Man-machine verification method, device, equipment and medium

Abstract

The invention discloses a man-machine verification method and device, equipment and a medium. The man-machine verification method comprises the following steps: acquiring first motion data of a mobile terminal acquired by a motion sensor; generating a motion map according to the first motion data; according to the motion map, querying in a database to determine the matching degree of the motion map and historical data in the database; under the condition that the matching degree is greater than a first preset threshold value and less than a second preset threshold value, generating a target track according to the motion map; sending the target track to the mobile terminal, so that an operation object of the mobile terminal performs feedback according to the target track; acquiring motion feedback information of the mobile terminal acquired by a motion sensor in real time; and according to the target trajectory and the motion feedback information, determining whether the operation object is a natural person to obtain a man-machine verification result. According to the embodiment of the invention, the accuracy of man-machine verification can be improved.

Description

Technical Field

[0001] The present application belongs to the field of network security technology, and in particular, relates to a human-machine verification method, device, equipment and medium. Background Art

[0002] In many scenarios, real people are required to participate in the interaction. However, due to the development of artificial intelligence, a large number of new methods have emerged to simulate the behavior of natural human users, such as AI (artificial intelligence) robots simulating human behavior, which leads to security risks. Therefore, it is necessary to reduce the risk by identifying the operation object of the mobile terminal to achieve human-machine verification.

[0003] In the related art, human-machine verification is usually implemented based on a pre-trained recognition model. However, since attackers can generate relevant data through AI algorithms to simulate human behavior and input the data into the recognition model to achieve the purpose of identifying AI as a human, that is, for the human-machine verification scheme in the related art, there is a risk of disguising AI behavior as human behavior and thus passing the human-machine verification and cracking the human-machine verification system. Summary of the invention

[0004] The embodiments of the present application provide a human-machine verification method, device, equipment and medium to improve the accuracy of human-machine verification.

[0005] In a first aspect, an embodiment of the present application provides a human-machine verification method, which is applied to a mobile terminal, wherein the mobile terminal is provided with a motion sensor;

[0006] The method comprises:

[0007] Acquire first motion data of the mobile terminal collected by a motion sensor;

[0008] generating a motion map according to the first motion data;

[0009] According to the motion graph, querying in a database to determine the degree of match between the motion graph and the historical data in the database;

[0010] When the matching degree is greater than a first preset threshold and less than a second preset threshold, generating a target trajectory according to the motion graph;

[0011] Sending the target trajectory to the mobile terminal so that an operation object of the mobile terminal provides feedback according to the target trajectory;

[0012] Acquire motion feedback information of the mobile terminal collected by a motion sensor in real time;

[0013] According to the target trajectory and motion feedback information, it is determined whether the operating object is a natural person to obtain a human-machine verification result.

[0014] In a second aspect, an embodiment of the present application provides a human-machine verification device, which is applied to a mobile terminal, wherein the mobile terminal is provided with a motion sensor;

[0015] The device comprises:

[0016] A first acquisition module, used to acquire first motion data of the mobile terminal collected by a motion sensor;

[0017] A first generating module, configured to generate a motion graph according to the first motion data;

[0018] A query module, for querying in a database according to the motion graph to determine the matching degree between the motion graph and the historical data in the database;

[0019] A second generating module, configured to generate a target trajectory according to the motion graph when the matching degree is greater than a first preset threshold and less than a second preset threshold;

[0020] A sending module, used for sending the target trajectory to the mobile terminal, so that the operation object of the mobile terminal can provide feedback according to the target trajectory;

[0021] A second acquisition module, used for acquiring in real time the motion feedback information of the mobile terminal collected by the motion sensor;

[0022] The determination module is used to determine whether the operation object is a natural person according to the target trajectory and motion feedback information, so as to obtain a human-machine verification result.

[0023] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the steps of the human-machine verification method described in any one of the embodiments of the first aspect are implemented.

[0024] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the steps of the human-machine verification method described in any embodiment of the first aspect are implemented.

