阅读说明：本技术 人机识别方法、滑块验证方法、装置、介质和设备 (Man-machine identification method, slider verification method, device, medium, and apparatus ) 是由 陈海龙 于 2020-04-23 设计创作，主要内容包括：本公开提供了一种人机识别方法、滑块验证方法、人机识别装置、滑块验证装置、计算机可读存储介质和电子设备,属于信息安全技术领域。所述方法包括：获取客户端进行滑块验证所产生的滑动轨迹数据,所述滑动轨迹数据包括所述滑块验证过程中滑块在各时刻的位置；对所述各时刻的位置进行分段线性拟合,根据每一段的斜率生成所述滑动轨迹数据对应的目标特征向量；将所述目标特征向量与历史滑块验证中所得到的历史特征向量进行聚类；如果所述目标特征向量所属类别中向量的数量或比例满足预设条件,则确定所述滑块验证由机器执行。本公开可以降低特征提取的复杂度,提高人机识别的准确率和效率。(The disclosure provides a man-machine identification method, a sliding block verification method, a man-machine identification device, a sliding block verification device, a computer readable storage medium and an electronic device, and belongs to the technical field of information security. The method comprises the following steps: acquiring sliding track data generated by a client for slider verification, wherein the sliding track data comprises the position of a slider at each moment in the slider verification process; performing piecewise linear fitting on the positions at all the moments, and generating a target characteristic vector corresponding to the sliding track data according to the slope of each section; clustering the target characteristic vector and the historical characteristic vector obtained in the historical slider verification; and if the quantity or proportion of the vectors in the category to which the target feature vector belongs meets a preset condition, determining that the slider verification is executed by a machine. The method and the device can reduce the complexity of feature extraction and improve the accuracy and efficiency of human-computer recognition.)

1. A human-computer recognition method is characterized by comprising the following steps:

acquiring sliding track data generated by a client for slider verification, wherein the sliding track data comprises the position of a slider at each moment in the slider verification process;

performing piecewise linear fitting on the positions at all the moments, and generating a target characteristic vector corresponding to the sliding track data according to the slope of each section;

clustering the target characteristic vector and the historical characteristic vector obtained in the historical slider verification;

and if the quantity or proportion of the vectors in the category to which the target feature vector belongs meets a preset condition, determining that the slider verification is executed by a machine.

2. The method according to claim 1, wherein the performing a piecewise linear fit on the positions at the respective times and generating the target feature vector corresponding to the sliding trajectory data according to the slope of each segment includes:

drawing the position of each moment as a corresponding data point by taking the moment as a horizontal axis and the position as a vertical axis;

segmenting each data point, and respectively performing linear fitting on the data points in each segment to obtain the slope of each segment;

and arranging the slope of each section into a vector to obtain a target characteristic vector corresponding to the sliding track data.

3. The method of claim 2, wherein said segmenting each of said data points comprises:

determining a first data point as a starting point of the current segment;

sequentially taking the data points subsequent to the starting point as the end point of the current segment, performing linear fitting on all the data points in the current segment, and calculating a fitting error;

if the fitting error is smaller than an error threshold value, continuing to take the next data point as the terminal point of the current segment;

if the fitting error is larger than the error threshold, moving the terminal point of the current segment to the last data point, and dividing the current segment;

and determining the end point of the current segment as the starting point of the next segment, and dividing the next segment until the segmentation is completed on the last data point.

4. The method of claim 2, wherein after obtaining the slope of each segment, the slope of each segment is rounded separately.

5. The method of claim 1, wherein clustering the target feature vector with historical feature vectors obtained from historical slider validation comprises:

determining vectors similar to the target feature vector from the historical feature vectors, and forming the target feature vector and the similar vectors into a category;

determining that the slider verification is performed by a machine if the number or proportion of vectors in the category to which the target feature vector belongs meets a preset condition, including:

and when the number of the vectors in the category is larger than a number threshold value, or the proportion of the vectors in the category to all the historical feature vectors and the target feature vectors is larger than a proportion threshold value, determining that the slider verification is performed by a machine.

6. The method of claim 1, wherein the slider slides along a direction of a preset axis during the slider validation process; the position of the slider at each time comprises: and the preset axis coordinates of the slide block at each moment.

7. The method of claim 1, wherein after clustering, the method further comprises:

adding the target feature vector to the historical feature vector.

8. A method of slider validation, the method comprising:

when the client side carries out slider verification, judging whether the position of the slider is correct or not;

identifying, if the slider position is correct, whether the slider validation is performed by a machine by the method of any of claims 1 to 7;

determining that the slider validation passed if it is determined that the slider validation is not machine-executed.

9. The method of claim 8, wherein the slider is displayed in a graphical verification area of the client, and wherein the graphical verification area comprises a movable graphic and a defective graphic; judging whether the slider position is correct includes:

moving the movable pattern synchronously while the slider slides;

when the sliding block stops sliding, detecting whether the movable graph and the incomplete graph are overlapped or not;

and when the movable graph is determined to be overlapped with the incomplete graph, determining that the position of the sliding block is correct.

