset image matching the image to be verified, and upload the image to be verified to the manual verification system for manual verification operation.
In some embodiments, the image determining module 31 is configured to input the image to be verified into a trained image type recognition model to perform an image type recognition operation, so as to obtain an image category corresponding to the image to be verified; and judging whether target preset images of the same image category exist in a preset list or not.
In some embodiments, the apparatus further comprises a pre-training module for obtaining a training sample of an image type recognition model to be trained, the training sample comprising image data provided with a tag; extracting features of image data in the training sample through the image type recognition model to be trained to obtain an image feature vector corresponding to the image data; identifying the image category of the image data in the training sample based on the image feature vector through the image type identification model to be trained, and obtaining an identification result of the image data; and adjusting parameters of the image type recognition model to be trained based on the recognition result and the label of the image data to obtain the pre-trained image type recognition model.
In some embodiments, the device further includes a second execution module, configured to send the image to be verified to the intelligent verification system for intelligent verification operation if there is no target preset image matching the image to be verified; judging whether the image to be verified is matched with the target expected image or not, and obtaining a judging result; and if the judging result is that the matching is consistent, determining that the image to be verified passes verification.
In some embodiments, the device further includes a third execution module, configured to upload the image to be verified to the manual verification system for manual verification operation if the determination result is that the matching is inconsistent.
In some embodiments, the device further includes a reminder module for obtaining a verification result according to the manual verification operation; if the verification result is that the verification is not passed, generating reminding information containing abnormal matters in the image to be verified; and sending the reminding information to a client of the requestor corresponding to the image to be verified.
In some embodiments, the alert information includes one or more of a sms, a mail, and a phone.
In specific implementation, each module may be implemented as a separate entity, or may be combined arbitrarily and implemented as the same entity or several entities.
As can be seen from the above, the image verification apparatus 30 provided in the embodiments of the present application, wherein the image obtaining module 31 is configured to obtain an image to be verified; the image judging module 32 is configured to judge whether a target preset image matched with the image to be verified exists in a preset list according to the image to be verified, where the preset image included in the preset list is an image with an error rate exceeding a preset threshold value after the intelligent verification operation is performed by the intelligent verification system; the first execution module 33 is configured to skip the intelligent verification operation if there is a target preset image matching the image to be verified, and upload the image to be verified to the manual verification system for manual verification operation.
Referring to fig. 3, fig. 3 is another schematic structural diagram of an image verification apparatus according to an embodiment of the present application, where the image verification apparatus 30 includes a memory 120, one or more processors 180, and one or more application programs, and the one or more application programs are stored in the memory 120 and configured to be executed by the processors 180; the processor 180 may include an image acquisition module 31, an image determination module 32, and a first execution module 33. For example, the structures and connection relationships of the above respective components may be as follows:
memory 120 may be used to store applications and data. The memory 120 stores application programs including executable code. Applications may constitute various functional modules. The processor 180 executes various functional applications and data processing by running application programs stored in the memory 120. In addition, memory 120 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 120 may also include a memory controller to provide access to the memory 120 by the processor 180.
The processor 180 is a control center of the device, connects various parts of the entire terminal using various interfaces and lines, and performs various functions of the device and processes data by running or executing application programs stored in the memory 120 and calling data stored in the memory 120, thereby performing overall monitoring of the device. Optionally, the processor 180 may include one or more processing cores; preferably, the processor 180 may integrate an application processor and a modem processor, wherein the application processor primarily processes an operating system, user interfaces, application programs, and the like.
In particular, in this embodiment, the processor 180 loads executable codes corresponding to the processes of one or more application programs into the memory 120 according to the following instructions, and the processor 180 executes the application programs stored in the memory 120, so as to implement various functions:
an image acquisition instruction, which is used for acquiring an image to be verified;
the image judging instruction is used for judging whether a target preset image matched with the image to be verified exists in a preset list or not according to the image to be verified, wherein the preset image contained in the preset list is an image with the error rate exceeding a preset threshold value after intelligent verification operation is performed by an intelligent verification system;
And the first execution instruction is used for skipping the intelligent verification operation if a target preset image matched with the image to be verified exists, and uploading the image to be verified to the manual verification system for manual verification operation.
