Anti-cheating method and electronic device

Abstract

An anti-cheating method and electronic device. A touch sensing device is used to detect the actual track information of a mouse device on the touch sensing device, wherein the mouse device moves on the touch sensing device. The mouse track information used to control the cursor is captured. Cheating operations are detected based on the actual track information and the mouse track information.

Description

Technical Field

[0001] The invention relates to an electronic device, and in particular to an anti-cheating method and an electronic device using touch technology. Background Art

[0002] In today's e-sports gaming industry, players are usually tested on their skills learned through practice. In this case, using additional software and hardware to gain unfair advantages is often one of the important factors that destroy the healthy competitive environment in the game. Therefore, if players who use the above cheating methods to play games can be detected and found in a timely manner, the fairness of the game should be better guaranteed.

[0003] Generally speaking, a common cheating method is that players play games with the help of auxiliary operation script tools (referred to as scripts). Scripts can be run on the operating system of a computer device in the form of programs, and are therefore also called plug-in programs. Alternatively, players or other relevant personnel can set scripts in the firmware inside a hardware device (such as a mouse device). Players can use the assistance of the above scripts to generate fake user operations, thereby destroying the balance of the original design of the game and also disrupting the normal development of the game. Therefore, how to detect players playing games through unfair cheating is indeed an important issue in the e-sports game industry. Summary of the invention

[0004] In view of this, the present invention provides an anti-cheating method and an electronic device, which can effectively determine whether a cheating operation occurs according to the movement trajectory of a mouse device sensed by a touch sensing device.

[0005] An embodiment of the present invention provides an anti-cheating method, which is applicable to an electronic device connected to a mouse device, and includes the following steps: using a touch sensing device to detect actual track information of a mouse device on the touch sensing device, wherein the mouse device moves on the touch sensing device; capturing mouse track information used to control a cursor; and detecting cheating operations based on the actual track information and the mouse track information.

[0006] An embodiment of the present invention provides an electronic device, which is connected to a touch sensing device and a mouse device and includes a storage device and a processor. The processor is connected to the storage device and is configured to perform the following steps. The touch sensing device is used to detect actual track information of the mouse device on the touch sensing device, wherein the mouse device moves on the touch sensing device. Mouse track information for controlling a cursor is captured. Cheating operations are detected based on the actual track information and the mouse track information.

[0007] Based on the above, in the embodiment of the present invention, when the mouse device moves on the touch sensing device, the electronic device can obtain the actual track information of the mouse device on the touch sensing device according to the touch data provided by the touch sensing device. Therefore, according to the actual track information and the mouse track information used to control the cursor, the electronic device can effectively and instantly detect whether the user controls the electronic device to perform cheating operations.

[0008] In order to make the above features and advantages of the present invention more obvious and easy to understand, embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 is a schematic diagram of an electronic device according to an embodiment of the present invention.

[0010] Figure 2 is a flow chart of an anti-cheating method according to an embodiment of the present invention.

[0011] Figure 3A is a schematic diagram of a touch sensing device according to an embodiment of the present invention.

[0012] Figure 3B FIG. 4 is a schematic diagram of a touch sensing frame according to an embodiment of the present invention.

[0013] Figure 4A It is a schematic diagram of obtaining actual trajectory information by using a machine learning model according to an embodiment of the present invention.

[0014] Figure 4B It is a schematic diagram of obtaining actual trajectory information by using a machine learning model according to an embodiment of the present invention.

[0015] Figure 5 is a flow chart of an anti-cheating method according to an embodiment of the present invention.

[0016] Figure 6 is a flow chart of an anti-cheating method according to an embodiment of the present invention.

[0017] Figure 7 is a flow chart of an anti-cheating method according to an embodiment of the present invention.

