CN114078016B - Anti-cheating behavior identification method and device, electronic equipment and storage medium - Google Patents

Anti-cheating behavior identification method and device, electronic equipment and storage medium Download PDF

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CN114078016B
CN114078016B CN202010804864.4A CN202010804864A CN114078016B CN 114078016 B CN114078016 B CN 114078016B CN 202010804864 A CN202010804864 A CN 202010804864A CN 114078016 B CN114078016 B CN 114078016B
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advertisement
cheating
rule
behavior
target
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CN114078016A (en
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孟嵩
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Tencent Technology Shenzhen Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0241—Advertisements
    • G06Q30/0248—Avoiding fraud
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0241—Advertisements
    • G06Q30/0242—Determining effectiveness of advertisements
    • G06Q30/0245—Surveys
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

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Abstract

The application relates to the technical field of Internet, in particular to an anti-cheating behavior identification method, an anti-cheating behavior identification device, electronic equipment and a storage medium, which are used for improving the accuracy and the speed of anti-cheating, wherein the method comprises the following steps: acquiring response behavior information of a target object to the advertisement put in the display page and advertisement picture information of the advertisement put in the display page; analyzing advertisement picture information according to advertisement display rules in pre-acquired target anti-cheating rules to obtain a first identification result which indicates whether the advertisement put in meets the advertisement display rules; analyzing the response behavior information according to the behavior rules in the target anti-cheating rules to obtain a second identification result which indicates whether the response behavior of the target object accords with the behavior rules; and summarizing the first recognition result and the second recognition result to obtain the anti-cheating behavior recognition result. The application improves the accuracy and speed of anti-cheating because the anti-cheating analysis is carried out on the client side based on the user behavior and the advertisement picture.

Description

Anti-cheating behavior identification method and device, electronic equipment and storage medium
Technical Field
The present application relates to the field of internet technologies, and in particular, to a method and apparatus for identifying anti-cheating behavior, an electronic device, and a storage medium.
Background
In the mobile internet era, with the widespread use of networks and computers, online advertising has become a new form of advertising. While online advertising brings a great flow benefit, the potential threat of advertising cheating is also increasing.
The anti-cheating schemes in the related art are based on that a SDK (Software Development Kit ) side collects some behavior parameters and sends the behavior parameters to a server, and the server determines whether anti-cheating behaviors exist or not through anti-cheating rules. However, part of the data relates to private information, and future control over local data uploading of users is getting tighter and tighter, which is a high risk. How to identify cheating advertising behavior is one of the very important issues in online advertising systems.
Disclosure of Invention
The embodiment of the application provides a method, a device, electronic equipment and a storage medium for identifying anti-cheating behaviors, which are used for providing a local anti-cheating method and improving the accuracy and speed of anti-cheating while ensuring the privacy of a user.
The first anti-cheating behavior identification method provided by the embodiment of the application comprises the following steps:
acquiring response behavior information of a target object to an advertisement put in a display page and advertisement picture information of the advertisement put in the display page;
analyzing the advertisement picture information according to advertisement display rules in pre-acquired target anti-cheating rules to obtain a first identification result used for indicating whether the put advertisement accords with the advertisement display rules; and
analyzing the response behavior information according to the behavior rules in the target anti-cheating rules to obtain a second identification result used for indicating whether the response behavior of the target object accords with the behavior rules;
and obtaining the anti-cheating behavior recognition result aiming at the target object by summarizing the first recognition result and the second recognition result.
Optionally, the method further comprises:
receiving a rule threshold value issued by the advertisement operation server;
if the received rule threshold is different from the rule threshold in the target anti-cheating rule, replacing the rule threshold in the target anti-cheating rule with the received rule threshold, wherein the received rule threshold is obtained by carrying out statistical analysis on response behavior information after removing information corresponding to each object according to the received anti-cheating behavior identification result of each object by the advertisement operation server.
The second anti-cheating behavior identification method provided by the embodiment of the application comprises the following steps:
receiving an advertisement request sent by a client, wherein the advertisement request is sent after the client responds to the operation of requesting advertisement by a target object;
according to the hardware parameters in the advertisement request, searching a target anti-cheating rule matched with the hardware parameters from a preset anti-cheating rule set, and issuing the target anti-cheating rule to the client so that the client can conduct anti-cheating behavior identification on the target object according to the target anti-cheating rule, wherein the hardware parameters are hardware parameters of terminal equipment where the client is located, the target anti-cheating rule comprises advertisement display rules and behavior rules, the advertisement display rules are used for analyzing advertisement picture information of the advertisements, and the behavior rules are used for analyzing response behavior information of the target object on the advertisements.
The third anti-cheating behavior identification method provided by the embodiment of the application comprises the following steps:
receiving response behavior information sent by each client after target information is removed and corresponding to each object, and identifying results of anti-cheating behaviors aiming at each object;
And carrying out statistical analysis on response behavior information after removing target information based on the anti-cheating behavior recognition result, determining rule thresholds in target anti-cheating rules, respectively issuing the rule thresholds to clients of all objects, so that when the clients of all objects determine that the rule thresholds in the pre-acquired target anti-cheating rules are different from the received rule thresholds, replacing the rule thresholds in the pre-acquired target anti-cheating rules with the received rule thresholds, wherein the target anti-cheating rules comprise advertisement display rules and behavior rules, the advertisement display rules are used for analyzing advertisement picture information of the advertisements, and the behavior rules are used for analyzing response behavior information of the objects aiming at the advertisements.
The first anti-cheating behavior recognition device provided by the embodiment of the application comprises:
the information acquisition unit is used for acquiring response behavior information of the target object to the advertisement put in the display page and advertisement picture information of the advertisement put in the display page;
the analysis unit is used for analyzing the advertisement picture information according to advertisement display rules in the target anti-cheating rules acquired in advance to obtain a first identification result used for indicating whether the put advertisement accords with the advertisement display rules; analyzing the response behavior information according to the behavior rules in the target anti-cheating rules to obtain a second identification result used for indicating whether the response behavior of the target object accords with the behavior rules;
And the summarizing unit is used for summarizing the first recognition result and the second recognition result to obtain an anti-cheating behavior recognition result aiming at the target object.
Optionally, the analysis unit is specifically configured to:
performing image recognition on the advertisement picture information to determine an advertisement area in the advertised picture;
and analyzing the picture ratio of the advertisement area in the put advertisement based on a target advertisement display rule corresponding to the advertisement type of the put advertisement in the advertisement display rule to obtain the first identification result.
Optionally, if the advertisement type of the advertised is a target advertisement type;
the identification unit is further configured to:
acquiring target area information contained in the delivered advertisement;
the identification unit is specifically configured to:
analyzing the target area information based on a target advertisement display rule corresponding to the advertisement type of the advertised in the advertisement display rule;
and obtaining the first identification result according to the analysis result of the target area information and the analysis result of the picture proportion of the advertisement area in the advertised.
Optionally, the response behavior information includes response information of the target object for various response behaviors of the advertised; the identification unit is specifically configured to:
and respectively comparing the response information corresponding to the various response behaviors with rule thresholds corresponding to the various response behaviors set in the behavior rules to obtain a second recognition result for representing whether the response behaviors of the target object accord with the behavior rules.
Optionally, the summarizing unit is specifically configured to:
if the first recognition result indicates that the placed advertisement accords with the advertisement display rule, determining that the anti-cheating behavior recognition result comprises that the placed advertisement is a normal advertisement; otherwise, determining that the anti-cheating behavior recognition result comprises that the put advertisement is a cheating advertisement;
if the second recognition result indicates that the target object accords with the behavior rule, determining that the anti-cheating behavior recognition result comprises that the target object is a normal object; otherwise, determining that the anti-cheating behavior recognition result comprises that the target object is a cheating object.
Optionally, the apparatus further includes:
the transmission unit is used for obtaining the target anti-cheating rule based on the following modes:
Responding to the operation of the target object for requesting advertisement, and sending an advertisement request to a local anti-cheating rule file server, wherein the advertisement request comprises hardware parameters of terminal equipment where a client is located;
and receiving a target anti-cheating rule matched with the hardware parameter returned by the local anti-cheating rule file server.
Optionally, the summarizing unit is further configured to:
removing target information in the response behavior information according to a preset information removal rule, and obtaining response behavior information after removing the target information;
uploading the response behavior information after the target information is removed and the anti-cheating behavior recognition result of the target object to an advertisement operation server, so that the advertisement operation server performs statistical analysis on the response behavior information after the target information is removed according to the recognition result of the anti-cheating behavior.
Optionally, the summarizing unit is specifically configured to:
classifying the response behavior information according to preset response categories, determining the original categories to which various response behaviors belong, and recording response time corresponding to the various response behaviors;
removing target information in the category to which the various response behaviors belong according to the information removing rule to obtain abstract categories of the various response behaviors;
Classifying and combining the abstract categories of the various response behaviors with a preset operation set to obtain target categories of the various response behaviors;
binding the target categories of the various response behaviors and the corresponding response time, and then using the target categories as response behavior information after removing the target information.