10. A human-machine identification apparatus, the apparatus comprising:

the device comprises an acquisition device and a verification device, wherein the acquisition device is used for acquiring sliding track data generated by a client terminal in sliding block verification, and the sliding track data comprises the position of a sliding block at each moment in the sliding block verification process;

the generating module is used for carrying out piecewise linear fitting on the positions at all the moments and generating a target characteristic vector corresponding to the sliding track data according to the slope of each section;

the clustering module is used for clustering the target characteristic vector and the historical characteristic vector obtained in the historical slider verification;

and the determining module is used for determining that the slider verification is executed by a machine if the quantity or proportion of the vectors in the category to which the target feature vector belongs meets a preset condition.

11. A slider validation apparatus, the apparatus comprising:

the judging module is used for judging whether the position of the sliding block is correct or not when the sliding block verification is carried out by the client;

an identification module for identifying whether the slider verification is performed by a machine if the slider position is correct by the method of any of claims 1 to 7;

a determination module to determine that the slider verification passes if it is determined that the slider verification is not machine-executed.

12. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method of any one of claims 1-7 or 8-9.

13. An electronic device, comprising:

a processor; and

a memory for storing executable instructions of the processor;

wherein the processor is configured to perform the method of any of claims 1-7 or 8-9 via execution of the executable instructions.

Technical Field

The present disclosure relates to the field of information security technologies, and in particular, to a human-machine recognition method, a slider verification method, a human-machine recognition apparatus, a slider verification apparatus, a computer-readable storage medium, and an electronic device.

Background

With the development of artificial intelligence technology, the machine can simulate human behaviors for operation, such as crawling data, accessing websites and the like. However, while bringing convenience to people, the machine may also generate some dangerous behaviors, such as breaking a user password violently, falsely registering or attacking a website, and the like.

Aiming at the dangerous behaviors of the machine, man-machine operations can be identified by setting verification codes so as to filter machine access. At present, a common identification mode is to classify verification data by adopting a machine learning model so as to judge that verification behaviors are generated by people or machines, but a certain amount of verification data needs to be collected in the identification mode, and the construction of machine data and models mainly depend on the thought of workers, so that the situation that the difference between the construction data and the on-line verification data is large is easily generated, and the accuracy and the efficiency of man-machine identification are low.

It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.

Disclosure of Invention

The present disclosure provides a human-machine identification method, a slider verification method, a human-machine identification device, a slider verification device, a computer-readable storage medium, and an electronic apparatus, thereby improving the problems of low human-machine identification accuracy and efficiency in the prior art at least to a certain extent.

Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.

According to a first aspect of the present disclosure, there is provided a human-machine recognition method, the method comprising: acquiring sliding track data generated by a client for slider verification, wherein the sliding track data comprises the position of a slider at each moment in the slider verification process; performing piecewise linear fitting on the positions at all the moments, and generating a target characteristic vector corresponding to the sliding track data according to the slope of each section; clustering the target characteristic vector and the historical characteristic vector obtained in the historical slider verification; and if the quantity or proportion of the vectors in the category to which the target feature vector belongs meets a preset condition, determining that the slider verification is executed by a machine.

In an exemplary embodiment of the present disclosure, the performing piecewise linear fitting on the positions at the respective times and generating a target feature vector corresponding to the sliding trajectory data according to a slope of each segment includes: drawing the position of each moment as a corresponding data point by taking the moment as a horizontal axis and the position as a vertical axis; segmenting each data point, and respectively performing linear fitting on the data points in each segment to obtain the slope of each segment; and arranging the slope of each section into a vector to obtain a target characteristic vector corresponding to the sliding track data.

In an exemplary embodiment of the present disclosure, the segmenting each of the data points includes: determining a first data point as a starting point of the current segment; sequentially taking the data points subsequent to the starting point as the end point of the current segment, performing linear fitting on all the data points in the current segment, and calculating a fitting error; if the fitting error is smaller than an error threshold value, continuing to take the next data point as the terminal point of the current segment; if the fitting error is larger than the error threshold, moving the terminal point of the current segment to the last data point, and dividing the current segment; and determining the end point of the current segment as the starting point of the next segment, and dividing the next segment until the segmentation is completed on the last data point.

In an exemplary embodiment of the present disclosure, after obtaining the slope of each segment, the slope of each segment is rounded separately.

In an exemplary embodiment of the present disclosure, the clustering the target feature vector with a history feature vector obtained in a history slider verification includes: determining vectors similar to the target feature vector from the historical feature vectors, and forming the target feature vector and the similar vectors into a category; determining that the slider verification is performed by a machine if the number or proportion of vectors in the category to which the target feature vector belongs meets a preset condition, including: and when the number of the vectors in the category is larger than a number threshold value, or the proportion of the vectors in the category to all the historical feature vectors and the target feature vectors is larger than a proportion threshold value, determining that the slider verification is performed by a machine.

