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

Abstract

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.