In some embodiments, the image determining instruction is configured to input the image to be verified into a trained image type recognition model to perform an image type recognition operation, so as to obtain an image category corresponding to the image to be verified; and judging whether target preset images of the same image category exist in a preset list or not.
In some embodiments, the program further comprises pre-training instructions for obtaining training samples of an image type recognition model to be trained, the training samples comprising image data provided with a tag; extracting features of image data in the training sample through the image type recognition model to be trained to obtain an image feature vector corresponding to the image data; identifying the image category of the image data in the training sample based on the image feature vector through the image type identification model to be trained, and obtaining an identification result of the image data; and adjusting parameters of the image type recognition model to be trained based on the recognition result and the label of the image data to obtain the pre-trained image type recognition model.
In some embodiments, the device further includes a second execution module, configured to send the image to be verified to the intelligent verification system for intelligent verification operation if there is no target preset image matching the image to be verified; judging whether the image to be verified is matched with the target expected image or not, and obtaining a judging result; and if the judging result is that the matching is consistent, determining that the image to be verified passes verification.
In some embodiments, the program further includes a third execution instruction, configured to upload the image to be verified to the manual verification system for performing a manual verification operation if the determination result is that the matching is inconsistent.
In some embodiments, the program further includes a reminder instruction for obtaining a verification result according to the manual verification operation; if the verification result is that the verification is not passed, generating reminding information containing abnormal matters in the image to be verified; and sending the reminding information to a client of the requestor corresponding to the image to be verified.
In some embodiments, the alert information includes one or more of a sms, a mail, and a phone call
The embodiment of the application also provides terminal equipment. The terminal equipment can be a server, a smart phone, a computer, a tablet personal computer and the like.
Referring to fig. 4, fig. 4 shows a schematic structural diagram of a terminal device provided in an embodiment of the present application, where the terminal device may be used to implement the image verification method provided in the foregoing embodiment. The terminal device 1200 may be a television or a smart phone or a tablet computer.
As shown in fig. 4, the terminal device 1200 may include an RF (Radio Frequency) circuit 110, a memory 120 including one or more (only one is shown in the figure) computer readable storage mediums, an input unit 130, a display unit 140, a sensor 150, an audio circuit 160, a transmission module 170, a processor 180 including one or more (only one is shown in the figure) processing cores, and a power supply 190. It will be appreciated by those skilled in the art that the configuration of the terminal device 1200 shown in fig. 4 does not constitute a limitation of the terminal device 1200, and may include more or fewer components than shown, or may combine certain components, or may have a different arrangement of components. Wherein:
the RF circuit 110 is configured to receive and transmit electromagnetic waves, and to perform mutual conversion between the electromagnetic waves and the electrical signals, so as to communicate with a communication network or other devices. RF circuitry 110 may include various existing circuit elements for performing these functions, such as an antenna, a radio frequency transceiver, a digital signal processor, an encryption/decryption chip, a Subscriber Identity Module (SIM) card, memory, and the like. The RF circuitry 110 may communicate with various networks such as the internet, intranets, wireless networks, or other devices via wireless networks.
