通过游戏回放分析玩家行为指纹,验证账号是否为同一人操作。
Account Consistency from Gameplay Traces: Same-Player Verification in Counter-Strike 2

- 用回放数据构建玩家行为指纹,包含瞄准、移动、经济等多维度特征。
- 在两个数据集上模型AUC分别达0.926和0.956,验证效果显著。
- 融合多段历史回放可大幅提升账号一致性判断准确率,适合反作弊场景。
在《反恐精英2》(CS2)等竞技FPS游戏中,账号完整性审查常需判断当前行为是否与历史操作一致。针对临时代打、排名提升及高玩使用低分号等场景,人工比对耗时且困难。本文将该问题建模为同玩家验证:从比赛回放中提取单个玩家的行为轨迹,构建行为指纹,并训练模型判断两段观察是否来自同一真实玩家。结合CS2领域知识,指纹涵盖准心控制、移动-停顿-开火协同、经济/购枪、战斗节奏及时间规律。在Perfect数据集(3,570个回放,35,700个行为观测)和Professional数据集(539个回放,5,390个行为观测)上进行六重严格评估,最终配对模型的ROC AUC分别为0.926和0.956。特征分析表明,瞄准/准心等底层操作是最强身份信号,远胜于单场表现结果。在固定查询群体下,将当前回放与多个历史回放进行配对证据聚合,使Perfect数据集的账号历史AUC从K=1时的0.923提升至K=10时的0.982;Professional数据集从K=1时的0.914升至K=5时的0.975。结果表明,基于多回放历史聚合的监督式同玩家验证可有效实现账号级身份一致性建模。
原文摘要 · Abstract (English)
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. Using CS2-specific domain knowledge, the fingerprints cover crosshair control, movement-stop-fire coordination, economy/buy, combat/engagement, and temporal rhythm. We construct strict six-fold evaluations on the Perfect dataset (3,570 demos and 35,700 demo-player observations) and the Professional dataset (539 demos and 5,390 demo-player observations). The final pairwise model reaches ROC AUCs of 0.926 and 0.956, respectively. Feature analysis shows that the strongest identity signals come from aiming/crosshair and other low-level mechanical behaviors, indicating that stable mechanics are more informative for this verification task than single-match performance outcomes. On fixed eligible query cohorts, aggregating pairwise evidence between a current demo and multiple historical demos raises account-history AUC on Perfect from 0.923 at K=1 to 0.982 at K=10, and on Professional from 0.914 at K=1 to 0.975 at K=5. These results show that CS2 demo behavior can support supervised same-player verification and account-level identity-consistency modeling through multi-demo history aggregation.
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