2021/03/18 by Aditya Jonnalagadda, Iuri Frosio, Jonnalagadda, Aditya +7 · 3 citations
Computer Science · #Advanced Malware Detection Techniques #Adversarial Robustness in Machine Learning #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Network Security and Intrusion Detection
paper · pdf · doi:10.48550/arxiv.2103.10031
openalex publication_date 2021/03/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.