The memory 120 may be used to store software programs and modules, such as program instructions/modules corresponding to the image verification method in the above embodiment, and the processor 180 executes various functional applications and data processing by running the software programs and modules stored in the memory 120, so that the vibration reminding mode can be automatically selected to perform image verification according to the current scene where the terminal device is located, thereby not only ensuring that the scenes such as a conference are not disturbed, but also ensuring that the user can perceive an incoming call, and improving the intelligence of the terminal device. Memory 120 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, memory 120 may further include memory remotely located relative to processor 180, which may be connected to terminal device 1200 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input unit 130 may be used to receive input numeric or character information and to generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control. In particular, the input unit 130 may comprise a touch sensitive surface 131 and other input devices 132. The touch sensitive surface 131, also referred to as a touch display screen or touch pad, may collect touch operations thereon or thereabout by a user (e.g., operations of the user on the touch sensitive surface 131 or thereabout by any suitable object or accessory such as a finger, stylus, etc.), and actuate the corresponding connection means according to a pre-set program. Alternatively, the touch sensitive surface 131 may comprise two parts, a touch detection device and a touch controller. The touch control detection device detects the touch control direction of a user, detects signals brought by touch control operation and transmits the signals to the touch control controller; the touch controller receives touch information from the touch detection device, converts the touch information into touch coordinates, sends the touch coordinates to the processor 180, and can receive and execute commands sent by the processor 180. In addition, the touch-sensitive surface 131 may be implemented in various types of resistive, capacitive, infrared, surface acoustic wave, and the like. In addition to the touch-sensitive surface 131, the input unit 130 may also comprise other input devices 132. In particular, other input devices 132 may include, but are not limited to, one or more of a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a trackball, mouse, joystick, etc.
The display unit 140 may be used to display information input by a user or information provided to the user and various graphical user interfaces of the terminal device 1200, which may be composed of graphics, text, icons, video, and any combination thereof. The display unit 140 may include a display panel 141, and alternatively, the display panel 141 may be configured in the form of an LCD (Liquid Crystal Display ), an OLED (Organic Light-Emitting Diode), or the like. Further, the touch-sensitive surface 131 may cover the display panel 141, and after the touch-sensitive surface 131 detects a touch operation thereon or thereabout, the touch-sensitive surface is transferred to the processor 180 to determine a type of touch event, and then the processor 180 provides a corresponding visual output on the display panel 141 according to the type of touch event. Although in fig. 4 the touch-sensitive surface 131 and the display panel 141 are implemented as two separate components for input and output functions, in some embodiments the touch-sensitive surface 131 may be integrated with the display panel 141 to implement the input and output functions.
The terminal device 1200 may also include at least one sensor 150, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor that may adjust the brightness of the display panel 141 according to the brightness of ambient light, and a proximity sensor that may turn off the display panel 141 and/or the backlight when the terminal device 1200 moves to the ear. As one of the motion sensors, the gravity acceleration sensor can detect the acceleration in all directions (generally three axes), and can detect the gravity and the direction when the mobile phone is stationary, and can be used for applications of recognizing the gesture of the mobile phone (such as horizontal and vertical screen switching, related games, magnetometer gesture calibration), vibration recognition related functions (such as pedometer and knocking), and the like; other sensors such as gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc. that may also be configured with the terminal device 1200 are not described in detail herein.
Audio circuitry 160, speaker 161, microphone 162 may provide an audio interface between a user and terminal device 1200. The audio circuit 160 may transmit the received electrical signal converted from audio data to the speaker 161, and the electrical signal is converted into a sound signal by the speaker 161 to be output; on the other hand, the microphone 162 converts the collected sound signal into an electrical signal, receives the electrical signal from the audio circuit 160, converts the electrical signal into audio data, outputs the audio data to the processor 180 for processing, transmits the audio data to, for example, another terminal via the RF circuit 110, or outputs the audio data to the memory 120 for further processing. Audio circuitry 160 may also include an ear bud jack to provide communication of the peripheral headphones with terminal device 1200.
Terminal device 1200 may facilitate user email, web browsing, streaming media access, etc. via a transmission module 170 (e.g., wi-Fi module) that provides wireless broadband internet access to the user. Although fig. 4 shows the transmission module 170, it is understood that it does not belong to the essential constitution of the terminal device 1200, and may be omitted entirely as needed within the scope of not changing the essence of the invention.
The processor 180 is a control center of the terminal device 1200, connects various parts of the entire mobile phone using various interfaces and lines, and performs various functions of the terminal device 1200 and processes data by running or executing software programs and/or modules stored in the memory 120, and calling data stored in the memory 120, thereby performing overall monitoring of the mobile phone. Optionally, the processor 180 may include one or more processing cores; in some embodiments, the processor 180 may integrate an application processor that primarily processes operating systems, user interfaces, applications, etc., with a modem processor that primarily processes wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 180.
