Detecting Aimbot Cheaters in MOGs

• URL: http://arxiv.org/abs/2606.07650v1
• Date: Tue, 02 Jun 2026 13:44:55 GMT
• Title: Detecting Aimbot Cheaters in MOGs
• Abstract summary: We present a novel proactive defense strategy that deploys adversarial patches to mitigate the presence of visual aimbot cheaters.<n>Our approach centers on deliberately triggering the cheaters' object model, enabling either direct detection, or rendering the game unplayable for the cheater via patch flooding on their viewport.<n>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.
• Score: 2.326937537253793
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Server-side Anti-cheat in FPS games for Aimbot detection using Deep learning and Machine learning [0.0]
  The rising popularity of FPS games emphasizes the need to combat cheating for fair and enjoyable gaming.<n>We need a way to detect players who are using cheating tools to gain an unfair advantage over regular players.<n>In this system, we focus exclusively on detecting aimbot cheats.
  arXiv  Detail & Related papers  (2026-07-05T14:34:09Z)
• Shoot the Honey, Cloak the Player: Towards Zero-Runtime-Overhead Proactive Defense and Detection for Visual Game Cheating [12.293978482912264]
  AimTrap is an end-to-end defense against visual aimbots in first-person shooter games.<n>It combines real-time protection with post-game detection using two adversarial texture mechanisms.<n>It attains extremely low false-positive rates, while incurring negligible runtime overhead.
  arXiv  Detail & Related papers  (2026-06-24T12:00:10Z)
• Watermarking Game-Playing Agents in Perfect-Information Extensive-Form Games [63.23335721758144]
  We show how the KGW watermark for LLMs can be adapted to watermark game-playing agents in perfect-information extensive-form games.<n>We show that the degradation in the quality of the watermarked strategy profile, quantified by the expected utility, can be bounded, but there is a tradeoff between detectability and quality.
  arXiv  Detail & Related papers  (2026-05-14T02:33:30Z)
• XGuardian: Towards Explainable and Generalized AI Anti-Cheat on FPS Games [13.543176436045833]
  Aim-assist cheats are the most prevalent and infamous form of cheating in First-Person Shooter (FPS) games.<n>XGuardian is a server-side generalized and explainable system for detecting aim-assist cheats.
  arXiv  Detail & Related papers  (2026-01-26T01:57:15Z)
• Adversarial Reinforcement Learning Framework for ESP Cheater Simulation [27.73673717943896]
  Extra-Sensory Perception (ESP) cheats are difficult to detect because their effects are not directly observable in player behavior.<n>We propose a simulation framework for controlled modeling of ESP cheaters, non-cheaters, and trajectory-based detectors.
  arXiv  Detail & Related papers  (2025-09-29T04:32:45Z)
• VIC: Evasive Video Game Cheating via Virtual Machine Introspection [1.7495213911983414]
  Video game cheats modify a video game behaviour to give unfair advantages to some players.<n>This destroys the experience of online gaming and can result in financial losses for game developers.<n>We present a new type of game cheat, Virtual machine Introspection Cheat (VIC), that takes advantage of virtual machines to execute stealthy game cheats.
  arXiv  Detail & Related papers  (2025-02-17T20:54:56Z)
• GAN-Aimbots: Using Machine Learning for Cheating in First Person Shooters [15.770861074132135]
  Game developers aim to improve the security and user experience of their games by preventing cheating. Traditional software-based methods and statistical systems have been successful in protecting against cheating. Recent advances in the automatic generation of content, such as images or speech, threaten the video game industry.
  arXiv  Detail & Related papers  (2022-05-14T13:33:23Z)
• Collusion Detection in Team-Based Multiplayer Games [57.153233321515984]
  We propose a system that detects colluding behaviors in team-based multiplayer games. The proposed method analyzes the players' social relationships paired with their in-game behavioral patterns. We then automate the detection using Isolation Forest, an unsupervised learning technique specialized in highlighting outliers.
  arXiv  Detail & Related papers  (2022-03-10T02:37:39Z)
• ObjectSeeker: Certifiably Robust Object Detection against Patch Hiding Attacks via Patch-agnostic Masking [95.6347501381882]
  Object detectors are found to be vulnerable to physical-world patch hiding attacks. We propose ObjectSeeker as a framework for building certifiably robust object detectors.
  arXiv  Detail & Related papers  (2022-02-03T19:34:25Z)
• Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detection [142.24869736769432]
  Adversarial patch attacks pose a serious threat to state-of-the-art object detectors. We propose Segment and Complete defense (SAC), a framework for defending object detectors against patch attacks. We show SAC can significantly reduce the targeted attack success rate of physical patch attacks.
  arXiv  Detail & Related papers  (2021-12-08T19:18:48Z)
• Unsupervised Visual Representation Learning by Tracking Patches in Video [88.56860674483752]
  We propose to use tracking as a proxy task for a computer vision system to learn the visual representations. Modelled on the Catch game played by the children, we design a Catch-the-Patch (CtP) game for a 3D-CNN model to learn visual representations.
  arXiv  Detail & Related papers  (2021-05-06T09:46:42Z)
• Certified Defenses for Adversarial Patches [72.65524549598126]
  Adversarial patch attacks are among the most practical threat models against real-world computer vision systems. This paper studies certified and empirical defenses against patch attacks.
  arXiv  Detail & Related papers  (2020-03-14T19:57:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.