[0018] in:

[0019] 10: Electronic devices;

[0020] 110: display;

[0021] 120: storage device;

[0022] 130: processor;

[0023] 20: Mouse device;

[0024] 30: Touch sensing device;

[0025] 111: sensing element array;

[0026] CS11, CS12, CS21: touch sensing elements;

[0027] 113: scanning driving circuit;

[0028] 112: receiving sensing circuit;

[0029] 114: scan line;

[0030] 115: sensing line;

[0031] d1: touch original data;

[0032] FC11, FC12, FC21: frame cells;

[0033] F1, F2, F3: touch sensing frame;

[0034] T1, T2: movement trajectory;

[0035] B1: Object frame;

[0036] P1: center point;

[0037] PL, PR, PB, PU: extreme points;

[0038] PC: center reference point;

[0039] Z1: semantic segmentation area;

[0040] Z2: other areas;

[0041] S210~S230, S510~S533, S610~S633, S710~S733: steps. DETAILED DESCRIPTION

[0042] Some embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The referenced element symbols in the following description will be regarded as the same or similar elements when the same element symbols appear in different drawings. These embodiments are only part of the present invention and do not disclose all possible implementation methods of the present invention. More specifically, these embodiments are only examples of the devices and methods in the claims of the present invention.

[0043] It should be understood that when an element is referred to as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or other intervening elements may exist. In other words, unless otherwise specified, the terms "connected" and "coupled" include direct and indirect connections and couplings between two elements.

[0044] Figure 1 1 is a schematic diagram of an electronic device according to an embodiment of the present invention, but this is only for the convenience of explanation and is not intended to limit the present invention. Figure 1 First, we will introduce all the components and configuration relationships of the electronic device, and then the detailed functions and operations will be Figure 2 Revealed together.

[0045] Please refer to Figure 1 The electronic device 10 of this embodiment is, for example, a notebook computer, a smart phone, a tablet computer, an e-book, a game console, etc., which can be connected to the mouse device 20 and the touch sensing device 30, but the present invention is not limited thereto. The electronic device 10 includes a display 110, a storage device 120, and a processor 130. In some embodiments, the electronic device 10 can be used to run a game, and the mouse device 20 can allow the user to control / operate the game in a mobile manner. In some embodiments, the electronic device 10 can be used to run an online test, and the mouse device 20 can allow the user to input the answer to the online test in a mobile manner.

[0046] The mouse device 20 can be connected to the electronic device 10 by wire or wirelessly, and the present invention is not limited to this. For example, the mouse device 20 can be a Bluetooth mouse or a wired mouse with a USB connector. In addition, according to the different working principles of the mouse device 20, the mouse device 20 can be a wheel mouse or an optical mouse, and the present invention is not limited to this.

[0047] The touch sensing device 30 can be used to sense contact of an object on the touch sensing surface. The touch sensing device 30 can be connected to the electronic device 10 by wire or wirelessly, and the present invention is not limited to this. In an embodiment of the present invention, the mouse device 20 moves on the touch sensing device 30. In more detail, under the control of the user's operation, the mouse device 20 moves on the touch sensing surface of the touch sensing device 30, and the touch sensing device 30 can thus sense the contact of the mouse device 20. The touch sensing device 30 can be implemented as a touchpad, a touch screen, or a mouse pad with a touch sensing function, etc., and the present invention is not limited to this. The touch sensing device 30 can, for example, include a capacitive touch panel, a resistive touch panel, an electromagnetic touch panel, an optical touch panel, or a touch panel using other touch sensing principles. In some embodiments, the touch sensing device 30 includes a plurality of touch sensing elements (eg, capacitive sensing elements or resistive sensing elements, etc.) arranged in an array and can perform touch sensing to obtain a touch sensing frame including touch raw data respectively corresponding to the touch sensing elements.

[0048] The display 110 is, for example, a liquid crystal display (LCD), a light-emitting diode (LED) display, a field emission display (FED), an organic light-emitting diode (OLED), or other types of display devices. In some embodiments, the display 110 is, for example, a screen that can be used to display the above-mentioned game screen or online test screen. In addition, in some embodiments, the display 110 can display a cursor controlled by the mouse device 20. In other words, the cursor in the screen displayed by the display 110 will react to the movement of the mouse device 20 and move accordingly.

