Method, device, electronic equipment and storage medium for identifying plug-ins

Abstract

The application provides a method, device, electronic equipment and storage medium for identifying plug-ins, which relate to the field of computer vision technology of artificial intelligence. The method includes: acquiring the portrait features and behavior sequence data of the user to be identified; Identify and obtain the policy sequence data of the user to be identified; according to the sequence pattern mining algorithm, mine the policy sequence data to obtain the sequence pattern characteristics of the user to be identified; based on the portrait features and the sequence pattern characteristics, determine the Identify whether the user has used cheats. The method provided by the present application can improve the accuracy of plug-in identification on the basis of controlling costs and ensuring practicability.

Description

technical field [0001] The embodiments of the present application relate to the technical field of computer vision of artificial intelligence, and more specifically, to a method, device, electronic device, and storage medium for identifying cheats. Background technique [0002] A complete strategic risk control system is inseparable from cheating. With the various scenarios driven by online and cloudification, more and more cheating behaviors inevitably appear in online competitions, advertisements, games, etc. Therefore, how to better identify and combat cheating has become an important part of many scenarios that concern and even involve life and death. For example, in the scene of an online chess competition, it is very important for the fairness and justice of the competition and the hosting platform to intelligently identify whether the contestants have cheated during the competition, so as to deal with it accordingly. [0003] At present, the methods for anti-cheat ma...

AI Technical Summary

Problems solved by technology

[0004] However, the above-mentioned method for anti-cheat has a problem in that: for the discriminant method based on manual setting of cheat rules, it is necessary to introduce experts and prior knowledge in related fields, and the specified rules are not easy to expand and flexible. For complex and changeable cheating environments, the rule coverage rate will be significantly reduced after a certain period of time, and it is necessary to manually update the knowledge base and anti-cheat rule base, which is costly

The anti-cheat method for multi-account user tracking first needs to obtain multiple accounts of the same user, which will be difficult in many actual application scenarios. Secondly, it is difficult for a single account user to identify its cheating behavior, which in turn reduces the number of multi-account users. The practicality of tracking anti-cheat method

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