In an exemplary embodiment of the present disclosure, the slider slides in a direction of a preset axis during the slider verification process; the position of the slider at each time comprises: and the preset axis coordinates of the slide block at each moment.

In an exemplary embodiment of the disclosure, after clustering, the method further comprises: adding the target feature vector to the historical feature vector.

According to a second aspect of the present disclosure, there is provided a slider verification method, the method comprising: when the client side carries out slider verification, judging whether the position of the slider is correct or not; if the slide is located correctly, identifying whether the slide verification is performed by a machine through the method of any one of the above-mentioned human-machine identification; determining that the slider validation passed if it is determined that the slider validation is not machine-executed.

In an exemplary embodiment of the present disclosure, the slider is displayed in a graphic verification area of the client, and the graphic verification area includes a movable graphic and a defective graphic therein; judging whether the slider position is correct includes: moving the movable pattern synchronously while the slider slides; when the sliding block stops sliding, detecting whether the movable graph and the incomplete graph are overlapped or not; and when the movable graph is determined to be overlapped with the incomplete graph, determining that the position of the sliding block is correct.

According to a third aspect of the present disclosure, there is provided a human-machine recognition apparatus comprising: the device comprises an acquisition device and a verification device, wherein the acquisition device is used for acquiring sliding track data generated by a client terminal in sliding block verification, and the sliding track data comprises the position of a sliding block at each moment in the sliding block verification process; the generating module is used for carrying out piecewise linear fitting on the positions at all the moments and generating a target characteristic vector corresponding to the sliding track data according to the slope of each section; the clustering module is used for clustering the target characteristic vector and the historical characteristic vector obtained in the historical slider verification; and the determining module is used for determining that the slider verification is executed by a machine if the quantity or proportion of the vectors in the category to which the target feature vector belongs meets a preset condition.

In an exemplary embodiment of the present disclosure, the generating module includes: the drawing unit is used for drawing the positions of all the moments into corresponding data points by taking the moments as a horizontal axis and the positions as a vertical axis; the fitting unit is used for segmenting each data point and respectively performing linear fitting on the data points in each segment to obtain the slope of each segment; and the arrangement unit is used for arranging the slope of each section into a vector so as to obtain a target characteristic vector corresponding to the sliding track data.

In an exemplary embodiment of the present disclosure, the fitting unit segments each of the data points by performing the following method: determining a first data point as a starting point of the current segment; sequentially taking the data points subsequent to the starting point as the end point of the current segment, performing linear fitting on all the data points in the current segment, and calculating a fitting error; if the fitting error is smaller than an error threshold value, continuing to take the next data point as the terminal point of the current segment; if the fitting error is larger than the error threshold, moving the terminal point of the current segment to the last data point, and dividing the current segment; and determining the end point of the current segment as the starting point of the next segment, and dividing the next segment until the segmentation is completed on the last data point.

In an exemplary embodiment of the disclosure, the generating module is further configured to, after obtaining the slope of each segment, respectively round the slope of each segment.

In an exemplary embodiment of the present disclosure, the clustering module is configured to determine a similar vector to the target feature vector from the historical feature vectors, and form the target feature vector and the similar vector into a category; the determining module is used for determining that the slider verification is executed by a machine when the number of the vectors in the category is larger than a number threshold or the proportion of the vectors in the category to all the historical feature vectors and the target feature vectors is larger than a proportion threshold.

In an exemplary embodiment of the present disclosure, the slider slides in a direction of a preset axis during the slider verification process; the position of the slider at each time comprises: and the preset axis coordinates of the slide block at each moment.

In an exemplary embodiment of the disclosure, after the clustering, the clustering module is further configured to add the target feature vector to the historical feature vector.

According to a fourth aspect of the present disclosure, there is provided a slider authentication device comprising: the judging module is used for judging whether the position of the sliding block is correct or not when the sliding block verification is carried out by the client; the identification module is used for identifying whether the slide verification is executed by a machine or not by the method of any one of the man-machine identification if the slide is positioned correctly; a determination module to determine that the slider verification passes if it is determined that the slider verification is not machine-executed.

In an exemplary embodiment of the present disclosure, the slider is displayed in a graphic verification area of the client, and the graphic verification area includes a movable graphic and a defective graphic therein; the judging module determines that the position of the sliding block is correct by executing the following method: moving the movable pattern synchronously while the slider slides; when the sliding block stops sliding, detecting whether the movable graph and the incomplete graph are overlapped or not; and when the movable graph is determined to be overlapped with the incomplete graph, determining that the position of the sliding block is correct.

According to a fifth aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements any one of the above-described human-machine recognition methods or slider verification methods.