Optionally, the apparatus further includes:
the updating unit is used for receiving a rule threshold value issued by the advertisement operation server;
if the received rule threshold is different from the rule threshold in the target anti-cheating rule, replacing the rule threshold in the target anti-cheating rule with the received rule threshold, wherein the received rule threshold is obtained by carrying out statistical analysis on response behavior information of each object after removing target information according to the received anti-cheating behavior identification result of each object by the advertisement operation server.
The second anti-cheating behavior recognition device provided by the embodiment of the application comprises:
the first receiving unit is used for receiving an advertisement request sent by the client, wherein the advertisement request is sent after the client responds to the operation of requesting the advertisement by the target object;
the rule issuing unit is used for searching a target anti-cheating rule matched with the hardware parameter from a preset anti-cheating rule set according to the hardware parameter in the advertisement request, issuing the target anti-cheating rule to the client so that the client can conduct anti-cheating behavior recognition on the target object according to the target anti-cheating rule, wherein the hardware parameter is the hardware parameter of the terminal equipment where the client is located, the target anti-cheating rule comprises an advertisement display rule and a behavior rule, the advertisement display rule is used for analyzing advertisement picture information of the advertisement, and the behavior rule is used for analyzing response behavior information of the target object on the advertisement.
The third anti-cheating behavior recognition device provided by the embodiment of the application comprises:
the second receiving unit is used for receiving response behavior information which is sent by each client and corresponds to each object and is subjected to target information removal, and anti-cheating behavior recognition results aiming at each object;
the statistical analysis unit is used for carrying out statistical analysis on the response behavior information after the target information is removed based on the anti-cheating behavior recognition result, determining rule thresholds in target anti-cheating rules, respectively issuing the rule thresholds to the clients of all the objects, so that when the clients of all the objects determine that the rule thresholds in the pre-acquired target anti-cheating rules are different from the received rule thresholds, the rule thresholds in the pre-acquired target anti-cheating rules are replaced with the received rule thresholds, the target anti-cheating rules comprise advertisement display rules and behavior rules, the advertisement display rules are used for analyzing advertisement picture information of the advertisements, and the behavior rules are used for analyzing the response behavior information of the objects aiming at the advertisements.
The electronic device provided by the embodiment of the application comprises a processor and a memory, wherein the memory stores program codes, and when the program codes are executed by the processor, the processor is caused to execute the steps of any anti-cheating behavior identification method.
An embodiment of the present application provides a computer readable storage medium including program code for causing an electronic device to perform the steps of any one of the anti-cheating behavior recognition methods described above, when the program code is run on the electronic device.
Embodiments of the present application provide a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer readable storage medium and the processor executes the computer instructions to cause the computer device to perform the steps of any of the anti-cheating behavior identification methods described above.
The application has the following beneficial effects:
according to the anti-cheating behavior identification method, device, electronic equipment and storage medium, the center of gravity of anti-cheating is shifted from the server side to the client side, the anti-cheating rule for anti-cheating behavior identification is obtained in advance by the client side, and then the locally collected object behaviors, advertisement pictures and the like are analyzed to obtain a final anti-cheating behavior identification result, privacy-related information in the information such as the object behaviors, the advertisement pictures and the like is not required to be uploaded to the server, but is directly analyzed by the client side, so that policy risks of privacy information collection can be avoided.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
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 specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
FIG. 1 is an alternative schematic diagram of an application scenario in an embodiment of the present application;
FIG. 2 is a flow chart of a first anti-cheating behavior recognition method according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a display page according to an embodiment of the present application;
FIG. 4 is a schematic flow chart of a desensitizing process for response behavior information according to an embodiment of the application;
FIG. 5 is a flowchart of a second anti-cheating behavior recognition method according to an embodiment of the present application;
FIG. 6 is a flow chart of a third anti-cheating behavior recognition method according to an embodiment of the present application;
FIG. 7A is a schematic diagram of an interactive timing sequence of an anti-cheating behavior recognition method according to an embodiment of the present application;
FIG. 7B is a schematic diagram of an interactive timing sequence of an anti-cheating behavior recognition method according to another embodiment of the present application;
FIG. 8 is a schematic diagram of the composition and structure of a first anti-cheating behavior recognition device according to an embodiment of the present application;
FIG. 9 is a schematic diagram of the composition and structure of a second anti-cheating behavior recognition device according to an embodiment of the present application;
FIG. 10 is a schematic diagram of a third anti-cheating behavior recognition device according to an embodiment of the present application;
fig. 11 is a schematic diagram of a hardware composition structure of an electronic device to which the embodiment of the present application is applied.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the technical solutions of the present application, but not all embodiments. All other embodiments, based on the embodiments described in the present document, which can be obtained by a person skilled in the art without any creative effort, are within the scope of protection of the technical solutions of the present application.
Some of the concepts involved in the embodiments of the present application are described below.
OpenCV: openCV is a cross-platform computer vision library based on BSD (Berkeley Software Distribution, berkeley software suite) licensing (open source) issues that can run on a variety of operating systems. It implements many general algorithms in terms of image processing and computer vision. In the embodiment of the application, the advertisement picture information can be analyzed by combining with the OpenCV technology.
SDK: an SDK is a collection of development tools that some software engineers create application software for a particular software package, software framework, hardware platform, operating system, etc. In the related art, some behavior parameters of a user are collected mainly based on an SDK side and sent to a server, and the server performs anti-cheating behavior recognition.
Advertisement exposure: refers to the advertisement being shown on the user side (e.g., in the page accessed by the user, in the application used by the user), and once shown on the user side is called one advertisement exposure.
Clicking an advertisement: the user accesses the page of the advertiser by clicking the advertisement on the user side equipment (such as a terminal equipment of a smart phone, a tablet computer and the like), and the user clicks the advertisement once to access the page of the advertiser, which is called advertisement clicking.
Advertisement cheating and cheating objects: in links of advertisement exposure, clicking, effect and the like, users have the behavior of improving indexes such as advertisement exposure, advertisement clicking amount, advertisement clicking rate, conversion rate and the like for some malicious purposes, and the malicious behavior of the cheating object is called advertisement cheating. Accordingly, the user who generated the cheating action is referred to as the cheating user, or the cheating object. The cheating object may be a network personnel who can achieve the purpose of profit or public opinion establishment by clicking advertisements, downloading applications or posting back, etc., or may be a natural person, or may be a cheating program for disguising users, etc.
Anti-cheating behavior identification: links such as advertisement exposure, clicking and effect are checked to judge whether the advertisement exposure, the advertisement clicking and the advertisement effect are triggered by normal access of a user side or are realized by an advertisement cheating means.
The client or the client refers to a program corresponding to a server and providing local service for clients. Except for some applications that only run locally, they are typically installed on a common client and need to run in conjunction with the server. After development of the internet, more commonly used clients include web browsers such as those used by the world wide web, email clients when receiving and sending email, and client software for instant messaging. For this type of application, there is a need for a corresponding server and service program in the network to provide corresponding services, such as database service, email service, etc., so that a specific communication connection needs to be established between the client and the server to ensure the normal operation of the application.
Artificial intelligence (ArtificialIntelligence, AI) is a theory, method, technique, and application system that simulates, extends, and extends human intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, obtains knowledge, and uses the knowledge to obtain optimal results. In other words, artificial intelligence is an integrated technology of computer science that attempts to understand the essence of intelligence and to produce a new intelligent machine that can react in a similar way to human intelligence. Artificial intelligence, i.e. research on design principles and implementation methods of various intelligent machines, enables the machines to have functions of sensing, reasoning and decision.
The artificial intelligence technology is a comprehensive subject, and relates to the technology with wide fields, namely the technology with a hardware level and the technology with a software level. Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.
With research and advancement of artificial intelligence technology, research and application of artificial intelligence technology is being developed in various fields, such as common smart home, smart wearable devices, virtual assistants, smart speakers, smart marketing, unmanned, automatic driving, unmanned aerial vehicles, robots, smart medical treatment, smart customer service, etc., and it is believed that with the development of technology, artificial intelligence technology will be applied in more fields and with increasing importance value.
The scheme provided by the embodiment of the application relates to an artificial intelligence machine learning technology. Machine learning is a way to realize artificial intelligence, has a certain similarity to data mining, is also a field interdisciplinary, and relates to multiple disciplines such as probability theory, statistics, approximation theory, convex analysis, computational complexity theory and the like. Compared with the data mining, the machine learning is more focused on the design of an algorithm when finding the mutual characteristics among big data, so that a computer can automatically learn rules from the data and predict unknown data by using the rules.
