XGuardian: Towards Explainable and Generalized AI Anti-Cheat on FPS Games

TL;DR

XGuardian is a novel server-side system that detects aim-assist cheats in FPS games using minimal data, offering high accuracy, explainability, and cross-game generalizability, validated on large-scale datasets from multiple games.

Contribution

It introduces a generalized, explainable, and efficient cheat detection system that works across different FPS games using only pitch and yaw data.

Findings

High detection accuracy across multiple games

Low computational overhead

Effective explanation of detection results

Abstract

Aim-assist cheats are the most prevalent and infamous form of cheating in First-Person Shooter (FPS) games, which help cheaters illegally reveal the opponent's location and auto-aim and shoot, and thereby pose significant threats to the game industry. Although a considerable research effort has been made to automatically detect aim-assist cheats, existing works suffer from unreliable frameworks, limited generalizability, high overhead, low detection performance, and a lack of explainability of detection results. In this paper, we propose XGuardian, a server-side generalized and explainable system for detecting aim-assist cheats to overcome these limitations. It requires only two raw data inputs, pitch and yaw, which are all FPS games' must-haves, to construct novel temporal features and describe aim trajectories, which are essential for distinguishing cheaters and normal players. XGuardian is evaluated with the latest mainstream FPS game CS2, and validates its generalizability with another two different games. It achieves high detection performance and low overhead compared to prior works across different games with real-world and large-scale datasets, demonstrating wide generalizability and high effectiveness. It is able to justify its predictions and thereby shorten the ban cycle. We make XGuardian as well as our datasets publicly available.