According to a sixth aspect of the present disclosure, there is provided an electronic device comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform any one of the above human machine identification methods or slider verification methods via execution of the executable instructions.

The present disclosure has the following beneficial effects:

the method comprises the steps of conducting piecewise linear fitting on sliding track data generated by sliding block verification, generating target characteristic vectors corresponding to the sliding track data according to the slope of each segment, clustering the target characteristic vectors and historical characteristic vectors obtained in historical sliding block verification to determine the categories of the target characteristic vectors, and determining that the sliding block verification is executed by a machine when the number of vectors in the categories to which the target characteristic vectors belong meets preset conditions. On one hand, the exemplary embodiment substantially realizes unsupervised learning of machine slider verification behaviors, clustering is carried out based on similarity between machine learning, the machine slider verification behaviors are distinguished from human behaviors, and the machine slider verification behaviors have strong generalization capability on various machine behaviors; particularly, for new machine behaviors, the behavior characteristics of the new machine behaviors are not required to be learned and can be recognized in an early stage, and the existing technology needs to label the behaviors and train a machine learning model, so that the scheme has high accuracy and timeliness. On the other hand, the target characteristic vector composed of slopes is obtained by piecewise linear fitting to represent the characteristics of sliding track data in the slider verification, the complexity of characteristic extraction is reduced, and the man-machine recognition is performed in a vector clustering mode.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is apparent that the drawings in the following description are only some embodiments of the present disclosure, and that other drawings can be obtained from those drawings without inventive effort for a person skilled in the art.

FIG. 1 illustrates a flow chart of a human recognition method in the present exemplary embodiment;

FIG. 2 illustrates a user interface diagram of a slider verification method in the exemplary embodiment;

FIG. 3 illustrates a graph of machine slip trace data in the exemplary embodiment;

fig. 4 is a graph showing slide trace data of a person in the present exemplary embodiment;

FIG. 5 illustrates a flow chart of a slider verification method in the present exemplary embodiment;

FIG. 6 illustrates a flow chart of another slider verification method in the exemplary embodiment;

fig. 7 is a block diagram showing a structure of a human recognition apparatus in the present exemplary embodiment;

fig. 8 is a block diagram showing the structure of a slider authentication apparatus in the present exemplary embodiment;

FIG. 9 illustrates a computer-readable storage medium for implementing the above-described method in the present exemplary embodiment;

fig. 10 shows an electronic device for implementing the above method in the present exemplary embodiment.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

The exemplary embodiments of the present disclosure first provide a human-machine identification method, which may be executed by a computer or a server in the background of an application program to determine whether a verification behavior of a client is triggered by a human or a machine.

Fig. 1 shows a flow of the human-machine recognition method in the present exemplary embodiment, which may include the following steps S110 to S140:

and S110, obtaining sliding track data generated by the client for slider verification, wherein the sliding track data comprises the position of the slider at each moment in the slider verification process.

The slider verification may be a verification method for moving a preset pattern to a target position by simulating a sliding operation.

When the client performs the slider verification, the position of the slider can be changed through corresponding operations, such as dragging the slider. Referring to fig. 2, a schematic diagram of a user interface for slider verification is shown, in response to an operation of sliding the slider 210 by a client, the slider 210 may move to the left and right along the slider to obtain positions, such as coordinates, of the slider 210 at various times during the movement process, that is, to obtain sliding trajectory data when the client performs slider verification.

Since the slider can slide along the linear direction between the slider and the target position during the movement of the slider to the target position, in an alternative embodiment, the slider can slide along the direction of the preset axis during the slider verification process, for example, in fig. 2, the slider can horizontally move along the slider to the left and right sides, and the x axis in the horizontal direction can be used as the preset axis, in this case, the above-mentioned sliding track data, that is, the position of the slider at each time can also be the preset axis coordinate of the slider at each time.

By acquiring the preset axis coordinates of the slider at each moment, the position data of the slider in other directions can be reduced, so that the calculated data amount can be reduced, and the slider verification efficiency is improved.

And S120, performing piecewise linear fitting on the position of each moment, and generating a target characteristic vector corresponding to the sliding track data according to the slope of each segment.

When the sliding block is verified, the positions of the sliding block at each moment can form a plurality of sections of position data, for example, the positions of one or more adjacent moments can form a section of position data, each section of position data is subjected to linear fitting to obtain a plurality of linear line segments, and the slopes of each linear line segment are sequentially arranged to obtain the target characteristic vector corresponding to the sliding track data.

Since the position of the slider at each time may be the preset axis coordinate of the slider at each time, in an alternative embodiment, step S120 may be implemented by:

drawing the position of each moment as a corresponding data point by taking the moment as a horizontal axis and the position as a vertical axis;

segmenting each data point, and respectively performing linear fitting on the data points in each segment to obtain the slope of each segment;

and arranging the slope of each section into a vector to obtain a target characteristic vector corresponding to the sliding track data.