The terminal device 1200 also includes a power supply 190 that provides power to the various components, and in some embodiments, may be logically coupled to the processor 180 via a power management system to perform functions such as managing discharge, and managing power consumption via the power management system. The power supply 190 may also include one or more of any of a direct current or alternating current power supply, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and the like.
Although not shown, the terminal device 1200 may further include a camera (such as a front camera, a rear camera), a bluetooth module, etc., which will not be described herein. In particular, in the present embodiment, the display unit 140 of the terminal device 1200 is a touch screen display, the terminal device 1200 further includes a memory 120, and one or more programs, wherein the one or more programs are stored in the memory 120 and configured to be executed by the one or more processors 180, the one or more programs include instructions for:
an information acquisition instruction for acquiring target crown word number information of currency to be identified;
the information comparison instruction is used for comparing the target crown word number information with preset crown word number information which is marked as counterfeit money in a database in advance;
The information marking instruction is used for marking currency to be identified corresponding to the target crown word number information as counterfeit currency if preset crown word number information consistent with the target crown word number information exists in the database;
the information determining instruction is used for acquiring market circulation information of the counterfeit money and determining historical user information of the counterfeit money according to the market circulation information;
and the responsibility judgment instruction is used for judging whether to send out warning information or not based on the historical user information and the current user information.
In some embodiments, the information obtaining instruction is configured to input the currency to be identified into a trained information extraction model to perform an information extraction operation, so as to obtain target crown word number information in the currency to be identified.
In some embodiments, the program further comprises information generating instructions for recording transaction data for the counterfeit money, the transaction data including a transaction place of occurrence, a transaction time of occurrence, and counterfeit money user information; and integrating according to the transaction data of each counterfeit money to generate market circulation information with a time chain relation.
In some embodiments, the responsibility determining instruction is configured to obtain the transaction data of the counterfeit money if the historical user information is consistent with the current user information, and determine whether there is a counterfeit money refund record in the transaction data; if the counterfeit money refund record exists in the transaction data, the counterfeit money user is judged to use the counterfeit money under the informed condition, and a warning message is sent out.
In some embodiments, the program further includes a warning instruction for sending a warning message to a current user of the counterfeit money, and binding the warning message with the current user and then having transaction data of the counterfeit money.
In some embodiments, the program further includes penalty instructions for generating a reputation record upload credit system if the historical user information is consistent with the current user information and there is warning information in the transaction data of the counterfeit money that is bound to the current user.
In some embodiments, the program further includes update instructions for storing target crown word size information corresponding to the counterfeit money in the database and updating the database.
The embodiment of the application also provides terminal equipment. The terminal equipment can be a smart phone, a computer and other equipment.
As can be seen from the above, the embodiments of the present application provide a terminal device 1200, where the terminal device 1200 performs the following steps:
acquiring an image to be verified;
judging whether a target preset image matched with the image to be verified exists in a preset list or not according to the image to be verified, wherein the preset image contained in the preset list is an image with the error rate exceeding a preset threshold value after intelligent verification operation is performed by an intelligent verification system;
If the target preset image matched with the image to be verified exists, skipping the intelligent verification operation, and uploading the image to be verified to a manual verification system for manual verification operation.
The embodiment of the application also provides a storage medium, in which a computer program is stored, and when the computer program runs on a computer, the computer executes the image verification method according to any one of the embodiments.
It should be noted that, for the image verification method described in the present application, it will be understood by those skilled in the art that all or part of the flow of implementing the image verification method described in the embodiments of the present application may be implemented by controlling related hardware through a computer program, where the computer program may be stored in a computer readable storage medium, such as a memory of a terminal device, and executed by at least one processor in the terminal device, and the execution may include the flow of the embodiment of the image verification method as described in the embodiment of the present application. The storage medium may be a magnetic disk, an optical disk, a Read Only Memory (ROM), a random access Memory (RAM, random Access Memory), or the like.
