论文标题
gan-aimbots：使用机器学习在第一人称射击者中作弊
GAN-Aimbots: Using Machine Learning for Cheating in First Person Shooters
论文作者
论文摘要
与作弊者一起玩游戏并不有趣，在数十亿美元的视频游戏行业中，拥有数亿名玩家，游戏开发人员的目标是提高安全性，并通过防止作弊来提高其游戏的用户体验。传统的基于软件的方法和统计系统都成功地保护了作弊，但是自动生成内容（例如图像或语音）的最新进展威胁到视频游戏行业。它们可以用来与合法的人类玩家产生人造游戏玩法。为了更好地理解这种威胁，我们首先要回顾当前的多人视频游戏作弊状态，然后继续构建概念验证方法Gan-Aimbot。通过在第一人称射击游戏中收集来自各种玩家的数据，我们表明该方法可以提高玩家的性能，同时隐藏在自动和手动保护机制中。通过分享这项工作，我们希望提高人们对这个问题的认识，并鼓励进一步研究保护游戏社区。
Playing games with cheaters is not fun, and in a multi-billion-dollar video game industry with hundreds of millions of players, game developers aim to improve the security and, consequently, the user experience of their games by preventing cheating. Both traditional software-based methods and statistical systems have been successful in protecting against cheating, but recent advances in the automatic generation of content, such as images or speech, threaten the video game industry; they could be used to generate artificial gameplay indistinguishable from that of legitimate human players. To better understand this threat, we begin by reviewing the current state of multiplayer video game cheating, and then proceed to build a proof-of-concept method, GAN-Aimbot. By gathering data from various players in a first-person shooter game we show that the method improves players' performance while remaining hidden from automatic and manual protection mechanisms. By sharing this work we hope to raise awareness on this issue and encourage further research into protecting the gaming communities.
