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Robust Vision-Based Cheat Detection in Competitive Gaming

Published:28 April 2021Publication History
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Abstract

Game publishers and anti-cheat companies have been unsuccessful in blocking cheating in online gaming. We propose a novel, vision-based approach that captures the frame buffer's final state and detects illicit overlays. To this aim, we train and evaluate a DNN detector on a new dataset, collected using two first-person shooter games and three cheating software. We study the advantages and disadvantages of different DNN architectures operating on a local or global scale. We use output confidence analysis to avoid unreliable detections and inform when network retraining is required. In an ablation study, we show how to use Interval Bound Propagation (IBP) to build a detector that is also resistant to potential adversarial attacks and study IBP's interaction with confidence analysis. Our results show that robust and effective anti-cheating through machine learning is practically feasible and can be used to guarantee fair play in online gaming.

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      • Published in

        cover image Proceedings of the ACM on Computer Graphics and Interactive Techniques
        Proceedings of the ACM on Computer Graphics and Interactive Techniques  Volume 4, Issue 1
        April 2021
        274 pages
        EISSN:2577-6193
        DOI:10.1145/3463840
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        Copyright © 2021 ACM

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 28 April 2021
        Published in pacmcgit Volume 4, Issue 1

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