[0025] The human-machine verification method, device, equipment and medium of the embodiment of the present application obtain information collected by the motion sensor carried by the mobile terminal, and generate a corresponding motion map from the obtained information to judge the matching degree with the historical data in the database, so as to realize the preliminary judgment based on the non-perception method, prevent the interruption of the original operation of the operation object, and improve the experience of the operation object; when the matching degree is within the preset range, the target trajectory for the operation object feedback is generated based on the matching degree, and the user's motion feedback information is used to verify whether the operation object is a natural person. Since the motion feedback information is the motion feedback information of the target trajectory, and the AI ​​technology consumes a large amount of computing resources to map the spatial motion information to the fusion data of the motion sensor, the attacker cannot complete a large-scale human-machine recognition attack through AI generated data. Therefore, it is difficult for AI technology to realize real-time motion feedback of the motion trajectory. Therefore, through the motion feedback information, it is possible to accurately identify whether the operation object is a natural person or a non-natural person, thereby improving the accuracy of human-machine verification. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solution of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0027] Figure 1 It is a flowchart of a human-machine verification method provided in an embodiment of the present application;

[0028] Figure 2 It is a structural schematic diagram of a human-machine verification device provided in an embodiment of the present application;

[0029] Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0031] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0032] It should be noted that the acquisition, storage, use and processing of data in the embodiments of the present application are in compliance with the relevant provisions of national laws and regulations.

[0033] The gait cycle is used to represent the process from the heel of one side touching the ground to the heel of the same side touching the ground again when walking, and is usually expressed in seconds (s). The gait cycle of an average adult is about 1-1.32s. Each gait cycle during walking includes a series of typical posture transfers, and this typical posture change is usually divided into a series of time periods, called gait phases (gait phase / period). A gait cycle can be divided into a stance phase and a swing phase, which can be further divided into 8 phases. It is usually expressed in terms of the percentage of the gait cycle occupied by the phase (GC%), and is sometimes expressed in seconds (s).

[0034] Weight decay, a regularization technique, is designed to solve overfitting. Overfitting is a statistical term used to refer to the phenomenon of matching a specific data set too closely or precisely to the point where it cannot fit other data well or predict future observations. Therefore, weight decay is used to indicate that there are multiple parameters, and all parameters have the same weight initially. When a parameter is too active and has too much influence on the overall judgment, or even plays a decisive role in the degree of influence of the parameter, overfitting will occur. Therefore, when a parameter has too much influence, its importance, that is, its weight, should be reduced to avoid the meaninglessness of other parameters.

[0035] Atomic actions, also known as atomic visual actions, originate from the computer term atomic operation. An operation composed of multiple operations, if the operation cannot be executed atomically, either all steps are executed or none of them are executed. It is impossible to execute only a subset of all steps.

[0036] In many scenarios, real people are required to participate in the interaction. However, due to the development of artificial intelligence, a large number of new methods have emerged to simulate the behavior of natural human users, such as AI (artificial intelligence) robots simulating human behavior, which leads to security risks. Therefore, it is necessary to reduce the risk by identifying the operation object of the mobile terminal to achieve human-machine verification.

[0037] In the related art, human-machine verification is usually implemented based on a pre-trained recognition model. However, since attackers can generate relevant data through AI algorithms to simulate human behavior and input the data into the recognition model to achieve the purpose of identifying AI as a human, that is, for the human-machine verification scheme in the related art, there is a risk of disguising AI behavior as human behavior and passing the human-machine verification, thereby cracking the human-machine verification system.

[0038] That is to say, based on the pre-trained model, the identity of the verifier is determined by matching during verification. The pre-trained recognition model requires a large number of data samples, and its purpose is to identify the identity through input data. If the attacker understands the intention of recognition, he can use the AI ​​algorithm to generate relevant sensor data to simulate human behavior, bypass the sensor workflow, and directly disguise the data as sensor-generated data and input it into the recognition model. Although the data generated by AI cannot correspond to the posture-sensor data pre-entered in the background database, and therefore cannot achieve the purpose of disguising as a specific identity, it can achieve the purpose of this type of algorithm to identify AI as a human, that is, during the human-machine verification process, the AI ​​behavior can be disguised as human behavior, thereby passing the human-machine verification.

[0039] Alternatively, in the related technology, an interactive method of image and text recognition is used to perform human-machine verification, so that the user can recognize the true intention of the image and text before performing related operations, which results in a poor user experience; or, a relatively fixed verification method or verification process is used, for example, by monitoring the user pulling the progress bar, clicking the verification point, sliding the screen and dragging the control on the page, entering the verification code, etc. to determine whether the user is a human-machine, which can be easily cracked by AI and easily bypassed.

[0040] In order to solve the problems of the related art, the embodiments of the present application provide a human-machine verification method, device, equipment and medium.

[0041] The human-machine verification method provided by the embodiment of the present application is described in detail below through specific embodiments and their application scenarios in conjunction with the accompanying drawings.

