CN112642161A - Cheating detection and model training method and device for shooting game and storage medium - Google Patents
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Abstract
The embodiment of the application provides cheating detection and model training methods and equipment for a shooting game and a storage medium. After the server acquires the video data of the shooting game, the aiming path of the virtual shooting operation of the user in the shooting game is detected based on the video data, and the aiming path is classified according to the neural network model to obtain the cheating detection result of the virtual shooting operation. The embodiment is easy to deploy on the server side, and the anti-cheating program does not need to be deployed on the game terminal, so that the cheating detection strategy is not easy to be specifically bypassed by the cheating user. Meanwhile, the method can accurately identify whether the virtual shooting operation is a cheating behavior or not based on the aiming path by depending on the strong learning and calculating capabilities of the neural network model, and the accuracy and the reliability of the cheating detection method are greatly improved.
Description
Technical Field
The present application relates to the field of internet technologies, and in particular, to a cheating detection and model training method and apparatus for a shooting game, and a storage medium.
Background
In a First-person shooter game (FPS), some users have a behavior of cheating by a plug-in program. For example, the enemy position can be quickly positioned through the plug-in program, the enemy can be quickly aimed through the plug-in program, and the like. These cheating activities break down the fairness and interest of the game, resulting in a large number of lost users.
At present, some client-based FPS game anti-cheating detection methods exist, and mainly prevent a plug-in program from accessing a game by additionally arranging a kernel or an upper-layer anti-cheating component on a game client, and collect and report plug-in feature codes so as to seal cheating users.
However, in the existing anti-cheating technology, the functions of preventing cheating and detecting plug-ins need to be deployed at the client, so that plug-in developers can easily debug anti-cheating components on the local computer and make a targeted bypass strategy. Further, it is difficult to detect the cheating action of the user. Therefore, a new solution is yet to be proposed.
Disclosure of Invention
Aspects of the present application provide a cheating detection method, a model training method, an apparatus, and a storage medium for a shooting game, so as to improve a detection accuracy rate for cheating in the shooting game.
The embodiment of the application provides a cheating detection method of a shooting game, which is suitable for a server and comprises the following steps: acquiring video data of a shooting game; acquiring continuous multi-frame images of a user before virtual shooting operation in the shooting game from the video data; respectively detecting aiming coordinates of the user from the continuous multi-frame images to obtain a plurality of aiming coordinates; generating an aiming path corresponding to the virtual shooting operation according to the plurality of aiming coordinates; and inputting the aiming path into a neural network model to obtain a cheating detection result corresponding to the virtual shooting operation.
The embodiment of the application also provides a training method of the cheating detection model of the shooting game, which comprises the following steps: obtaining a plurality of sample data, the plurality of sample data comprising: aiming paths of virtual shooting operations of non-cheating users and aiming paths of virtual shooting operations of the cheating users; the real value of the sample data corresponding to the non-cheating user is marked as non-cheating, and the real value of the sample data corresponding to the cheating user is marked as cheating; inputting the plurality of sample data into a neural network model to obtain respective cheating prediction results of the plurality of sample data; calculating a loss function of the neural network model according to the true values of the sample data and the cheating prediction results of the sample data; and optimizing the model parameters of the neural network model based on the loss function until the loss function converges to a specified range.
An embodiment of the present application further provides a server, including: a memory and a processor; the memory is to store one or more computer instructions; the processor is to execute the one or more computer instructions to: the steps in the method provided by the embodiments of the present application are performed.
Embodiments of the present application further provide a computer-readable storage medium storing a computer program, where the computer program can implement the steps in the method provided in the embodiments of the present application when executed.
In the cheating detection method for the shooting game provided by the embodiment of the application, after the server obtains the video data of the shooting game, the aiming path of the virtual shooting operation of the user in the shooting game is detected based on the video data, and the aiming path is classified according to the neural network model to obtain the cheating detection result of the virtual shooting operation. The embodiment is easy to deploy on the server side, and the anti-cheating program does not need to be deployed on the game terminal, so that the cheating detection strategy is not easy to be specifically bypassed by the cheating user. Meanwhile, the method can accurately identify whether the virtual shooting operation is a cheating behavior or not based on the aiming path by depending on the strong learning and calculating capabilities of the neural network model, and the accuracy and the reliability of the cheating detection method are greatly improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a schematic flowchart of a cheat detection method for a shooting game according to an exemplary embodiment of the present application;
FIGS. 2a, 2b and 2c are schematic diagrams of aiming paths for virtual shooting operations by non-cheating users;
fig. 2d, 2e and 2f are schematic diagrams of aiming paths of virtual shooting operations of cheating users;
FIG. 3 is a schematic diagram of a neural network model provided in an exemplary embodiment of the present application;
FIG. 4 is a schematic flow chart illustrating a training method of a cheat-detection model of a shooting game according to an exemplary embodiment of the present application;
fig. 5 is a schematic structural diagram of a server according to an exemplary embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In a First-person shooter game (FPS), some users have a behavior of cheating by a plug-in program. For example, the enemy position can be quickly positioned through the plug-in program, the enemy can be quickly aimed through the plug-in program, and the like. These cheating activities break down the fairness and interest of the game, resulting in a large number of lost users.
