CN115471775A - Information verification method, device and equipment based on screen recording video and storage medium - Google Patents
Information verification method, device and equipment based on screen recording video and storage medium Download PDFInfo
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- G06—COMPUTING; CALCULATING OR COUNTING
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- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
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- G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
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- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
Abstract
The invention relates to an artificial intelligence technology, and discloses an information verification method based on screen recording video, which comprises the following steps: acquiring a screen recording video when information is collected; converting the screen recording video into a video frame sequence ordered according to time; classifying each video frame in the video frame sequence by utilizing a pre-trained lightweight neural network to obtain a key video frame sequence; identifying an operation scene of the key video frame sequence to obtain an operation scene sequence corresponding to the key video frame sequence; and carrying out information verification on the screen recording video according to the operation scene sequence. The invention also provides an information verification device based on the screen recording video, electronic equipment and a storage medium. The invention can improve the accuracy of filling in the user information.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to an information verification method and device based on screen recording video, electronic equipment and a computer readable storage medium.
Background
The acquisition of the credit condition of the user is an important ring in the financial industry, and generally needs to cooperate with a third-party credit investigation institution to acquire the credit information of the user, but the strict control on the wind control of the third-party credit investigation institution is required, and meanwhile, data which is not mastered by the third-party credit investigation institution also needs to be filled and submitted by the user.
At present, information needing to be uploaded by a user is mainly filled in by the user or automatically screenshot uploaded by the user, but the two methods cannot be used for obtaining evidence in the process of uploading the user information, so that the accuracy and the authenticity of the information cannot be guaranteed, wrong user information is obtained, and the accuracy rate of the obtained user credit information is low.
Disclosure of Invention
The invention provides an information verification method and device based on screen recording video and a computer readable storage medium, and mainly aims to solve the problem of low accuracy in information collection.
In order to achieve the above object, the information verification method based on screen recording video provided by the invention comprises the following steps:
acquiring a screen recording video when user information is collected;
converting the screen recording video into a video frame sequence ordered according to time;
classifying each video frame in the video frame sequence by utilizing a pre-trained lightweight neural network to obtain a key video frame sequence;
identifying an operation scene of the key video frame sequence to obtain an operation scene sequence corresponding to the key video frame sequence;
and carrying out information verification on the screen recording video according to the operation scene sequence.
Optionally, the converting the screen recording video into a time-ordered sequence of video frames includes:
acquiring a video frame rate of the screen recording video and a preset target frame rate;
calculating the interval of the video frames to be extracted according to the video frame rate and the preset target frame rate;
and extracting a video frame set from the screen recording video according to the video frame interval, and sequencing the video frame set according to extraction time to obtain a video frame sequence.
Optionally, the calculating the interval of the video frames to be extracted according to the video frame rate and the preset target frame rate includes:
calculating the interval of the video frames to be extracted by using the following formula:
i＝int{float(f 1 /f 2 )+1}
wherein i is a video frame interval, f 1 For the video frame rate, said f 2 For the target frame rate, int is the integer function, float is the floating point data type.
Optionally, the classifying each video frame in the sequence of video frames by using a pre-trained lightweight neural network includes:
inputting each video frame in the video frame sequence into the pre-trained lightweight neural network one by one;
convolving said each video frame with a convolutional layer in said lightweight neural network, obtaining a convolution characteristic map of each video frame;
pooling the convolution feature map by using a pooling layer in the lightweight neural network to obtain a feature vector of each video frame;
and classifying the feature vector of each video frame by using a preset activation function to obtain a key video frame sequence.
Optionally, the performing operation scene recognition on the key video frame sequence includes:
extracting characters of key video frames in the key video frame sequence one by one to obtain text information in the key video frames;
performing text word segmentation on the character information, and determining text keywords according to the result of the text word segmentation;
and matching the text keywords with a pre-constructed operation scene set to obtain the operation scene of the key video frame.
Optionally, the performing text extraction on the key video frames in the sequence of key video frames one by one includes:
carrying out character area boundary detection on the key video frame to obtain a boundary coordinate point of a character area;
segmenting the key video frame into a plurality of character areas according to the boundary coordinate points;
and performing character recognition on the character areas by using a preset character recognition model, and extracting a text according to a text recognition result.
