Machine learning anti-cheating algorithm and a test against computer vision aimbot

Gefei Yang; Yuqi Zhang; Feihong Liu; Zishuo Gao

doi:10.54254/2755-2721/6/20230589

Research Article

Open access

Published on 14 June 2023

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Yang,G.;Zhang,Y.;Liu,F.;Gao,Z. (2023). Machine learning anti-cheating algorithm and a test against computer vision aimbot. Applied and Computational Engineering,6,1048-1054.

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Machine learning anti-cheating algorithm and a test against computer vision aimbot

Gefei Yang *^,1, Yuqi Zhang ², Feihong Liu ³, Zishuo Gao ⁴

¹ College of Engineering, School of Computing, University of Utah, Salt Lake City, 84112, USA
² Champaign Central High School, Chapaign, 61822, USA
³ Crean Lutheran High School, Irvine, 92618, USA
⁴ Computer science, University of Bristol, Bristol, BS8 1QU, UK

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/6/20230589

Abstract

In this article, we introduced a recurrent neural network (RNN) serves as a cheating detection method in CS: GO against traditional and AI cheating methods. Cheating methods today are growingly stronger, making traditional anti-cheating methods less and less helpful. The CV AimBot, which does not include any illegal operation but only reading the game footage, has made all traditional anti-cheating methods de facto useless. We developed a CV AimBot from scratch and used it to generate the sample data. We gathered datasets from traditional AimBot and newly invented CV AimBot and run it through an RNN. Our RNN shows high accuracy against both traditional and CV AimBot. It also shows that introducing data from CV AimBot with significantly lower the accuracy of the result from RNN.

Keywords

Machine Learning, Anti-Cheating, Aimbot, RNN.

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References

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Cite this article

Yang,G.;Zhang,Y.;Liu,F.;Gao,Z. (2023). Machine learning anti-cheating algorithm and a test against computer vision aimbot. Applied and Computational Engineering,6,1048-1054.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

Conference website: http://www.confspml.org

ISBN：978-1-915371-59-1(Print) / 978-1-915371-60-7(Online)

Conference date: 25 February 2023

Editor：Omer Burak Istanbullu

Series: Applied and Computational Engineering

Volume number: Vol.6

ISSN：2755-2721(Print) / 2755-273X(Online)

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