In the embodiment of the application, the anti-cheating behavior recognition can be performed on the target object based on the anti-cheating model by deploying the anti-cheating model at the client. The anti-cheating model is trained based on machine learning technology. The following detailed description will be made with reference to specific embodiments.
The following briefly describes the design concept of the embodiment of the present application:
and entering the mobile internet era, the advertisement brings a great flow benefit to advertisers. Currently, with the widespread use of networks and computers, online advertising has become a new form of advertising. In an online advertising system, advertisers pay an ad publisher to place their own ads via web pages, browser, application, or other online media.
However, with the rapid development of the internet, online advertising brings a great flow benefit, and simultaneously, the potential threat of the gray industrial chain related to advertising cheating is also increasing. In the related art, some behavior parameters are collected based on the SDK side and sent to a server, and the server determines whether anti-cheating behaviors exist or not through anti-cheating rules.
However, the related art solution needs to upload the collected behavior parameters to the server, and because some data relate to private information, such as some browsing websites of the user, or some specific interaction and behavior data, the control of uploading the local data of the user will be more and more stringent in the future, which has a high risk. Furthermore, the related art scheme only supports uploading structured data with limited data length, such as click coordinates, but cannot be uploaded for some data, such as advertisement screen information in the user's current screen.
In view of the above, the embodiment of the application provides a method for locally judging whether a user has a cheating action at a client, which adds the anti-cheating action judgment based on user action analysis locally and the anti-cheating judgment method based on local screenshot and analysis on the basis of original online anti-cheating action judgment, and the client performs anti-cheating action identification according to the pre-acquired target anti-cheating rule without transmitting privacy information and the like to a server, so that the privacy information or some complex structured data and the like can be directly acquired locally, the policy risk of collecting the privacy information in the future is avoided, the accuracy and speed of anti-cheating are improved, and the bandwidth resources of the server are saved.
The preferred embodiments of the present application will be described below with reference to the accompanying drawings of the specification, it being understood that the preferred embodiments described herein are for illustration and explanation only, and not for limitation of the present application, and embodiments of the present application and features of the embodiments may be combined with each other without conflict.
Fig. 1 is a schematic diagram of an application scenario according to an embodiment of the present application. The application scenario diagram of the embodiment of the application is shown. The application scenario diagram includes two terminal devices 110 and a server 130, and the advertisement interface 120 can be logged in through the terminal devices 110. Communication between the terminal device 110 and the server 130 may be performed through a communication network. In fig. 1, a user corresponding to each terminal device is taken as an example, in which a user corresponds to a left terminal device 110, and a user B corresponds to a right terminal device 110, and the number of terminal devices is not limited in practice. In some cases, the terminal devices may communicate with each other through the server 130, and direct communication may be established from terminal device to terminal device, and the manner in which the terminal devices communicate directly from terminal device to terminal device may be referred to as point-to-point communication, in which case some interaction between the terminal devices 110 may not require the transfer of the server 130.
Wherein each terminal device may have an online document client installed therein. In the embodiment of the present application, the client may be social software, such as instant messaging software, short video software, and may also be an applet, a web page, etc., which is not limited herein. The terminal device 110 needs to be installed with a client, where the client may be software, or may be a client such as a web page or an applet, and the server 130 is a server corresponding to the software, the web page or the applet.
In an alternative embodiment, the communication network is a wired network or a wireless network. The terminal device 110 and the server 130 may be directly or indirectly connected through wired or wireless communication, and the present application is not limited herein.
In the embodiment of the present application, the terminal device 110 is an electronic device used by a user, and the electronic device may be a computer device having a certain computing capability, such as a personal computer, a mobile phone, a tablet computer, a notebook, an electronic book reader, etc., and running instant messaging software and a website or social software and a website. Each terminal device 110 is connected to the server 130 through a wireless network, where the server 130 may be an independent physical server, or may be a server cluster or a distributed system formed by a plurality of physical servers, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network ), and basic cloud computing services such as big data and artificial intelligence platforms.
In an embodiment of the present application, a user may log on to the advertisement interface 120 through the terminal device 110, and the terminal device 110 may respond to various operations, such as clicking, sliding, etc., triggered by the user on the advertisement interface 120.
Wherein after the terminal device 110 responds to the user operation, there may be interaction with the server 130, for example, the terminal device 110 sends an advertisement request to the server 130, obtains a target anti-cheating rule from the server 130, and so on.
Referring to fig. 2, a flowchart of an implementation of an anti-cheating behavior recognition method provided by an embodiment of the present application is applied to a client, and the specific implementation flow of the method is as follows:
s21: acquiring response behavior information of a target object to the advertisement put in the display page and advertisement picture information of the advertisement put in the display page;
in the embodiment of the application, taking the target object as an example of a user, after the user requests the advertisement at the client side, interactive behaviors such as clicking, sliding and the like can be generated on the advertisement which is displayed in the display page, the behaviors are collectively called as response behaviors of the user, and the corresponding response behavior information refers to data related to the behaviors such as the clicking times, the clicking time and the like. The advertisement picture information may be an advertisement picture obtained by means of screenshot, screen recording, etc. Hereinafter, the advertisement screen information mainly refers to a screenshot of the advertisement screen.
S22: analyzing advertisement picture information according to advertisement display rules in pre-acquired target anti-cheating rules to obtain a first identification result used for indicating whether the put advertisement accords with the advertisement display rules; analyzing the response behavior information according to the behavior rules in the target anti-cheating rules to obtain a second identification result used for indicating whether the response behavior of the target object accords with the behavior rules;
in an alternative embodiment, the target anti-cheating rule pre-acquired by the client side may be pre-pulled from the server side, which specifically includes the following steps:
the client responds to the operation of requesting the advertisement by the target object, and sends an advertisement request to the local anti-cheating rule file server, wherein the advertisement request comprises hardware parameters of terminal equipment where the client is located; after receiving the advertisement request sent by the client, the local anti-cheating rule file server searches a target anti-cheating rule matched with the hardware parameter from a preset anti-cheating rule set according to the hardware parameter in the advertisement request, and issues the target anti-cheating rule to the client.
Wherein, the anti-cheating rule in the anti-cheating rule set stored on the local anti-cheating rule file server side can be derived from any one or more of the following three modes:
The anti-cheating rule of the partial behavior data which is already run on line in the first mode can comprise at least one of advertisement display rules and behavior rules;
and secondly, collecting historical behavior data uploaded by the user, and formulating corresponding behavior rules by analyzing the historical behavior data. For example, the residence time of the user opening the page may be recorded, and the average, median, and variance of the residence time may be viewed by behavior rules. If the dwell time of each page is approximately the same, or the data has a periodic law, there is a suspicion that the machine operates the script to cheat. Assuming statistical analysis based on historical behavioral data uploaded by the user, the final specification is: the residence time of the page is longer than 1 second, if the residence time is shorter than 1 second, cheating behavior is shown;
and thirdly, manually giving advertisement display rules of different advertisement categories, for example, setting the height of the advertisement main body view to be less than 75% of the whole screen height for a certain advertisement type, and if the height is less than 75%, indicating that advertisement cheating exists.
The anti-cheating rule set constructed in the above-listed manner comprises behavior rules corresponding to different hardware parameters and advertisement display rules, wherein the rule thresholds in the behavior rules corresponding to the different hardware parameters are different, or the rule thresholds in the advertisement display rules corresponding to the different hardware parameters are different.
The rule threshold is a reference standard set in the anti-cheating rule, for example, for the behavior rule that the page stay time is greater than 1 second, the rule threshold is 1 second, and the rule threshold is a reference standard of the user page browsing time, wherein the page stay time is the time for the user to browse advertisements. For different hardware parameters, for example, for some new types of terminal equipment, since the speed of the CPU (central processing unit ) is faster, the rule threshold of the page stay time length is lower; for some old terminal devices, the rule threshold of the page stay time length is higher because the CPU speed is slower.
For example, the rule "the height of the advertisement subject view must not be less than 75% of the overall screen height" is displayed for advertisements, wherein the rule threshold is 75%, and the rule threshold is a reference standard for the advertisement subject view height.
It should be noted that, in the embodiment of the present application, the local anti-cheating rule file server will have a rule version number +1 when updating the anti-cheating rule each time, and the final rule will be issued with the request of the client, so that the client can update in time.
When a user requests an advertisement, the user simultaneously requests the latest anti-cheating rule in the local anti-cheating rule file server. The local anti-cheating rule file server will adapt different anti-cheating rules according to the hardware parameters in the advertisement request. For example, for the rule threshold of the page stay time, the rule threshold corresponding to the model a is 1 second, the rule threshold corresponding to the model B is 1.5 seconds, and the rule threshold corresponding to the model C is 2 seconds. For another example, for a rule threshold of the advertisement main view height, the rule threshold corresponding to the model a is 75%, the rule threshold corresponding to the model B is 70%, and the rule threshold corresponding to the model C is 80%.