Optionally, the performing information verification on the screen recording video according to the operation scene sequence includes:
acquiring a preset check rule, and performing rule analysis on the check rule to obtain a rule expression of the check rule;
and verifying the operation scene sequence by using the regular expression to obtain a verification result of the screen recording video.
In order to solve the above problem, the present invention further provides an information verification apparatus based on a screen recording video, the apparatus comprising:
the screen recording video acquisition module is used for acquiring a screen recording video during user information collection;
the video frame sequence conversion module is used for converting the screen recording video into a video frame sequence ordered according to time;
the key video frame sequence classification module is used for classifying each video frame in the video frame sequence by utilizing a pre-trained lightweight neural network to obtain a key video frame sequence;
an operation scene identification module, configured to perform operation scene identification on the key video frame sequence to obtain an operation scene sequence corresponding to the key video frame sequence;
and the screen recording video checking module is used for checking the information of the screen recording video according to the operation scene sequence.
In order to solve the above problem, the present invention also provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the screen recording video-based information verification method described above.
In order to solve the above problem, the present invention further provides a computer-readable storage medium, in which at least one computer program is stored, and the at least one computer program is executed by a processor in an electronic device to implement the screen-recording video-based information verification method described above.
The embodiment of the invention converts the screen recording video into the video frame sequence ordered according to time, converts the video stream into the video frame image, and can acquire the specific image information in the screen recording video; classifying each video frame in the video frame sequence by utilizing the pre-trained lightweight neural network to obtain a key video frame sequence, so that the accuracy of subsequent verification can be improved; identifying an operation scene of the key video frame sequence to obtain an operation scene sequence corresponding to the key video frame sequence; and verifying the screen recording video according to the operation scene sequence, and judging whether the user violates rules in the information filling process, so that whether the information filled by the user is correct is determined, and the accuracy of filling the information by the user can be improved. Therefore, the information verification method, the information verification device, the electronic equipment and the computer readable storage medium based on the screen recording video can solve the problem of low accuracy in information collection.
Drawings
Fig. 1 is a schematic flowchart of an information verification method based on a screen recording video according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a video frame sequence conversion according to an embodiment of the present invention;
fig. 3 is a schematic flowchart of operation scenario identification according to an embodiment of the present invention;
fig. 4 is a functional block diagram of an information verification apparatus based on a video recording according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device for implementing the information verification method based on the screen recording video 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 do not limit the invention.
The embodiment of the application provides an information verification method based on screen recording video. The execution subject of the information verification method based on the screen recording video includes, but is not limited to, at least one of electronic devices such as a server and a terminal, which can be configured to execute the method provided by the embodiment of the present application. In other words, the information verification method based on the screen recording video can be executed by software or hardware installed in the terminal device or the server device, and the software can 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 server may be an independent server, or may be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like.
Fig. 1 is a schematic flow chart of an information verification method based on a screen recording video according to an embodiment of the present invention. In this embodiment, the information verification method based on screen recording video includes the following steps S1 to S5:
s1, acquiring a screen recording video during user information collection;
in the embodiment of the invention, the screen recording video is a screen recording video when the user performs information filling, the screen recording can be initiated and finished by the user, and specifically, embedded screen recording software can be called on a user information collection platform to record the screen when the user performs information filling.
In another optional embodiment of the present invention, the user information collecting platform is an enterprise platform that requires the user to fill in the real information, for example, a financial insurance industry, a third-party credit investigation enterprise, and the like, and it is necessary to ensure the correctness of the user when collecting the user information.
S2, converting the screen recording video into a video frame sequence ordered according to time;
in the embodiment of the invention, the video frame sequence is obtained by sequencing single images in the screen recording video according to time, so that the screen recording video can be displayed in the form of images.