In the coordinate system, when the time t in the sliding trajectory data is taken as the horizontal axis and the position x is taken as the vertical axis, the sliding trajectory data can be expressed as (x)₁，t₁)，(x₂，t₂),…,(x_n，t_n) (ii) a Drawing the position of the sliding block at each moment as a corresponding data point in a coordinate system, segmenting each data point, for example, dividing two adjacent data points into one data segment, and performing linear fitting on each data segment to obtain slopes corresponding to a plurality of data segments; and sequentially arranging the slopes corresponding to the data segments according to the sequence, wherein the obtained vector is the target characteristic vector corresponding to the sliding track data.

Since one or more data points can also be divided into one data segment when segmenting the data points, segmenting each data point can be achieved in an alternative embodiment by:

determining a first data point as a starting point of the current segment;

sequentially taking the data points subsequent to the starting point as the end point of the current segment, performing linear fitting on all the data points in the current segment, and calculating a fitting error;

if the fitting error is smaller than the error threshold, continuing to take the next data point as the terminal point of the current segment;

if the fitting error is larger than the error threshold, moving the terminal point of the current segment to the previous data point, and dividing the current segment;

and determining the end point of the current segment as the starting point of the next segment, and dividing the next segment until the segmentation is completed on the last data point.

As previously mentioned, the sliding trajectory data may be represented as (x)₁,t₁),(x₂,t₂)，…，(x_n，t_n)，x₁The linear fitting of the data points of the current segment for the first data point, i.e. the start point of the current segment, and thus taking the data points subsequent to the start point as the end point of the current segment in turn, can be realized by the following equations (1) to (4):

for example, let i equal 1, j equal 2, pair with the starting point x₁Adjacent first data point x₂Linear fitting is carried out with the data segment formed by the starting point to obtain corresponding x₁And x₂The equation of a straight line x of a data segment formed by two data points is at + b; a is the slope and b is the intercept. For another example, if i is 1 and j is 3, the corresponding x can be obtained₁、x₂And x₃The equation of a line of the data segment formed by the three data points.

After the fitted straight line x ═ at + b is obtained by equations (1) to (4), the linear fitting error of the data segment formed by the corresponding data points is calculated by equation (5):

as can be seen from equation (5), the linear fitting error represents a mean square error of the corresponding data point to the fitting straight line, each data point may be divided into corresponding data segments according to a preset error threshold, and the error threshold may be determined according to an error sum of the corresponding data point to the fitting straight line, and may be set to be, for example, an average value or 1/3 value of the error sum of the corresponding data point. Specifically, if the linear fitting error is smaller than the error threshold, the next data point is used as the end point of the current segment to form a new current segment, and then the linear fitting is performed again on the data of the current segment through the formulas (1) to (4), and when the data corresponds to x, the linear fitting is performed again₁And x₂When the linear fitting error of a data segment formed by two data points is less than an error threshold value, x is determined₃As the end point of the current segment, form a segment consisting of x₁、x₂And x₃The new current segment data is formed, and linear fitting is carried out on the new current segment data again; if the fitting error is larger than the error threshold, moving the end point of the current segment to the previous data point, determining the current segment with the previous data point as the end point as an independent data segment, storing the slope a of the data segment, then determining the end point of the current segment, namely the previous data point, as the start point of the next segment, and dividing the next segment until the last data point is segmented, for example, when the corresponding x data point is segmented₁And x₂And x₃When the linear fitting error of a data segment formed by three data points is larger than an error threshold value, moving the terminal point of the current segment to the previous data point x₂Store by x₁And x₂The slope of a data segment formed by two data is the first element of the target feature vector, and then the end point of the current segment, namely x₂And as the starting point of the next current segment, dividing the new current segment until the segmentation of the last data point is completed.

By performing piecewise linear fitting on the sliding trajectory data as described above, a plurality of linearly fitted slope vectors (a) can be obtained₁,a₂,…,a_k) And the slope vector is the target characteristic vector corresponding to the sliding track data.

It should be added that, when performing piecewise linear fitting, the number of segments of different sliding trajectory data may be different, and the dimensionality of the obtained target feature vector may be different. In order to unify the vector dimension for subsequent processing, a fixed length of the vector may be set, such as a fixed length of 32, and if the number of segments of the sliding track data is less than 32, for example, there are only 30 segments, resulting in a slope number of 30, two preset values may be filled later, for example, two 0 s are filled after 30 slopes, to form a target feature vector with 32 dimensions.

And S130, clustering the target characteristic vector and the historical characteristic vector obtained in the historical slider verification.

The historical feature vector is a feature vector obtained through historical sliding track data in the historical verification behavior, and can be stored by a specific database or a data platform.

