AI 中文总结
针对眼部生物识别面临的复杂呈现攻击，提出时空亮度传感器融合协议，构建同步矩阵评估相关生物延迟，通过蒙特卡罗模拟证明理论可分离性及多轮挑战设计的优势，为眼部和虹膜生物识别动态欺骗防御提供理论框架。
AI 中文摘要
眼部生物识别系统面临复杂的呈现攻击，如高分辨率视频回放和实时生成的深度伪造，容易绕过静态活体检测。当前的呈现攻击检测（PAD）框架通常依赖孤立的生理指标，这些指标可能被独立伪造。本文提出了一种时空亮度传感器融合协议，引入双流挑战-响应框架，通过将这些指标统一到同步认证挑战中进行眼部活体检测。通过生成随机、随时间变化的视觉刺激，构建同步矩阵评估眼动追踪和瞳孔收缩预期生物延迟之间的连续互相关。利用蒙特卡罗模拟证明了真实和模拟攻击条件之间的理论可分离性，表明多轮挑战设计可提高对深度伪造的检测。这项工作为下一代眼部和虹膜生物识别动态欺骗防御提供了模拟支持的理论框架，在进行部署声明之前，人体受试者验证是必要的未来工作。
英文摘要
Ocular biometric systems face sophisticated presentation attacks, including high-resolution video replays and real-time generative deepfakes, which easily bypass static liveness checks. Current Presentation Attack Detection (PAD) frameworks typically rely on isolated physiological metrics, such as gaze tracking or the Pupillary Light Reflex (PLR), which can be spoofed independently. This paper proposes a Spatio-Luminance Sensor Fusion protocol, which introduces a dual-stream challenge-response framework for ocular liveness verification by uniting these metrics into a simultaneous authentication challenge. By generating a randomized, time-varying visual stimulus that fluctuates in both spatial trajectory and luminance intensity, we construct a mathematically coupled state-space likelihood model, termed the Synchronization Matrix, to evaluate the continuous cross-correlation between the expected biological latencies of smooth pursuit tracking and pupillary constriction. Using Monte Carlo simulation grounded in literature-derived latency distributions, we demonstrate theoretical separability between genuine and simulated attack conditions, and show that a multi-round challenge design improves the detection of generative deepfakes when a non-zero rendering-latency gap exists. This work provides a simulation-supported theoretical framework for next-generation dynamic spoofing defense in ocular and iris biometrics; human-subject validation is identified as necessary future work before deployment claims can be made.
Comments6 pages, 2 figures. Simulation-based theoretical framework; human-subject validation identified as future work