[0049] The storage device 120 is used to store touch data, instructions, source code, software components and other data, and can be, for example, any type of fixed or removable random access memory (RAM), read-only memory (ROM), flash memory, hard disk or other similar devices, integrated circuits and combinations thereof.

[0050] The processor 130 is coupled to the display 110 and the storage device 120 to control the actions of the components of the electronic device 10. The processor 130 is, for example, a central processing unit (CPU), or other programmable general-purpose or special-purpose microprocessors, digital signal processors (DSP), programmable controllers, application specific integrated circuits (ASIC), programmable logic devices (PLD), graphics processing units (GPU) or other similar devices or combinations of these devices. The processor 130 can execute source code, software modules, instructions, etc. recorded in the storage device 120 to implement the anti-cheating method using touch data in the embodiment of the present invention.

[0051] However, in addition to the display 110, the storage device 120, and the processor 130, the electronic device 10 may further include other components not shown in the figure. Figure 1 Other components, such as speakers, microphones, cameras, communication modules, keyboards, etc., are not limited to the present invention.

[0052] Figure 2 is a flow chart of an anti-cheating method according to an embodiment of the present invention. Figure 2 The method of this embodiment is applicable to the electronic device 10 in the above embodiment. The following describes the detailed steps of this embodiment in conjunction with the various components in the electronic device 10.

[0053] In step S210, the processor 130 uses the touch sensing device 30 to detect the actual trajectory information of the mouse device 20 on the touch sensing device 30. The mouse device 20 moves on the touch sensing device 30. Specifically, the user places the mouse device 20 on the touch sensing surface of the touch sensing device 30, and the user can provide user instructions to the electronic device 10 by moving the mouse device 20 and pressing buttons on the mouse device 20. It should be noted that since the touch sensing device 30 can sense the contact of the mouse device 20, the processor 130 can sense the movement of the mouse device 20 through the touch sensing device 30 and obtain the actual trajectory information of the mouse device 20 on the touch sensing device 30. The above-mentioned actual trajectory information may include at least two or more touch positioning coordinates.

[0054] In one embodiment, the processor 130 may obtain multiple touch sensing frames through the touch sensing device 30. Each touch sensing frame may include multiple frame cells corresponding to the touch sensing elements, and each frame cell in each touch sensing frame has touch raw data. For example, assuming that the touch sensing device 30 has m*n touch sensing elements, each touch sensing frame will include m*n frame cells corresponding to m*n pen touch raw data. In one embodiment, the processor 130 may also normalize the touch raw data so that the touch data of each frame cell in the normalized touch sensing frame can be within a preset value range, such as 0 to 255.

[0055] It should be noted that, in one embodiment, in order to detect the actual trajectory information of the mouse device 20 in the touch sensing device 30, the processor 130 obtains the raw touch data that has not been filtered. The reason is that compared with the touch of a human finger, which can cause a huge change in the touch sensing signal generated by the touch sensing element, the touch of the mouse device 20 usually only causes a small change in the touch sensing signal generated by the touch sensing element. If the raw touch data is filtered, the touch sensing frame may not show the touch of the mouse device 20. In one embodiment, the capacitive touch sensing element may cause a small charge change in response to the proximity of the metal element inside the mouse device 20, and the raw touch data in the touch sensing frame can therefore be used to sense the touch of the mouse device 20.

[0056] Furthermore, Figure 3Ais a schematic diagram of a touch sensing device according to an embodiment of the present invention. Figure 3B is a schematic diagram of a touch sensing frame according to an embodiment of the present invention. Figure 3A , the touch sensing device 30 may include a sensing element array 111, a scan driving circuit 113 and a receiving sensing circuit 112. The sensing element array 111 includes a plurality of touch sensing elements (e.g., touch sensing elements CS11, CS12, CS21) arranged in an array. The scan driving circuit 113 applies a driving signal to the touch sensing elements row by row through a scan line (e.g., a scan line 114). The receiving sensing circuit 112 receives a touch sensing signal provided by the touch sensing element through a sensing line (e.g., a sensing line 115) and outputs touch raw data d1. The receiving sensing circuit 112 may use an analog-to-digital converter (ADC) to convert the touch sensing signal generated by the touch sensing element into digital touch raw data d1 and output it.

