Robust Vision-Based Cheat Detection in Competitive Gaming

by   Aditya Jonnalagadda, et al.

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.



There are no comments yet.


page 1

page 12

page 14


ASSERT: Anti-Spoofing with Squeeze-Excitation and Residual neTworks

We present JHU's system submission to the ASVspoof 2019 Challenge: Anti-...

ActiveNet: A computer-vision based approach to determine lethargy

The outbreak of COVID-19 has forced everyone to stay indoors, fabricatin...

Vision-based Navigation of Autonomous Vehicle in Roadway Environments with Unexpected Hazards

Vision-based navigation of modern autonomous vehicles primarily depends ...

An Anti-fraud System for Car Insurance Claim Based on Visual Evidence

Automatically scene understanding using machine learning algorithms has ...

GGT: Graph-Guided Testing for Adversarial Sample Detection of Deep Neural Network

Deep Neural Networks (DNN) are known to be vulnerable to adversarial sam...

Metamorphic Relation Based Adversarial Attacks on Differentiable Neural Computer

Deep neural networks (DNN), while becoming the driving force of many nov...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.