Related papers: VIC: Evasive Video Game Cheating via Virtual Machine Introspection

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Server-side Anti-cheat in FPS games for Aimbot detection using Deep learning and Machine learning [0.0]
The rising popularity of FPS games emphasizes the need to combat cheating for fair and enjoyable gaming.<n>We need a way to detect players who are using cheating tools to gain an unfair advantage over regular players.<n>In this system, we focus exclusively on detecting aimbot cheats.
arXiv Detail & Related papers (2026-07-05T14:34:09Z)
Cheating in Multiplayer Online Games: a Dataset [1.140634831132165]
This paper presents a dataset compiling records of game sessions played by both real players and automated game clients.<n>To the best of our knowledge, this is the first dataset that provides logs of network flow disruption cheats.<n>This dataset can be used by researchers in academia and industry seeking to develop cheating detection mechanisms for online games.
arXiv Detail & Related papers (2026-06-04T11:03:18Z)
Detecting Aimbot Cheaters in MOGs [2.326937537253793]
We present a novel proactive defense strategy that deploys adversarial patches to mitigate the presence of visual aimbot cheaters.<n>Our approach centers on deliberately triggering the cheaters' object model, enabling either direct detection, or rendering the game unplayable for the cheater via patch flooding on their viewport.<n>We evaluate our approach on various criteria; analyzing the effectiveness of different patch sizes, scalability of patches to different screen resolutions, efficacy against diverse visual aimbot cheat configurations and also explore various YOLO models to assess patch transferability.
arXiv Detail & Related papers (2026-06-02T13:44:55Z)
Do Synthetic Trajectories Reflect Real Reward Hacking? A Systematic Study on Monitoring In-the-Wild Hacking in Code Generation [53.024513172383195]
We present a systematic analysis of the synthetic vs. in-the-wild discrepancy in reward hacking.<n>We examine what extent hacking behaviors induced by prompting resemble those emerging during RL training.<n>We find that synthetic-data-trained monitors fail to generalize to "in-the-wild" hacking.
arXiv Detail & Related papers (2026-04-26T01:26:50Z)
Adversarial Reward Auditing for Active Detection and Mitigation of Reward Hacking [69.06218054848803]
We propose Adrial Reward Auditing (ARA), a framework that reconceptualizes reward hacking as a dynamic, competitive game.<n>ARA operates in two stages: first, a Hacker policy discovers reward model vulnerabilities while an Auditor learns to detect exploitation from latent representations.<n>ARA achieves the best alignment-utility tradeoff among all baselines.
arXiv Detail & Related papers (2026-02-02T07:34:57Z)
XGuardian: Towards Explainable and Generalized AI Anti-Cheat on FPS Games [13.543176436045833]
Aim-assist cheats are the most prevalent and infamous form of cheating in First-Person Shooter (FPS) games.<n>XGuardian is a server-side generalized and explainable system for detecting aim-assist cheats.
arXiv Detail & Related papers (2026-01-26T01:57:15Z)
Adversarial Reinforcement Learning Framework for ESP Cheater Simulation [27.73673717943896]
Extra-Sensory Perception (ESP) cheats are difficult to detect because their effects are not directly observable in player behavior.<n>We propose a simulation framework for controlled modeling of ESP cheaters, non-cheaters, and trajectory-based detectors.
arXiv Detail & Related papers (2025-09-29T04:32:45Z)
AntiCheatPT: A Transformer-Based Approach to Cheat Detection in Competitive Computer Games [0.0]
Anti-cheat systems face challenges in keeping pace with evolving cheating methods without imposing invasive measures on users' systems.<n>This paper presents AntiCheatPT_256, a transformer-based machine learning model designed to detect cheating behaviour in Counter-Strike 2 using gameplay data.
arXiv Detail & Related papers (2025-08-08T14:22:41Z)
Addressing Network Packet-based Cheats in Multiplayer Games: A Secret Sharing Approach [3.304472814852163]
We present a new approach for detecting network packet-based cheats, such as forgery and timing cheats, within the context of multiplayer games.<n>We show that our proposal is practical in addressing some widely used attacks in online gaming.
arXiv Detail & Related papers (2025-01-18T21:11:38Z)
Identify As A Human Does: A Pathfinder of Next-Generation Anti-Cheat Framework for First-Person Shooter Games [25.04499987558611]
Cheating in online games poses a significant threat to the integrity of the gaming experience. The paper proposes HAWK, a server-side FPS anti-cheat framework for the popular game CS:GO.