[0057] Please refer to Figure 3B , the touch sensing frame F1 includes a plurality of frame cells (e.g., frame cells FC11, FC12, FC21) corresponding to a plurality of touch sensing elements (e.g., touch sensing elements CS11, CS12, CS21), respectively. Moreover, each frame cell has corresponding touch raw data. For example, frame cell FC11 has touch raw data 'r1'; frame cell FC12 has touch raw data 'r2'; frame cell FC21 has touch raw data 'r3'. In other words, the touch sensing frame F1 can also be regarded as an m*n data array, and the array elements in this data array are the touch raw data.

[0058] Next, the processor 130 may input each touch sensing frame into a machine learning model to obtain actual trajectory information of the mouse device 20 on the touch sensing device 30. Specifically, each time the processor 130 inputs a touch sensing frame into the machine learning model, the machine learning model may detect the mouse touch area of ​​the mouse device 20 from the touch sensing frame.

[0059] In one embodiment, the machine learning model is pre-constructed by performing machine learning (e.g., deep learning) based on a training frame set, and can be stored in the storage device 120. In other words, the model parameters of the trained machine learning model (e.g., the number of neural network layers and the weights of each neural network layer, etc.) have been determined by prior training and stored in the storage device 120. The machine learning model can be an object detection model used for object detection in a convolutional neural network (CNN) model, such as YOLO or SSD, etc., and the present invention is not limited to this. Alternatively, the machine learning model can be a semantic segmentation model in a convolutional neural network model, such as DeepLabv3+, etc., and the present invention is not limited to this.

[0060] In one embodiment, the processor 130 may input each touch sensing frame into a machine learning model to obtain an object frame (bounding box) corresponding to the mouse device 20 in each touch sensing frame. Then, the processor 130 may obtain actual trajectory information according to the position of a reference point in the object frame in each touch sensing frame. Specifically, when the processor 130 applies a machine learning model belonging to an object detection model, the machine learning model may output an object frame corresponding to the mouse device 20 in the touch sensing frame. Then, the processor 130 may take the coordinate position of a reference point in the object frame as the current touch positioning coordinate of the mouse device 20. The above-mentioned reference point is, for example, the center point of the object frame, but is not limited thereto. In this way, by inputting multiple touch sensing frames into the machine learning model one by one in sequence, the processor 130 may obtain multiple touch positioning coordinates in the multiple touch sensing frames to obtain actual trajectory information including multiple touch positioning coordinates.

[0061] For example, Figure 4A is a schematic diagram of using a machine learning model to obtain actual trajectory information according to an embodiment of the present invention. Figure 4A By inputting the touch sensing frame F2 provided by the touch sensing device 30 into the machine learning model, the processor 130 can obtain the object frame B1 corresponding to the mouse device 20, and locate the coordinate position of the center point P1 of the object frame B1 as the touch positioning coordinates of the mouse device 20. By collecting multiple touch positioning coordinates in multiple touch sensing frames, the processor 130 can obtain the movement trajectory T1 (i.e., actual trajectory information) of the mouse device 20 on the touch sensing device 30.

[0062] Alternatively, in one embodiment, the processor 130 may input each touch sensing frame into a machine learning model to obtain a semantic segmentation region corresponding to the mouse device 20 in each touch sensing frame. Then, the processor 130 may obtain actual trajectory information based on the boundaries of the semantic segmentation regions in each touch sensing frame. Specifically, when the processor 130 applies a machine learning model belonging to a semantic segmentation model, the machine learning model may classify each frame cell in the touch sensing frame to obtain a semantic segmentation region corresponding to the mouse device 20 in the touch sensing frame. Then, the processor 130 may determine a reference point based on the boundaries of the semantic segmentation region, and use the coordinate position of this reference point as the current touch positioning coordinates of the mouse device 20. Thus, by inputting multiple touch sensing frames into the machine learning model one by one in sequence, the processor 130 may obtain multiple touch positioning coordinates in the multiple touch sensing frames to obtain actual trajectory information including multiple touch positioning coordinates.