[0042] Figure 1 FIG. 1 is a flow chart of a human-machine verification method 100 according to an embodiment of the present application. Figure 1 As shown, the human-machine verification method 100 is applied to a mobile terminal, and the mobile terminal is provided with a motion sensor. The human-machine verification method 100 may specifically include the following steps:

[0043] S101, acquiring first motion data of the mobile terminal collected by a motion sensor;

[0044] S102, generating a motion spectrum according to the first motion data;

[0045] S103, querying in a database according to the motion graph to determine the matching degree between the motion graph and the historical data in the database;

[0046] S104, generating a target trajectory according to the motion graph when the matching degree is greater than a first preset threshold and less than a second preset threshold;

[0047] S105, sending the target trajectory to the mobile terminal, so that the operation object of the mobile terminal provides feedback according to the target trajectory;

[0048] S106, acquiring in real time motion feedback information of the mobile terminal collected by a motion sensor;

[0049] S107: Determine whether the operation object is a natural person according to the target trajectory and motion feedback information to obtain a human-machine verification result.

[0050] Therefore, by acquiring the information collected by the motion sensor carried by the mobile terminal, and generating the corresponding motion map with the acquired information to judge the matching degree with the historical data in the database, a preliminary judgment based on a non-perceptual method is achieved to prevent the interruption of the original operation of the operation object and improve the experience of the operation object; when the matching degree is within the preset range, a target trajectory for feedback of the operation object is generated based on the matching degree, and the user's motion feedback information is used to verify whether the operation object is a natural person. Since the motion feedback information is the motion feedback information of the target trajectory, and the AI ​​technology consumes a lot of computing resources to map the spatial motion information to the fusion data of the motion sensor, the attacker cannot complete a large-scale human-machine recognition attack through AI generated data. Therefore, it is difficult for AI technology to achieve real-time motion feedback of the motion trajectory. Therefore, through the motion feedback information, it is possible to accurately identify whether the operation object is a natural person or a non-natural person, thereby improving the accuracy of human-machine verification.

[0051] The specific implementation methods of the above steps are introduced below.

[0052] It should be noted that the mobile terminal in the embodiment of the present application may include a smart phone, a handheld terminal device or a wearable device such as a VR glasses, or other large human-controllable equipment equipped with sensors, etc., and this embodiment does not make any specific limitations on this.

[0053] In addition, the mobile terminal in the embodiment of the present application is provided with a motion sensor, and considering the universal applicability of the human-machine verification method in the embodiment of the present application, the motion sensor may include an accelerometer, a gyroscope, etc., because terminal devices equipped with these sensors are relatively common.

[0054] In some embodiments, in step S101, the motion sensor carried by the mobile terminal collects its spatial motion information in real time. When the mobile terminal is in the target scene, for example, entering a pre-set business page (target page), or meeting other preset conditions, the acquisition signal of the motion sensor is obtained and defined as the first motion data. Specifically, since the motion sensor may include an accelerometer, a gyroscope, etc., the first motion data may include linear acceleration information and angular velocity change information.

[0055] In some embodiments, before step S102, the state information of the operation object of the mobile terminal is determined according to the first motion data; when the state information of the operation object is static, invalid data that does not meet the target scene requirements in the first motion data is determined according to the target scene requirements, and the invalid data is removed from the first motion data. The state information of the operation object may include motion and static.

[0056] It should be understood that when the state information of the operating object is motion, the corresponding mobile terminal will generate a relatively obvious acceleration signal within a certain period of time because the operating object is in motion. Therefore, the moving speed of the operating object can be calculated based on the acceleration information collected by the accelerometer, and the acceleration information and the angular velocity change information collected by the gyroscope can be combined to determine the posture change of the terminal device to generate the first motion data in real time, so as to determine whether the operating object is in motion (for example, walking) based on the first motion data.

[0057] Alternatively, when the state information of the operating object is static, the corresponding mobile terminal may move in space within a certain range, which may include two types of movements: the first type is that the operating object needs to interact according to the target scene requirements or business requirements. Since it is related to the business, it can be determined based on the state of the operating object in the business scene; the second type is not related to the business and cannot be identified and determined based on the operating object information in the business scene, but it can be understood that since the operating object will not swing or shake the mobile device meaninglessly when interacting (for example, reading and understanding the information conveyed by the terminal device), the meaningless movement of the mobile terminal can be identified.