At present, some client-based FPS game anti-cheating detection methods exist, and mainly prevent a plug-in program from accessing a game by additionally arranging a kernel or an upper-layer anti-cheating component on a game client, and collect and report plug-in feature codes so as to seal cheating users.
For example, in CSGO (computer-Strike: Global Offenive, anti-terrorist elite: Global attack) games, anti-cheating techniques are mainly provided by CSGO official Steam (game platform) and various third party battle platforms (e.g., Faceit,5E, etc.). The technical scheme of anti-cheating of the platform mainly focuses on the client side, the kernel or the upper-layer anti-cheating component is additionally arranged on the client side, the plug-in program is prevented from accessing the CSGO game, plug-in feature codes are collected and reported, and cheating users in the CSGO game are prohibited.
However, in the existing anti-cheating technology, the functions of preventing cheating and detecting plug-ins need to be deployed at the client, so that plug-in developers can easily debug anti-cheating components on the local computer and make a targeted bypass strategy. For example, a plug-in developer may customize bypass policies by restricting the network, prohibiting anti-cheating reports; or, the circumvention strategy can be customized by modifying the anti-cheating protocol to make it only report normal behavior; alternatively, patches may be added to the anti-cheating code to make it no longer work, thereby customizing the bypass policy. Based on the above, the game anti-cheating technology deployed at the client cannot accurately detect the cheating behavior of the user.
Meanwhile, when developing the anti-cheating technology, a developer needs to consider the stability and performance of the anti-cheating technology on a large number of user machines and the compatibility of the anti-cheating technology with some third-party software, so that the strength of the anti-cheating technology is sacrificed to a certain extent. However, the plug-in developer does not need to consider the above factors, and can obtain more cooperation operations of the plug-in user, for example, the plug-in user can unload the anti-cheating program first and then start the plug-in program to avoid detecting the cheating behavior. The above factors also result in the failure of the client-based anti-cheating technique to accurately and reliably detect the cheating behavior of the user.
In view of the above technical problem, in some embodiments of the present application, a solution is provided, and the technical solutions provided by the embodiments of the present application will be described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic flowchart of a cheating detection method for a shooting game according to an exemplary embodiment of the present application, as shown in fig. 1, when the method is executed on a server side, the method mainly includes the following steps:
And 102, acquiring continuous multi-frame images of the user before virtual shooting operation in the shooting game from the video data.
And 103, detecting aiming coordinates of the user from the continuous multi-frame images respectively to obtain a plurality of aiming coordinates.
And 104, generating an aiming path corresponding to the virtual shooting operation according to the plurality of aiming coordinates.
And 105, inputting the aiming path into a neural network model to obtain a cheating detection result corresponding to the virtual shooting operation.
The embodiment can be executed by the server and does not depend on the game terminal, so as to avoid the interference of the cheating detection process by the cheating user. The server may be implemented as a conventional server, a cloud host, a virtual center, or other devices, which is not limited in this embodiment. The server device mainly includes a processor, a hard disk, a memory, a system bus, and the like, and is similar to a general computer architecture, and is not described in detail.
In step 101, a shooting game, which may include a variety of FPS games. The video data of the shooting game can be acquired by recording the screen during the game process or can be acquired based on a frame synchronization technology. The video data acquisition based on the frame synchronization technology is a video data acquisition mode which is not perceived by a user (a game player), and a bypass strategy is not easy to be specified in a targeted manner. An embodiment of acquiring video data based on the frame synchronization technique will be described below.
In some FPS games, a game framework is typically built using frame synchronization techniques. The frame synchronization is to divide the data of the FPS game in the same second into N status frames, where each status frame includes complete status information of the game, such as character position, view direction, equipment and blood volume. When the game server accesses a plurality of game clients, the plurality of game clients can synchronize the status frames of the game in real time in order to ensure that each user sees the same game world. That is, the game server can issue N status frames corresponding to each second to N game clients, so as to ensure that the game server and all clients accessing the game synchronize the status frames of the game in real time.
For example, in the case of the CSGO game, the CSGO game will usually generate a video record at the end of each game. The recorded data may be imported into a CSGO game for playback to replay the game from various perspectives. The video data may be referred to as Demo, which may be regarded as a collection of game status frames of the game.