The terminal device requesting the advertisement is of type A, so the behavior rules in the target anti-cheating rules issued to the client by the server comprise: the page dwell time is greater than 1 second. The advertisement presentation rules in the target anti-cheating rules include: the height of the advertisement body view must not be less than 75% of the overall screen height.
After the client receives the target anti-cheating rule issued by the local anti-cheating rule file server, comparing the version number in the anti-cheating rule information with the local version number to determine whether the local anti-cheating rule needs to be updated, if so, replacing the local anti-cheating rule and lasting, and after detecting the user request advertisement again, directly adopting the local target anti-cheating rule without pulling the target anti-cheating rule from the local anti-cheating rule file server.
For example, in the above embodiment, the version number of the target anti-cheating rule issued by the local anti-cheating file server is V2, and the version number of the anti-cheating rule locally stored by the client is V1, in which case the target anti-cheating rule issued by the local anti-cheating file server is used to replace the local anti-cheating rule and persist. And then, performing anti-cheating behavior analysis on the target object based on the target anti-cheating rule to obtain a first recognition result and a second recognition result.
S23: and obtaining the anti-cheating behavior recognition result aiming at the target object by summarizing the first recognition result and the second recognition result.
When a user triggers advertisement operations, such as exposure (advertisement is displayed), clicking (advertisement is clicked), conversion (one-time downloading is completed), and the like, the local anti-cheating module can judge the local anti-cheating behavior by using the data collected by the local behavior data collector in the process, and the local anti-cheating behavior is mainly divided into two parts: the first part is to analyze advertisement picture information locally at the client based on advertisement display rules; the second part is to analyze the response behavior information of the user locally at the client based on the behavior rules.
The following first describes in detail the process of analyzing advertisement screen information based on advertisement presentation rules:
in an alternative embodiment, the advertisement display rule is related to the advertisement type, and when the advertisement picture information is analyzed based on the advertisement display rule in the target anti-cheating rule, it is required to determine whether the advertisement picture information of the delivered advertisement meets the target advertisement display rule corresponding to the corresponding advertisement type, which specifically includes the following steps:
firstly, carrying out image recognition on advertisement picture information of the advertisements which are displayed in a display page, and determining advertisement areas in the advertisements; and further, analyzing the picture ratio of the advertisement area in the put advertisement based on a target advertisement display rule corresponding to the advertisement type of the put advertisement in the advertisement display rule, so as to obtain a first recognition result.
In the embodiment of the application, the required advertisement area ratio or other picture information in the advertisement pictures of different advertisement types may be different, so that the application adopts the corresponding target advertisement display rule to judge when analyzing, and mainly judges whether the picture ratio of the advertisement area in the put advertisement meets the regulation.
Taking advertisement screen information as a screenshot, as shown in fig. 3, it is a schematic diagram of an open screen advertisement screenshot in an embodiment of the present application. Firstly, picture marking and picture character recognition are carried out on advertisement screenshot in the modes of OpenCV and the like, and basic data and coordinates of pictures are divided. The basic data of the picture comprises characters in the picture, basic picture data in the picture and the like, and the coordinates mainly refer to the coordinates of the view part of the advertisement main body, or can also comprise the coordinates of boundary points of the footer part of the advertisement and the like.
Wherein for any one advertisement, the advertisement can be roughly divided into at most three areas, depending on the advertisement type. Taking an open screen advertisement as an example, the advertisement area may be divided into two parts of Body and Footer, and the advertisement display rule corresponding to the advertisement type of the first screen advertisement in the advertisement display rule received by the client is assumed to specify that the Body area is not less than 75% of the screen area, the Footer area is not more than 25% of the screen area, and the height ratio may be specified except the area ratio, which is not particularly limited herein.
After the data is acquired, matching is carried out according to the target advertisement display rule corresponding to the advertisement type of the advertisement shown in fig. 3, and whether the target advertisement display rule is met is judged. Assume that the Body area of the advertisement screenshot shown in FIG. 3 occupies 80% of the screen area, exceeding 75%; the Footer area occupies 20 percent of the screen area and is smaller than 25 percent, namely, the Footer area accords with the target advertisement display rule, and the corresponding first recognition result can be represented by 1; if the target advertisement display rule is not met, the corresponding first recognition result can be represented by 0.
Optionally, for the advertisement of the target type, some advertisement icons or text identifiers may exist in the advertisement frame, so that in addition to determining the advertisement area ratio, it is further required to determine whether the information of the target area in the advertisement frame meets the corresponding advertisement display rule. In this case, when determining the first recognition result, it is necessary to analyze the target area information in addition to analyzing the screen ratio of the advertisement area in the advertisement that has been placed, and obtain the first recognition result based on the analysis results of the two parts. If any one of the two parts does not accord with the target advertisement display rule, the corresponding first recognition result can be represented by 0; if both the two parts meet the target advertisement display rule, the corresponding first recognition result is denoted by 1.
Taking an X-type advertisement with a target type as a certain advertisement delivery system as an example, it is assumed that the advertisement must have a corresponding logo (icon) and an "advertisement" two word, for example, as shown by a dashed box S30 in fig. 3, these information are target area information, and the target area is a partial area shown by the dashed box S30, that is, a lower right corner portion of the Body area.
If the X-type open screen advertisement includes the target area information indicated by the dashed box S30, and the Body area is not less than 75% of the screen area, and the Foote area is not more than 25% of the screen area, the advertisement is indicated to be a valid X-type advertisement, that is, a normal advertisement, otherwise, the advertisement can be determined to be a cheating advertisement, that is, an invalid advertisement, and the benefits of the advertisement delivery party cannot be or are influenced. For example, for a normal open screen advertisement, the user can jump to the corresponding link of the advertisement by clicking, but the cheating advertisement cannot achieve the effect, and may be just a cheating advertisement picture. In the embodiment of the application, whether the user has cheating behavior is further judged on the basis that the advertisement picture information accords with the advertisement display rule.
It should be noted that, the above embodiment realizes the analysis of the advertisement cheating behavior by comparing the local advertisement screenshot, and can be used as an important supplement to the anti-cheating capability based on the online mobile phone data in the related technology, so as to promote the development of migrating the anti-cheating gravity center from the server to the client, and receive the cost of the related resources of the server.
After the advertisement presentation rule is introduced, the following detailed description is given of the process of analyzing the response behavior information according to the behavior rule in the target anti-cheating rule:
in an alternative implementation manner, response behavior information of the target object is analyzed according to the behavior rules in the target anti-cheating rules, when a second recognition result for representing whether the response behavior of the target object accords with the behavior rules is obtained, the response behavior information of the target object is preferably needed to be analyzed, and various response behaviors of the target object aiming at the delivered advertisements and response information corresponding to the various response behaviors are determined; and then, respectively comparing the response information corresponding to the various response behaviors with rule thresholds corresponding to the various response behaviors set in the behavior rules to obtain a second recognition result for indicating whether the response behaviors of the target object accord with the behavior rules.
The response behavior information of the user on the placed advertisement comprises data such as click coordinates, gesture sliding distance, historical browsing page information and the like, and the corresponding response behaviors are respectively click, sliding and browsing pages.
And respectively carrying out behavior rule matching on response information corresponding to various response behaviors to see whether the cheating problem exists. For example, it is determined whether the click coordinates of the user meet the coordinate rule threshold corresponding to the click coordinates, whether the gesture sliding distance of the user meets the distance rule threshold corresponding to the click coordinates, whether the duration of the user browsing the advertisement display page meets the duration rule threshold, and the like. If at least N items of response information corresponding to the response behaviors do not accord with the corresponding rule threshold, the second recognition result can be determined to indicate that the response behaviors of the target object do not accord with the behavior rule, and at the moment, the second recognition result can be represented by 0, wherein N is a positive integer and can be determined according to actual conditions; otherwise, it may be determined that the response behavior of the second recognition result indicating the target object meets the behavior rule, where the second recognition result may be indicated by 1.
The following list a target anti-cheating rule obtained by the client, and the specific rule format is as follows:
In the above-listed target anti-cheating rules:
the Version field represents the Version number of the target anti-cheating rule, the BehavinorRules field represents the behavior rule, and the Screen ShotRules represents the advertisement presentation rule.
Specifically, the rule format of the behavior rule is: [ < formula, value range >, … ], wherein each group of < formula, value range > represents behavior information of a type of response behavior, and the value range (i.e., rule threshold) corresponding to the type of response behavior. For example, < page stay length, > = 1> means that the average page browsing length is greater than 1s, and the length rule threshold is 1s.