[0063] For example, Figure 4B is a schematic diagram of using a machine learning model to obtain actual trajectory information according to an embodiment of the present invention. Figure 4B By inputting the touch sensing frame F3 provided by the touch sensing device 30 into the machine learning model, the processor 130 can segment the touch sensing frame F3 into the semantic segmentation area Z1 corresponding to the mouse device 20 and the other area Z2. In this example, the processor 130 can extract the four extreme points PL, PR, PB, and PU of the semantic segmentation area Z1 in the vertical axis and the horizontal axis, and obtain the center reference point PC according to the extreme points PL, PR, PB, and PU. Then, the processor 130 can locate the coordinate position of the center reference point PC1 as the touch positioning coordinates of the mouse device 20. By collecting multiple touch positioning coordinates in multiple touch sensing frames, the processor 130 can obtain the movement trajectory T2 (i.e., actual trajectory information) of the mouse device 20 on the touch sensing device 30.

[0064] Next, in step S220, the processor 130 captures the mouse track information for controlling the cursor. In one embodiment, the processor 130 may obtain the mouse track information for controlling the cursor from the operating system or application executed by the electronic device 10. For example, the processor 130 may obtain the cursor position coordinates of the cursor on the screen or in a certain working window through the application program interface (such as GetCursorPosAPI, etc.) provided by the Microsoft operating system to obtain the mouse track information including multiple cursor position coordinates. Alternatively, in other embodiments, the processor 130 may obtain the mouse track information for controlling the cursor by performing cursor detection on the screen (such as a game screen or an application screen) displayed by the display 110 to obtain the cursor position coordinates.

[0065] Therefore, in step S230, the processor 130 detects cheating operation based on the actual trajectory information and the mouse trajectory information. In detail, the actual trajectory information is generated in response to the actual operation of the user controlling the mouse device 20. Therefore, based on the actual trajectory information and the mouse trajectory information provided by the operating system or the application, the processor 130 can determine whether the user uses an illegal script to forge user input information to control the cursor. For example, the processor 130 can know that the mouse device 20 is in a static state based on the actual trajectory information, but the mouse trajectory information used to control the cursor is constantly changing, and the processor 130 can determine that the user's cheating operation is detected. Alternatively, the processor 130 can know the moving direction of the mouse device 20 based on the actual trajectory information, but the mouse trajectory information used to control the cursor indicates an inconsistent cursor moving direction, and the processor 130 can determine that the user's cheating operation is detected.

[0066] Accordingly, by using the touch data sensed by the touch sensing device 30, the processor 130 can effectively determine whether the user controls the electronic device 10 to perform a cheating operation, thereby ensuring the fairness of e-sports games and online tests. It should be particularly noted that the processor 130 can detect cheating operations based on actual trajectory information and mouse trajectory information and in a variety of different ways, and the following examples will be listed for explanation.

[0067] Figure 5 is a flow chart of an anti-cheating method according to an embodiment of the present invention. Figure 5 The method of this embodiment is applicable to the electronic device 10 in the above embodiment. The following describes the detailed steps of this embodiment in conjunction with the various components in the electronic device 10.

[0068] In step S510, the processor 130 uses the touch sensing device 30 to detect the actual track information of the mouse device 20 on the touch sensing device 30. In step S520, the processor 130 captures the mouse track information used to control the cursor. The implementation of steps S510 to S520 is the same as Figure 2 The embodiments described above are not described in detail here. It should be noted that in step S530, the processor 130 detects cheating operations according to the actual trajectory information and the mouse trajectory information. In this embodiment, step S530 can be implemented as steps S531 to S533.

[0069] In step S531, the processor 130 obtains actual movement data within a preset time period according to multiple touch positioning coordinates in the actual trajectory information. The actual movement data may include an actual movement direction and an actual displacement. The above-mentioned preset time period is, for example, 1 second or 3 seconds, etc., which can be configured according to actual needs, and the present invention is not limited to this. In more detail, each touch positioning coordinate in the actual trajectory information corresponds to a positioning time point. The processor 130 can decide to take out two touch positioning coordinates based on the length of the preset time period, and calculate the actual movement direction and actual displacement within the preset time period based on these two touch positioning coordinates.