For the image verification device in the embodiment of the present application, each functional module may be integrated in one processing chip, or each module may exist separately and physically, or two or more modules may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated module, if implemented as a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium such as read-only memory, magnetic or optical disk, etc.
The image verification method, the device, the medium and the equipment provided by the embodiment of the application are described in detail. The principles and embodiments of the present application are described herein with specific examples, the above examples being provided only to assist in understanding the methods of the present application and their core ideas; meanwhile, those skilled in the art will have variations in the specific embodiments and application scope in light of the ideas of the present application, and the present description should not be construed as limiting the present application in view of the above.

Claims (10)

1. An image verification method, comprising:
acquiring an image to be verified;
judging whether a target preset image matched with the image to be verified exists in a preset list or not according to the image to be verified, wherein the preset image contained in the preset list is an image with the error rate exceeding a preset threshold value after intelligent verification operation is performed by an intelligent verification system;
if the target preset image matched with the image to be verified exists, skipping the intelligent verification operation, and uploading the image to be verified to a manual verification system for manual verification operation.
2. The method for verifying an image according to claim 1, wherein the determining whether a target preset image matched with the image to be verified exists in the preset list comprises:
inputting the image to be verified into a trained image type recognition model to perform image type recognition operation, so as to obtain an image type corresponding to the image to be verified;
and judging whether target preset images of the same image category exist in a preset list or not.
3. The image verification method according to claim 2, wherein before said inputting the image to be verified into the trained image type recognition model for image type recognition operation, the method further comprises:
Acquiring a training sample of an image type recognition model to be trained, wherein the training sample comprises image data provided with a label;
extracting features of image data in the training sample through the image type recognition model to be trained to obtain an image feature vector corresponding to the image data;
identifying the image category of the image data in the training sample based on the image feature vector through the image type identification model to be trained, and obtaining an identification result of the image data;
and adjusting parameters of the image type recognition model to be trained based on the recognition result and the label of the image data to obtain the pre-trained image type recognition model.
4. The image verification method according to claim 2, wherein the method further comprises:
if the target preset image matched with the image to be verified does not exist, the image to be verified is sent to the intelligent verification system for intelligent verification operation;
judging whether the image to be verified is matched with the target expected image or not, and obtaining a judging result;
and if the judging result is that the matching is consistent, determining that the image to be verified passes verification.
5. The image verification method according to claim 4, wherein after said sending said image to be verified to said intelligent verification system for intelligent verification operation, said method further comprises:
and if the judging result is inconsistent, uploading the image to be verified to a manual verification system for manual verification operation.
6. The image authentication method according to claim 5, wherein after said uploading the image to be authenticated to a manual authentication system for a manual authentication operation, the method further comprises:
obtaining a verification result according to the manual verification operation;
if the verification result is that the verification is not passed, generating reminding information containing abnormal matters in the image to be verified;
and sending the reminding information to a client of the requestor corresponding to the image to be verified.
7. The image verification method of claim 6, wherein the alert message comprises one or more of a short message, a mail, and a telephone.
8. An image verification apparatus, characterized in that the image verification apparatus comprises:
the image acquisition module is used for acquiring an image to be verified;
the image judging module is used for judging whether a target preset image matched with the image to be verified exists in a preset list or not according to the image to be verified, wherein the preset image contained in the preset list is an image with the error rate exceeding a preset threshold value after intelligent verification operation is performed by the intelligent verification system;
And the first execution module is used for skipping the intelligent verification operation if a target preset image matched with the image to be verified exists, and uploading the image to be verified to the manual verification system for manual verification operation.
9. A computer readable storage medium storing a plurality of instructions adapted to be loaded by a processor to perform the image verification method of any one of claims 1 to 7.
10. A terminal device comprising a processor and a memory, the memory storing a plurality of instructions, the processor loading the instructions to perform the image authentication method of any one of claims 1 to 7.
CN202310413549.2A 2023-04-11 2023-04-11 Image verification method, device, medium and equipment Pending CN116486121A (en)

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Applications Claiming Priority (1)

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