Shoot the Honey, Cloak the Player: Towards Zero-Runtime-Overhead Proactive Defense and Detection for Visual Game Cheating

Computer Science > Cryptography and Security [Submitted on 24 Jun 2026] Shoot the Honey, Cloak the Player: Towards Zero-Runtime-Overhead Proactive Defense and Detection for Visual Game Cheating Jianing Wang, Chuqi Zhang, Yuancheng Jiang, Adil Ahmad, Shanqing Guo Visual aimbots have emerged as a serious cheating threat in first-person shooter (FPS) games, as they evade existing anti-cheat defenses by operating only on rendered frames rather than game memory. However, existing defenses fail to provide an end-to-end solution: post-hoc behavior detectors cannot protect match integrity in real time and are increasingly fragile against human-mimicking aimbots, while proactive runtime defenses often lack accountability, incur substantial overhead, or require intrusive system integration. We present AimTrap, the first end-to-end defense against visual aimbots that combines real-time protection with post-game detection using two adversarial texture mechanisms. Adversarial Camouflage Textures (ACT) hide real players from aimbots, while Adversarial Honeypot Textures (AHT) lure aimbots into locking onto fake targets, yielding strong evidence of cheating. To make this practical, AimTrap integrates differentiable rendering with Expectation over Renderings for robust 3D texture synthesis, secure texture management, and a novel honeypot-interaction trajectory analysis pipeline for accurate cheating attribution. In real-game evaluation against a state-of-the-art visual aimbot, ACT achieves 85.1% defense success, AHT achieves 96.9%. Compared with prior baselines, AimTrap attains extremely low false-positive rates, while incurring negligible runtime overhead. These results show that AimTrap provides a practical and effective end-to-end defense against visual aimbots. Comments: 15 pages Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2606.25734 [cs.CR]   (or arXiv:2606.25734v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2606.25734 Focus to learn more Submission history From: Jianing Wang [view email] [v1] Wed, 24 Jun 2026 12:00:10 UTC (10,624 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)