WO2021218338A1 - User identity verification method and apparatus, medium, and electronic device - Google Patents

Abstract

The present disclosure relates to the technical field of artificial intelligence, and provides a user identity verification method and apparatus, a medium, and an electronic device. The method comprises: in response to human-machine interaction of a user on a terminal interface, collecting a first behaviour data set of the user; on the basis of the first behaviour data set, determining a first feature data sequence used for verifying the user identity; inputting the first feature data sequence into a pre-determined user identity verification model to obtain a user identity verification index, the user identity verification index being used for representing the degree of authenticity of the user identity; and, on the basis of the user identity verification index, giving a verification response to the authenticity of the user identity. In the present method, the behaviour data produced by the user performing human-machine interaction with the terminal interface serves as the basis for verifying the authenticity of the user identity, thereby improving the security of verifying the authenticity of the user identity. The present application also relates to blockchain technology; the first feature data sequence is stored in the blockchain.

Description

User identity verification method, device, medium and electronic equipment

This application claims the priority of a Chinese patent application filed with the Chinese Patent Office with the application number CN202010349662.5 and titled "User Identity Verification Method, Device, Medium, and Electronic Equipment" on April 28, 2020, the entire content of which is by reference Incorporated in this application.

Technical field

The present disclosure relates to the field of artificial intelligence technology, and in particular, to a user identity verification method, device, medium, and electronic equipment.

Background technique

Nowadays, verifying user identity is an important part of computer security management and Internet security management. At present, with the development and maturity of biometric authentication technology, the verification of user identity has gradually changed from a password verification method to a biometric verification method, for example, the user's identity is verified through biometric methods such as user fingerprints, face, and voice.

However, with the development of artificial intelligence, the inventor realized that human biological characteristics can be easily imitated and copied, for example, to copy human faces through the "face-changing" technology.

It can be seen that in the above method of verifying user identity through biometrics, there is still a technical problem of low user identity verification security.

Summary of the invention

According to one aspect of the embodiments of the present disclosure, there is provided a user identity verification method, the method includes: in response to a user's human-computer interaction on a terminal interface, collecting a user's first behavior data set; based on the first behavior A data set to determine a first characteristic data sequence used to verify a user’s identity; input the first characteristic data sequence into a predetermined user identity verification model to obtain a user identity verification index, and the user identity verification index is used to characterize the The authenticity of the user's identity; according to the user identity verification index, a verification response is made to the authenticity of the user's identity.

According to one aspect of the embodiments of the present disclosure, there is provided a user identity verification device, including: a collection unit, which is used to collect a user's first behavior data set in response to a user's human-computer interaction on a terminal interface; and a determination unit, Is used to determine the first characteristic data sequence used to verify the user's identity based on the first behavior data set; the input unit is used to input the first characteristic data sequence into the predetermined user identity verification model to obtain the user The identity verification index is used to characterize the authenticity of the user identity; the response unit is used to respond to the authenticity of the user identity according to the user identity verification index.

According to one aspect of the embodiments of the present disclosure, there is provided a computer-readable storage medium having a computer program stored thereon, the computer program including executable instructions, and when the executable instructions are executed by a processor, the following steps are implemented:

In response to the user's human-computer interaction on the terminal interface, collect the user's first behavior data set; based on the first behavior data set, determine the first characteristic data sequence used to verify the user's identity; combine the first characteristic data Input the predetermined user identity verification model in sequence to obtain the user identity verification index, the user identity verification index is used to characterize the authenticity of the user identity; according to the user identity verification index, the authenticity of the user identity is determined Verify the response.

According to one aspect of the embodiments of the present disclosure, there is provided an electronic device, including: one or more processors; When multiple processors are executed, the one or more processors are caused to implement the following steps:

In response to the user's human-computer interaction on the terminal interface, collect the user's first behavior data set; based on the first behavior data set, determine the first characteristic data sequence used to verify the user's identity; combine the first characteristic data Input the predetermined user identity verification model in sequence to obtain the user identity verification index, the user identity verification index is used to characterize the authenticity of the user identity; according to the user identity verification index, the authenticity of the user identity is determined Verify the response.

Description of the drawings

By describing in detail the exemplary embodiments thereof with reference to the accompanying drawings, the above and other features and advantages of the present disclosure will become more apparent.

Fig. 1 shows a schematic diagram of an application scenario of a user identity verification method according to an embodiment of the present disclosure;

Fig. 2 shows a flow chart of user identity verification according to an embodiment of the present disclosure;

FIG. 3 shows a schematic diagram of a scene when touching or sliding on a terminal interface according to an embodiment of the present disclosure;

FIG. 4 shows a detailed flowchart of determining a first characteristic data sequence used to verify the identity of a user according to an embodiment of the present disclosure;

Fig. 5 shows a detailed flowchart of determining a user identity verification model according to an embodiment of the present disclosure;

FIG. 6 shows a detailed flowchart of inputting the first characteristic data sequence into a predetermined user identity verification model to obtain a user identity verification index according to an embodiment of the present disclosure;

FIG. 7 shows a detailed flowchart of a verification response to the authenticity of the user identity according to the user identity verification index according to an embodiment of the present disclosure;

Fig. 8 shows a block diagram of a user identity verification device according to an embodiment of the present disclosure;

FIG. 9 shows a computer-readable storage medium for implementing a user identity verification method according to an embodiment of the present disclosure;

Fig. 10 shows an exemplary block diagram of an electronic device implementing a user identity verification method according to an embodiment of the present disclosure.