Because machine behaviors are generally executed according to scripts and the generated sliding track data often have greater similarity, referring to fig. 3, a graph of the sliding track data generated by a machine for slider verification is shown, where time t is a horizontal axis and a position coordinate x is a vertical axis, and as shown in the graph, the sliding track data are parallel in stages, that is, the difference of the sliding track data is small at corresponding stages; the human verification behavior is related to personal habits, operation characteristics and other factors, and the sliding track data generally has a large difference, as shown in fig. 4, a graph of the sliding track data generated by the manual slider verification is shown, each sliding track data is in an obvious irregular state, that is, the human-machine behavior can be judged through the similarity between the sliding track data, specifically, after the target feature vector is obtained through step S210, the target feature vector can be clustered with the historical feature vector obtained in the historical slider verification, so that the feature vector similar to the target feature vector in the historical feature vector can be determined.

Since the human-machine identification method is actually a classification method, in an alternative embodiment, step S130 may be implemented by:

and determining vectors similar to the target feature vector from the historical feature vectors, and forming the target feature vector and the similar vectors into a category.

By calculating the similarity between the historical feature vector and the target feature vector, the vector with the similarity larger than a preset threshold value is used as the similar vector of the target feature vector, and therefore the target feature vector and the similar vector are used as a category.

It should be noted that, determining the feature vector similar to the target feature vector in the historical feature vector may be implemented in various ways, for example, after obtaining the target feature vector, the feature vector similar to the target feature vector may be determined by calculating an euclidean distance, a cosine similarity, a correlation coefficient, and the like between the target feature vector and the historical feature vector.

In addition, when determining the similarity vector of the target feature vector, the target feature vector may be approximated so that the same vector as the target feature vector is used as the similarity vector of the target feature vector. For example, after the slope of each segment is obtained in step S120, the slope of each segment may be rounded, so that a plurality of linearly-fitted integer slope vectors may be obtained, and the integer slope vectors may be used as the target feature vectors corresponding to the sliding trajectory data. And after the target characteristic vector is obtained, taking a vector which is the same as the target characteristic vector in the historical characteristic vector as a similar vector of the target characteristic vector.

And S140, if the quantity or the proportion of the vectors in the category to which the target characteristic vector belongs meets a preset condition, determining that the slide block verification is executed by a machine.

The preset condition may be a quantity condition of the vectors in the category to which the target feature vector belongs, and may include, for example, a quantity threshold or a proportion threshold of the vectors in the category to which the target feature vector belongs, and the quantity threshold and the proportion threshold may be generally set by a developer according to an operation experience and the like.

When the number or the proportion of the vectors in the category to which the target feature vector belongs meets a preset condition, it is indicated that in the current slider verification and the historical verification, the total number of the target feature vectors exceeds a preset artificial verification number range, and therefore, the slider verification behavior corresponding to the target feature vector can be determined to be executed by a machine.

After determining the vectors similar to the target feature vector and forming the target feature vector and the similar vectors into a category, whether the slider verification behavior corresponding to the target feature vector is executed by a machine can be determined by counting the number of the vectors in the category. Specifically, in an alternative embodiment, it is determined that the slider verification is performed by the machine when the number of vectors in the category is greater than a number threshold, or the ratio of the vectors in the category to all the historical feature vectors and the target feature vector is greater than a ratio threshold.

In an alternative embodiment, after step S130, the target feature vector may be added to the historical feature vector, so that the historical feature vector is continuously accumulated for performing human-machine recognition on subsequent slider verification behaviors.

In summary, the exemplary embodiment of the present disclosure provides a human-machine recognition method, in which sliding trajectory data generated by slider verification is subjected to piecewise linear fitting, a target feature vector corresponding to the sliding trajectory data is generated according to a slope of each segment, the target feature vector is clustered with a history feature vector obtained in history slider verification to determine a category of the target feature vector, and when the number of vectors in the category to which the target feature vector belongs meets a preset condition, it is determined that the slider verification is performed by a machine. On one hand, the exemplary embodiment substantially realizes unsupervised learning of machine slider verification behaviors, clustering is carried out based on similarity between machine learning, the machine slider verification behaviors are distinguished from human behaviors, and the machine slider verification behaviors have strong generalization capability on various machine behaviors; particularly, for new machine behaviors, the behavior characteristics of the new machine behaviors are not required to be learned and can be recognized in an early stage, and the existing technology needs to label the behaviors and train a machine learning model, so that the scheme has high accuracy and timeliness. On the other hand, the target characteristic vector composed of slopes is obtained by piecewise linear fitting to represent the characteristics of sliding track data in the slider verification, the complexity of characteristic extraction is reduced, and the man-machine recognition is performed in a vector clustering mode.

Further, the present exemplary embodiment also provides a method for slider verification, which may be applied to a computer or a server in an application background performing slider verification, so as to determine whether a verification behavior of a client passes or not when the client performs slider verification.

FIG. 5 shows a flowchart of a slider verification method in the exemplary embodiment, which may include the following steps S510-S530:

and S510, when the client side carries out slide block verification, judging whether the slide block position is correct or not.