The advertisement type1 is exemplified by the advertisement type1, and for the advertisement of the type, the imageMustInclude field represents a picture URL which can be used for judging whether the advertisement URL is correct; the include Description field indicates that the text is included, and can be used for judging whether the advertisement screenshot includes the target text; the body view height range [ a, b ] can be used to determine whether the height of the advertisement subject view accords with the interval defined by the rule threshold a and the rule threshold b, for example, the value of a is 75, and the value of b is 100, namely, the height of the advertisement subject view must not be less than 75% of the whole screen height. Similarly, [ a, b ] represents the duty cycle interval of the advertisement header view height compared with the whole screen height, and [ a, b ] represents the duty cycle interval of the advertisement footer view height compared with the whole screen height.
After the first recognition result and the second recognition result are obtained, the first recognition result and the second recognition result are summarized by the client. If the cheating information belongs to the cheating, the specific reasons of the cheating information are taken, and the specific reasons are uploaded to the advertisement operation server along with exposure, clicking and conversion operation to serve as important basis for judging whether the cheating information is cheated or not by the advertisement operation server. Meanwhile, the local interaction data and the historical behavior data can be uploaded to an advertisement operation server after desensitization, or uploaded to a local anti-cheating rule file server to reach a user interaction information database and serve as reference data when the local anti-cheating rule file server updates the anti-cheating rule set.
In the embodiment of the application, when the client performs summarization, for the first recognition result, if the value of the first recognition result is 1, the first recognition result indicates that the advertisement is put in accordance with the advertisement display rule, and at the moment, the anti-cheating behavior recognition result comprises that the advertisement is put as a normal advertisement; if the first recognition result is 0, the fact that the advertisement is put does not accord with the advertisement display rule is indicated, and the anti-cheating behavior recognition result comprises that the advertisement is put as a cheating advertisement.
For the second recognition result, if the value of the second recognition result is 1, the target object is indicated to accord with the behavior rule, and the anti-cheating behavior recognition result comprises that the target object is a normal object; if the second recognition result is 0, the target object is not in accordance with the behavior rule, and the anti-cheating behavior recognition result comprises that the target object is a cheating object.
Therefore, when the first recognition result and the second recognition result are summarized, the following four cases can be classified together:
in the first case, the first recognition result indicates that the put advertisement accords with the advertisement display rule, and the second recognition result indicates that the target object accords with the behavior rule, in this case, the anti-cheating behavior recognition result comprises: the advertisements which are put are normal advertisements, the target objects are normal objects, that is to say, the advertisements are put normally, and the target objects are not cheated.
In the second case, the first recognition result indicates that the put advertisement accords with the advertisement display rule, and the second recognition result indicates that the target object does not accord with the behavior rule, and in the case, the anti-cheating behavior recognition result comprises: the advertisements which are put are normal advertisements, the target objects are cheating objects, that is to say, the advertisements are put normally, and the behaviors of the target objects belong to the cheating behaviors.
In the third case, the first recognition result indicates that the put advertisement does not accord with the advertisement display rule, and the second recognition result indicates that the target object accords with the behavior rule, and in the case, the anti-cheating behavior recognition result comprises: the advertisement is a cheating advertisement, the target object is a normal object, that is, the advertisement is abnormally placed, the screenshot of the advertisement does not accord with the advertisement display rule, and is a false advertisement, but the behavior of the target object belongs to a normal behavior, and the target object is not cheated.
In the fourth case, the first recognition result indicates that the advertisement put in does not accord with the advertisement display rule, and the second recognition result indicates that the target object does not accord with the behavior rule, and in this case, the anti-cheating behavior recognition result includes: the advertisement is a cheating advertisement, the target object is a cheating object, that is, the advertisement is abnormally put, the screenshot of the advertisement does not accord with the advertisement display rule, the advertisement is a false advertisement, and the behavior of the target object belongs to the cheating behavior.
In the embodiment of the application, after the client obtains the anti-cheating behavior recognition result aiming at the target object through summarizing the first recognition result and the second recognition result, the anti-cheating behavior recognition result can be uploaded to the advertisement operation server for statistical analysis by the advertisement operation server.
Besides, the client analyzes the response behavior information of the user and the advertisement picture information of the advertised according to the target anti-cheating rule, and further desensitizes the response behavior information of the user and then uploads the desensitized response behavior information to the user interaction information database and the advertisement operation server, wherein the response behavior information of the user comprises local interaction data, historical behavior data and the like.
In an optional implementation manner, the client removes target information in the response behavior information according to a preset information removal rule, and obtains response behavior information after the target information is removed, namely response behavior information after desensitization processing is performed on the response behavior information of the user; further uploading the response behavior information after the target information is removed and the anti-cheating behavior recognition result of the target object to the advertisement operation server; after receiving response behavior information which is sent by each client and corresponds to each object and is subjected to target information removal and anti-cheating behavior recognition results aiming at each object, the advertisement operation server carries out statistical analysis on the response behavior information which is subjected to target information removal based on the anti-cheating behavior recognition results, determines rule thresholds in target anti-cheating rules, and respectively sends the determined rule thresholds to the clients of each object.
In the embodiment of the application, after the client receives the rule threshold issued by the advertisement operation server, the received rule threshold is compared with the rule threshold in the target anti-cheating rule stored locally, and when the rule threshold in the target anti-cheating rule is determined to be different from the rule threshold issued by the advertisement operation server, the rule threshold in the target anti-cheating rule is replaced by the rule threshold issued by the advertisement operation server.
For example, the target anti-cheating rules issued to the client by the local anti-cheating rule file server include: the residence time of the page is longer than 1 second; the height of the advertisement body view must not be less than 75% of the overall screen height. The rule threshold corresponding to the page stay time is 1 second, and the rule threshold corresponding to the advertisement main body view height is the same. Assuming that a new page stay time rule threshold value issued by the advertisement operation server is 1.5 seconds, determining that the page stay time rule threshold value in the target anti-cheating rule is different from the page stay time rule threshold value in the target anti-cheating rule by the client, and adjusting the behavior rule about the page stay time in the target anti-cheating rule to be: the page dwell time is greater than 1.5 seconds.
It should be noted that, in the embodiment of the present application, the advertisement operation server may further adjust the rule threshold by combining the anti-cheating behavior recognition result uploaded by the client and the response behavior information of the user, and then send the rule threshold to the client, where the client adjusts the corresponding target anti-cheating rule, so as to improve the accuracy of anti-cheating rule behavior recognition.
The removal of the target information refers to desensitizing the response behavior information of the user, and removing the sensitive information. Referring to fig. 4, a flowchart of a desensitizing method according to an embodiment of the present application is shown, and the specific process is as follows:
In step S41, the response behavior information is classified according to preset response categories, original categories to which various response behaviors belong are determined, and response time corresponding to the various response behaviors is recorded;
wherein the original class of response behavior is a specific class containing sensitive information. When classifying response behavior information according to preset response categories, the response behavior information is mainly classified according to category names using stay or interaction, such as UserView, click button, message dialog, inputtextfield, and the like, and specific time of stay or interaction is recorded in ms.
For example, userView corresponds to a dwell time of 2s; the residence time corresponding to the ClickButton behavior is 0.5s; the action of MessageDialogue corresponds to a dwell time of 5 sfinputtextfield and the action corresponds to a dwell time of 3s.
In step S42, removing target information in the category to which each type of response behavior belongs according to the information removal rule, so as to obtain an abstract category of each type of response behavior;
the process is a process of abstracting the response behaviors of the user, and abstracts the concrete categories of the various response behaviors obtained based on the division in the above manner, for example, four types of original categories listed above can be abstracted into View, button, dialog and Input. The specific category is abstracted, and the abstract category is obtained, namely, the target information is removed, and in the embodiment of the application, the target information refers to sensitive information related to some privacy-related behaviors of a user.
In step S43, the abstract categories of the various response behaviors are classified and combined with the preset operation set to obtain target categories of the various response behaviors;
after the abstract class is obtained, the abstract class can be merged into a preset operation set, and the preset operation set listed in the embodiment of the application is as follows: after classifying and combining the four listed abstract categories with a preset operation set, the above examples are classified into a static View element, a Button interaction element, a static View element, an Input box interaction element, namely, a target category corresponding to View is a static View element, a target category corresponding to Button is a Button interaction element, a target category corresponding to dialog is a static View element, and a target category corresponding to Input is an Input box interaction element.
In step S44, the target class of each type of response behavior and the corresponding response time are bound and then used as response behavior information after the target information is removed.
In the embodiment of the application, the binding of the target class and the stay time can be obtained: < static element, 2s >, < button interaction element, 0.5s >, < input box interaction element, 5s >, < static element, 3s >, upload advertisement operations server in time order, for example: { < static element, 2s >, < button interaction element, 0.5s >, < input box interaction element, 5s >, < static element, 3s > … }.
In the above embodiment, by desensitizing the response behavior information of the user, the policy risk of future privacy information collection can be avoided.
