文章背景与核心概要
在像《反恐精英2》（Counter-Strike 2, CS2）这样的竞技类第一人称射击（FPS）游戏中，维护账号完整性面临着持久的挑战。临时顶替、代练以及小号虐菜（高分玩家使用低分账号）等违规行为，都需要平台能够验证账号近期的行为是否与其历史操作者相符。传统的审核手段往往耗时且依赖人工，缺乏一种能够通过客观数据自动化、精准化识别身份变化的有效方案。
为了解决这一痛点，该论文提出了一种监督式的同名玩家验证框架。该方法通过解析比赛回溯（Demo）文件，将玩家在单场比赛中的行为轨迹编码为独特的“行为指纹”（涵盖准星控制、急停开火协调、经济习惯、交战模式以及时间节奏等）。通过在大规模数据集上的严格评估，研究表明：低级别的机械行为（如瞄准和准星控制）能够提供极具鲁棒性的身份信号，从而在多场比赛中可靠地验证玩家的一致性。多场历史回溯数据的聚合更进一步将模型准确率推向了新高，为游戏安全和账号级别的身份一致性建模提供了强有力的技术支撑。
Account Consistency from Gameplay Traces: Same-Player Verification in Counter-Strike 2
Authors: Xuchen Zhang
Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv: 2608.24893v2 [cs.AI]
Published: 20 Jun 2026 (Last revised: 27 Aug 2026)
DOI: 10.48550/arXiv.2608.24893
📌 Executive Summary
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项长期的挑战。诸如临时替打、代练冲分以及小号虐菜（高水平玩家操作低段位账号）等问题，都需要验证账号最近的行为是否与其历史操作者相符。
本文引入了一种监督式的同名玩家验证（same-player verification）框架，该框架将比赛回放（demo）中的玩家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数据集）上的评估，该方法证明了低级机械行为（如瞄准和准星控制）能够提供强大的身份信号，从而在多场比赛中可靠地验证玩家的一致性。
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.
🔍 Abstract
在《反恐精英2》（CS2）等竞技类第一人称射击（FPS）游戏中，账号完整性审查通常需要判断账号最近的行为与其历史操作者是否保持一致。这种一致性问题出现在临时替打、代练以及高水平玩家使用低分账号等场景中，在这些场景下，人工审查需要将当前比赛与多个历史比赛进行对比。
我们将此审查任务归纳为同名玩家验证： 1. 我们将比赛回放（demo）中单名玩家的行为轨迹编码为回放-玩家行为指纹（demo-player behavioral fingerprint）。 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特定的领域知识，这些指纹涵盖了： * 准星控制 * 移动-急停-开火协调 * 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
📊 Evaluation & Results
该模型在两个全面的数据集上使用严格的六折交叉验证进行了评估： * Perfect 数据集： 3,570 场回放和 35,700 个回放-玩家观测值 * Professional 数据集： 539 场回放和 5,390 个回放-玩家观测值
核心发现
- 成对匹配性能： 成对模型的 ROC AUC 在 Perfect 数据集上达到了 0.926，在 Professional 数据集上达到了 0.956。
- 特征重要性： 特征分析表明，最强的身份信号来自于瞄准、准星操控以及其他低级机械行为。对于身份验证而言，稳定的机械操作比单场比赛的竞技表现结果提供的信息要丰富得多。
- 多回放历史聚合： 通过聚合当前回放与多个历史回放（\(K\)）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 回放轨迹可以有效支持监督式的同名玩家验证以及账号级别的身份一致性建模。
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.
🔗 Full-Text & Resources
🔗 Full-Text & Resources