Same-Player Verification for Account Consistency in Counter-Strike 2

Abstract:In competitive first-person shooter (FPS) games such as Counter-Strike 2 (CS2), account-integrity review often asks whether an account's recent behavior remains consistent with its historical operator. This consistency question arises in cases such as temporary substitution, rank boosting, and high-skill players using lower-ranked accounts, where manual review requires comparing a current match against multiple historical matches. We formulate this review task as same-player verification: we encode the behavioral trajectory of a single player in a match replay (demo) as a demo-player behavioral fingerprint, and train a model to judge whether two behavioral observations come from the same real player. Grounded in CS2 game understanding, the fingerprints cover crosshair control, movement-stop-fire coordination, economy/buy, combat/engagement, and temporal rhythm. From 1,330 CS2 demos we extract 13,300 demo-player observations, and sample 663,590 same/different pairs from an 88.4M candidate-pair space for supervised training and evaluation. The final pairwise model reaches an average ROC AUC of 0.931 and achieves 0.722 different-player recall at 95% precision. Feature analysis shows that the strongest identity signals come from low-level operations, especially crosshair control, firing rhythm, and movement-stop-fire coordination, indicating that stable low-level mechanical habits are more informative for this verification task than single-match performance outcomes. In the account-history aggregation evaluation, increasing history depth raises AUC from the K=1 single-pair baseline of 0.931 to 0.986 at K=10. These results show that CS2 demo behavior can support supervised same-player verification and account-level identity-consistency modeling through multi-demo history aggregation.

10 pages, 2 figures, 8 tables. Submitted to IEEE for possible publication

Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2608.24893 [cs.AI]

(or arXiv:2608.24893v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.24893 Focus to learn more arXiv-issued DOI via DataCite

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