[0058] For example, when the target scene is the operating object reading content, the mobile terminal should swing according to the natural body of the operating object, and a small number of incoherent and discrete-direction signals will appear, but a large number of acceleration signals should not appear; or, when the target scene is the movement of the operating object (such as moving forward, turning around, etc.), the mobile terminal should generate a relatively continuous linear velocity signal and a relatively uniform angular velocity signal.

[0059] In addition, when the operating object is in walking movement, the first motion data within a certain time range can be used to generate multiple gait cycles according to the periodic characteristics of walking movement; when the operating object is in a stationary state, it can be determined in real time whether the operating object is in an interactive state according to the spatial movement and posture change of the terminal device. When it is determined that the operating object is not in interaction, data collection should be stopped. When it is determined that the operating object is in interaction, data collection can be triggered according to the device movement generated by the interaction of the operating object in the business scenario. For example, a VR user can confirm or turn the page in the scene by nodding or shaking his head, and the collected data can be uploaded for subsequent matching verification.

[0060] In addition, since meaningless movement of the mobile terminal is not conducive to interaction, the data generated by the meaningless movement is invalid data, and it is necessary to discard the invalid data. That is, the invalid data is removed from the first movement data.

[0061] In this way, when the operation object is in a static state, the rationality of its static state can be judged according to the scene in which the operation object is located. For example, a motion scene should not be in a static state, and the operation object will inevitably change the posture of the terminal device when processing related services. Therefore, the acquired motion information (i.e., the first motion data) can be removed from unreasonable invalid data to achieve denoising.

[0062] Furthermore, in some embodiments, in step S102, the spatial position change of the mobile terminal is mapped according to the linear acceleration information and the angular velocity change information in the first motion data to construct a graph model, ie, a motion graph.

[0063] Specifically, the motion map includes second motion data corresponding to three directions (i.e., XYZ axes in three-dimensional space), and the straight lines in the three directions are perpendicular to each other. The second motion data may include the speed, acceleration, angular velocity in the corresponding direction and the change information of these parameters over time.

[0064] In some embodiments, in step S103, it is queried whether there is data matching the motion graph in the historical data in the database, so as to determine the matching degree between the motion graph and the historical data in the database.

[0065] For example, when the operating object is walking, the gait information generated by the terminal is used to generate a motion map, and the generated motion map is input into the gait information database to analyze the motion map, thereby querying whether similar gait information already exists in the database. The gait information includes gait data within a period of time (i.e., multiple gait cycles).

[0066] Thus, in some implementations, in step S104, when the degree of match is greater than the first preset threshold and less than the second preset threshold, the target trajectory is generated according to the motion map. Alternatively, the degree of match can be set to three levels: high, medium, and low, with 30% being low, 60% being medium, and 80% being high. Then, when the degree of match is medium, the target trajectory is generated according to the motion map.

[0067] In addition, in some embodiments, when the degree of match is less than or equal to a first preset threshold, the operation object of the mobile terminal is determined to be a natural person to obtain a human-machine verification result, and an association relationship is established between the corresponding motion map and the mobile terminal identification and stored in a database; when the degree of match is greater than or equal to a second preset threshold, the operation object of the mobile terminal is determined to be a non-natural person to obtain a human-machine verification result.

[0068] In another embodiment, when the degree of match is greater than or equal to a second preset threshold, the matching data corresponding to the motion map and the terminal information corresponding to the matching data based on a pre-established association relationship are further determined; it is determined whether the terminal information is consistent with the identifier of the mobile terminal; when the terminal information is consistent with the identifier of the mobile terminal, the target trajectory can be further generated according to the motion map.

[0069] Therefore, this embodiment adopts a large-scale human-machine verification method of performing big data repetition verification on the generated data, which can prevent the use of AI based on a pre-trained sample set to cause a single user to pass the verification repeatedly or multiple users to pass the verification at the same time.

[0070] In this way, human-computer interaction based on a non-perceptual method can be achieved through matching judgment, so that it can be preliminarily determined whether the terminal device is operated by a natural person or a non-natural person (such as AI) to achieve human-computer verification. It should be noted that when the preliminary verification cannot be determined, a secondary verification is performed in a perceptual manner based on the generated target trajectory.

[0071] In addition, in some embodiments, the same initial scoring weights are set for the three directions of the motion map. When the matching degree is greater than the first preset threshold and less than the second preset threshold, for the generation of the target trajectory, the initial scoring weights of the corresponding directions can be weight-decayed according to the second motion data to obtain updated scoring weights; and then the target trajectory is generated according to the updated scoring weights.

[0072] In a specific implementation, the stronger the movement in a certain direction (for example, the stronger the acceleration signal), the faster the weight of the direction decays.

