AI 中文总结
本文提出BAVD框架，通过自适应视觉干扰在保障数字评估防捕获安全的同时，兼顾考生可访问性，为可信数字评估平台提供理论基础。
AI 中文摘要
机构日益依赖浏览器锁定、网络摄像头监控和行为分析来保障高风险数字评估的安全性，但这些机制通常是独立设计和评估的，且往往忽视学习者的可访问性。本文提出了行为自适应视觉干扰（BAVD）理论框架，该框架将合成的非语义视觉场与评估内容合成，并根据观察到的考生行为进行自适应调节。底层评估内容从不被修改，仅修改其视觉呈现方式，以降低未经授权的屏幕捕获或屏幕共享的实用性，同时对合法考生的干扰最小。该框架还包含一个可访问性感知衰减机制，可为获得批准的视觉处理便利条件的考生降低或抑制干扰强度。我们使用耦合动力系统表示来构建模型，该表示包括干扰场生成器、渲染张量、行为张量、复合完整性函数和多维熵模型，并确立了内容保真度、渲染稳定性、熵有界性、完整性跟踪和闭环自适应稳定性的理论特性。该框架明确阐述了其威胁模型，确定了部署假设和局限性，并讨论了可访问性与抗捕获性之间的权衡。本工作为行为自适应和可访问性感知的评估交付提供了基于数学的基础，并为可信数字评估平台的未来实证验证提供了依据。
英文摘要
Institutions increasingly rely on browser lockdown, webcam monitoring, and behavioral analytics to secure high-stakes digital assessments, yet these mechanisms are commonly designed and evaluated independently and often overlook learner accessibility. This paper introduces Behaviorally-Adaptive Visual Diversion (BAVD), a theoretical framework in which a synthetic, non-semantic visual field is composited with assessment content and adaptively modulated according to observed candidate behavior. The underlying assessment content is never altered; only its visual presentation is modified to reduce the usefulness of unauthorized screen capture or screen sharing while remaining minimally intrusive for legitimate candidates. The framework further incorporates an accessibility-aware attenuation mechanism that reduces or suppresses diversion intensity for candidates with approved visual-processing accommodations. We formulate the model using a coupled dynamical-systems representation comprising a Diversion Field Generator, Rendering Tensor, Behavior Tensor, Composite Integrity Functional, and Multi-dimensional Entropy Model, and establish theoretical properties for content fidelity, rendering stability, entropy boundedness, integrity tracking, and closed-loop adaptation stability. The framework explicitly states its threat model, identifies deployment assumptions and limitations, and discusses the trade-off between accessibility and capture resistance. This work provides a mathematically grounded foundation for behaviorally adaptive and accessibility-aware assessment delivery and offers a basis for future empirical validation in trusted digital assessment platforms.
Comments15 Pages, 7 Figures, 25 Equations