When the game is subjected to frame synchronization, real-time game data can be sampled according to the set state frame sampling frequency. For example, the CSGO game can be sampled according to a set frame sampling frequency to obtain the state frame. The state frame sampling frequency may be 32tick (32 frames/second), 64tick (64 frames/second), 128tick (128 frames/second), or other sampling frequencies, and the embodiment is not limited.
The collection of the state frames of each game obtained by the game server may be referred to as video data corresponding to the game. In one game, a user may perform one or more virtual shooting operations. Generally, before the virtual shooting operation is executed, in order to improve the hit rate of the shooting operation, a user can control the virtual shooting prop to adjust the angle and the position in the game world so as to realize the aim of the target object in the game world.
It should be appreciated that when the game play is sampled at a higher frame sampling frequency, the user's aiming operation in the game may be analyzed at a microscopic time scale. On a microscopic time scale, the stream of aiming operations (e.g., aiming curve, mouse movement curve, etc.) for each user should be smooth, inertial, and abrupt-free, as shown in fig. 2a, 2b, and 2 c. When a user uses plug-in cheating software in a game, the plug-in software generally needs to forcibly correct the input of the user so as to realize the high shooting hit rate effects of 'lock head', 'one-shot death', and the like of the virtual shooting prop. Therefore, the aiming operation flow of the cheating user in the game often presents features of jumping, non-inertial steering and the like which do not conform to the human operation rule at the microscopic time scale, as shown in fig. 2d, 2e and 2 f.
Based on the above analysis, in the present embodiment, by analyzing the operation flow of the user at the microscopic time scale, it is possible to detect whether the virtual shooting operation of the user has a cheating behavior.
In step 102 and step 103, after the video data of the FPS is acquired, the virtual shooting operation of the user may be detected based on the video data, and the key frame image for the user to perform the virtual shooting operation may be determined. After the key frame image is determined, successive K frame images preceding the key frame image may be acquired from the video data as an image for analyzing the aiming path. K is a positive integer, and the value of K can be set according to actual requirements. In some alternative embodiments, K may be 6, 8, 10, or other alternative values, and this embodiment is not limited.
Next, aiming coordinates of the user may be detected from the consecutive multi-frame images, respectively. Wherein one aiming coordinate can be detected per frame of image, and a plurality of aiming coordinates can be detected by continuous multi-frame images. After the plurality of aiming coordinates are obtained, the aiming path corresponding to the virtual shooting operation can be generated according to the plurality of aiming coordinates.
In step 104, the aiming path is used to describe a moving path formed by the user controlling the virtual shooting prop to shoot and aim before the virtual shooting operation is performed, that is, an operation flow of the user performing the virtual shooting operation.
In the FPS game, a game player carries out shooting aiming through the subjective visual angle of a virtual game character, and the subjective visual angle of the virtual game character is the aiming visual angle. Therefore, in some embodiments, the change in the angle of view of the virtual game character may be detected based on the video data, thereby detecting the stream of aiming operations of the user.
For each virtual shooting operation of the user, when the aiming path is detected, the multi-frame image before the virtual shooting operation, namely the multi-frame image corresponding to the aiming process of the user, can be obtained from the video data. The multi-frame image typically contains perspective status information at which a user is aiming a virtual shooting prop (e.g., a virtual gun) of the virtual game character. And detecting the visual angle based on the multi-frame image, and calculating the change curve of the detected visual angle, so as to determine the aiming path of the virtual shooting operation.
It should be understood that in the process of aiming at the target, when the angle of view of the game character changes, the falling point of the line of sight of the game character also changes. Therefore, in other embodiments, the coordinates of the landing point of the line of sight of the game character may be detected, and the change in the coordinates of the landing point may be calculated, resulting in the aiming path for the virtual shooting operation. After the aiming path of the virtual shooting operation is obtained, the aiming path can be input into a pre-trained neural network model.
In step 105, the neural network model may be obtained by training in advance according to a large amount of sample data. In the process of training the neural network model, the neural network model can learn the characteristics of the normal aiming curve under the non-cheating condition and the characteristics of the aiming curve under the cheating condition based on sample data adopted by training, and further can achieve the purpose of intelligently classifying the input aiming curves. An alternative implementation of training the neural network model will be described in the following embodiments, which are not described herein.
Based on this, in this embodiment, after the aiming path of the virtual shooting operation is acquired, the aiming path may be input into the neural network model, and the cheating detection result corresponding to the virtual shooting operation is determined according to the output of the neural network model. Wherein the cheating detection result may include; the virtual shooting operation is a cheating action or the virtual shooting operation is a non-cheating action.
