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.

翻译：游戏出版商和反热公司在阻止网上赌博中作弊方面没有成功。 我们提出一种新的、基于愿景的方法,捕捉框架缓冲的最终状态,并发现非法重叠。 为此,我们用两个第一人射击游戏和三个欺骗软件对新数据集的DNN探测器进行培训和评价。 我们研究了在当地或全球范围运行的不同DNN结构的利弊。 我们利用输出信心分析来避免不可靠的检测,并在需要网络再培训时提供信息。 在一项反通货膨胀研究中,我们展示了如何使用Interval Bound Propagation来建立一个也能够抵抗潜在对抗性攻击的探测器,并用信心分析来研究其互动。 我们的结果表明,通过机器学习进行有力和有效的抗切除是可行的,并且可以用来保证在线游戏中的公平游戏。