[0070] In step S532, the processor 130 obtains the cursor movement data within the preset period according to the multiple cursor positioning coordinates in the mouse trajectory information. The cursor movement data may include the cursor movement direction and the cursor displacement. In detail, each cursor positioning coordinate in the mouse trajectory information may represent the coordinate position of the cursor on the screen at a certain time point. The processor 130 may determine to take out two cursor positioning coordinates based on the length of the preset period, and calculate the cursor movement direction and the cursor displacement within the preset period according to the two cursor positioning coordinates.

[0071] In step S533, the processor 130 compares the actual movement data with the cursor movement data to determine whether a cheating operation is detected. In one embodiment, the processor 130 can determine whether a cheating operation is detected based on the angle between the actual movement direction and the cursor movement direction. If the angle between the actual movement direction and the cursor movement direction is greater than the preset critical value, it means that the direction in which the user moves the mouse device 20 is inconsistent with the cursor movement direction, and the processor 130 can determine that a cheating operation is detected. On the contrary, if the angle between the actual movement direction and the cursor movement direction is not greater than the preset critical value, the processor 130 can determine that no cheating operation is detected. Alternatively, in the absence of interference from other cheating scripts, there should be a specific proportional relationship between the cursor displacement and the actual displacement. Therefore, the processor 130 can determine whether a cheating operation is detected based on the ratio between the actual movement direction and the cursor movement direction. If the ratio between the actual movement direction and the cursor movement direction does not match the specific proportional relationship, the processor 130 can determine that a cheating operation is detected. On the contrary, if the ratio between the actual moving direction and the cursor moving direction matches the specific ratio relationship, the processor 130 may determine that no cheating operation is detected.

[0072] Figure 6 is a flow chart of an anti-cheating method according to an embodiment of the present invention. Figure 6 The method of this embodiment is applicable to the electronic device 10 in the above embodiment. The following describes the detailed steps of this embodiment in conjunction with the various components in the electronic device 10.

[0073] In step S610, the processor 130 uses the touch sensing device 30 to detect the actual track information of the mouse device 20 on the touch sensing device 30. In step S620, the processor 130 captures the mouse track information used to control the cursor. The implementation of steps S610 to S620 is the same as Figure 2 The embodiment described is not described in detail here. It should be noted that in step S630, the processor 130 detects cheating operation according to the actual trajectory information and the mouse trajectory information. In this embodiment, step S630 can be implemented as steps S631 to S633.

[0074] In step S631, the processor 130 obtains an actual trajectory image according to the multiple touch positioning coordinates in the actual trajectory information. In step S632, the processor 130 obtains a cursor trajectory image according to the multiple cursor positioning coordinates in the mouse trajectory information. In detail, by simultaneously presenting the touch positioning coordinates in the actual trajectory information in a single actual trajectory image, the actual trajectory image can present the movement trajectory of the mouse device 20 on the touch sensing device 30. Similarly, by simultaneously presenting the multiple cursor positioning coordinates in the mouse trajectory information in a single cursor trajectory image, the actual trajectory image can present the movement trajectory of the cursor on the screen.

[0075] In step S633, the processor 130 determines whether a cheating operation is detected based on the actual trajectory image and the cursor trajectory image. In other words, the processor 130 can compare the actual moving trajectory in the actual trajectory image with the cursor moving trajectory in the cursor trajectory image to determine whether a cheating operation is detected. In one embodiment, the processor 130 can input the actual trajectory image and the cursor trajectory image into a trained machine learning model to determine whether a cheating operation is detected through the output of the machine learning model. Alternatively, in one embodiment, the processor 130 can perform image scaling or other image processing on the actual trajectory image and / or the cursor trajectory image, and then determine whether a cheating operation is detected by comparing the trajectory shapes of the moving trajectories in the two processed images.