In some embodiments, if it is detected that all of the multiple virtual shooting operations of a certain user in the game process are cheating behaviors, a warning message may be sent to the user, or a game account of the user may be prohibited, and the like.
In this embodiment, after the server obtains the video data of the shooting game, the server detects the targeting path of the virtual shooting operation of the user in the shooting game based on the video data, and classifies the targeting path according to the neural network model to obtain the cheating detection result of the virtual shooting operation. The embodiment is easy to deploy on the server side, and the anti-cheating program does not need to be deployed on the game terminal, so that the cheating detection strategy is not easy to be specifically bypassed by the cheating user. Meanwhile, the method can accurately identify whether the virtual shooting operation is a cheating behavior or not based on the aiming path by depending on the strong learning and calculating capabilities of the neural network model, and the accuracy and the reliability of the cheating detection method are greatly improved.
In addition, the cheating detection scheme of non-client deployment provided by the application embodiment can avoid influencing normal installation and operation of the client on one hand, and can achieve the effects that a user does not sense and the cheating detection accuracy is high on the other hand.
The foregoing examples describe embodiments of detecting a targeting path based on a change in viewing angle and embodiments of detecting a targeting path based on a gaze drop point, both of which are further exemplified below.
An alternative embodiment for detecting the aiming path will be described below by taking any one of consecutive multi-frame images as an example.
Embodiment A: based on the change in the viewing angle, an aiming path is detected.
In the FPS game, a game player performs shooting aiming through the subjective view of a virtual game character, and thus, the view of the game character is an aiming view. Based on this, optionally, for the t-th frame image in the continuous multi-frame images, a target ray may be emitted in the image with the aiming viewpoint of the game character as an end point to determine the aiming direction. The aiming viewpoint may be a position of an eye of the game character, a position of a muzzle of the virtual item used by the game character, or a position of a sight on a gun of the virtual item, which includes but is not limited to this embodiment.
Next, the targets can be computed separatelyThe included angle between the ray and the horizontal plane and the vertical plane is obtained to obtain a first angle deviation value xtAnd a second angular offset value ytAnd according to a first angle offset value xtAnd a second angular offset value ytObtaining the aiming coordinate (x) of the user in the imaget,yt)。
For example, in cheating analysis based on Demo at 64tick for CSGO game, the user's aiming coordinates may be sampled every 1/64 seconds (15.6 milliseconds) and recorded as an x, y two angular offset in the game. When the aiming coordinate is sampled, a ray can be emitted from the eyes of a character in a game, x represents the included angle between the ray and a horizontal plane and has the value range of (-180 degrees and 180 degrees), y represents the included angle between the ray and a vertical plane and has the value range of (-180 degrees and 180 degrees).
Embodiment B: based on the change of the sight line drop point, the aiming path is detected.
In this embodiment, for any frame of image, the target ray is shot with the aiming viewpoint of the game character as the end point, and based on the image, the intersection point where the target ray intersects with the object in the game world is determined. Next, the coordinates of the intersection in the game world are calculated as the aiming coordinates of the user in the image.
After the aiming coordinates corresponding to the continuous multi-frame images are acquired, the aiming path corresponding to the virtual shooting operation can be generated based on the aiming coordinates. The following is an exemplary description with reference to embodiment a.
In some alternative embodiments, a coordinate sequence formed by a plurality of aiming coordinates may be used as the aiming path for the shooting operation. For example, the targeting path may be described as { (x)1,y1),(x2,y2)，…，(xK,yK)}。
In other alternative embodiments, the aiming vector for each aiming coordinate may be calculated, a plurality of aiming vectors may be derived, and based on the plurality of aiming vectors, an aiming path corresponding to the virtual shooting operation may be determined.
Optionally, the targeting coordinate may be related to any one of the plurality of targeting coordinatesAnd vectorizing the next adjacent aiming coordinate to obtain an aiming vector of the aiming coordinate. Continue with aiming coordinates (x) in the image of the t-th framet,yt) For example, the change between the aiming coordinate of the t +1 th frame and the aiming coordinate of the t th frame can be vectorized and recorded as the aiming vector v of the image of the t th framet＝(xt+1-xt,yt+1–yt)。
After obtaining the aiming vector of each aiming coordinate, the matrix formed by the aiming vectors can be used for describing the aiming path of the virtual shooting operation. Wherein the aiming path may be described as: { v1,v2…vkThe dimension of the matrix is m × K, m represents vtThe characteristic dimension of (c).
In some embodiments, at a computational level, v may be described using four numerical valuestThe four values include: v. oftProjection on the x-axis, vtProjection on the y-axis, vtV.d. oftAngle relative to positive x-axis. In this calculation, vtWith 4 feature dimensions, the matrix corresponding to the targeting path has 4 × K feature dimensions. When the value of K is 8, the matrix corresponding to the aiming path has 4 × 8-32 characteristic dimensions.
