  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.

Computer Vision and Pattern Recognition