Detailed ways

Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms, and should not be construed as being limited to the examples set forth herein; on the contrary, the provision of these embodiments makes this application more comprehensive and complete, and fully conveys the concept of the example embodiments To those skilled in the art.

In addition, the described features, structures, or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to give a sufficient understanding of the embodiments of the present application. However, those skilled in the art will realize that the technical solutions of the present application can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. can be used. In other cases, well-known methods, devices, implementations or operations are not shown or described in detail in order to avoid obscuring various aspects of the present application.

The block diagrams shown in the drawings are merely functional entities, and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in the form of software, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and/or processor devices and/or microcontroller devices. entity.

The flowchart shown in the drawings is only an exemplary description, and does not necessarily include all contents and operations/steps, nor does it have to be executed in the described order. For example, some operations/steps can be decomposed, and some operations/steps can be combined or partially combined, so the actual execution order may be changed according to actual conditions.

First, the present disclosure briefly describes the application scenarios of the user identity verification method.

In an embodiment of the present application, the user identity verification method may be implemented in the scenario shown in FIG. 1. Referring to FIG. 1, a schematic diagram of an application scenario of the user identity verification method according to an embodiment of the present disclosure is shown.

In Fig. 1, an application scenario where a user is authenticated to log in to a device is shown. Specifically, the user identity verification method is implemented based on the mobile phone as shown in the figure. Of course, the mobile phone as shown in the figure can also be replaced by an electronic device with a touch screen such as a tablet computer or a notebook computer.

In the scenario illustrated in the figure, when the user needs to log in to his account registered in the device, first turn on the device, that is, the mobile phone interface shown in 101 in Figure 1 is displayed. In the interface 101 shown, the user can choose A login method including password login and man-machine interactive login. If the user clicks on the word "Man-machine interactive login", the mobile phone interface immediately jumps to the interface shown in 102. An input box is displayed in the interface 102. The input box reminds the user to input characters through "Please enter at least 20 characters at will...". The user clicks on the blank space in the input box as shown in the interface 102, and enters the words "JJNjnOjkn515H874664@!#％311!" (as shown in the interface 103) through the keyboard installed in the mobile phone interface. In the process of the user inputting the word, the first behavior data of the human-computer interaction generated when the user clicks or slides on the interface is collected. Further, when the user clicks the virtual touch button with the words "login", the mobile phone verifies whether the logged-in user is legal according to the collected first behavior data, and if it is legal, the user is authorized to log in to the device, and the mobile phone interface The words "login successful" as shown in interface 104 are displayed on the screen. If it is illegal, the words "login failed" as shown in interface 105 are displayed on the mobile phone interface, and the words "password login" and "human-computer interactive login" are also displayed. , So that the user can re-select the corresponding login method to log in.

It should be noted that, in the foregoing embodiment, it may also be an application scenario in which a user logs in to a website or an APP account.

In an embodiment of the present application, the user identity verification method may also be implemented in other scenarios, such as a scenario where the authenticity of the user identity is monitored in real time. Specifically, for example, in some existing scenarios, users no longer need to verify the authenticity of their user identity after logging in to an account or device. This situation may cause some serious problems, for example, When the user’s account is in the login state of the device, and the device is not within the user’s controllable range, it is possible for illegal users to control or operate the device, which may further result in bringing real users Come to lose. In view of this, when the user's account is in the login state of the device, it is very necessary to verify and monitor the authenticity of the user who controls or operates the device in real time.

In this application, when the user’s account is in the login state of the device, and the device is being operated by the user (for example, the user views some private information in the device through the device interface operation), at this time, the user’s presence is collected. The first behavior data of human-computer interaction generated when clicking or sliding operations in the interface, and verifying whether the user operating the device is legal based on the collected first behavior data, and if it is legal, allowing the user to continue to operate the device If it is illegal, it will be forced to log out of the login status of the real user’s account in the device.

The following describes in detail the implementation details of the technical solutions of the embodiments of the present application:

According to the first aspect of the present disclosure, a user identity verification method is provided.

Referring to FIG. 2, there is shown a flowchart of user identity verification according to an embodiment of the present disclosure. The user identity verification method can be executed by a device with computing processing function, for example, it can be executed by the mobile phone shown in FIG. 1. As shown in Figure 2, the user identity verification method at least includes step 210 to step 270:

Step 210: In response to the user's human-computer interaction on the terminal interface, collect the user's first behavior data set.

Step 230: Determine a first characteristic data sequence for verifying the identity of the user based on the first behavior data set.

Step 250: Input the first characteristic data sequence into a predetermined user identity verification model to obtain a user identity verification index, where the user identity verification index is used to characterize the authenticity of the user identity.

Step 270: Based on the user identity verification index, a verification response is made to the authenticity of the user identity.

The following will describe the above implementation steps in detail:

In step 210, in response to the user's human-computer interaction on the terminal interface, the user's first behavior data set is collected.

In this application, the human-computer interaction of the user on the terminal interface may refer to the user operating and controlling the terminal by touching or sliding on the terminal interface.

Specifically, the collection of first behavior data of the user may specifically include at least one of the following:

The first is to collect the collection of the touch pressure value and the touch time value when the user touches at different positions in the terminal interface.