After the aiming path corresponding to the virtual shooting operation is acquired based on the above embodiment, the aiming path can be input into the neural network model. Optionally, in this embodiment, the neural network model may be implemented as: MLP (Multi-Layer Perceptron), RNN (Recurrent Neural Network), transform model, etc., but the present embodiment includes but is not limited thereto. The following will exemplify MLP.
The structure of the neural network model can be shown in fig. 3, and is an MLP full-connection network, the output layer of the neural network model is a two-dimensional regression function softmax, and the softmax is used as a classifier and can output probability distribution that the aiming path belongs to normal operation and cheating operation.
As shown in fig. 3, the input layer (first layer) and each intermediate hidden layer of the neural network model may respectively include a number of neurons and an activation function Relu. The input layer of the neural network model can comprise 400 neurons, and the number of the neurons in the hidden layer between the input layer and the output layer is gradually reduced. The rule of decreasing neuron number is related to the drop rate (the proportion of neurons dropped in the network layer). Where the discharge rate may be set to 0.25 or other optional value. It should be understood that the structure shown in fig. 3 is only used for exemplary illustration of the structure of the neural network model, and in practice, the structure of the neural network model may be adjusted according to the detection requirement, and is not limited to the structure shown in fig. 3.
After the aiming path is input into the input layer of the neural network model, the aiming path can be subjected to feature calculation in the neural network model based on the pre-learned model parameters. Based on the calculated features, the probability that the virtual shooting operation corresponding to the aiming path is cheating can be calculated. And if the calculated probability is larger than the set probability threshold, outputting a cheating detection result that the virtual shooting operation is a cheating behavior. That is, the trajectory of the virtual shooting operation by the user is regarded as a suspicious trajectory.
The set probability threshold may be set according to actual requirements, for example, may be set to 80%, 85%, 90%, or other probability thresholds, which is not limited in this embodiment. For example, in some embodiments, if the probability that one virtual shooting operation of the user is a cheating behavior is greater than 80% or 90%, the trajectory of the current virtual shooting operation of the user may be determined to be a suspicious trajectory.
For a user, if the number of virtual shooting operations using plug-in is determined to be greater than a set number threshold in the virtual shooting operations of the user in the game, the user can be marked as a cheating user. The set number threshold may be set according to actual requirements, for example, may be set to 5, 8, 10, and the like, and this embodiment is not limited. For example, if the number of trajectories of a virtual shooting operation of a certain user in a game is detected as suspicious trajectories is more than 5, the user may be marked as a cheating user. And warning processing or account number blocking processing can be performed for the cheating user, and repeated description is omitted.
It is worth to be noted that the cheating detection method based on the server provided by the embodiment of the present application may be used alone, or may be used in cooperation with a cheating detection method deployed by a client. When the method is matched with a cheating detection method deployed by a client, on one hand, the accuracy of a cheating detection result can be improved, and on the other hand, the cheating detection result deployed at a server and the detection result deployed at the client can be cross-quoted so as to continuously optimize the two detection methods. As will be exemplified below.
Optionally, after the targeting path of the virtual shooting operation of the user is input into the neural network model and the cheating detection result output by the neural network model is obtained, the cheating detection data of the user sent by the client may be further obtained. Wherein the client runs a cheating detection program.
Then, suspicious cheating characteristics of the user can be obtained from the cheating detection data; wherein the suspected cheating feature may include: the user uses a driver with unknown signature at the client, or an unknown application program initiates access to the game system, and the like, which is not limited in this embodiment.
Next, a cheating judgment score of the user may be calculated based on the suspicious cheating features detected by the client and the cheating detection result calculated by the server. Alternatively, a weight coefficient may be set in advance for each suspected cheating feature and the cheating detection result. After the suspicious cheating features and the cheating detection results are obtained, weighting calculation can be performed on the suspicious cheating features and the cheating detection results based on the weight coefficients, and cheating judgment scores are obtained. And if the cheating judgment score is larger than the set score threshold, determining that the user is a cheating user. The score threshold may be set according to actual requirements, which is not limited in this embodiment.
Continuing with the CSGO game as an example, the anti-cheating function module running on the CSGO game client can detect whether the user uses a driver with unknown signature or whether an unknown program accesses the CSGO during the game. If the suspicious cheating characteristics are detected and the server detects that the user has the cheating behavior of self-aiming based on machine learning, the CSGO user can be determined to have the cheating behavior. At this point, the programs used by the user may be blacklisted. If it is detected again that the user or other users start the programs in the blacklist on the client, account blocking processing can be performed on the user.
