基于游戏轨迹的账号一致性：反恐精英2中的同玩家验证
文章背景与核心概要
在《反恐精英2》（CS2）等竞技类第一人称射击（FPS）游戏中，维护账号完整性始终是一项严峻挑战。代练、账号共享以及“炸鱼”（高水平玩家使用低分段账号）等行为严重破坏了游戏环境。传统的审核方式往往依赖人工对比，效率低下且难以量化。
本文提出了一种基于监督学习的“同玩家验证”框架，通过将比赛录像（Demo）中的玩家行为轨迹编码为独特的“行为指纹”，实现了对账号操作者身份的自动验证。研究表明，瞄准控制、移动射击协调等底层机械行为具有极高的身份识别度，能够有效识别账号是否由同一玩家操作，为解决账号一致性问题提供了强有力的技术支撑。
📌 执行摘要
在《反恐精英2》（CS2）等竞技类第一人称射击（FPS）游戏中，维护账号完整性是一个持续存在的挑战。诸如临时代打、排位代练以及“炸鱼”（高水平玩家操作低分段账号）等问题，都需要验证账号近期的行为是否与其历史操作者相符。
本文引入了一个监督式同玩家验证框架，将玩家在比赛录像（Demo）中的行为轨迹编码为独特的行为指纹。通过在大型数据集（Perfect 和 Professional）上的评估，该方法证明了低级机械行为（如瞄准和准星控制）提供了稳健的身份信号，能够可靠地验证跨多场比赛的玩家一致性。
Maintaining account integrity in competitive first-person shooter (FPS) games like Counter-Strike 2 (CS2) is a persistent challenge. Issues such as temporary substitution, rank boosting, and smurfing (high-skill players operating lower-ranked accounts) require verifying whether an account's recent behavior matches its historical operator.
This paper introduces a supervised same-player verification framework that encodes a player's behavioral trajectory from match replays (demos) into distinct behavioral fingerprints. Evaluated across large datasets (Perfect and Professional), the proposed approach demonstrates that low-level mechanical behaviors (such as aiming and crosshair control) provide robust identity signals capable of reliably verifying player consistency across multiple matches.
🔍 摘要
在《反恐精英2》（CS2）等竞技类第一人称射击游戏中，账号完整性审查通常需要判断账号近期的行为是否与历史操作者保持一致。这种一致性问题出现在临时代打、排位代练以及高水平玩家使用低分段账号等场景中，而人工审核通常需要将当前比赛与多场历史比赛进行对比。
我们将此审查任务定义为同玩家验证： 1. 我们将比赛录像（Demo）中单个玩家的行为轨迹编码为“Demo-玩家行为指纹”。 2. 我们训练一个模型来判断两个行为观测值是否来自同一个真实玩家。
利用 CS2 特有的领域知识，这些指纹涵盖了： * 准星控制 * 移动-停止-射击协调 * 经济/购买习惯 * 战斗/交战模式 * 时间节奏
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: 1. We encode the behavioral trajectory of a single player in a match replay (demo) as a demo-player behavioral fingerprint. 2. We 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 habits * Combat/engagement patterns * Temporal rhythm
📊 评估与结果
该模型在两个综合数据集上进行了严格的六折交叉验证： * Perfect 数据集： 3,570 个 Demo 和 35,700 个 Demo-玩家观测值 * Professional 数据集： 539 个 Demo 和 5,390 个 Demo-玩家观测值
关键发现
- 成对性能： 成对模型在 Perfect 数据集上的 ROC AUC 达到 0.926，在 Professional 数据集上达到 0.956。
- 特征重要性： 特征分析表明，最强的身份信号源于瞄准、准星操控以及其他低级机械行为。稳定的机械操作在身份验证方面比单场比赛的胜负结果具有显著更高的信息量。
- 多 Demo 历史聚合： 通过聚合当前 Demo 与多个历史 Demo (\(K\)) 之间的成对证据，账号历史的 AUC 得到了显著提升：
- Perfect 数据集： 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 \(0.914\) 提升至 \(K=5\) 时的 \(0.975\)。
这些结果证实，CS2 的 Demo 轨迹可以有效支持监督式同玩家验证和账号级身份一致性建模。
The model was evaluated using strict six-fold evaluations on two comprehensive datasets: * Perfect Dataset: 3,570 demos and 35,700 demo-player observations * Professional Dataset: 539 demos and 5,390 demo-player observations
Key Findings
- Pairwise Performance: The pairwise model reached ROC AUCs of 0.926 (Perfect dataset) and 0.956 (Professional dataset).
- Feature Importance: Feature analysis indicates that the strongest identity signals originate from aiming, crosshair handling, and other low-level mechanical behaviors. Stable mechanics are significantly more informative for identity verification than single-match performance outcomes.
- Multi-Demo History Aggregation: By aggregating pairwise evidence between a current demo and multiple historical demos (\(K\)), account-history AUC improved substantially:
- Perfect Dataset: Increased from \(0.923\) at \(K=1\) to \(0.982\) at \(K=10\).
- Professional Dataset: Increased from \(0.914\) at \(K=1\) to \(0.975\) at \(K=5\).
These results confirm that CS2 demo trajectories can effectively support supervised same-player verification and account-level identity-consistency modeling.