The anti-cheating behavior recognition method in the embodiment of the application can also be realized by combining an artificial intelligence technology, and the specific realization mode is as follows: acquiring response behavior information of a target object aiming at the advertised and advertisement picture information of the advertised; and inputting the acquired response behavior information and advertisement picture information into a trained anti-cheating model to obtain an anti-cheating behavior recognition result which is output by the anti-cheating model and aims at the target object, wherein the trained anti-cheating model is obtained through machine learning training based on a training sample set. Wherein the training sample set comprises a plurality of sets of data, each set of data in the plurality of sets of data comprising:
a first group of data: the advertisement system comprises advertisement picture information of sample advertisements and a first label, wherein the first label is used for labeling whether the sample advertisements are real sample advertisements or cheating sample advertisements;
a second group of data: the sample object is used for marking whether the sample object is a normal sample object or a cheating sample object.
Furthermore, the anti-cheating model in the embodiment of the application is obtained by adopting a plurality of groups of data and utilizing machine learning training, based on the data, the data is trained into an anti-cheating model through machine learning and is issued to the client, and then the data processing is carried out based on the machine learning framework on the client.
In the embodiment of the application, when the local anti-cheating behavior recognition is performed through the machine learning model, the recognition efficiency and accuracy can be effectively improved.
After introducing the anti-cheating behavior recognition method of the client side, briefly introducing the anti-cheating behavior recognition methods of the advertisement operation server side and the local anti-cheating rule file server side as follows:
referring to fig. 5, a flowchart of an implementation of an anti-cheating behavior recognition method according to an embodiment of the present application is applied to a local anti-cheating rule file server, and the specific implementation flow of the method is as follows:
s51: receiving an advertisement request sent by a client, wherein the advertisement request is sent after the client responds to the operation of requesting the advertisement by a target object;
s52: according to hardware parameters in the advertisement request, searching target anti-cheating rules matched with the hardware parameters from a preset anti-cheating rule set, and issuing the target anti-cheating rules to the client so that the client can conduct anti-cheating behavior recognition on a target object according to the target anti-cheating rules, wherein the hardware parameters are hardware parameters of terminal equipment where the client is located, the target anti-cheating rules comprise advertisement display rules and behavior rules, the advertisement display rules are used for analyzing advertisement picture information of the advertisements, and the behavior rules are used for analyzing response behavior information of the target object aiming at the advertisements.
In the above embodiment, the local anti-cheating rule file server configures the anti-cheating rule set, and updates the anti-cheating rules corresponding to each hardware parameter continuously, and in addition, the local anti-cheating rule file server does not need to receive the user response behavior information and the advertisement picture information uploaded by the client, but issues the target anti-cheating rule corresponding to the hardware parameter in the advertisement request sent by the client to the client, and shifts the anti-cheating center of gravity from the server to the client, so that the cost of resources related to the local anti-cheating rule file server can be effectively saved. The specific implementation manner may be referred to the above embodiments, and the detailed description is not repeated here.
Referring to fig. 6, a flowchart of an implementation of an anti-cheating behavior recognition method provided by an embodiment of the present application is applied to an advertisement operation server, and the specific implementation flow of the method is as follows:
s61: receiving response behavior information sent by each client after removing target information corresponding to each object and anti-cheating behavior identification results aiming at each object;
s62: and carrying out statistical analysis on the response behavior information after removing the target information based on the anti-cheating behavior recognition result, determining a rule threshold value in the target anti-cheating rule, respectively issuing the rule threshold value to the client side of each object, so that when the client side of each object determines that the rule threshold value in the pre-acquired target anti-cheating rule is different from the received rule threshold value, replacing the rule threshold value in the pre-acquired target anti-cheating rule with the received rule threshold value, wherein the target anti-cheating rule comprises an advertisement display rule and a behavior rule, the advertisement display rule is used for analyzing advertisement picture information of the advertisement, and the behavior rule is used for analyzing the response behavior information of the object aiming at the advertisement.
In the above embodiment, when the client uploads the identification result, if the identification result indicates cheating, the client may further bring specific reasons for the cheating, and upload response behavior information after removing the target information corresponding to the operations such as exposure, clicking, conversion, etc. to the advertisement operation server. The advertisement operation server can make anti-cheating behavior judgment on the side of the server according to the received anti-cheating behavior recognition result, and can also perform statistical analysis on the received response behavior information according to the received anti-cheating behavior recognition result, for example, based on the rule threshold corresponding to the page stay time up through statistical analysis, the original rule threshold is adjusted to 1.5 seconds, the newly adjusted rule threshold is sent to the client, and the client updates the threshold.
In the above embodiment, the advertisement operation server may further adjust the rule threshold by combining the anti-cheating behavior recognition result uploaded by the client and the response behavior information of the user, and further send the rule threshold to the client, where the client adjusts the corresponding target anti-cheating rule, so as to improve the accuracy of anti-cheating rule behavior recognition.
Fig. 7A is a schematic diagram of an interaction timing sequence of an anti-cheating behavior recognition method. The specific implementation flow of the method is as follows:
Step S701: the client responds to the operation of the user for requesting the advertisement and sends an advertisement request to the local anti-cheating rule file server, wherein the advertisement request comprises hardware parameters of terminal equipment where the client is located;
step S702: the local anti-cheating rule file server searches a target anti-cheating rule corresponding to the hardware parameter according to the hardware parameter in the advertisement request;
step S703: the local anti-cheating rule file server sends the target anti-cheating rule to the client;
step S704: the client analyzes advertisement picture information of the target advertisement according to advertisement display rules in the target anti-cheating rules to obtain a first identification result;
step S705: the client analyzes response behavior information of the target object aiming at the target advertisement according to the behavior rules in the target anti-cheating rules to obtain a second identification result;
step S706: the client gathers the first recognition result and the second recognition result to obtain an anti-cheating behavior recognition result aiming at the target object;
step S707: the client uploads the obtained anti-cheating behavior recognition result to the advertisement operation server;
step S708: the client desensitizes the response behavior information of the target object aiming at the target advertisement to obtain the desensitized response behavior information;
Step S709: the client uploads the desensitized response behavior information to the advertisement operation server;
step S710: the advertisement operation server performs statistical analysis on the response behavior information according to the received cheating behavior recognition result, and determines a rule threshold value in the target anti-cheating rule;
step S711: the advertisement operation server transmits the determined rule threshold to the client;
step S712: when the client determines that the rule threshold value issued by the advertisement operation server is different from the rule threshold value in the local target anti-cheating rule, the rule threshold value in the target anti-cheating rule is replaced by the rule threshold value issued by the advertisement operation server.
Referring to fig. 7B, a schematic diagram of an interactive timing sequence of an anti-cheating behavior recognition method in another form according to an embodiment of the present application is shown. The anti-cheating behavior recognition system in the embodiment of the application comprises: the system comprises a client, a local anti-cheating rule file server and an advertisement operation server.
When a user requests an advertisement, the client pulls the anti-cheating rule from the local anti-cheating rule file server; after the anti-cheating rule is pulled, whether local updating is needed or not is judged, and if so, the local updating is updated to the local storage. The process of comparing the version number of the target anti-cheating rule issued by the local anti-cheating rule file server with the version number of the client local anti-cheating rule, which is listed in the above embodiment, is not repeated here.
In addition, the client local anti-cheating module collects advertisement operation of a user, advertisement picture information and the like, wherein the advertisement operation of the user, namely data information related to response behaviors of the user to the delivered advertisements, including exposure, clicking and the like, is obtained through client local behavior data collection, and advertisement screenshot is obtained based on image processing, and further comprises coordinates, basic data and the like obtained through image labeling and recognition.
Based on the information collected by the client local anti-cheating rule module, two parts of analysis can be executed: 1. analyzing advertisement picture information based on advertisement display rules; 2. analysis of the user's responsive behavior information based on behavior rules. The specific analysis procedure can be found in the above examples and is not repeated here.
Finally, the client gathers and processes the analysis results of the two parts and reports the analysis results to the advertisement operation server, in addition, the client can also report the desensitization processing of response behavior information such as advertisement operation of the user and the like to the advertisement operation server, and finally the advertisement operation server records and performs statistical analysis.
In addition, the client can upload response behavior information such as advertisement operation after desensitization treatment to the user interaction information database, the cloud can formulate anti-cheating rules based on the information in the user interaction information database and store the anti-cheating rules into the local anti-cheating rule file server, and in addition, the anti-cheating rules in the local anti-cheating rule file server can also be derived from an anti-cheating strategy set and the like obtained based on analysis of partial behavior data which is already operated on line.
In the embodiment of the application, the policy risk of future privacy information collection can be avoided through the desensitization processing of the response behavior information of the user; in addition, through local screenshot comparison, local user behavior analysis and the like, the anti-cheating gravity center is migrated from the server side to the client side, so that the cost of relevant resources of the server can be effectively saved.
