# GAN-Aimbots: Using Machine Learning for Cheating in First Person Shooters

arXiv: https://arxiv.org/abs/2205.07060 · HTML: https://mlsanity.com/paper/2205.07060/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 53 references that are in the map.
- First posted on arXiv 14 May 2022.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [FaceForensics++: Learning to Detect Manipulated Facial Images](https://mlsanity.com/paper/1901.08971/) (2019)
- [Learning Robust Rewards with Adversarial Inverse Reinforcement Learning](https://mlsanity.com/paper/1710.11248/) (2017)
- [ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning](https://mlsanity.com/paper/1605.02097/) (2016)
- [Forbidden knowledge in machine learning -- Reflections on the limits of research and publication](https://mlsanity.com/paper/1911.08603/) (2019)
- [Hacking Google reCAPTCHA v3 using Reinforcement Learning](https://mlsanity.com/paper/1903.01003/) (2019)
- [The Social Responsibility of Game AI](https://mlsanity.com/paper/2105.15122/) (2021)

## Built on by (most cited first, 7 of 7)

- [Identify As A Human Does: A Pathfinder of Next-Generation Anti-Cheat Framework for First-Person Shooter Games](https://mlsanity.com/paper/2409.14830/) (2024)
- [AntiCheatPT: A Transformer-Based Approach to Cheat Detection in Competitive Computer Games](https://mlsanity.com/paper/2508.06348/) (2025)
- [Human-AI Collaborative Bot Detection in MMORPGs](https://mlsanity.com/paper/2508.20578/) (2025)
- [Adversarial Reinforcement Learning Framework for ESP Cheater Simulation](https://mlsanity.com/paper/2509.24274/) (2025)
- [XGuardian: Towards Explainable and Generalized AI Anti-Cheat on FPS Games](https://mlsanity.com/paper/2601.18068/) (2026)
- [Detecting Aimbot Cheaters in MOGs](https://mlsanity.com/paper/2606.07650/) (2026)
- [Server-side Anti-cheat in FPS games for Aimbot detection using Deep learning and Machine learning](https://mlsanity.com/paper/2607.04336/) (2026)

## Abstract (arXiv)

Playing games with cheaters is not fun, and in a multi-billion-dollar video game industry with hundreds of millions of players, game developers aim to improve the security and, consequently, the user experience of their games by preventing cheating. Both traditional software-based methods and statistical systems have been successful in protecting against cheating, but recent advances in the automatic generation of content, such as images or speech, threaten the video game industry; they could be used to generate artificial gameplay indistinguishable from that of legitimate human players. To better understand this threat, we begin by reviewing the current state of multiplayer video game cheating, and then proceed to build a proof-of-concept method, GAN-Aimbot. By gathering data from various players in a first-person shooter game we show that the method improves players' performance while remaining hidden from automatic and manual protection mechanisms. By sharing this work we hope to raise awareness on this issue and encourage further research into protecting the gaming communities.