AimTrap is an end-to-end system using Adversarial Camouflage Textures (ACT) and Adversarial Honeypot Textures (AHT) synthesized via differentiable rendering to defend against and detect visual aimbots, with reported success rates of 85.1% and 96.9% and negligible overhead.
In: The International Joint Conference on Neural Networks (IJCNN) (2012)
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CR 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
PATCH uses adversarial patches deployed as in-game honeytokens to detect or disrupt visual aimbot cheaters relying on screen-capture object detection, showing over 90% white-box detection and 60-90% cross-model transferability on a custom Unreal Engine game and Fortnite.
citing papers explorer
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Shoot the Honey, Cloak the Player: Towards Zero-Runtime-Overhead Proactive Defense and Detection for Visual Game Cheating
AimTrap is an end-to-end system using Adversarial Camouflage Textures (ACT) and Adversarial Honeypot Textures (AHT) synthesized via differentiable rendering to defend against and detect visual aimbots, with reported success rates of 85.1% and 96.9% and negligible overhead.
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Detecting Aimbot Cheaters in MOGs
PATCH uses adversarial patches deployed as in-game honeytokens to detect or disrupt visual aimbot cheaters relying on screen-capture object detection, showing over 90% white-box detection and 60-90% cross-model transferability on a custom Unreal Engine game and Fortnite.