AI 中文总结
该研究提出多层上下文伪装理论（MCCT），构建含六个耦合结构的数学框架，通过语义叠加保护在线评估内容，保障合法用户恢复，为抗作弊在线评估提供理论基础。
AI 中文摘要
当代在线评估系统主要依赖浏览器锁定、网络摄像头监控和行为分析，但仍易受通过截图、屏幕共享、光学字符识别和自动抓取提取评估内容的攻击。本文在MARS（多模态评估抗扰套件）内扩展了多维时空上下文伪装模型（MSCCM），引入多层上下文伪装理论（MCCT），这是一种通过语义叠加保护渲染后评估内容的数学框架。真实评估内容与合成生成的伪装被表示为统一渲染，仅合法考生可恢复。该框架通过显式提取通道算子对对抗性提取过程建模，开发了六个耦合结构：上下文逆算子、上下文分层算子、分离通道、人类可读性函数、计算歧义函数和上下文伪装张量。计算歧义用条件熵公式化，得到量化未授权提取期间不确定性的闭式表达式，而合法恢复通过精确过滤恒等式保证。我们进一步建立了控制歧义、伪装密度、语义保留、多观测泄漏和时间复用的理论性质，并提出了具有计算复杂度的渲染算法和预注册评估协议。MCCT通过在保护渲染后评估内容的同时保留合法用户的可读性，为行为自适应、可访问性感知和计算弹性的数字评估提供了数学严谨的基础。
英文摘要
Contemporary online assessment systems rely primarily on browser lockdown, webcam monitoring, and behavioural analytics, yet remain vulnerable to attacks that extract the assessment content itself through screenshots, screen sharing, optical character recognition, and automated scraping. This paper extends the Multi-dimensional Spatio-Temporal Context Camouflaging Model (MSCCM) within the MARS (Multi-modal Assessment Resilience Suite) by introducing the Multi-Layer Context Camouflaging Theory (MCCT), a mathematical framework that protects rendered assessment content through semantic superposition. Authentic assessment content and synthetically generated camouflage are represented as a unified rendering while remaining recoverable only by legitimate candidates. The framework models the adversarial extraction process through an explicit extraction-channel operator and develops six coupled constructs: the Context Inversion Operator, Contextual Lamination Operator, Separation Channel, Human Readability Functional, Computational Ambiguity Functional, and Context Camouflage Tensor. Computational ambiguity is formulated using conditional entropy, yielding a closed-form expression that quantifies uncertainty during unauthorized extraction, while legitimate recovery is guaranteed through an exact filtering identity. We further establish theoretical properties governing ambiguity, camouflage density, semantic preservation, multi-observation leakage, and temporal multiplexing, and present a rendering algorithm with computational complexity and a pre-registered evaluation protocol. MCCT provides a mathematically rigorous foundation for behaviorally adaptive, accessibility-aware, and computationally resilient digital assessment by securing rendered assessment content while preserving readability for legitimate users.
Comments11 Pages, 36 Equations, 8 Figures