In detail, referring to fig. 2, the converting the screen recording video into a time-ordered sequence of video frames includes the following steps S21 to S23:
s21, acquiring a video frame rate of the screen recording video and a preset target frame rate;
s22, calculating the interval of the video frames to be extracted according to the video frame rate and the preset target frame rate;
s23, extracting a video frame set from the screen recording video according to the video frame interval, and sequencing the video frame set according to extraction time to obtain a video frame sequence.
Further, the calculating the interval of the video frames to be extracted according to the video frame rate and the preset target frame rate includes:
calculating the interval of the video frames to be extracted by using the following formula:
i＝int{float(f 1 /f 2 )+1}
wherein i is a video frame interval, and f is 1 For the video frame rate, said f 2 For the target frame rate, int is the integer function, float is the floating point data type.
In the embodiment of the present invention, the video frame rate of the screen recording video is a frame rate of a screen recording video captured image, for example, 25fps, that is, 1 second can send 25 color code streams, the target frame rate is a required capture frame rate, for example, a preset target frame rate may be 10fps, and then the video frame interval to be extracted is calculated through the video frame rate and the preset target frame rate.
In the embodiment of the invention, the screen recording video is converted into the video frame sequence, the video stream is converted into the video frame images, the specific image information in the screen recording video can be obtained, and the video frames are extracted according to the calculated video frame intervals, so that the content accuracy of extracting the video frames at the target frame rate from the screen recording video at the original frame rate is ensured.
S3, classifying each video frame in the video frame sequence by utilizing a light weight neural network finished by pre-training to obtain a key video frame sequence;
in the embodiment of the invention, the pre-trained lightweight neural network is a MnasNet (classical lightweight neural network), and because the complexity of the scene for classifying video frames is not high, the model complexity can be reduced and the operation efficiency can be improved by selecting the lightweight neural network.
In another optional embodiment of the present invention, the pre-trained lightweight neural network is obtained by recording a normal operation video by using a mainstream mobile phone model, extracting all video frames in the normal operation video, performing scene labeling on the video frames, and performing neural network iterative training by using the video frames after the scene labeling.
In another optional embodiment of the present invention, the key video frames are video frames with contents, including a jump page, a login page, an information filling page, and a screen recording ending page, and the key video frames may include key frames during information filling and cheating frames during cheating, video frames without contents, such as black screens and blank frames, are removed, and then the key video frames are sequenced based on the position of each key video frame in the video frame sequence, so as to obtain the key video frame sequence.
In detail, the classifying each video frame in the sequence of video frames by using the pre-trained lightweight neural network includes:
inputting each video frame in the video frame sequence into the pre-trained lightweight neural network one by one;
performing convolution on each video frame by utilizing a convolution layer in the lightweight neural network to obtain a convolution characteristic diagram of each video frame;
pooling the convolution feature map by using a pooling layer in the lightweight neural network to obtain a feature vector of each video frame;
and classifying the feature vector of each video frame by using a preset activation function to obtain a key video frame sequence.
In the embodiment of the invention, the activation function in the full connection layer is a sigmoid activation function, the activation function is utilized to calculate the video frame category corresponding to the characteristic vector to obtain the key video frame, and the key video frame sequence is obtained based on the position of each key video frame in the video frame sequence.
In the embodiment of the invention, each video frame in the video frame sequence is classified by utilizing the light weight neural network finished by pre-training, so that the identification efficiency of the video frame can be improved, the subsequent information verification efficiency can be further improved, useless video frames can be deleted, and the subsequent verification accuracy can be improved.
S4, identifying the operation scene of the key video frame sequence to obtain an operation scene sequence corresponding to the key video frame sequence;
in the embodiment of the present invention, the identifying the operation scene of the key video frame sequence is to further identify the specific operation content of the key video frame, for example, the key video frame sequence is an operation scene of page jump, an operation scene of login, an operation scene of ending screen recording, an operation scene of information cheating, and the like.
In detail, referring to fig. 3, the performing operation scene recognition on the key video frame sequence includes the following steps S31 to S33:
s31, extracting characters of the key video frames in the key video frame sequence one by one to obtain text information in the key video frames;
s32, performing text word segmentation on the character information, and determining text keywords according to the result of the text word segmentation;
and S33, matching the text keywords with a pre-constructed operation scene set to obtain the operation scene of the key video frame.