In the process of interacting with the terminal interface, the user will perform a touch operation on the terminal interface due to actual needs, thereby realizing the user's purpose. Specifically, due to the different operating behaviors and operating habits of each user in the process of operating the terminal interface, the touch pressure on the terminal interface and the touch time staying in the terminal interface will be different when the terminal interface is touched. The difference. Therefore, in this application, the collection of the touch pressure value and the touch time value when the user touches at different positions in the terminal interface can be collected.

In order to make it easier for those skilled in the art to understand the technical features, further description will be made below with reference to FIG. 3.

FIG. 3 shows a schematic diagram of a scene when a touch or slide is performed on a terminal interface according to an embodiment of the present disclosure. In this application, the terminal interface can be divided into several cells, such as the 60 cells shown in Figure 3. Each cell represents a position. When the user touches a certain position (that is, a certain cell) in the terminal interface, it will produce a certain pressure on this position and stay in this position for a certain time, for example As shown in Figure 3, when the user touches the No. 6 position in the terminal interface, a pressure will be generated on the No. 6 position and will stay for a certain period of time.

In actual operation, according to different needs, the user will continuously select any position (cell) in the terminal interface to touch, and then collect the touch pressure value and point when the user touches any position in the terminal interface A collection of touch time values.

It should be noted that for those skilled in the art, it should be understood that for the same user, due to the different positions of different cells in the terminal interface, the user’s difficulty in tapping different cells is also different. This makes the touch pressure value and touch time value different when the user touches different cells in the terminal interface. For example, a user is accustomed to holding the phone with his right hand and tapping on the phone interface with his thumb. Then, due to the distance, the user taps the point produced by cell 1 as shown in Figure 3. The touch pressure value can be smaller than the touch pressure value generated by touching the 28th cell shown in Figure 3, and the generated touch time value can also be different.

The second is to collect the collection of sliding pressure values and sliding speed values when the user slides at different positions in the terminal interface.

In one case, in the process of interacting with the terminal interface, the user may also perform a sliding operation on the terminal interface due to actual needs. Specifically, when the user performs a sliding operation on the terminal interface, the collection of the sliding pressure value generated on the terminal interface and the sliding speed value in the interface when the user slides in the terminal interface can be collected

The third type is to collect the touch frequency value when the user touches in the terminal interface;

The fourth is to collect the sliding track when the user slides in different positions on the terminal interface.

Specifically, in this application, the trajectory arc or trajectory radius of the sliding trajectory at different positions in the terminal interface can be obtained according to the sliding trajectory when the user slides at different positions in the terminal interface. Therefore, the trajectory arc or trajectory radius of the sliding trajectory can be adjusted according to the trajectory arc or trajectory radius of the sliding trajectory. The user’s identity is verified.

It should be noted that the first row data set may also be a combination of multiple data types among the above four data types.

As described above, it can be understood that the collection method of collecting the user's first behavior data can be arbitrary, and is not limited to those shown above.

Continuing to refer to FIG. 2, in step 230, based on the first behavior data set, a first characteristic data sequence for verifying the identity of the user is determined.

In an embodiment of the present application, based on the first behavior data set, determining the first characteristic data sequence for verifying the user's identity may be implemented through the steps shown in FIG. 4. It should be emphasized that, in order to further ensure the privacy and security of the first characteristic data sequence, the first characteristic data sequence may also be stored in a node of a blockchain.

Referring to FIG. 4, there is shown a detailed flowchart of determining the first characteristic data sequence used to verify the user's identity according to an embodiment of the present disclosure. Specifically, it includes steps 231 to 232:

Step 231: Determine a first behavior data segment from the first behavior data set, where the first behavior data segment includes behavior data generated when a user touches or slides on the terminal interface at least once.

Step 232: Perform dimensionless preprocessing on the first behavior data segment to obtain a first characteristic data sequence.

In a specific implementation of this embodiment, determining the first behavior data segment from the first behavior data set may refer to a segment of first behavior data generated when the user continuously taps or slides on the terminal interface.

In order to make it easier for those skilled in the art to understand this specific implementation, further description will be given below with reference to FIG. 3. For example, the user sequentially touches the position of the interface No. 1 to No. 60 in the terminal interface as shown in FIG. 3 to obtain a set including 60 first behavior data. Determining the first behavior data segment from the first behavior data set may be determining the first behavior data corresponding to the first behavior data from the 28th to 48th interface positions from the set of the first behavior data corresponding to the positions of the 1st to the 60th interface.

In a specific implementation of this embodiment, the dimensionless preprocessing of the first behavior data segment may be standardized preprocessing of the first behavior data in the first behavior data segment.

For example, the first behavior data includes a touch pressure value and a touch time value when the user touches at different positions on the terminal interface. Specifically, it includes the touch pressure value and the touch time value when the 5 positions are touched, and the standard preprocessing is performed on the touch pressure value and the touch time value when the 5 positions are touched, and the result includes 5 A first characteristic data sequence of a first characteristic data. As shown in Table 1.

Location (cell)	Touch pressure value	Pressure characteristic data (sequence)	Tap time value	Time characteristic data (sequence)
number 1	0.001N (Newton)	1	0.01S (seconds)	1
number 2	0.002N	2	0.02S	2
number 3	0.003N	3	0.03S	3
No 4	0.004N	4	0.04S	4
Number 5	0.005N	5	0.05S	5

Table 1

In a specific implementation of this embodiment, the dimensionless preprocessing of the first behavior data segment may also be the normalization preprocessing of the first behavior data in the first behavior data segment.

