Combating Computer Vision-Based Aim Assist Tools in Competitive Online Games

Abstract

This work presents a novel approach to the application of adversarial attacks to the domain of video games, specifically, the exploitation of computer vision-based aim assist tools in the first-person shooter genre. As one of the greatest issues plaguing modern shooters, aim assist (also referred to as aimbots) greatly increase the speed and accuracy of cheating players, giving them an unfair advantage over their competitors. The latest versions of these aim assisting tools make use of object detection models such as YOLO (You Only Look Once); fortunately, these models are vulnerable to attack via small perturbations to their input space which results in the misclassification of objects. The purpose of this work is to formulate an attack on a black-box object detection model which can be feasibly implemented in a commercial game environment. What makes our solution unique is the generation of attack images in the form of in-game objects rendered by the game engine itself, instead of a set of screenshots or from a generic differentiable renderer. Results show that our approach is capable of generating adversarial examples which can fool an object detection model in a black-box environment, as well as recreating the game’s original textures such that these perturbations go unnoticed by players.