Robust Vision-Based Cheat Detection in Competitive Gaming

TL;DR

This paper introduces a vision-based deep learning method for detecting cheating in online games by analyzing frame buffer states, demonstrating robustness against adversarial attacks and improving fairness.

Contribution

It presents a novel DNN-based cheat detection approach using a new dataset, confidence analysis, and Interval Bound Propagation for adversarial resistance, advancing anti-cheat technology.

Findings

Effective cheat detection with high accuracy.

Robustness against adversarial attacks demonstrated.

Confidence analysis improves detection reliability.

Abstract

Game publishers and anti-cheat companies have been unsuccessful in blocking cheating in online gaming. We propose a novel, vision-based approach that captures the final state of the frame buffer and detects illicit overlays. To this aim, we train and evaluate a DNN detector on a new dataset, collected using two first-person shooter games and three cheating software. We study the advantages and disadvantages of different DNN architectures operating on a local or global scale. We use output confidence analysis to avoid unreliable detections and inform when network retraining is required. In an ablation study, we show how to use Interval Bound Propagation to build a detector that is also resistant to potential adversarial attacks and study its interaction with confidence analysis. Our results show that robust and effective anti-cheating through machine learning is practically feasible and can be used to guarantee fair play in online gaming.