Multiplayer Online Games have become a multibillion dollar industry in the entertainment sector. However, the presence of cheaters undermines the experience of honest players and devalues the effort of game developers, as it directly affects player retention, competitive integrity, the legitimacy and trustworthiness of a game, and most importantly the overall revenue streams. Among various cheating techniques, visual aimbots represent an emerging threat. They use computer vision models to detect opponents from client screen captures rather than accessing game memory, making them completely undetectable by commercial kernel level anti cheat solutions. In this paper, we introduce PATCH, a novel proactive defense strategy that deploys adversarial patches as in game honeytokens to mitigate the presence of visual aimbot cheaters. Our approach centers on deliberately triggering the cheaters' object detection model, enabling either direct detection, or rendering the game unplayable for the cheater via patch flooding on their viewport. We evaluate our approach on various criteria; analyzing the effectiveness of different patch sizes, scalability of patches to different screen resolutions, efficacy against diverse visual aimbot cheat configurations and also explore various YOLO models to assess patch transferability. Evaluation on a custom Unreal Engine game demonstrates over 90 percent detection rate in white box scenarios for almost all patch sizes, and reaches 60 to 90 percent cross model transferability with larger patches. We further validate our approach on Fortnite, a commercial MOG, demonstrating real world applicability.

翻译：多人网游已成为娱乐产业中价值数十亿美元的行业。然而，作弊者的存在破坏了诚实玩家的游戏体验，降低了游戏开发者的劳动价值，并直接影响到玩家留存率、竞技公平性、游戏的可信度与合法性，更重要的是，影响了整体收入来源。在众多作弊技术中，视觉自瞄技术构成了一项新兴威胁。该类作弊手段通过计算机视觉模型从客户端屏幕截图中检测对手，而非访问游戏内存，因此完全无法被商业内核级反作弊解决方案检测到。本文提出PATCH，一种新型主动防御策略，通过在游戏中部署对抗性补丁作为蜜标来缓解视觉自瞄作弊者的威胁。我们的核心思路是故意触发作弊者的目标检测模型，从而能够直接检测作弊行为，或通过在其视口中进行补丁泛滥使作弊者无法正常游戏。我们从多个维度评估该方法：分析不同补丁大小的有效性、补丁对不同屏幕分辨率的可扩展性、对多种视觉自瞄作弊配置的对抗效果，并探索多种YOLO模型以评估补丁的跨模型迁移性。在自研虚幻引擎游戏上的评估表明，几乎所有补丁大小在白盒场景下均可实现超过90%的检测率，较大补丁的跨模型迁移率达到60%至90%。我们进一步在商业多人网游《堡垒之夜》中验证了该方法的现实适用性。