[0076] Figure 7 is a flow chart of an anti-cheating method according to an embodiment of the present invention. Figure 7 The method of this embodiment is applicable to the electronic device 10 in the above embodiment. The following describes the detailed steps of this embodiment in conjunction with the various components in the electronic device 10.

[0077] In step S710, the processor 130 uses the touch sensing device 30 to detect the actual track information of the mouse device 20 on the touch sensing device 30. In step S720, the processor 130 captures the mouse track information used to control the cursor. The implementation of steps S710 to S720 is the same as Figure 2The embodiment described is not described in detail here. It should be noted that in step S730, the processor 130 detects cheating operation according to the actual trajectory information and the mouse trajectory information. In this embodiment, step S730 can be implemented as steps S731 to S733.

[0078] In step S731, the processor 130 obtains actual motion related data according to the multiple touch positioning coordinates in the actual trajectory information. The actual motion related data may include actual displacement, actual moving direction, actual moving speed, actual moving acceleration, or statistics of the above data.

[0079] In step S732, the processor 130 obtains cursor motion related data according to the multiple cursor position coordinates in the mouse track information. The cursor motion related data may include cursor displacement, cursor movement direction, cursor movement speed, cursor movement acceleration, or statistics of the above data.

[0080] In step S733, the processor 130 inputs the actual motion-related data and the cursor motion-related data into the machine learning model, so that the machine learning model determines whether a cheating operation is detected. The processor 130 may input the actual motion-related data and the cursor motion-related data into a trained machine learning model and determine whether a cheating operation is detected based on the output of the machine learning model. For example, the machine learning model may be a support vector machine (SVM) classification model or a K-nearest neighbor algorithm (KNN) classification model, but is not limited thereto. In one embodiment, the machine learning model may output, for example, the probability of a cheating operation, and the processor 130 may determine whether a cheating operation is detected based on the probability of existence.

[0081] In summary, in the embodiment of the present invention, the actual trajectory information of the mouse device can be detected by the touch sensing device, and the actual trajectory information can be compared with the mouse trajectory information used to control the cursor. Therefore, based on the actual trajectory information based on the actual action of the user and the mouse trajectory information used to control the cursor, the existence of cheating operations that forge user operations can be effectively and accurately detected. In this way, it is possible to effectively grasp which players have used improper scripts and prevent players from cheating, thereby better maintaining the fairness of the game.

[0082] Although the present invention has been disclosed as above by way of embodiments, it is not intended to limit the present invention. Any person skilled in the art may make some changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be determined by the definition of the claims.

Claims

1. An anti-cheating method, applicable to an electronic device connected to a mouse device, the method comprising: Using a touch sensing device to detect actual track information of the mouse device on the touch sensing device, wherein the mouse device moves on the touch sensing device, the step of using the touch sensing device to detect the actual track information of the mouse device on the touch sensing device comprises: Acquiring a plurality of touch sensing image frames through the touch sensing device; and Inputting each of the touch sensing frames into a machine learning model to obtain the actual trajectory information of the mouse device on the touch sensing device; Retrieving mouse track information for controlling a cursor; and A cheating operation is detected according to the actual track information and the mouse track information.

2. The anti-cheating method according to claim 1, characterized in that: The step of inputting each of the touch sensing frames into the machine learning model to obtain the actual trajectory information of the mouse device on the touch sensing device includes: Inputting each of the touch sensing frames into the machine learning model to obtain an object frame corresponding to the mouse device in each of the touch sensing frames; and The actual track information is obtained according to the position of a reference point in the object frame in each of the touch sensing frames.

3. The anti-cheating method according to claim 1, characterized in that: The step of inputting each of the touch sensing frames into the machine learning model to obtain the actual trajectory information of the mouse device on the touch sensing device includes: Inputting each of the touch sensing frames into the machine learning model to obtain a semantic segmentation region corresponding to the mouse device in each of the touch sensing frames; and The actual trajectory information is obtained according to the boundaries of the semantic segmentation areas in the touch sensing frames.