In a specific implementation of this embodiment, performing dimensionless preprocessing on the first behavior data segment may also be performing interval scaling preprocessing on the first behavior data in the first behavior data segment.

In an embodiment of the present application, based on the first behavior data set, determining the first characteristic data sequence used to verify the user's identity may also be randomly selected from the first behavior data set at least once when the user is in the first behavior data set. The behavior data generated when the user touches or slides on the terminal interface is dimensionlessly preprocessed to the randomly selected behavior data generated when the user touches or slides on the terminal interface at least once, to obtain the first characteristic data sequence.

Continuing to refer to FIG. 2, in step 250, the first characteristic data sequence is input into a predetermined user identity verification model to obtain a user identity verification index, which is used to characterize the authenticity of the user identity.

In an embodiment of the present application, the user identity verification model can be determined through the steps shown in FIG. 5.

Referring to FIG. 5, a detailed flowchart of determining a user identity verification model according to an embodiment of the present disclosure is shown. Specifically, it includes steps 251 to 254:

Step 251: Obtain a second behavior data set generated when a real user taps or slides on various positions in the terminal interface.

In a specific implementation of an embodiment, the acquisition of the second behavior data set generated when a real user taps or slides on various positions on the terminal interface may specifically include at least one of the following:

The first type is to obtain the collection of the touch pressure value and the touch time value when the real user touches each position on the terminal interface.

The second method is to obtain a collection of sliding pressure values and sliding speed values when a real user slides on various positions in the terminal interface.

The third type is to obtain the touch frequency value when the real user touches on the terminal interface;

The fourth is to obtain the sliding track when the real user slides on each position in the terminal interface.

It should be noted that each position on the terminal interface refers to all positions on the terminal interface, for example, all positions from No. 1 to No. 60 shown in FIG. 3.

It should also be noted that the second behavior data set may also be a combination of multiple data types among the above four data types.

Step 252: Perform dimensionless preprocessing on the second behavior data in the second behavior data set to obtain a second characteristic data sequence.

Step 253: Based on the second characteristic data sequence, construct model learning data for determining the user identity verification model.

Step 254: Learn the model learning data through the initial user identity verification model to obtain the user identity verification model.

It should be understood that in this application, the learning of the model learning data through the initial user authentication model is actually learning the behavior characteristics of real users when they touch or slide on the terminal interface, for example, touch the terminal interface The pressure at different positions in the terminal, or the speed of sliding at different positions in the terminal interface, or the frequency of tapping in the terminal interface, etc.

In a specific implementation of an embodiment, the second behavior data set includes a set of tapping pressure values and tapping time values when the real user taps on various positions in the terminal interface, and the set is based on the The second characteristic data sequence, which is used to construct the model learning data used to determine the user identity verification model, includes:

Based on the second characteristic data sequence, the following model learning data matrix S is constructed, and each column of the model learning data matrix S is used as a piece of model learning data:

Wherein, n indicates that a real user performs a touch operation on the nth position in the terminal interface, F indicates pressure characteristic data, T indicates time characteristic data, and "1" indicates a learning goal, which is used to characterize all Describes the degree of authenticity when a real user touches various positions in the terminal interface.

What needs to be explained is that in the above model learning data matrix S, the learning target "1" represents a probability, and the learning target in each model learning data is set to "1". The reason is that each model learning data The pressure characteristic data and time characteristic data in both come from real users, so "1" means that the corresponding pressure characteristic data and time characteristic data come from real users to an absolute degree. Therefore, for those skilled in the art, it can be understood that the learning goal is also set to "100%", "100", "10" and so on.

In a specific implementation of an embodiment, the second behavior data set includes a set of sliding pressure values and sliding speed values when the real user slides on various positions in the terminal interface, and the set is based on the second feature The data sequence, which is used to construct the model learning data used to determine the user identity verification model, includes:

Based on the second characteristic data sequence, the following model learning data matrix T is constructed, and each column of the model learning data matrix T is used as a piece of model learning data:

Wherein, n indicates that a real user performs a sliding operation on the nth position in the terminal interface, R indicates pressure characteristic data, V indicates time characteristic data, and "1" indicates a learning target, and the learning target is used to characterize the The degree of realism when a real user slides on various positions in the terminal interface.

In an embodiment of the present application, the first characteristic data sequence includes at least one piece of first characteristic data, and the first characteristic data sequence is input into a predetermined user identity verification model to obtain a user identity verification index, This can be achieved through the steps shown in Figure 6.

Referring to FIG. 6, there is shown a detailed flowchart of inputting the first characteristic data sequence into a predetermined user identity verification model to obtain a user identity verification index according to an embodiment of the present disclosure. Specifically, it includes steps 255 to 256:

Step 255: Input the at least one piece of first characteristic data into a predetermined user identity verification model to obtain at least one user identity verification sub-index.

Specifically, in an implementation, the user identity verification sub-index may be a probability value, that is, the probability value at which the user who taps or slides at a certain position on the terminal interface is judged to be a real user.

Step 256: Perform mathematical statistics on the at least one user identity verification sub-index to obtain a user identity verification index.

In a specific implementation of an embodiment, performing mathematical statistics on the at least one user identity verification sub-index may be averaging the at least one user identity verification sub-index, for example, Table 2, for 5 user identity verification The sub-indices are averaged.