[0073] In specific implementation, the score decay value of the direction corresponding to the second motion data may be determined, and then the initial score weight may be decayed according to the score decay value to obtain an updated score weight.

[0074] In specific implementation, for each direction, the corresponding atomic action can be determined in the pre-established atomic action library according to the updated scoring weight; thus, the atomic actions corresponding to the three directions are randomly combined to determine the standard verification action; and then, the target trajectory is generated according to the standard verification action.

[0075] In specific implementation, the smaller the score weight of the update in a certain direction, the more intense the movement in that direction is, and the movement in that direction needs to be weakened, otherwise it needs to be strengthened.

[0076] In addition, in some embodiments, an atomic action library is constructed, including several atomic actions. The atomic actions are atomic, and the final values ​​obtained according to the changes in the scoring weights of the action process on the three axes of X, Y, and Z are divided into three groups, namely, group X, group Y, and group Z. The three groups can each be set with 30 atomic actions, numbered 1-30, and the actions 1-30 are numbered from small to large in terms of strength.

[0077] Taking the atomic action of group Z No. 1 as an example, the atomic action of group Z indicates that the movement in the Z-axis direction needs to be strengthened. Z1 indicates the weakest strengthening degree. It can be understood that the strength of the motion signal on the three axes is equivalent, but the Z-axis motion signal is slightly stronger than the X and Y axes. In terms of motion design, taking a handheld terminal device as an example, the action is to raise the mobile phone from the head position as the starting position, stretch one arm to the height of the abdomen, and the direction is the angle of 45° between the same side of the arm and the facing direction as the end point. A Bezier curve is generated according to the starting point and the end point. The handheld terminal device moves in space according to the generated Bezier curve. The process from the starting point to the end point is a Z1 atomic action. Z30 indicates the strongest strengthening degree. Z30 is understood as the Z-axis signal is much larger than the X-axis signal and the Y-axis signal in the three-axis motion. In terms of motion design, taking a head-mounted terminal (VR glasses) as an example, the action is the head position after putting on the device as the starting point, squatting vertically, and the head position after squatting as the end point. A straight line is generated from the starting point to the end point, and the process from the starting point to the end point is a Z30 atomic action.

[0078] In specific implementation, the number of atomic actions can be set based on experimental experience and commonly used actions, that is, actions that are not too complex and have a large degree of distinction. This embodiment does not limit this.

[0079] In specific implementation, for the determination of atomic actions, after completing the imperceptible verification, the corresponding atomic action can be selected according to the ratio of the final weight value to the initial weight value. For example, the initial weight of the X-axis is 33, the final weight is 11, and the selected X-axis atomic action is 11\33*30=10, which is X10. Furthermore, after selecting the atomic actions corresponding to the X, Y, and Z axes respectively, the three atomic actions are combined in a random order to generate the standard verification action for the current verification, thereby generating the target trajectory according to the standard verification action.

[0080] Therefore, in step S105, the target trajectory can be sent to the mobile terminal so that the operating object of the mobile terminal can provide feedback according to the target trajectory; and in step S106, the motion feedback information of the mobile terminal collected by the motion sensor is obtained in real time.

[0081] In some embodiments, before step S107, an updated target trajectory may be generated according to the motion feedback information, and the process returns to the step of sending the target trajectory to the mobile terminal until the end point of the motion feedback information coincides with the end point of the target trajectory.

[0082] In specific implementation, key check points can be randomly selected within a preset distance range of the target trajectory according to motion feedback information; the key check points are used to replace any passing point in the target trajectory to obtain an updated target trajectory.

[0083] That is to say, these key check points are randomly distributed near the path of the standard check action. The key check points will replace the points in the standard trajectory and generate new Bezier curves in real time, so that the check action and the standard check action are significantly different locally.

[0084] Moreover, these key points are not pre-generated, but dynamically generated. The basis for dynamic generation is to determine the movement that needs to be strengthened according to multiple scoring indicators (conformity with the target trajectory, reaction rate, dwell interval, speed, etc.) corresponding to the real-time motion feedback information. For example, if the completion trajectory shown in the motion feedback information is highly consistent with the target trajectory, more and more key checkpoints will be generated more frequently, making the trajectory of subsequent movements increasingly different from the preset standard check movements.

[0085] It can be seen that the selected atomic action group is generated based on the result of the non-perceptual verification. The random arrangement of the atomic action group will generate different standard verification actions, and the performance when completing the standard action will generate the verification action by deforming the standard action in real time. This makes the verification action that each operation object participating in the verification needs to complete completely different, that is, a completely dynamic verification action.