In the embodiment of the present invention, the word with the highest frequency of occurrence in the text word segments may be extracted as a text keyword, and the text keyword is matched with a pre-constructed operation scene set according to the text keyword, where the pre-constructed operation scene set is a set of a preset keyword and a corresponding operation scene, for example, if the text keyword is "login" and corresponds to a "login operation scene" in the pre-constructed operation scene set, the operation scene corresponding to the video key frame is a login operation scene.
Further, the extracting the texts from the key video frames in the sequence of key video frames comprises:
carrying out character area boundary detection on the key video frame to obtain a boundary coordinate point of a character area;
segmenting the key video frame into a plurality of character areas according to the boundary coordinate points;
and performing character recognition on the character areas by using a preset character recognition model, and extracting a text according to a text recognition result.
In the embodiment of the present invention, the preset character recognition model may be a pretrained CRNN (Convolutional Recurrent Neural Network), which can solve the character recognition problem based on an image sequence, and therefore, the character recognition model may be used to perform character recognition on the key video frame sequence.
In the embodiment of the invention, the operation flow of the user during information filling can be obtained by identifying the operation scene sequence corresponding to the key video frame, and whether the user is in compliance during information filling is further judged.
And S5, performing information verification on the screen recording video according to the operation scene sequence.
In the embodiment of the invention, the information verification is to judge whether the operation flow of the user is in compliance in the information filling process according to the operation scene sequence, and further judge whether the cheating action exists in the screen recording video.
In detail, the performing information verification on the screen recording video according to the operation scene sequence includes:
acquiring a preset check rule, and performing rule analysis on the check rule to obtain a rule expression of the check rule;
and verifying the operation scene sequence by using the regular expression to obtain a verification result of the screen recording video.
In the embodiment of the present invention, the preset check rule is a rule requirement that an operation scene sequence needs to meet, and the check rule is converted into a case where the operation scene sequence is a page jump operation scene, a login operation scene, and a screen recording operation scene is ended, and no information is filled, so that the screen recording video does not meet the check rule, or a case where the operation scene jump is performed, the login operation scene, the information filling operation scene, and the screen recording operation scene is ended, and a user has multiple discontinuous login behaviors, so that the corresponding screen recording video does not meet the requirement.
In the embodiment of the invention, whether the user violates the rules in the information filling process can be judged through the operation scene sequence, so that whether the information filled by the user is correct or not can be judged, and the accuracy rate of filling the information by the user can be improved.
The embodiment of the invention converts the screen recording video into the video frame sequence ordered according to time, converts the video stream into the video frame image, and can acquire the specific image information in the screen recording video; classifying each video frame in the video frame sequence by utilizing the pre-trained lightweight neural network to obtain a key video frame sequence, so that the accuracy of subsequent verification can be improved; identifying an operation scene of the key video frame sequence to obtain an operation scene sequence corresponding to the key video frame sequence; and verifying the screen recording video according to the operation scene sequence, and judging whether the user violates rules in the information filling process, so that whether the information filled by the user is correct is determined, and the accuracy of filling the information by the user can be improved. Therefore, the information verification method based on the screen recording video can solve the problem of low accuracy in information collection.
Fig. 4 is a functional block diagram of an information verification apparatus based on a screen recording video according to an embodiment of the present invention.
The information verification apparatus 100 based on screen recording video according to the present invention may be installed in an electronic device. According to the realized functions, the information verification device 100 based on the screen recording video can comprise a screen recording video acquisition module 101, a video frame sequence conversion module 102, a key video frame sequence classification module 103, an operation scene identification module 104 and a screen recording video verification module 105. The module of 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 screen recording video acquisition module 101 is configured to acquire a screen recording video during user information collection;
the video frame sequence conversion module 102 is configured to convert the screen recording video into a time-ordered video frame sequence;
the key video frame sequence classification module 103 is configured to classify each video frame in the video frame sequence by using a pre-trained lightweight neural network to obtain a key video frame sequence;
the operation scene recognition module 104 is configured to perform operation scene recognition on the key video frame sequence to obtain an operation scene sequence corresponding to the key video frame sequence;
the screen recording video checking module 105 is configured to perform information checking on the screen recording video according to the operation scene sequence.