User identity verification sub-index 1	0.7
User authentication sub-index 2	0.9
User Identity Verification Sub-Index 3	0.8
User Identity Verification Sub-Index 4	0.6
User Identity Verification Sub-index 5	1.0
User authentication index (average)	0.8

Table 2

In a specific implementation of an embodiment, performing mathematical statistics on the at least one user identity verification sub-index may be a summation of the at least one user identity verification sub-index.

In a specific implementation of an embodiment, performing mathematical statistics on the at least one user identity verification sub-index may include counting the number of user identity verification sub-indexes that exceed a predetermined threshold in the at least one user identity verification sub-index. .

Continuing to refer to FIG. 2, in step 270, a verification response is made to the authenticity of the user identity according to the user identity verification index.

In an embodiment of the present application, according to the user identity verification index, the verification response to the authenticity of the user identity can be implemented according to the steps shown in FIG. 7.

Referring to FIG. 7, there is shown a detailed flowchart of a verification response to the authenticity of the user identity according to the user identity verification index according to an embodiment of the present disclosure. Specifically, it includes steps 271 to 272:

Step 271: When the user identity verification index falls within a predetermined index range, a response that the authenticity of the user identity is verified is made.

For example, in an application scenario where the user identity is verified to log in to the device, when the user identity verification index belongs to a predetermined index range, the user is authorized to log in to the device.

For another example, in a scenario where the authenticity of a user's identity is monitored in real time, when the user identity verification index falls within a predetermined index range, the user is allowed to continue to operate the device.

Step 272: When the user identity verification index does not belong to a predetermined index range, a response of verification failure is made to the authenticity of the user identity.

For example, in an application scenario where the user identity is verified to log in to the device, when the user identity verification index does not belong to a predetermined index range, the user is denied to log in to the device.

For another example, in a scenario where the authenticity of a user's identity is monitored in real time, when the user identity verification index does not fall within a predetermined index range, the login state of the real user's account in the device is forcibly logged out.

In summary, in the technical solutions of some embodiments of the present disclosure, the first characteristic data sequence is obtained through the collected first behavior data set of the user, and the first characteristic data sequence is further determined by the user identity verification model determined in advance. The data sequence is verified to obtain a user identity verification index that can be used to verify the authenticity of the user's identity. Since the behavior data generated by different users when interacting with the terminal interface is different and different, and the behavior data is difficult to be copied, the behavior data generated by the human-computer interaction between the user and the terminal interface is used as the verification user identity The basis of authenticity can improve the security of authenticity verification of the user's identity, and thus can solve the technical problem of low user identity verification in the prior art.

The following describes the device embodiments of the present disclosure, which can be used to implement the user identity verification method in the foregoing embodiments of the present disclosure. For details that are not disclosed in the embodiment of the device of the present disclosure, please refer to the embodiment of the user identity verification method of the present disclosure.

Fig. 8 shows a block diagram of a user identity verification device according to an embodiment of the present disclosure.

Referring to FIG. 8, a user identity verification device 800 according to an embodiment of the present disclosure includes: a collection unit 801, a determination unit 802, an input unit 803, and a response unit 804.

Among them, the collection unit 801 is used to collect the user's first behavior data set in response to the user's human-computer interaction on the terminal interface; the determination unit 802 is used to determine the first behavior data set for verification based on the first behavior data set The first characteristic data sequence of the user identity; the input unit 803 is used to input the first characteristic data sequence into a predetermined user identity verification model to obtain a user identity verification index, and the user identity verification index is used to characterize the The authenticity of the user's identity; the response unit 804 is used to respond to the authenticity of the user's identity according to the user's identity verification index.

In some embodiments of the present disclosure, based on the foregoing solution, the collection unit 801 is configured to collect a first behavior data set including at least one of the following: collect the touch pressure when the user touches at different positions in the terminal interface The collection of the value and the touch time value; the collection of the sliding pressure value and the sliding speed value when the user slides in different positions in the terminal interface; the collection of the touch frequency value when the user touches in the terminal interface; the collection of the user's terminal interface The sliding track when sliding in different positions in the middle.

In some embodiments of the present disclosure, based on the foregoing solution, the determining unit 802 is configured to determine a first behavior data segment from the first behavior data set, and the first behavior data segment includes at least one user The behavior data generated when the terminal interface is touched or swiped; the first behavior data segment is non-dimensionally preprocessed to obtain the first characteristic data sequence. It should be emphasized that, in order to further ensure the privacy and security of the first characteristic data sequence, the first characteristic data sequence may also be stored in a node of a blockchain.

In some embodiments of the present disclosure, based on the foregoing solution, the user identity verification model is determined in the following manner: acquiring a second behavior data set generated when a real user taps or slides on various positions in the terminal interface; Perform dimensionless preprocessing on the second behavior data in the second behavior data set to obtain a second characteristic data sequence; based on the second characteristic data sequence, construct model learning data for determining the user identity verification model ; Learning the model learning data through the initial user identity verification model to obtain the user identity verification model.

In some embodiments of the present disclosure, based on the foregoing solution, the second behavior data set includes the set of the touch pressure value and the touch time value when the real user touches each position in the terminal interface, so The input unit 803 is further configured to construct the following model learning data matrix S based on the second characteristic data sequence, and each column of the model learning data matrix S is used as a piece of model learning data:

Wherein, n indicates that a real user performs a touch operation on the nth position in the terminal interface, F indicates pressure characteristic data, T indicates time characteristic data, and "1" indicates a learning goal, which is used to characterize all Describes the degree of authenticity when a real user touches various positions in the terminal interface.