And S520, if the position of the slide block is correct, identifying whether the slide block verification is executed by a machine or not through the man-machine identification method.

Step S530, if the sliding block verification is determined to be not executed by a machine, the sliding block verification is determined to be passed.

When the client side carries out slider verification, whether the final position of the slider is correct or not can be determined through the sliding track data generated by the slider verification. For example, the verification can be performed by dragging the verification graph at the client to be located at the corresponding position, and therefore, in an alternative embodiment, the slider can be displayed in the graph verification area of the client, and the graph verification area can include the movable graph and the default graph, and as shown in fig. 2, the slider 210 is displayed in the graph verification area 240 and the graph verification area includes the movable graph 220 and the default graph 230, and thus, the step S510 can be implemented by:

when the slide block slides, the movable graph is synchronously moved;

when the sliding block stops sliding, detecting whether the movable graph and the incomplete graph are overlapped or not;

and when the movable graph is determined to be overlapped with the incomplete graph, determining that the position of the sliding block is correct.

When the sliding block moves, the movable graph is moved at the same speed and direction at the client, when the sliding of the sliding block is detected to stop, the position of the movable graph is determined, whether the movable graph is overlapped with the incomplete graph or not is determined according to the position of the movable graph, and when the movable graph and the incomplete graph are determined to be overlapped, the position of the sliding block is determined to be correct.

When the sliding block is manually slid, the movable graph and the incomplete graph are often difficult to completely coincide, so that a certain coincidence threshold value, such as 80% or 90%, can be set. When the sliding block stops moving, the overlapping proportion of the movable graph and the incomplete graph is calculated, if the proportion is larger than the overlapping threshold value, the movable graph and the incomplete graph are determined to be overlapped, the position of the sliding block is correct, otherwise, the movable graph and the incomplete graph are determined to be not overlapped, and the position of the sliding block is incorrect.

When the slider is determined to be positioned correctly, whether the slider verification is executed by a machine or not can be identified through the man-machine identification method, and when the slider verification is determined to be executed by the machine, the slider verification is determined to be not passed, otherwise, the slider verification is determined to be passed.

In addition, the movable graph can also be directly used as a slider, so that a user directly drags the movable graph to slide, and slider verification is completed.

Since the calculation of the human-machine recognition method in the exemplary embodiment is simplified compared to a machine learning model method, the slider verification method also improves the verification efficiency to a certain extent.

Fig. 6 shows a flowchart of another slider verification method of the present exemplary embodiment, which may include the following steps S610 to S650:

and S610, acquiring sliding track data generated by the client for slider verification.

And S620, when the sliding block stops moving, determining whether the position of the sliding block is correct.

When the slide block stops moving, the position of the slide block at the current moment is not changed any more, so that whether the position of the slide block meets the superposition condition of the graph or not can be determined according to the position of the slide block at the final moment in the sliding track data, when the position of the slide block meets the superposition condition of the graph is determined, the position of the slide block is determined to be correct, step S630 is executed, otherwise, the position of the slide block is determined to be incorrect, and step S650 is executed.

And S630, judging whether the sliding block verification behavior corresponding to the sliding track data is a machine behavior or not through the man-machine identification method.

When it is determined that the sliding behavior is not the machine behavior by the above-described human-machine recognition method, step S640 is performed; when it is determined that the sliding behavior is the machine behavior by the above-described human-machine recognition method, step S650 is performed.

And step 640, determining that the client slider passes verification.

And S650, determining that the client side sliding block verification is not passed.

An exemplary embodiment of the present disclosure also provides a human-machine recognition device, and referring to fig. 7, a structural block diagram of the human-machine recognition device is shown, and the human-machine recognition device 700 may include: the obtaining device 710 may be configured to obtain sliding track data generated by the client performing the slider verification, where the sliding track data may include positions of the slider at various times in the slider verification process; the generating module 720 may be configured to perform piecewise linear fitting on the positions at each time, and generate a target feature vector corresponding to the sliding trajectory data according to a slope of each segment; the clustering module 730 may be configured to cluster the target feature vector with the historical feature vector obtained in the historical slider verification; the determining module 740 may be configured to determine that the slider verification is performed by the machine if the number or the proportion of the vectors in the category to which the target feature vector belongs satisfies a preset condition.

In an exemplary embodiment of the present disclosure, the generating module 720 may include: the drawing unit can be used for drawing the position of each moment into a corresponding data point by taking the moment as a horizontal axis and the position as a vertical axis; the fitting unit can be used for segmenting each data point and respectively performing linear fitting on the data points in each segment to obtain the slope of each segment; and the arranging unit can be used for arranging the slope of each section into a vector so as to obtain a target characteristic vector corresponding to the sliding track data.