Based on the same inventive concept, the embodiment of the application also provides an anti-cheating behavior recognition device. As shown in fig. 8, which is a schematic structural diagram of a first anti-cheating behavior recognition apparatus 800 in an embodiment of the present application, the apparatus may include:
an information obtaining unit 801, configured to obtain response behavior information of a target object to an advertisement placed in a display page, and advertisement frame information of the advertisement placed;
an analysis unit 802, configured to analyze advertisement frame information according to an advertisement display rule in a pre-acquired target anti-cheating rule, and obtain a first recognition result that is used to indicate whether a put advertisement meets the advertisement display rule; analyzing the response behavior information according to the behavior rules in the target anti-cheating rules to obtain a second identification result used for indicating whether the response behavior of the target object accords with the behavior rules;
And a summarizing unit 803, configured to obtain an anti-cheating behavior recognition result for the target object by summarizing the first recognition result and the second recognition result.
Optionally, the analysis unit 802 is specifically configured to:
carrying out image recognition on the advertisement picture information to determine an advertisement area in the put advertisement;
and analyzing the picture ratio of the advertisement area in the put advertisement based on a target advertisement display rule corresponding to the advertisement type of the put advertisement in the advertisement display rule to obtain a first identification result.
Optionally, if the advertisement type of the delivered advertisement is a target advertisement type;
the identification unit 804 is further configured to:
acquiring target area information contained in the put advertisement;
the identifying unit 804 is specifically configured to:
analyzing the target area information based on a target advertisement display rule corresponding to the advertisement type of the advertised advertisement in the advertisement display rule;
and obtaining a first identification result according to the analysis result of the target area information and the analysis result of the picture ratio of the advertising area in the advertised.
Optionally, the response behavior information comprises response information of the target object aiming at various response behaviors of the advertised; the identifying unit 804 is specifically configured to:
And respectively comparing the response information corresponding to the various response behaviors with rule thresholds corresponding to the various response behaviors set in the behavior rules to obtain a second identification result for indicating whether the response behaviors of the target object accord with the behavior rules.
Optionally, the summarizing unit 803 is specifically configured to:
if the first recognition result shows that the placed advertisement accords with the advertisement display rule, determining that the anti-cheating behavior recognition result comprises that the placed advertisement is a normal advertisement; otherwise, determining that the anti-cheating behavior recognition result comprises that the put advertisement is a cheating advertisement;
if the second recognition result indicates that the target object accords with the behavior rule, determining that the anti-cheating behavior recognition result comprises that the target object is a normal object; otherwise, determining that the anti-cheating behavior recognition result comprises that the target object is the cheating object.
Optionally, the apparatus further comprises:
a transmission unit 805 configured to obtain a target anti-cheating rule based on the following manner:
responding to the operation of the target object for requesting the advertisement, and sending an advertisement request to a local anti-cheating rule file server, wherein the advertisement request comprises hardware parameters of terminal equipment where a client is located;
and receiving a target anti-cheating rule matched with the hardware parameter returned by the local anti-cheating rule file server.
Optionally, the summarizing unit 803 is further configured to:
removing target information in response behavior information according to a preset information removal rule, and obtaining response behavior information after removing the target information;
and uploading the response behavior information after the target information is removed and the anti-cheating behavior recognition result of the target object to the advertisement operation server, so that the advertisement operation server performs statistical analysis on the response behavior information after the target information is removed according to the recognition result of the anti-cheating behavior.
Optionally, the summarizing unit 803 is specifically configured to:
classifying response behavior information according to preset response categories, determining the original categories to which various response behaviors belong, and recording response time corresponding to the various response behaviors;
target information in the category to which each type of response behavior belongs is removed according to an information removal rule, and an abstract category of each type of response behavior is obtained;
classifying and combining abstract categories of various response behaviors with a preset operation set to obtain target categories of various response behaviors;
binding the target types of various response behaviors and the corresponding response time, and taking the bound target types as response behavior information after removing target information.
Optionally, the apparatus further comprises:
An updating unit 806, configured to receive a rule threshold issued by the advertisement operation server;
if the received rule threshold is different from the rule threshold in the target anti-cheating rule, replacing the rule threshold in the target anti-cheating rule with the received rule threshold, wherein the received rule threshold is obtained by carrying out statistical analysis on response behavior information of each object after removing target information according to the received anti-cheating behavior identification result of each object by the advertisement operation server.
Based on the same inventive concept, the embodiment of the application also provides an anti-cheating behavior recognition device. As shown in fig. 9, which is a schematic structural diagram of a second anti-cheating behavior recognition apparatus 900 according to an embodiment of the present application, the apparatus may include:
a first receiving unit 901, configured to receive an advertisement request sent by a client, where the advertisement request is sent by the client after responding to an operation that a target object requests an advertisement;
the rule issuing unit 902 is configured to find a target anti-cheating rule matched with the hardware parameter from a preset anti-cheating rule set according to the hardware parameter in the advertisement request, and issue the target anti-cheating rule to the client, so that the client identifies the anti-cheating behavior of the target object according to the target anti-cheating rule, where the hardware parameter is a hardware parameter of a terminal device where the client is located, the target anti-cheating rule includes an advertisement display rule and a behavior rule, the advertisement display rule is used for analyzing advertisement picture information of the advertisement, and the behavior rule is used for analyzing response behavior information of the target object for the advertisement.
Based on the same inventive concept, the embodiment of the application also provides an anti-cheating behavior recognition device. As shown in fig. 10, which is a schematic structural diagram of a third anti-cheating behavior recognition apparatus 1000 according to an embodiment of the present application, the apparatus may include:
a second receiving unit 1001, configured to receive response behavior information sent by each client after removing target information corresponding to each object, and anti-cheating behavior recognition results for each object;
the statistics analysis unit 1002 is configured to perform statistics analysis on the response behavior information after the target information is removed based on the anti-cheating behavior recognition result, determine rule thresholds in the target anti-cheating rules, and respectively issue the rule thresholds to clients of the objects, so that when the clients of the objects determine that the rule thresholds in the pre-acquired target anti-cheating rules are different from the received rule thresholds, the rule thresholds in the pre-acquired target anti-cheating rules are replaced with the received rule thresholds, the target anti-cheating rules include advertisement display rules and behavior rules, the advertisement display rules are used for analyzing advertisement picture information of the advertisements, and the behavior rules are used for analyzing response behavior information of the objects with respect to the advertisements.
For convenience of description, the above parts are described as being functionally divided into modules (or units) respectively. Of course, the functions of each module (or unit) may be implemented in the same piece or pieces of software or hardware when implementing the present application.
Having described the anti-cheating behavior recognition method and apparatus of an exemplary embodiment of the present application, next, an electronic device according to another exemplary embodiment of the present application is described.
Those skilled in the art will appreciate that the various aspects of the application may be implemented as a system, method, or program product. Accordingly, aspects of the application may be embodied in the following forms, namely: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may be referred to herein as a "circuit," module "or" system.
Fig. 11 is a block diagram of an electronic device 1100, according to an example embodiment, the apparatus comprising:
a processor 1110;
a memory 1120 for storing instructions executable by the processor 1110;
wherein the processor 1110 is configured to execute instructions to implement the anti-cheating behavior identification method in an embodiment of the present application.
In an exemplary embodiment, a storage medium is also provided that includes instructions, such as memory 1120 including instructions, that are executable by processor 1110 of electronic device 1100 to perform the above-described methods. Alternatively, the storage medium may be a non-transitory computer readable storage medium, for example, a ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, and the like.
In some possible implementations, the electronic device in the present embodiment further includes a bus 1130 that connects the different system components (including the memory and the processor).
Bus 1130 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a processor, and a local bus using any of a variety of bus architectures.
The memory 1120 may include readable media in the form of volatile memory, such as Random Access Memory (RAM) 1121 and/or cache memory 1122, and may further include Read Only Memory (ROM) 1123.