4. The anti-cheating method according to claim 1, characterized in that: The step of capturing the mouse movement trajectory for controlling the cursor includes: The mouse track information for controlling the cursor is obtained from an operating system or an application program executed by the electronic device.

5. The anti-cheating method according to claim 1, characterized in that: The step of detecting the cheating operation according to the actual trajectory information and the mouse trajectory information includes: Acquire actual movement data within a preset time period according to a plurality of touch positioning coordinates in the actual trajectory information; Acquire a cursor movement data within the preset time period according to a plurality of cursor positioning coordinates in the mouse track information; and The actual movement data is compared with the cursor movement data to determine whether the cheating operation is detected.

6. The anti-cheating method according to claim 1, characterized in that: The step of detecting the cheating operation according to the actual trajectory information and the mouse trajectory information includes: Acquire an actual trajectory image according to a plurality of touch positioning coordinates in the actual trajectory information; Acquiring a cursor track image according to a plurality of cursor positioning coordinates in the mouse track information; and Determine whether the cheating operation is detected according to the actual track image and the cursor track image.

7. The anti-cheating method according to claim 1, characterized in that: The step of detecting the cheating operation according to the actual trajectory information and the mouse trajectory information includes: Acquire actual motion related data according to a plurality of touch positioning coordinates in the actual trajectory information; Acquire cursor motion related data according to the multiple cursor positioning coordinates in the mouse trajectory information; and The actual motion related data and the cursor motion related data are input into a machine learning model, so that the machine learning model determines whether the cheating operation is detected.

8. An electronic device connected to a mouse device and a touch sensing device, comprising: a storage device; as well as A processor is coupled to the touch sensing device and the storage device and is configured to: The actual track information of the mouse device on the touch sensing device is detected by the touch sensing device, wherein the mouse device moves on the touch sensing device, and the step of detecting the actual track information of the mouse device on the touch sensing device by the touch sensing device comprises: Acquiring a plurality of touch sensing image frames through the touch sensing device; and Inputting each of the touch sensing frames into a machine learning model to obtain the actual trajectory information of the mouse device on the touch sensing device; Retrieving mouse track information for controlling a cursor; and A cheating operation is detected according to the actual track information and the mouse track information.

9. The electronic device as claimed in claim 8, characterized in that: The processor is also configured to: Inputting each of the touch sensing frames into the machine learning model to obtain an object frame corresponding to the mouse device in each of the touch sensing frames; and The actual track information is obtained according to the position of a reference point in the object frame in each of the touch sensing frames.

10. The electronic device as claimed in claim 8, characterized in that The processor is also configured to: Inputting each of the touch sensing frames into the machine learning model to obtain a semantic segmentation region corresponding to the mouse device in each of the touch sensing frames; and The actual trajectory information is obtained according to the boundaries of the semantic segmentation areas in the touch sensing frames.

11. The electronic device according to claim 8, wherein: The processor is also configured to: The mouse track information for controlling the cursor is obtained from an operating system or an application program executed by the electronic device.

12. The electronic device as claimed in claim 8, characterized in that: The processor is also configured to: Acquire actual movement data within a preset time period according to a plurality of touch positioning coordinates in the actual trajectory information; Acquire a cursor movement data within the preset time period according to a plurality of cursor positioning coordinates in the mouse track information; and The actual movement data is compared with the cursor movement data to determine whether the cheating operation is detected.

13. The electronic device as claimed in claim 8, characterized in that: The processor is also configured to: Acquire an actual trajectory image according to a plurality of touch positioning coordinates in the actual trajectory information; Acquire a cursor track image according to a plurality of cursor positioning coordinates in the mouse track information; as well as Determine whether the cheating operation is detected according to the actual track image and the cursor track image.

14. The electronic device as claimed in claim 8, wherein: The processor is also configured to: Acquire actual motion related data according to a plurality of touch positioning coordinates in the actual trajectory information; Acquire cursor motion related data according to the multiple cursor positioning coordinates in the mouse trajectory information; and The actual motion related data and the cursor motion related data are input into a machine learning model, so that the machine learning model determines whether the cheating operation is detected.

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