arXiv Detail & Related papers (2024-09-23T09:00:07Z)
Not All Prompts Are Secure: A Switchable Backdoor Attack Against Pre-trained Vision Transformers [51.0477382050976]
An extra prompt token, called the switch token in this work, can turn the backdoor mode on, converting a benign model into a backdoored one. To attack a pre-trained model, our proposed attack, named SWARM, learns a trigger and prompt tokens including a switch token. Experiments on diverse visual recognition tasks confirm the success of our switchable backdoor attack, achieving 95%+ attack success rate.
arXiv Detail & Related papers (2024-05-17T08:19:48Z)
Turn Fake into Real: Adversarial Head Turn Attacks Against Deepfake Detection [58.1263969438364]
We propose adversarial head turn (AdvHeat) as the first attempt at 3D adversarial face views against deepfake detectors. Experiments validate the vulnerability of various detectors to AdvHeat in realistic, black-box scenarios. Additional analyses demonstrate that AdvHeat is better than conventional attacks on both the cross-detector transferability and robustness to defenses.
arXiv Detail & Related papers (2023-09-03T07:01:34Z)
Promptable Game Models: Text-Guided Game Simulation via Masked Diffusion Models [68.85478477006178]
We present a Promptable Game Model (PGM) for neural video game simulators. It allows a user to play the game by prompting it with high- and low-level action sequences. Most captivatingly, our PGM unlocks the director's mode, where the game is played by specifying goals for the agents in the form of a prompt. Our method significantly outperforms existing neural video game simulators in terms of rendering quality and unlocks applications beyond the capabilities of the current state of the art.
arXiv Detail & Related papers (2023-03-23T17:43:17Z)
Inspector: Pixel-Based Automated Game Testing via Exploration, Detection, and Investigation [116.41186277555386]
Inspector is a game testing agent that can be easily applied to different games without deep integration with games. Inspector is based on purely pixel inputs and comprises three key modules: game space explorer, key object detector, and human-like object investigator. Experiment results demonstrate the effectiveness of Inspector in exploring game space, detecting key objects, and investigating objects.
arXiv Detail & Related papers (2022-07-18T04:49:07Z)
GAN-Aimbots: Using Machine Learning for Cheating in First Person Shooters [15.770861074132135]
Game developers aim to improve the security and user experience of their games by preventing cheating. Traditional software-based methods and statistical systems have been successful in protecting against cheating. Recent advances in the automatic generation of content, such as images or speech, threaten the video game industry.
arXiv Detail & Related papers (2022-05-14T13:33:23Z)
Collusion Detection in Team-Based Multiplayer Games [57.153233321515984]
We propose a system that detects colluding behaviors in team-based multiplayer games. The proposed method analyzes the players' social relationships paired with their in-game behavioral patterns. We then automate the detection using Isolation Forest, an unsupervised learning technique specialized in highlighting outliers.
arXiv Detail & Related papers (2022-03-10T02:37:39Z)
An Unsupervised Video Game Playstyle Metric via State Discretization [20.48689549093258]
We propose the first metric for video game playstyles directly from the game observations and actions. Our proposed method is built upon a novel scheme of learning discrete representations. We demonstrate high playstyle accuracy of our metric in experiments on some video game platforms.
arXiv Detail & Related papers (2021-10-03T08:30:51Z)
Robust Vision-Based Cheat Detection in Competitive Gaming [12.124621973070164]
We propose a vision-based approach that captures the final state of the frame buffer and detects illicit overlays. Our results show that robust and effective anti-cheating through machine learning is practically feasible.
arXiv Detail & Related papers (2021-03-18T06:06:52Z)
DeepCrawl: Deep Reinforcement Learning for Turn-based Strategy Games [137.86426963572214]
We introduce DeepCrawl, a fully-playable Roguelike prototype for iOS and Android in which all agents are controlled by policy networks trained using Deep Reinforcement Learning (DRL) Our aim is to understand whether recent advances in DRL can be used to develop convincing behavioral models for non-player characters in videogames.
arXiv Detail & Related papers (2020-12-03T13:53:29Z)
Learning by Cheating [72.9701333689606]
We show that this challenging learning problem can be simplified by decomposing it into two stages. We use the presented approach to train a vision-based autonomous driving system that substantially outperforms the state of the art. Our approach achieves, for the first time, 100% success rate on all tasks in the original CARLA benchmark, sets a new record on the NoCrash benchmark, and reduces the frequency of infractions by an order of magnitude compared to the prior state of the art.
arXiv Detail & Related papers (2019-12-27T18:59:04Z)

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