In detail, when the modules in the information verification apparatus 100 based on the screen recording video according to the embodiment of the present invention are used, the same technical means as the information verification method based on the screen recording video described in fig. 1 to fig. 3 are used, and the same technical effect can be produced, which is not described again here.
Fig. 5 is a schematic structural diagram of an electronic device for implementing an information verification method based on a screen recording video according to an embodiment of the present invention.
The electronic device 1 may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13, and may further include a computer program, such as a screen-recording video-based information verification program, stored in the memory 11 and executable on the processor 10.
In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same function or different functions, and includes one or more Central Processing Units (CPUs), a microprocessor, a digital Processing chip, a graphics processor, a combination of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing programs or modules stored in the memory 11 (for example, executing a video-recording-based information verification program, etc.), and calling data stored in the memory 11.
The memory 11 includes at least one type of readable storage medium including flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, for example a removable hard disk of the electronic device. The memory 11 may also be an external storage device of the electronic device 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. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only to store application software installed in the electronic device and various types of data, such as a code of an information verification program based on a video on a screen, etc., but also to temporarily store data that has been output or is to be output.
The communication bus 12 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.
The communication interface 13 is used for communication between the electronic device and other devices, and includes a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which are typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (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, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.
Only electronic devices having components are shown, and those skilled in the art will appreciate that the structures shown in the figures do not constitute limitations on the electronic devices, and may include fewer or more components than shown, or some components in combination, or a different arrangement of components.
For example, although not shown, the electronic device 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 that functions of charge management, discharge management, power consumption management and the like are realized 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 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
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 information verification program based on the screen recording video stored in the memory 11 of the electronic device 1 is a combination of a plurality of instructions, and when running in the processor 10, can realize that:
acquiring a screen recording video during user information collection;
converting the screen recording video into a video frame sequence ordered according to time;
classifying each video frame in the video frame sequence by utilizing a pre-trained lightweight neural network to obtain a key video frame sequence;
identifying an operation scene of the key video frame sequence to obtain an operation scene sequence corresponding to the key video frame sequence;
and performing information verification on the screen recording video according to the operation scene sequence.
Specifically, the specific implementation method of the instruction by the processor 10 may refer to the description of the relevant steps in the embodiment corresponding to the drawings, which is not described herein again.
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 storage medium may be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying said computer program code, a recording medium, a usb-disk, a removable hard disk, a magnetic diskette, an optical disk, a computer Memory, a Read-Only Memory (ROM).
The present invention also provides a computer-readable storage medium storing a computer program which, when executed by a processor of an electronic device, implements:
acquiring a screen recording video when user information is collected;
converting the screen recording video into a video frame sequence ordered according to time;
classifying each video frame in the video frame sequence by utilizing a pre-trained lightweight neural network to obtain a key video frame sequence;
identifying an operation scene of the key video frame sequence to obtain an operation scene sequence corresponding to the key video frame sequence;
and carrying out information verification on the screen recording video according to the operation scene sequence.
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 reference signs in the claims shall not be construed as limiting the claim concerned.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
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 first, 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. An information verification method based on screen recording video is characterized by comprising the following steps:
acquiring a screen recording video when user information is collected;
converting the screen recording video into a video frame sequence ordered according to time;
classifying each video frame in the video frame sequence by utilizing a pre-trained lightweight neural network to obtain a key video frame sequence;
identifying an operation scene of the key video frame sequence to obtain an operation scene sequence corresponding to the key video frame sequence;
and performing information verification on the screen recording video according to the operation scene sequence.
2. The screen-recording video-based information verification method according to claim 1, wherein the converting the screen-recording video into a time-ordered sequence of video frames comprises:
acquiring a video frame rate of the screen recording video and a preset target frame rate;
calculating the interval of the video frames to be extracted according to the video frame rate and the preset target frame rate;
and extracting a video frame set from the screen recording video according to the video frame interval, and sequencing the video frame set according to extraction time to obtain a video frame sequence.