Memory 1120 may also include a program/utility 1125 having a set (at least one) of program modules 1124, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
The electronic device 1100 may also communicate with one or more external devices 1140 (e.g., keyboard, pointing device, etc.), one or more devices that enable a user to interact with the electronic device 1100, and/or any devices (e.g., routers, modems, etc.) that enable the electronic device 1100 to communicate with one or more other electronic devices. Such communication may occur through an input/output (I/O) interface 1150. Also, electronic device 1100 can communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 1160. As shown, network adapter 1160 communicates with other modules for electronic device 1100 via bus 1130. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 1100, including, but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
In some possible embodiments, aspects of the anti-cheating behavior identification method provided by the present application may also be implemented in the form of a program product comprising program code for causing a computer device to perform the steps of the anti-cheating behavior identification method according to the various exemplary embodiments of the present application described herein above, when the program product is run on a computer device, e.g., the computer device may perform the steps as shown in fig. 2.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The program product of embodiments of the present application may employ a portable compact disc read only memory (CD-ROM) and include program code and may run on a computing device. However, the program product of the present application is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with a command execution system, apparatus, or device.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims (11)

1. An anti-cheating behavior recognition method, which is characterized by being applied to a client, comprises the following steps:
acquiring response behavior information of a target object to an advertisement put in a display page, advertisement picture information of the advertisement put in the display page, and a target anti-cheating rule for identifying anti-cheating behaviors; wherein the target anti-cheating rule comprises an advertisement display rule and a behavior rule; the response behavior information comprises response information of the target object aiming at various response behaviors of the advertised;
Performing image recognition on the advertisement picture information to determine an advertisement area in the advertised picture; analyzing the picture ratio of the advertisement area in the put advertisement based on a target advertisement display rule corresponding to the advertisement type of the put advertisement in the advertisement display rule to obtain a first identification result; and
respectively comparing the response information corresponding to the various response behaviors with rule thresholds corresponding to the various response behaviors set in the behavior rules to obtain a second recognition result for representing whether the response behaviors of the target object accord with the behavior rules;
if the first recognition result indicates that the placed advertisement accords with the advertisement display rule, determining that the anti-cheating behavior recognition result comprises that the placed advertisement is a normal advertisement; otherwise, determining that the anti-cheating behavior recognition result comprises that the put advertisement is a cheating advertisement;
if the second recognition result indicates that the target object accords with the behavior rule, determining that the anti-cheating behavior recognition result comprises that the target object is a normal object; otherwise, determining that the anti-cheating behavior recognition result comprises that the target object is a cheating object.
2. The method of claim 1, wherein if the advertisement type of the advertised advertisement is a targeted advertisement type; the method further comprises the steps of:
acquiring target area information contained in the delivered advertisement;
the analyzing the picture ratio of the advertisement area in the advertisement based on the target advertisement display rule corresponding to the advertisement type of the advertisement to obtain the first recognition result specifically includes:
analyzing the target area information based on a target advertisement display rule corresponding to the advertisement type of the advertised in the advertisement display rule;
and obtaining the first identification result according to the analysis result of the target area information and the analysis result of the picture proportion of the advertisement area in the advertised.
3. The method of claim 1, wherein the method further comprises:
removing target information in the response behavior information according to a preset information removal rule, and obtaining response behavior information after removing the target information;
uploading the response behavior information after the target information is removed and the anti-cheating behavior recognition result of the target object to an advertisement operation server, so that the advertisement operation server performs statistical analysis on the response behavior information after the target information is removed according to the recognition result of the anti-cheating behavior.
4. The method of claim 3, wherein the removing the target information in the response behavior information according to the preset information removing rule, to obtain the response behavior information after removing the target information, specifically includes:
classifying the response behavior information according to preset response categories, determining the original categories to which various response behaviors belong, and recording response time corresponding to the various response behaviors;
removing target information in the category to which the various response behaviors belong according to the information removing rule to obtain abstract categories of the various response behaviors;
classifying and combining the abstract categories of the various response behaviors with a preset operation set to obtain target categories of the various response behaviors;
binding the target categories of the various response behaviors and the corresponding response time, and then using the target categories as response behavior information after removing the target information.
5. A method for identifying anti-cheating behavior, the method comprising:
receiving an advertisement request sent by a client, wherein the advertisement request is sent after the client responds to the operation of requesting advertisement by a target object;
According to the hardware parameters in the advertisement request, searching a target anti-cheating rule matched with the hardware parameters from a preset anti-cheating rule set, and issuing the target anti-cheating rule to the client so that the client can conduct anti-cheating behavior recognition on the target object according to the target anti-cheating rule by adopting the anti-cheating behavior recognition method according to claim 1, wherein the hardware parameters are hardware parameters of terminal equipment where the client is located, the target anti-cheating rule comprises an advertisement display rule and a behavior rule, the advertisement display rule is used for analyzing advertisement picture information of the advertisement which is put on, and the behavior rule is used for analyzing response behavior information of the target object for the advertisement which is put on.
6. A method for identifying anti-cheating behavior, the method comprising:
receiving response behavior information sent by each client after target information is removed and corresponding to each object, and identifying results of anti-cheating behaviors aiming at each object;
and carrying out statistical analysis on response behavior information after target information is removed based on the anti-cheating behavior recognition result, determining rule thresholds in target anti-cheating rules, respectively issuing the rule thresholds to clients of all objects, so that when the clients of all objects determine that the rule thresholds in the pre-acquired target anti-cheating rules are different from the received rule thresholds, replacing the rule thresholds in the pre-acquired target anti-cheating rules with the received rule thresholds, and then, carrying out anti-cheating behavior recognition on the objects by the clients according to the replaced target anti-cheating rules by adopting the anti-cheating behavior recognition method according to claim 1, wherein the target anti-cheating rules comprise advertisement display rules and behavior rules, the advertisement display rules are used for analyzing advertisement picture information of the objects aiming at the response behavior information of the released advertisements.
7. An anti-cheating behavior recognition device, applied to a client, comprising:
the information acquisition unit is used for acquiring response behavior information of a target object to the advertisement which is put in the display page, advertisement picture information of the advertisement which is put in the display page and target anti-cheating rules for identifying anti-cheating behaviors; wherein the target anti-cheating rule comprises an advertisement display rule and a behavior rule; the response behavior information comprises response information of the target object aiming at various response behaviors of the advertised;
the analysis unit is used for carrying out image recognition on the advertisement picture information and determining an advertisement area in the advertised; analyzing the picture ratio of the advertisement area in the put advertisement based on a target advertisement display rule corresponding to the advertisement type of the put advertisement in the advertisement display rule to obtain a first identification result; and
respectively comparing the response information corresponding to the various response behaviors with rule thresholds corresponding to the various response behaviors set in the behavior rules to obtain a second recognition result for representing whether the response behaviors of the target object accord with the behavior rules;
The summarizing unit is used for determining that the anti-cheating behavior recognition result comprises that the put advertisement is a normal advertisement if the first recognition result indicates that the put advertisement accords with the advertisement display rule; otherwise, determining that the anti-cheating behavior recognition result comprises that the put advertisement is a cheating advertisement;
if the second recognition result indicates that the target object accords with the behavior rule, determining that the anti-cheating behavior recognition result comprises that the target object is a normal object; otherwise, determining that the anti-cheating behavior recognition result comprises that the target object is a cheating object.
8. An anti-cheating behavior recognition apparatus, comprising:
the first receiving unit is used for receiving an advertisement request sent by the client, wherein the advertisement request is sent after the client responds to the operation of requesting the advertisement by the target object;
the rule issuing unit is used for searching a target anti-cheating rule matched with the hardware parameter from a preset anti-cheating rule set according to the hardware parameter in the advertisement request, issuing the target anti-cheating rule to the client so that the client can conduct anti-cheating behavior recognition on the target object according to the target anti-cheating rule by adopting the anti-cheating behavior recognition method according to claim 1, wherein the hardware parameter is a hardware parameter of a terminal device where the client is located, the target anti-cheating rule comprises an advertisement display rule and a behavior rule, the advertisement display rule is used for analyzing advertisement picture information of the advertisement which is put in, and the behavior rule is used for analyzing response behavior information of the target object for the advertisement which is put in.
9. An anti-cheating behavior recognition apparatus, comprising:
the second receiving unit is used for receiving response behavior information which is sent by each client and corresponds to each object and is subjected to target information removal, and anti-cheating behavior recognition results aiming at each object;
the statistical analysis unit is used for carrying out statistical analysis on the response behavior information after the target information is removed based on the anti-cheating behavior recognition result, determining rule thresholds in target anti-cheating rules, respectively issuing the rule thresholds to clients of all objects, so that when the clients of all objects determine that the rule thresholds in the pre-acquired target anti-cheating rules are different from the received rule thresholds, the rule thresholds in the pre-acquired target anti-cheating rules are replaced with the received rule thresholds, and then, the clients are used for carrying out anti-cheating behavior recognition on the objects according to the replaced target anti-cheating rules by adopting the anti-cheating behavior recognition method according to claim 1, wherein the target anti-cheating rules comprise advertisement display rules and behavior rules, the advertisement display rules are used for analyzing advertisement picture information of the advertisements, and the behavior rules are used for analyzing the response behavior information of the objects aiming at the advertisements.
10. An electronic device comprising a processor and a memory, wherein the memory stores program code that, when executed by the processor, causes the processor to perform the method of any one of claims 1-4 or the method of claim 5 or the method of claim 6.
11. A computer readable storage medium, characterized in that it comprises a program code for causing an electronic device to perform the method of any one of claims 1-4 or the method of claim 5 or the method of claim 6 when said program code is run on the electronic device.
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