In such an embodiment, the strength of the anti-cheating function of the cheating-detection program running on the client can be appropriately reduced (i.e., the strength of the anti-cheating function is reduced) by the auxiliary detection function of the serverSacrifice the strength of the anti-plug-in). Furthermore, on one hand, the performance of the game run by the client can be ensured, the stability of the game run by the client is enhanced, and the compatibility of third-party software is improved, on the other hand, the cheating behavior can be comprehensively judged by combining various detection data, the accuracy of the cheating detection result can be improved, and 1+1>Anti-cheating effect of 2 ″。
In the following examples, alternative embodiments of training neural network models will be illustrated with reference to the accompanying drawings.
Fig. 4 is a flowchart illustrating a training method of a cheat-detection model of a shooting game according to an exemplary embodiment of the present application, where the method includes:
And 404, optimizing model parameters of the neural network model based on the loss function until the loss function converges to a specified range.
In this embodiment, the video data of the shooting game of the cheating user and the video data of the shooting game of the non-cheating user can be acquired. Based on the video data of the cheating users, the aiming path of the virtual shooting operation of the cheating users can be obtained; based on the video data of the non-cheating user, the aiming path of the virtual shooting operation of the cheating user and the aiming path of the virtual shooting operation of the non-cheating user can be obtained. For an optional implementation of obtaining the aiming path based on the video data, reference may be made to the descriptions in the foregoing embodiments, which are not repeated herein.
A plurality of sample data required for training the neural network model, including aiming paths of virtual shooting operations of cheating users and aiming paths of virtual shooting operations of non-cheating users; and marking the true value of each sample data according to the type of the user to which the true value belongs. Wherein the user types refer to a cheating user type and a non-cheating user type. That is, the true value of the aiming path of the virtual shooting operation of the cheating user is marked as non-cheating, and the true value of the aiming path of the virtual shooting operation of the cheating user is marked as cheating.
In the sample labeling mode, the idea of 'approximate labeling' is adopted, and the truth marks of all aiming paths of cheating users who clearly have cheating behaviors are taken as cheating. Although each virtual shooting operation of the cheating user is not modified by the plug-in program, and may also include some normal aiming data, the true value of the normal aiming data is marked as cheating and can be used as noise in the training data. During training, a large amount of non-cheating user data can automatically correct this noise, while the obvious cheating features can be preserved. That is, the normal targeting data of the cheating user and the data of a large number of non-cheating users have a certain characteristic coincidence, and in the training, the normal targeting data of the cheating user can be re-corrected by the targeting data of the large number of non-cheating users, while the data of the cheating characteristics can not be corrected. Based on the mode, the performance of the trained neural network model can be improved while the labeling cost is reduced.
After a plurality of sample data are obtained, the plurality of sample data can be input into the neural network model to obtain respective cheating prediction results of the plurality of sample data.
After the cheating prediction result of each sample data is obtained, a loss function of the neural network model can be calculated according to the respective true values of the sample data and the cheating prediction results of the sample data, model parameters of the neural network model are optimized based on the loss function, and the trained result model is output until the loss function converges to a specified range.
It should be noted that the execution subjects of the steps of the methods provided in the above embodiments may be the same device, or different devices may be used as the execution subjects of the methods. For example, the execution subjects of steps 401 to 404 may be device a; for another example, the execution subject of steps 401 and 402 may be device a, and the execution subject of step 403 may be device B; and so on.
In addition, in some of the flows described in the above embodiments and the drawings, a plurality of operations are included in a specific order, but it should be clearly understood that the operations may be executed out of the order presented herein or in parallel, and the sequence numbers of the operations, such as 401, 402, etc., are merely used to distinguish various operations, and the sequence numbers themselves do not represent any execution order. Additionally, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel.
It should be noted that, the descriptions of "first", "second", etc. in this document are used for distinguishing different messages, devices, modules, etc., and do not represent a sequential order, nor limit the types of "first" and "second" to be different.
Fig. 5 is a schematic structural diagram illustrating a server provided in an exemplary embodiment of the present application, where the server is suitable for the cheating detection method of the shooting game provided in the foregoing embodiment. As shown in fig. 5, the server includes: a memory 501 and a processor 502.
The memory 501 is used for storing a computer program and may be configured to store other various data to support operations on the server. Examples of such data include instructions for any application or method operating on the server, contact data, phonebook data, messages, pictures, videos, and so forth.
The memory 501 may be implemented by any type or combination of volatile or non-volatile memory devices, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
A processor 502, coupled to the memory 501, for executing computer programs in the memory 501 for: acquiring video data of a shooting game; detecting an aiming path of a virtual shooting operation of a user in the shooting game based on the video data; and inputting the aiming path into a neural network model to obtain a cheating detection result corresponding to the virtual shooting operation.
