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

2026-06-24

AI Summary

This work addresses the challenge of vision-based cheats in first-person shooter games that evade traditional anti-cheat mechanisms by proposing AimTrap, a zero-runtime-overhead, end-to-end defense framework. AimTrap integrates adversarial camouflage textures to conceal legitimate players and adversarial honeypot textures to lure and trap cheating agents. It leverages differentiable and expected rendering to generate secure 3D textures and employs honeypot interaction trajectory analysis for effective detection and attribution. Evaluated in real-world gaming environments, AimTrap achieves an 85.1% defense success rate and a 96.9% honeypot trapping rate with negligible false positives, marking the first unified approach that seamlessly combines proactive defense with post-incident forensic tracing.

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.

Problem

• visual aimbots
• anti-cheat defense
• real-time protection
• cheating detection
• FPS games

Innovation

• adversarial textures
• visual aimbot defense
• differentiable rendering
• honeypot interaction analysis
• zero-runtime-overhead