In some embodiments of the present disclosure, based on the foregoing solution, the first characteristic data sequence includes at least one piece of first characteristic data, and the input unit 803 is further configured to: input the at least one piece of first characteristic data into a predetermined To obtain at least one user identity verification sub-index; mathematical statistics are performed on the at least one user identity verification sub-index to obtain the user identity verification index.

In some embodiments of the present disclosure, based on the foregoing solution, the response unit 804 is configured to: when the user identity verification index belongs to a predetermined index range, make a response that the authenticity of the user identity is verified; When the user identity verification index does not belong to the predetermined index range, a response of verification failure is made to the authenticity of the user identity.

It should be noted that although the user identity verification method and several units of the user identity verification device are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, two or more units and functions described above may be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided into multiple units to be embodied. A component displayed as a unit may or may not be a physical unit, that is, it may be located in one place, or it may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the objectives of the solutions of the present disclosure. Those of ordinary skill in the art can understand and implement without creative work.

As another aspect, the present disclosure also provides a computer-readable storage medium. The computer-readable storage medium may be volatile or non-volatile, and a program capable of implementing the above method of this specification is stored thereon. product. In some possible implementation manners, various aspects of the present disclosure may also be implemented in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to enable the The terminal device performs the following steps:

In response to the user's human-computer interaction on the terminal interface, collect the user's first behavior data set; based on the first behavior data set, determine the first characteristic data sequence used to verify the user's identity; combine the first characteristic data Input the predetermined user identity verification model in sequence to obtain the user identity verification index, the user identity verification index is used to characterize the authenticity of the user identity; according to the user identity verification index, the authenticity of the user identity is determined Verify the response.

Referring to FIG. 9, a program product 900 for implementing the above method according to an embodiment of the present disclosure is described. It can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be installed in a terminal device, For example, running on a personal computer. However, the program product of the present disclosure is not limited thereto. In this document, the readable storage medium can be any tangible medium that contains or stores a program, and the program can be used by or in combination with an instruction execution system, device, or device.

The program product can use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or a combination of any of the above. More specific examples (non-exhaustive list) of readable storage media include: electrical connections with one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable Type programmable read only memory (EPROM or flash memory), optical fiber, portable compact disk read only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.

The computer-readable signal medium may include a data signal propagated in baseband or as a part of a carrier wave, and readable program code is carried therein. This propagated data signal can take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. The readable signal medium may also be any readable medium other than a readable storage medium, and the readable medium may send, propagate, or transmit a program for use by or in combination with the instruction execution system, apparatus, or device.

The program code contained on the readable medium can be transmitted by any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

The program code used to perform the operations of the present disclosure can be written in any combination of one or more programming languages. The programming languages include object-oriented programming languages—such as Java, C++, etc., as well as conventional procedural programming languages. Programming language-such as "C" language or similar programming language. The program code can be executed entirely on the user's computing device, partly on the user's device, executed as an independent software package, partly on the user's computing device and partly executed on the remote computing device, or entirely on the remote computing device or server Executed on. In the case of a remote computing device, the remote computing device can be connected to a user computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (for example, using Internet service providers). Business to connect via the Internet).

As another aspect, the present disclosure also provides an electronic device capable of implementing the above method.

Those skilled in the art can understand that various aspects of the present disclosure can be implemented as a system, a method, or a program product. Therefore, various aspects of the present disclosure can be specifically implemented in the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which may be collectively referred to herein as "Circuit", "Module" or "System".

The electronic device 1000 according to this embodiment of the present disclosure will be described below with reference to FIG. 10. The electronic device 1000 shown in FIG. 10 is only an example, and should not bring any limitation to the function and scope of use of the embodiments of the present disclosure.

As shown in FIG. 10, the electronic device 1000 is represented in the form of a general-purpose computing device. The components of the electronic device 1000 may include, but are not limited to: the aforementioned at least one processing unit 1010, the aforementioned at least one storage unit 1020, and a bus 1030 connecting different system components (including the storage unit 1020 and the processing unit 1010).

Wherein, the storage unit stores program code, and the program code can be executed by the processing unit 1010, so that the processing unit 1010 executes the various exemplary methods described in the "Methods of Embodiments" section of this specification. Steps of implementation.

The storage unit 1020 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 1021 and/or a cache storage unit 1022, and may further include a read-only storage unit (ROM) 1023.

The storage unit 1020 may also include a program/utility tool 1024 having a set (at least one) program module 1025. Such program module 1025 includes but is not limited to: an operating system, one or more application programs, other program modules, and program data, Each of these examples or some combination may include the implementation of a network environment.

The bus 1030 may represent one or more of several types of bus structures, including a storage unit bus or a storage unit controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local area using any bus structure among multiple bus structures. bus.

The electronic device 1000 may also communicate with one or more external devices 1200 (such as keyboards, pointing devices, Bluetooth devices, etc.), and may also communicate with one or more devices that enable a user to interact with the electronic device 1000, and/or communicate with Any device (such as a router, modem, etc.) that enables the electronic device 1000 to communicate with one or more other computing devices. This communication can be performed through an input/output (I/O) interface 1050. In addition, the electronic device 1000 may also communicate with one or more networks (for example, a local area network (LAN), a wide area network (WAN), and/or a public network, such as the Internet) through the network adapter 1060. As shown in the figure, the network adapter 1060 communicates with other modules of the electronic device 1000 through the bus 1030. It should be understood that although not shown in the figure, other hardware and/or software modules can be used in conjunction with the electronic device 1000, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives And data backup storage system, etc.