Further optionally, the processor 502, when detecting, based on the video data, a targeting path of a virtual shooting operation of a user in the shooting game, is specifically configured to: acquiring continuous multi-frame images before the shooting operation from the video data; respectively detecting aiming coordinates of the user from the continuous multi-frame images to obtain a plurality of aiming coordinates; and generating an aiming path corresponding to the virtual shooting operation according to the aiming coordinates.
Further optionally, when the processor 502 detects the aiming coordinate of the user from the consecutive multi-frame images respectively to obtain a plurality of aiming coordinates, it is specifically configured to: aiming at any frame of image in the continuous multi-frame images, in the image, aiming at a sight point of a game role as an end point, and emitting a target ray; respectively calculating included angles between the target ray and a horizontal plane and included angles between the target ray and a vertical plane to obtain a first angle deviation value and a second angle deviation value; and obtaining the aiming coordinate of the user in the image according to the first angle deviation value and the second angle deviation value.
Further optionally, when the processor 502 generates the aiming path corresponding to the virtual shooting operation according to the plurality of aiming coordinates, it is specifically configured to: aiming at any aiming coordinate in the multiple aiming coordinates, vectorizing the aiming coordinate and the next adjacent aiming coordinate to obtain an aiming vector of the aiming coordinate; and acquiring a matrix formed by aiming vectors of the aiming coordinates as an aiming path corresponding to the virtual shooting operation.
Further optionally, when the processor 502 generates the aiming path corresponding to the virtual shooting operation according to the plurality of aiming coordinates, it is specifically configured to: and taking a coordinate sequence formed by the aiming coordinates as an aiming path corresponding to the virtual shooting operation.
Further optionally, when the processor 502 inputs the aiming path into the neural network model to obtain a cheating detection result corresponding to the virtual shooting operation, the processor is specifically configured to: inputting the aiming path into a neural network model; in the neural network model, performing feature calculation on the aiming path based on a pre-learned model parameter; calculating the probability that the virtual shooting operation corresponding to the aiming path is cheating according to the calculated characteristics; and if the calculated probability is larger than a set probability threshold value, outputting a cheating detection result that the virtual shooting operation is a cheating behavior.
Further optionally, the processor 502 is further configured to: and if the number of the virtual shooting operations detected as cheating behaviors in the virtual shooting operations of the user is larger than a set number threshold, determining that the user is a cheating user.
Further optionally, the processor 502 is further configured to: the method comprises the steps of obtaining cheating detection data of a user, sent by a client, wherein a cheating detection program runs on the client; obtaining suspicious cheating characteristics of the user from the cheating detection data; calculating a cheating judgment score of the user based on the suspicious cheating characteristics and the cheating detection result; and if the cheating judgment score is larger than a set score threshold value, determining that the user is a cheating user.
Further optionally, the processor 502 is further configured to: obtaining a plurality of sample data, the plurality of sample data comprising: aiming paths of virtual shooting operations of cheating users and aiming paths of virtual shooting operations of non-cheating users; the real value of the sample data corresponding to the non-cheating user is marked as non-cheating, and the real value of the sample data corresponding to the cheating user is marked as cheating; inputting the plurality of sample data into the neural network model to obtain respective cheating prediction results of the plurality of sample data; calculating a loss function of the neural network model according to the true values of the sample data and the cheating prediction results of the sample data; and optimizing the model parameters of the neural network model based on the loss function until the loss function converges to a specified range.
Further, as shown in fig. 5, the server further includes: communication component 503, power component 504, and the like. Only some of the components are schematically shown in fig. 5, and it is not meant that the server includes only the components shown in fig. 5.
Wherein the communication component 503 is configured to facilitate communication between the device in which the communication component is located and other devices in a wired or wireless manner. The device in which the communication component is located may access a wireless network based on a communication standard, such as WiFi, 2G, 3G, 4G, or 5G, or a combination thereof. In an exemplary embodiment, the communication component receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component may be implemented based on Near Field Communication (NFC) technology, Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
The power supply 504 is configured to provide power to various components of the device in which the power supply is located. The power components may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the device in which the power component is located.
In this embodiment, after the server obtains the video data of the shooting game, the server detects the targeting path of the virtual shooting operation of the user in the shooting game based on the video data, and classifies the targeting path according to the neural network model to obtain the cheating detection result of the virtual shooting operation. The embodiment is easy to deploy on the server side, and the anti-cheating program does not need to be deployed on the game terminal, so that the cheating detection strategy is not easy to be specifically bypassed by the cheating user. Meanwhile, the method can accurately identify whether the virtual shooting operation is a cheating behavior or not based on the aiming path by depending on the strong learning and calculating capabilities of the neural network model, and the accuracy and the reliability of the cheating detection method are greatly improved.
