Detecting Aimbot Cheaters in MOGs

2026-06-02

AI Summary

This work addresses the threat posed by vision-based aimbot cheats that leverage computer vision models to bypass kernel-level anti-cheat systems, severely compromising game fairness. The authors propose PATCH, a novel method that deploys adversarial patches as highly stealthy honeypots directly within the game rendering pipeline to actively trigger misclassifications in cheat-enabled object detectors, thereby enabling precise identification or functional disruption of cheating clients. By integrating adversarial example generation, YOLO-family model adaptation, resolution-adaptive deployment, and cross-model transferability optimization, PATCH achieves over 90% white-box detection accuracy in a custom Unreal Engine game. Large patches demonstrate 60%–90% transferable detection rates across diverse models and are validated for real-world efficacy in Fortnite.

Abstract

Multiplayer Online Games have become a multibillion dollar industry in the entertainment sector. However, the presence of cheaters undermines the experience of honest players and devalues the effort of game developers, as it directly affects player retention, competitive integrity, the legitimacy and trustworthiness of a game, and most importantly the overall revenue streams. Among various cheating techniques, visual aimbots represent an emerging threat. They use computer vision models to detect opponents from client screen captures rather than accessing game memory, making them completely undetectable by commercial kernel level anti cheat solutions. In this paper, we introduce PATCH, a novel proactive defense strategy that deploys adversarial patches as in game honeytokens to mitigate the presence of visual aimbot cheaters. Our approach centers on deliberately triggering the cheaters' object detection model, enabling either direct detection, or rendering the game unplayable for the cheater via patch flooding on their viewport. We evaluate our approach on various criteria; analyzing the effectiveness of different patch sizes, scalability of patches to different screen resolutions, efficacy against diverse visual aimbot cheat configurations and also explore various YOLO models to assess patch transferability. Evaluation on a custom Unreal Engine game demonstrates over 90 percent detection rate in white box scenarios for almost all patch sizes, and reaches 60 to 90 percent cross model transferability with larger patches. We further validate our approach on Fortnite, a commercial MOG, demonstrating real world applicability.

Problem

• aimbot
• cheating detection
• multiplayer online games
• visual aimbot
• anti-cheat

Innovation

• adversarial patches
• visual aimbot detection
• proactive anti-cheat
• honeytokens
• cross-model transferability