The blockchain referred to in this application is a new application mode of computer technology such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm. Blockchain, essentially a decentralized database, is a series of data blocks associated with cryptographic methods. Each data block contains a batch of network transaction information for verification. The validity of the information (anti-counterfeiting) and the generation of the next block. The blockchain can include the underlying platform of the blockchain, the platform product service layer, and the application service layer.

Through the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described here can be implemented by software, or can be implemented by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, U disk, mobile hard disk, etc.) or on the network , Including several instructions to make a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) execute the method according to the embodiments of the present disclosure.

In addition, the above-mentioned drawings are merely schematic illustrations of the processing included in the method according to the exemplary embodiments of the present disclosure, and are not intended for limitation. It is easy to understand that the processing shown in the above drawings does not indicate or limit the time sequence of these processings. In addition, it is easy to understand that these processes can be executed synchronously or asynchronously in multiple modules, for example.

It should be understood that the present disclosure is not limited to the precise structure that has been described above and shown in the drawings, and various modifications and changes can be performed without departing from its scope. The scope of the present disclosure is only limited by the appended claims.

Claims (20)

1. A user identity verification method, wherein the method includes:

   In response to the user's human-computer interaction on the terminal interface, collect the user's first behavior data set;

   Based on the first behavior data set, determining a first characteristic data sequence used to verify the identity of the user;

   Inputting the first characteristic data sequence into a predetermined user identity verification model to obtain a user identity verification index, where the user identity verification index is used to characterize the authenticity of the user identity;

   According to the user identity verification index, a verification response is made to the authenticity of the user identity.
2. The method according to claim 1, wherein the collection of the first behavior data set of the user specifically includes at least one of the following:

   Collect the collection of the touch pressure value and the touch time value when the user touches at different positions in the terminal interface;

   Collect the collection of sliding pressure values and sliding speed values when the user slides at different positions in the terminal interface;

   Collect the touch frequency value when the user touches in the terminal interface;

   Collect the sliding track when the user slides in different positions on the terminal interface.
3. The method according to claim 1, wherein the first characteristic data sequence is stored in a blockchain, and the determining the first characteristic data sequence for verifying the identity of the user based on the first behavior data set comprises :

   Determine a first behavior data segment from the first behavior data set, where the first behavior data segment includes behavior data generated when a user touches or slides on the terminal interface at least once;

   The dimensionless preprocessing is performed on the first behavior data segment to obtain a first characteristic data sequence.
4. The method according to claim 1, wherein the user identity verification model is determined in the following manner:

   Acquiring a second behavior data set generated when a real user taps or slides on various positions in the terminal interface;

   Performing dimensionless preprocessing on the second behavior data in the second behavior data set to obtain a second characteristic data sequence;

   Based on the second characteristic data sequence, constructing model learning data for determining the user identity verification model;

   The model learning data is learned through the initial user identity verification model to obtain the user identity verification model.
5. The method according to claim 4, wherein the second behavior data set includes a set of the touch pressure value and the touch time value when the real user makes a touch on each position in the terminal interface, and the data is based on The second characteristic data sequence, which is used to construct the model learning data used to determine the user identity verification model, includes:

   Based on the second characteristic data sequence, the following model learning data matrix S is constructed, and each column of the model learning data matrix S is used as a piece of model learning data:

   

   Wherein, n indicates that a real user performs a touch operation on the nth position in the terminal interface, F indicates pressure characteristic data, T indicates time characteristic data, and "1" indicates a learning goal, which is used to characterize all Describes the degree of authenticity when a real user touches various positions in the terminal interface.
6. The method according to claim 1, wherein the first characteristic data sequence includes at least one piece of first characteristic data, and the first characteristic data sequence is input into a predetermined user identity verification model to obtain user identity verification Index, including:

   Inputting the at least one piece of first characteristic data into a predetermined user identity verification model to obtain at least one user identity verification sub-index;

   Perform mathematical statistics on the at least one user identity verification sub-index to obtain a user identity verification index.
7. The method according to claim 1, wherein the responding to the authenticity of the user identity according to the user identity verification index comprises:

   When the user identity verification index falls within a predetermined index range, respond to the authenticity of the user identity with a verification pass;

   When the user identity verification index does not belong to a predetermined index range, a response of verification failure is made to the authenticity of the user identity.
8. A user identity verification device, wherein the device includes:

   The collection unit is used to collect the user's first behavior data collection in response to the user's human-computer interaction on the terminal interface;

   The determining unit is used to determine the first characteristic data sequence used to verify the identity of the user based on the first behavior data set;

   The input unit is used to input the first characteristic data sequence into a predetermined user identity verification model to obtain a user identity verification index, where the user identity verification index is used to characterize the authenticity of the user identity;

   The response unit is used to respond to the authenticity of the user identity according to the user identity verification index.
9. A computer-readable storage medium, in which computer program instructions are stored, and when the computer program instructions are executed by a computer, the computer executes the following steps:

   In response to the user's human-computer interaction on the terminal interface, collect the user's first behavior data set;

   Based on the first behavior data set, determining a first characteristic data sequence used to verify the identity of the user;

   Inputting the first characteristic data sequence into a predetermined user identity verification model to obtain a user identity verification index, where the user identity verification index is used to characterize the authenticity of the user identity;