Accordingly, the present application further provides a computer-readable storage medium storing a computer program, where the computer program can implement the steps that can be executed by the server in the foregoing method embodiments when executed.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (11)
1. A cheating detection method of a shooting game is suitable for a server and is characterized by comprising the following steps:
acquiring video data of a shooting game;
acquiring continuous multi-frame images of a user before virtual shooting operation in the shooting game from the video data;
respectively detecting aiming coordinates of the user from the continuous multi-frame images to obtain a plurality of aiming coordinates;
generating an aiming path corresponding to the virtual shooting operation according to the plurality of aiming coordinates; and inputting the aiming path into a neural network model to obtain a cheating detection result corresponding to the virtual shooting operation.
2. The method of claim 1, wherein detecting aiming coordinates of the user from the consecutive frames of images, respectively, to obtain a plurality of aiming coordinates comprises:
aiming at any frame of image in the continuous multi-frame images, in the image, aiming at a sight point of a game role as an end point, and emitting a target ray;
respectively calculating included angles between the target ray and a horizontal plane and included angles between the target ray and a vertical plane to obtain a first angle deviation value and a second angle deviation value;
and obtaining the aiming coordinate of the user in the image according to the first angle deviation value and the second angle deviation value.
3. The method of claim 1, wherein generating the aiming path corresponding to the virtual shooting operation from the plurality of aiming coordinates comprises:
aiming at any aiming coordinate in the multiple aiming coordinates, vectorizing the aiming coordinate and the next adjacent aiming coordinate to obtain an aiming vector of the aiming coordinate;
and acquiring a matrix formed by aiming vectors of the aiming coordinates as an aiming path corresponding to the virtual shooting operation.
4. The method of claim 1, wherein generating the aiming path corresponding to the virtual shooting operation from the plurality of aiming coordinates comprises:
and taking a coordinate sequence formed by the aiming coordinates as an aiming path corresponding to the virtual shooting operation.
5. The method according to any one of claims 1-4, wherein inputting the aiming path into a neural network model to obtain a cheating detection result corresponding to the virtual shooting operation comprises:
inputting the aiming path into a neural network model;
in the neural network model, performing feature calculation on the aiming path based on a pre-learned model parameter;
calculating the probability that the virtual shooting operation corresponding to the aiming path is cheating according to the calculated characteristics;
and if the calculated probability is larger than a set probability threshold value, outputting a cheating detection result that the virtual shooting operation is a cheating behavior.
6. The method of claim 5, further comprising:
and if the number of the virtual shooting operations detected as cheating behaviors in the virtual shooting operations of the user is larger than a set number threshold, determining that the user is a cheating user.
7. The method of claim 5, further comprising:
the method comprises the steps of obtaining cheating detection data of a user, sent by a client, wherein a cheating detection program runs on the client;
obtaining suspicious cheating characteristics of the user from the cheating detection data;
calculating a cheating judgment score of the user based on the suspicious cheating characteristics and the cheating detection result;
and if the cheating judgment score is larger than a set score threshold value, determining that the user is a cheating user.
8. The method of claim 6, further comprising:
obtaining a plurality of sample data, the plurality of sample data comprising: the aiming path of the virtual shooting operation of the cheating user is not the aiming path of the virtual shooting operation of the cheating user and the aiming path of the virtual shooting operation of the cheating user; the real value of the sample data corresponding to the non-cheating user is marked as non-cheating, and the real value of the sample data corresponding to the cheating user is marked as cheating;
inputting the plurality of sample data into the neural network model to obtain respective cheating prediction results of the plurality of sample data;
calculating a loss function of the neural network model according to the true values of the sample data and the cheating prediction results of the sample data;
and optimizing the model parameters of the neural network model based on the loss function until the loss function converges to a specified range.
9. A training method of a cheat-detecting model of a shooting game, comprising:
obtaining a plurality of sample data, the plurality of sample data comprising: aiming paths of virtual shooting operations of non-cheating users and aiming paths of virtual shooting operations of the cheating users; the real value of the sample data corresponding to the non-cheating user is marked as non-cheating, and the real value of the sample data corresponding to the cheating user is marked as cheating;
inputting the plurality of sample data into a neural network model to obtain respective cheating prediction results of the plurality of sample data;
calculating a loss function of the neural network model according to the true values of the sample data and the cheating prediction results of the sample data;
and optimizing the model parameters of the neural network model based on the loss function until the loss function converges to a specified range.
10. A server, comprising: a memory and a processor;
the memory is to store one or more computer instructions;
the processor is to execute the one or more computer instructions to: performing the steps of the method of any one of claims 1-9.
11. A computer-readable storage medium storing a computer program, wherein the computer program is capable of performing the steps of the method of any one of claims 1-9 when executed.
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