Robust Vision-Based Cheat Detection in Competitive Gaming
Open accessAbstract
Game publishers and anti-cheat companies have been unsuccessful in blocking cheating in online gaming. We propose a novel, vision-based approach that captures the frame buffer's final state and detects illicit overlays. To this aim, we train and evaluate a DNN detector on a new dataset, collected using two first-person shooter games and three cheating software. We study the advantages and disadvantages of different DNN architectures operating on a local or global scale. We use output confidence analysis to avoid unreliable detections and inform when network retraining is required. In an ablation study, we show how to use Interval Bound Propagation (IBP) to build a detector that is also resistant to potential adversarial attacks and study IBP's interaction with confidence analysis. Our results show that robust and effective anti-cheating through machine learning is practically feasible and can be used to guarantee fair play in online gaming.
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Researchers and developers have seen much success in bringing AI to bear on major problems in the industry, such as cheat detection in competitive games, as well as to improve common development processes such as playtesting, bug reporting, and other labor intensive aspects of game production.
“Researchers and developers have seen much success in bringing AI to bear on major problems in the industry, such as cheat detection in competitive games [26], as well as to improve common development processes such as playtesting, bug reporting, and other labor intensive aspects of game production [17,42].”
From Is Resistance Futile?: Early Career Game Developers, Generative AI, and Ethical Skepticism (2024) · cites this paper as [26]
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References 17 · Cited by 23
Research works printed but not linked: 2
- [16]16. Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2018. Towards Deep Learning Models Resistant to Adversarial Attacks. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 -May 3, 2018, Conference Track Proceedings. OpenReview.net. https://openreview. net/forum?id=rJzIBfZAb
- [29]29. Han Xu, Yao Ma, Haochen Liu, Debayan Deb, Hui Liu, Jiliang Tang, and Anil K. Jain. 2019. Adversarial Attacks and Defenses in Images, Graphs and Text: A Review. arXiv:1909.08072 [cs.LG] · arXiv
- Authors
- Jonnalagadda, A., Frosio, I., Schneider, S., McGuire, M., & Kim, J.
- Affiliations
- University of California, Santa Barbara (US)
- Nvidia (United States) (US)
- Venue
- Proceedings of the ACM on Computer Graphics and Interactive Techniques
- Published
- 2021-04-26
- Volume
- 4
- Issue
- 1
- Pages
- 1-18
- ISSN
- 2577-6193 (ISSN-L)
- OpenAlex
- W3159416126
- Cited by
- 23
- References
- 17
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 5 · 2023: 5 · 2022: 2
- FWCI
- 1.08
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the ACM on Computer Graphics and Interactive Techniques (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Security and Verification in Computing
- Keywords
- anti-cheating, online games, vision-based detection, first-person shooter games, deep neural network, adversarial robustness, confidence analysis, sportsmanship
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