[0086] Finally, in some embodiments, in step S107, the motion feedback information is scored according to the target trajectory to obtain a scoring result; in response to the scoring result being greater than or equal to a third preset threshold, the operation object is determined to be a natural person to obtain a human-machine verification result; in response to the scoring result being less than the third preset threshold, the operation object is determined to be a non-natural person to obtain a human-machine verification result.

[0087] That is, when the motion feedback information passes through a key verification point, it will be scored based on multiple indicators such as its completion degree relative to the target trajectory, and finally a scoring result will be obtained. Based on this scoring result, human-machine judgment will be made, and finally a conclusion of human-machine verification will be made.

[0088] In this way, a combination of an imperceptible verification process and a perceptual verification process is adopted. The imperceptible process can complete the preliminary human-machine verification without the operation object being aware of it to prevent interruption of the original operation of the operation object, thereby improving the interactive experience, and the imperceptible human-machine verification can complete the human-machine verification of most operation objects, which can save time cost and improve human-machine recognition effect and efficiency; the perceptual process uses the imperceptible preliminary verification result to influence the dynamic generation of selected atomic actions, and guides the motion tracking of the operation object for human-machine verification in a real-time dynamic path generation manner. Since the verification action is completely dynamically generated and unpredictable, it can effectively prevent AI simulation and improve the accuracy of human-machine verification.

[0089] It should be noted that the above describes some embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0090] Based on the same technical concept, corresponding to any of the above-mentioned embodiments and methods, the present application further provides a human-machine verification device 200. Specifically, the human-machine verification device 200 is applied to a mobile terminal, and the mobile terminal is provided with a motion sensor.

[0091] like Figure 2 As shown, the human-machine verification device 200 may include:

[0092] A first acquisition module 201 is used to acquire first motion data of the mobile terminal collected by a motion sensor;

[0093] A first generating module 202, configured to generate a motion graph according to the first motion data;

[0094] A query module 203, configured to query a database according to the motion graph to determine a matching degree between the motion graph and the historical data in the database;

[0095] A second generating module 204, configured to generate a target trajectory according to the motion graph when the matching degree is greater than a first preset threshold and less than a second preset threshold;

[0096] A sending module 205, configured to send the target trajectory to the mobile terminal, so that an operation object of the mobile terminal can provide feedback according to the target trajectory;

[0097] The second acquisition module 206 is used to acquire the motion feedback information of the mobile terminal collected by the motion sensor in real time;

[0098] The determination module 207 is used to determine whether the operating object is a natural person according to the target trajectory and motion feedback information, so as to obtain a human-machine verification result.

[0099] In some embodiments, the motion map includes second motion data corresponding to three directions, the straight lines of the three directions are perpendicular to each other, and the three directions correspond to the same initial scoring weight.

[0100] In some embodiments, the second generation module 204 is specifically used to perform weight decay processing on the initial scoring weight of the direction corresponding to the second motion data when the matching degree is greater than the first preset threshold and less than the second preset threshold to obtain an updated scoring weight; and generate a target trajectory according to the updated scoring weight.

[0101] In some embodiments, the second generation module 204 is further specifically used to determine the score decay value of the direction corresponding to the second motion data according to the second motion data when the matching degree is greater than the first preset threshold and less than the second preset threshold; and to decay the initial score weight according to the score decay value to obtain an updated score weight.

[0102] In some embodiments, the human-machine verification device 200 further includes an update module ( Figure 2 (not shown), the updating module is used to generate an updated target trajectory according to the motion feedback information, and return to the step of sending the target trajectory to the mobile terminal until the end point of the motion feedback information coincides with the end point of the target trajectory.

[0103] In some embodiments, the updating module is specifically used to randomly select key check points within a preset distance range of the target trajectory according to the motion feedback information; and replace any waypoint in the target trajectory with the key check points to obtain an updated target trajectory.

[0104] In some embodiments, the update module is further specifically used to determine, for each direction, a corresponding atomic action in a pre-established atomic action library according to the updated scoring weight; randomly combine the atomic actions corresponding to each of the three directions to determine a standard verification action; and generate a target trajectory according to the standard verification action.

[0105] In some embodiments, the human-machine verification device 200 further includes a removal module ( Figure 2 (not shown), the removal module is used to determine the state information of the operation object of the mobile terminal according to the first motion data; when the state information of the operation object is static, according to the target scene requirements, determine the invalid data in the first motion data that does not meet the target scene requirements, and remove the invalid data in the first motion data.

