AI 中文总结
该研究针对CS2账号一致性审查问题，提出基于demo行为指纹的同玩家验证模型，经实验验证其能有效判断账号身份一致性，且多demo历史聚合可提升模型性能。
AI 中文摘要
在《反恐精英2》（Counter-Strike 2, CS2）这类竞技第一人称射击（FPS）游戏中，账号完整性审查常需判断某账号近期行为是否与其历史操作者保持一致。当出现临时代打、等级代练、高玩使用低等级账号等情况时，一致性问题便会凸显，人工审查需将当前对局与多场历史对局进行对比。我们将该审查任务形式化为同玩家验证：把对局录像（demo）中单个玩家的行为轨迹编码为demo玩家行为指纹，并训练模型判断两个行为观测是否来自同一真实玩家。基于对CS2游戏的理解，指纹涵盖准星控制、移动-停火协调、经济/购买、战斗/交火及时间节奏维度。我们从1330个CS2 demo中提取13300个demo玩家观测值，从8840万候选对空间中采样663590个相同/不同对用于监督训练与评估。最终的成对模型达到平均ROC AUC为0.931，且在95%精度下实现0.722的不同玩家召回率。特征分析显示，最强的身份信号来自低层级操作，尤其是准星控制、开火节奏及移动-停火协调，表明稳定的低层级机械习惯对该验证任务而言，比单场对局的表现结果更具信息价值。在账号历史聚合评估中，历史深度从K=1单对基线的0.931提升至K=10时的0.986。这些结果表明，CS2 demo行为可通过多demo历史聚合，支持有监督的同玩家验证及账号级身份一致性建模。
英文摘要
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.
Comments10 pages, 1 figure, 9 tables. Major revision: title updated; expanded datasets, strict six-fold evaluation, and additional cross-dataset and robustness analyses