   According to the user identity verification index, a verification response is made to the authenticity of the user identity.
10. 8. The computer-readable storage medium according to claim 9, wherein the collection of the first behavior data collection of the user specifically includes at least one of the following:

    Collect the collection of the touch pressure value and the touch time value when the user touches at different positions in the terminal interface;

    Collect the collection of sliding pressure values and sliding speed values when the user slides at different positions in the terminal interface;

    Collect the touch frequency value when the user touches in the terminal interface;

    Collect the sliding track when the user slides in different positions on the terminal interface.
11. The computer-readable storage medium according to claim 9, wherein the first characteristic data sequence is stored in a blockchain, and the first characteristic for verifying the identity of the user is determined based on the first behavior data set Data sequence, including:

    Determine a first behavior data segment from the first behavior data set, where the first behavior data segment includes behavior data generated when a user touches or slides on the terminal interface at least once;

    The dimensionless preprocessing is performed on the first behavior data segment to obtain a first characteristic data sequence.
12. The computer-readable storage medium according to claim 9, wherein the user authentication model is determined in the following manner:

    Acquiring a second behavior data set generated when a real user taps or slides on various positions in the terminal interface;

    Performing dimensionless preprocessing on the second behavior data in the second behavior data set to obtain a second characteristic data sequence;

    Based on the second characteristic data sequence, constructing model learning data for determining the user identity verification model;

    The model learning data is learned through the initial user identity verification model to obtain the user identity verification model.
13. The computer-readable storage medium according to claim 12, wherein the second behavior data set includes a set of touch pressure values and touch time values when the real user touches each position in the terminal interface , Said constructing the model learning data used to determine the user identity verification model based on the second characteristic data sequence includes:

    Based on the second characteristic data sequence, the following model learning data matrix S is constructed, and each column of the model learning data matrix S is used as a piece of model learning data:

    

    Wherein, n indicates that a real user performs a touch operation on the nth position in the terminal interface, F indicates pressure characteristic data, T indicates time characteristic data, and "1" indicates a learning goal, which is used to characterize all Describes the degree of authenticity when a real user touches various positions in the terminal interface.
14. 8. The computer-readable storage medium according to claim 9, wherein the first characteristic data sequence includes at least one piece of first characteristic data, and the inputting the first characteristic data sequence into a predetermined user identity verification model, Obtain the user identity verification index, including:

    Inputting the at least one piece of first characteristic data into a predetermined user identity verification model to obtain at least one user identity verification sub-index;

    Perform mathematical statistics on the at least one user identity verification sub-index to obtain a user identity verification index.
15. 8. The computer-readable storage medium according to claim 9, wherein said responding to the authenticity of said user identity according to said user identity verification index comprises:

    When the user identity verification index falls within a predetermined index range, respond to the authenticity of the user identity with a verification pass;

    When the user identity verification index does not belong to a predetermined index range, a response of verification failure is made to the authenticity of the user identity.
16. An electronic device for user identity verification, wherein the electronic device includes:

    processor;

    A memory, where computer-readable instructions are stored, and when the computer-readable instructions are executed by the processor, the following steps are implemented:

    Based on the first behavior data set, determining a first characteristic data sequence used to verify the identity of the user;

    Inputting the first characteristic data sequence into a predetermined user identity verification model to obtain a user identity verification index, where the user identity verification index is used to characterize the authenticity of the user identity;

    According to the user identity verification index, a verification response is made to the authenticity of the user identity.
17. The user identity verification electronic device according to claim 16, wherein the collection of the first behavior data set of the user specifically includes at least one of the following:

    Collect the collection of the touch pressure value and the touch time value when the user touches at different positions in the terminal interface;

    Collect the collection of sliding pressure values and sliding speed values when the user slides at different positions in the terminal interface;

    Collect the touch frequency value when the user touches in the terminal interface;

    Collect the sliding track when the user slides in different positions on the terminal interface.
18. The user identity verification electronic device according to claim 16, wherein the first characteristic data sequence is stored in a blockchain, and the first characteristic for verifying the user identity is determined based on the first behavior data set Data sequence, including:

    Determine a first behavior data segment from the first behavior data set, where the first behavior data segment includes behavior data generated when a user touches or slides on the terminal interface at least once;

    The dimensionless preprocessing is performed on the first behavior data segment to obtain a first characteristic data sequence.
19. The user identity verification electronic device according to claim 16, wherein the user identity verification model is determined in the following manner:

    Acquiring a second behavior data set generated when a real user taps or slides on various positions in the terminal interface;

    Performing dimensionless preprocessing on the second behavior data in the second behavior data set to obtain a second characteristic data sequence;

    Based on the second characteristic data sequence, constructing model learning data for determining the user identity verification model;

    The model learning data is learned through the initial user identity verification model to obtain the user identity verification model.
20. The user identity verification electronic device according to claim 19, wherein the second behavior data set includes a set of the touch pressure value and the touch time value when the real user touches each position in the terminal interface , Said constructing the model learning data used to determine the user identity verification model based on the second characteristic data sequence includes:

    Based on the second characteristic data sequence, the following model learning data matrix S is constructed, and each column of the model learning data matrix S is used as a piece of model learning data:

    

    Wherein, n indicates that a real user performs a touch operation on the nth position in the terminal interface, F indicates pressure characteristic data, T indicates time characteristic data, and "1" indicates a learning goal, which is used to characterize all Describes the degree of authenticity when a real user touches various positions in the terminal interface.

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