[0106] In some embodiments, the determination module 207 is specifically used to score the motion feedback information according to the target trajectory to obtain a scoring result; in response to the scoring result being greater than or equal to a third preset threshold, determine that the operation object is a natural person to obtain a human-machine verification result; in response to the scoring result being less than the third preset threshold, determine that the operation object is a non-natural person to obtain a human-machine verification result.

[0107] It should be noted that, for the convenience of description, the above device is described in various modules according to their functions. Of course, when implementing this application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0108] The device of the above embodiment is used to implement the corresponding human-machine verification method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0109] Based on the same technical concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides an electronic device.

[0110] Figure 3 A more specific schematic diagram of the hardware structure of an electronic device provided by this embodiment is shown.

[0111] The electronic device 300 may include a processor 301 and a memory 302 storing computer program instructions.

[0112] Specifically, the processor 301 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0113] The memory 302 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 302 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory 302 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 302 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 302 is a non-volatile solid-state memory.

[0114] In certain embodiments, the memory may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical or other physical / tangible memory storage device. Thus, typically, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to one aspect of the present application.

[0115] The processor 301 implements any one of the human-machine verification methods in the above embodiments by reading and executing the computer program instructions stored in the memory 302 .

[0116] In some examples, the electronic device 300 may further include a communication interface 303 and a bus 310. Figure 3 As shown, the processor 301, the memory 302, and the communication interface 303 are connected via a bus 310 and communicate with each other.

[0117] The communication interface 303 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0118] Bus 310 includes hardware, software or both, and the components of online data flow billing equipment are coupled to each other. For example, but not limitation, bus 310 may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front-end bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 310 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the present application considers any suitable bus or interconnection.

[0119] Exemplarily, the electronic device 300 may be a mobile phone, a tablet computer, a laptop computer, a PDA, an in-vehicle electronic device, an ultra-mobile personal computer (UMPC), a netbook, or a personal digital assistant (PDA).

[0120] Based on the same technical concept, corresponding to any of the above-mentioned embodiments, the present application also provides a non-transitory computer-readable storage medium. The computer-readable storage medium stores computer program instructions; when the computer program instructions are executed by the processor, any of the human-machine verification methods in the above-mentioned embodiments is implemented. Examples of computer-readable storage media include non-transitory computer-readable storage media, such as portable disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, etc.

[0121] Based on the same technical concept, corresponding to any of the above-mentioned embodiments, the present application also provides a computer program product, which includes computer program instructions. In some embodiments, the computer program instructions can be executed by one or more processors of a computer so that the computer and / or the processor execute the human-machine verification method. Corresponding to the execution subject corresponding to each step in each embodiment of the human-machine verification method, the processor that executes the corresponding step can belong to the corresponding execution subject.

[0122] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0123] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0124] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0125] The above reference is according to the method of the embodiment of the present application, the flow chart of the device (system) and the computer program product and / or the block diagram described various aspects of the present application.It should be understood that each square box in the flow chart and / or the block diagram and the combination of each square box in the flow chart and / or the block diagram can be realized by computer program instructions.These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the realization of the function / action specified in one or more square boxes of the flow chart and / or the block diagram.Such a processor can be but is not limited to a general-purpose processor, a special-purpose processor, a special application processor or a field programmable logic circuit.It can also be understood that each square box in the block diagram and / or the flow chart and the combination of the square boxes in the block diagram and / or the flow chart can also be realized by the dedicated hardware that performs the specified function or action, or can be realized by the combination of dedicated hardware and computer instructions.

[0126] The above is only a specific implementation of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.

Claims

1. A human-machine verification method, characterized in that: Applied to a mobile terminal, the mobile terminal being provided with a motion sensor; The method comprises: Acquire first motion data of the mobile terminal collected by a motion sensor; generating a motion map according to the first motion data; According to the motion graph, querying in a database to determine the degree of match between the motion graph and the historical data in the database; When the matching degree is greater than a first preset threshold and less than a second preset threshold, generating a target trajectory according to the motion graph; Sending the target trajectory to the mobile terminal so that an operation object of the mobile terminal provides feedback according to the target trajectory; Acquire motion feedback information of the mobile terminal collected by a motion sensor in real time; According to the target trajectory and motion feedback information, it is determined whether the operating object is a natural person to obtain a human-machine verification result.

