  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.