3. The information verification method based on screen recording video of claim 2, wherein the calculating the interval of the video frames to be extracted according to the video frame rate and the preset target frame rate comprises:
calculating the interval of the video frames to be extracted by using the following formula:
i＝int{float(f 1 /f 2 )+1}
wherein i is a video frame interval, and f is 1 For video frame rate, said f 2 For the target frame rate, int is the integer function, float is the floating point data type.
4. The screen-recording video-based information verification method of claim 1, wherein the classifying each video frame in the sequence of video frames by using a pre-trained lightweight neural network comprises:
inputting each video frame in the video frame sequence into the pre-trained lightweight neural network one by one;
performing convolution on each video frame by utilizing a convolution layer in the lightweight neural network to obtain a convolution characteristic diagram of each video frame;
pooling the convolution feature map by using a pooling layer in the lightweight neural network to obtain a feature vector of each video frame;
and classifying the feature vector of each video frame by using a preset activation function to obtain a key video frame sequence.
5. The screen-recording video-based information verification method according to claim 1, wherein the performing operation scene recognition on the key video frame sequence comprises:
extracting characters of key video frames in the key video frame sequence one by one to obtain text information in the key video frames;
performing text word segmentation on the character information, and determining text keywords according to the result of the text word segmentation;
and matching the text keywords with a pre-constructed operation scene set to obtain the operation scene of the key video frame.
6. The method for verifying information based on screen recording video as claimed in claim 1, wherein said extracting the key video frames of said sequence of key video frames one by one comprises:
carrying out character area boundary detection on the key video frame to obtain a boundary coordinate point of a character area;
segmenting the key video frame into a plurality of character areas according to the boundary coordinate points;
and performing character recognition on the character areas by using a preset character recognition model, and extracting a text according to a text recognition result.
7. The information verification method based on screen recording video of claim 1, wherein the information verification of the screen recording video according to the operation scene sequence comprises:
acquiring a preset check rule, and performing rule analysis on the check rule to obtain a rule expression of the check rule;
and verifying the operation scene sequence by using the regular expression to obtain a verification result of the screen recording video.
8. An information verification apparatus based on screen recording video, the apparatus comprising:
the screen recording video acquisition module is used for acquiring a screen recording video during user information collection;
the video frame sequence conversion module is used for converting the screen recording video into a video frame sequence ordered according to time;
the key video frame sequence classification module is used for classifying each video frame in the video frame sequences by utilizing a pre-trained lightweight neural network to obtain a key video frame sequence;
an operation scene identification module, configured to perform operation scene identification on the key video frame sequence to obtain an operation scene sequence corresponding to the key video frame sequence;
and the screen recording video checking module is used for checking the information of the screen recording video according to the operation scene sequence.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the screen recording video-based information authentication method according to 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 the screen-recording video-based information authentication method according to any one of claims 1 to 7.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116647727A (en) * | 2023-07-27 | 2023-08-25 | 中邮消费金融有限公司 | Screen recording information collection method, device, equipment and storage medium |
| CN117727282A (en) * | 2023-12-20 | 2024-03-19 | 东莞明崴电子科技有限公司 | Driving method and system of liquid crystal display screen |
| CN117727282B (en) * | 2023-12-20 | 2024-05-14 | 东莞明崴电子科技有限公司 | Driving method and system of liquid crystal display screen |
-
2022
- 2022-09-29 CN CN202211200519.5A patent/CN115471775A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116647727A (en) * | 2023-07-27 | 2023-08-25 | 中邮消费金融有限公司 | Screen recording information collection method, device, equipment and storage medium |
| CN116647727B (en) * | 2023-07-27 | 2024-02-06 | 中邮消费金融有限公司 | Screen recording information collection method, device, equipment and storage medium |
| CN117727282A (en) * | 2023-12-20 | 2024-03-19 | 东莞明崴电子科技有限公司 | Driving method and system of liquid crystal display screen |
| CN117727282B (en) * | 2023-12-20 | 2024-05-14 | 东莞明崴电子科技有限公司 | Driving method and system of liquid crystal display screen |
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