Detecting Aimbot Cheats in FPS Games Using Computer Visi on: Effective Methods and Challenges

Abstract

Aimbot cheats in first-person shooter (FPS) games automatically manipulate player input to target opponents with unnatural precision, undermining fairness and player experience. Recent research has explored computervision and machine-learning methods to detect such cheats by analyzing either raw game visuals or user input data. The paper surveys four representative approaches: VADNet , a vision-based convolutional neural network with focus modules and feature pyramids; Pinto et al., a multivariate time-series CNN using mouse and keyboard input; BotScreen, a distributed RNN-based detector operating in secure enclaves; and GAN-Aimbot, a ge nerative approach to simulate adaptive aimbot behavior. The paper compares their reported (and simulated) performance—precision, recall, F1, detection latency, and cross-game generalizability—across multiple FPS titles. Key challenges emerge in distinguishing skilled legitimate play from cheats, evading adversarially adaptive cheats, achieving real-time processing, and maintaining player privacy and trust. The paper also discusses ethical considerations such as data privacy and the risk of falsely flagging skilled players, and outlines future directions including explainable ML, federated anti-cheat systems, and robustness against GAN-generated cheats. This synthesis highlights the trade-offs and open problems in vision-based aimbot detection, guiding researchers and practitioners in designing fair and effective anti-cheat systems.