In an exemplary embodiment of the present disclosure, the fitting unit segments each data point by performing the following method: determining a first data point as a starting point of the current segment; sequentially taking the data points subsequent to the starting point as the end point of the current segment, performing linear fitting on all the data points in the current segment, and calculating a fitting error; if the fitting error is smaller than the error threshold, continuing to take the next data point as the terminal point of the current segment; if the fitting error is larger than the error threshold, moving the terminal point of the current segment to the previous data point, and dividing the current segment; and determining the end point of the current segment as the starting point of the next segment, and dividing the next segment until the segmentation is completed on the last data point.

In an exemplary embodiment of the disclosure, the generating module 720 may be further configured to, after obtaining the slope of each segment, respectively round the slope of each segment.

In an exemplary embodiment of the present disclosure, the clustering module 730 may be configured to determine a similar vector to the target feature vector from the historical feature vectors, and form the target feature vector and the similar vector into a category; the determination module 740 may be configured to determine that slider verification is performed by the machine when the number of vectors in the category is greater than a number threshold, or the ratio of vectors in the category to all historical feature vectors to target feature vectors is greater than a ratio threshold.

In an exemplary embodiment of the present disclosure, the slider slides in a direction of a preset axis during the slider verification process; the position of the slider at each time may include: the preset axis coordinates of the slider at each moment.

In an exemplary embodiment of the disclosure, after clustering, the clustering module 740 may be further configured to add the target feature vector to the historical feature vector.

The present exemplary embodiment also provides a slider verification apparatus, which is shown in fig. 8 and shows a block diagram of a slider verification apparatus, and the slider verification apparatus 800 may include: the determining module 810 may be configured to determine whether the position of the slider is correct when the client performs the slider verification; an identification module 820, which can be used to identify whether the slider verification is performed by the machine by any one of the above methods of human-machine identification if the slider is correctly positioned; the determining module 830 may be configured to determine that the slider verification passes if it is determined that the slider verification is not machine-executed.

In an exemplary embodiment of the present disclosure, the slider is displayed in a graphic verification area of the client, and the graphic verification area may include a movable graphic and a defective graphic; the decision module 810 determines that the slider position is correct by performing the following method: when the slide block slides, the movable graph is synchronously moved; when the sliding block stops sliding, detecting whether the movable graph and the incomplete graph are overlapped or not; and when the movable graph is determined to be overlapped with the incomplete graph, determining that the position of the sliding block is correct.

The specific details of each module in the above apparatus have been described in detail in the method section, and details of an undisclosed scheme may refer to the method section, and thus are not described again.

As will be appreciated by one skilled in the art, aspects of the present disclosure may be embodied as a system, method or program product. Accordingly, various aspects of the present disclosure may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.

Exemplary embodiments of the present disclosure also provide a computer-readable storage medium having stored thereon a program product capable of implementing the above-described method of the present specification. In some possible embodiments, various aspects of the disclosure may also be implemented in the form of a program product comprising program code for causing a terminal device to perform the steps according to various exemplary embodiments of the disclosure described in the above-mentioned "exemplary methods" section of this specification, when the program product is run on the terminal device.

Referring to fig. 9, a program product 900 for implementing the above method according to an exemplary embodiment of the present disclosure is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

Program product 900 may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).

The exemplary embodiment of the present disclosure also provides an electronic device capable of implementing the above method. An electronic device 1000 according to such an exemplary embodiment of the present disclosure is 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 functions and the scope of use of the embodiments of the present disclosure.

As shown in fig. 10, the electronic device 1000 may be embodied in the form of a general purpose computing device. The components of the electronic device 1000 may include, but are not limited to: the at least one processing unit 1010, the at least one memory unit 1020, a bus 1030 connecting different system components (including the memory unit 1020 and the processing unit 1010), and a display unit 1040.

Wherein the storage unit 1020 stores program code that may be executed by the processing unit 1010 such that the processing unit 1010 performs the steps according to various exemplary embodiments of the present disclosure described in the above-mentioned "exemplary methods" section of this specification. For example, the processing unit 1010 may perform the method steps shown in fig. 1, 5-6, and so on.

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

Storage unit 1020 may also include a program/utility 1024 having a set (at least one) of program modules 1025, such program modules 1025 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.

Bus 1030 may be any one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, and a local bus using any of a variety of bus architectures.

The electronic device 1000 may also communicate with one or more external devices 1100 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 1000, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 1000 to communicate with one or more other computing devices. Such communication may occur through input/output (I/O) interfaces 1050. Also, the electronic device 1000 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the internet) via the network adapter 1060. As shown, the network adapter 1060 communicates with the other modules of the electronic device 1000 over the bus 1030. It should be appreciated that although not shown, other hardware and/or software modules may 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 systems, among others.

It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functions of two or more modules or units described above may be embodied in one module or unit, according to exemplary embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.

Furthermore, the above-described figures are merely schematic illustrations of processes included in methods according to exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.

Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the exemplary embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes 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 exemplary embodiments of the present disclosure.

Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.