2. The method according to claim 1, characterized in that Before determining whether the operation object is a natural person according to the target trajectory and the motion feedback information to obtain a human-machine verification result, the method further includes: An updated target trajectory is generated according to the motion feedback information, and the step of sending the target trajectory to the mobile terminal is returned until the end point of the motion feedback information coincides with the end point of the target trajectory.

3. The method according to claim 1, characterized in that The motion graph includes second motion data corresponding to three directions, the straight lines in the three directions are perpendicular to each other, and the three directions correspond to the same initial scoring weight; When the matching degree is greater than a first preset threshold and less than a second preset threshold, generating a target trajectory according to the motion graph includes: When the matching degree is greater than a first preset threshold and less than a second preset threshold, performing weight decay processing on an initial scoring weight of a direction corresponding to the second motion data to obtain an updated scoring weight; A target trajectory is generated according to the updated scoring weights.

4. The method according to claim 2, characterized in that: The step of generating an updated target trajectory according to the motion feedback information comprises: According to the motion feedback information, randomly selecting key check points within a preset distance range of the target trajectory; The key checkpoint replaces any waypoint in the target trajectory to obtain an updated target trajectory.

5. The method according to claim 3, characterized in that: When the matching degree is greater than a first preset threshold and less than a second preset threshold, performing weight decay processing on an initial scoring weight of a direction corresponding to the second motion data to obtain an updated scoring weight, including: When the matching degree is greater than a first preset threshold and less than a second preset threshold, determining a score decay value of a direction corresponding to the second motion data according to the second motion data; The initial scoring weight is decayed according to the scoring decay value to obtain an updated scoring weight.

6. The method according to claim 3, characterized in that Generating a target trajectory according to the updated scoring weight includes: For each direction, according to the updated scoring weight, a corresponding atomic action is determined in a pre-established atomic action library; The atomic actions corresponding to the three directions are randomly combined to determine the standard verification action; According to the standard verification action, a target trajectory is generated.

7. The method according to claim 1, characterized in that Before generating a motion map according to the first motion data, the method further includes: determining, according to the first motion data, state information of an operation object of the mobile terminal; When the state information of the operation object is static, invalid data that does not meet the target scene requirement is determined in the first motion data according to the target scene requirement, and the invalid data is removed from the first motion data.

8. The method according to claim 1, characterized in that After querying in a database according to the motion graph to determine the matching degree between the motion graph and the historical data in the database, the method further includes: When the matching degree is greater than or equal to the second preset threshold, it is determined that the operation object of the mobile terminal is a non-natural person to obtain a human-machine verification result.

9. The method according to claim 1, characterized in that: After querying in a database according to the motion graph to determine the matching degree between the motion graph and the historical data in the database, the method further includes: When the matching degree is less than or equal to the first preset threshold, it is determined that the operation object of the mobile terminal is a natural person to obtain a human-machine verification result.

10. The method according to claim 1, characterized in that The determining whether the operation object is a natural person according to the target trajectory and the motion feedback information to obtain a human-machine verification result includes: Scoring the motion feedback information according to the target trajectory to obtain a scoring result; In response to the scoring result being greater than or equal to a third preset threshold, the operation object is determined to be a natural person to obtain a human-machine verification result; in response to the scoring result being less than the third preset threshold, the operation object is determined to be a non-natural person to obtain a human-machine verification result.

11. A human-machine verification device, characterized in that: Applied to a mobile terminal, the mobile terminal being provided with a motion sensor; The device comprises: A first acquisition module, used to acquire first motion data of the mobile terminal collected by a motion sensor; A first generating module, configured to generate a motion graph according to the first motion data; A query module, for querying in a database according to the motion graph to determine the matching degree between the motion graph and the historical data in the database; A second generating module, configured to generate a target trajectory according to the motion graph when the matching degree is greater than a first preset threshold and less than a second preset threshold; A sending module, used for sending the target trajectory to the mobile terminal, so that the operation object of the mobile terminal can provide feedback according to the target trajectory; A second acquisition module, used for acquiring in real time the motion feedback information of the mobile terminal collected by the motion sensor; The determination module is used to determine whether the operation object is a natural person according to the target trajectory and motion feedback information, so as to obtain a human-machine verification result.

12. An electronic device, characterized in that: The device comprises: a processor and a memory storing computer program instructions; when the processor calls the computer program instructions, the human-machine verification method as described in any one of claims 1-10 is implemented.

13. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions, and when the computer program instructions are called by the processor, the human-machine verification method according to any one of claims 1 to 10 is implemented.

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