检测镜头遮挡与光照突变，实现低误报的摄像头异常实时监控。
Clean-Reference Streaming Detection of Lens Occlusion and Photometric Transitions for Camera Tamper Monitoring
- 通过对比亮度和局部梯度统计与纯净参考，识别镜头遮挡和光照突变。
- 在320个测试序列上达0.800 F1，5%误报率下部分AUC最优，召回率达0.925。
- 适合需要可审计、低误报的工业级摄像头健康监测场景。
监控摄像头的物理退化会无声破坏其数据下游应用。为实现就地完整性报警，需具备低误报率、计算开销可控、对干扰光照变化可诊断的特性。本文设计一种面向两类低成本传感器故障信号（纹理坍缩的镜头遮挡、突发性光度场景变化）的窄范围流式完整性监测器。该检测器将采样后的亮度与局部梯度统计与仅含纯净样本的滑动参考进行比较，采用粗网格结构光剔除与模式/快速亮度抑制策略，每类篡改事件最多触发一次通知。论文形式化了判定谓词，并推导出快速亮度抑制使场景过渡路径不可达的一致性规则。在320个受控测试序列上，默认状态机达到0.800 F1和0.822平衡准确率（显著优于最强基线，但F1差异未达统计显著）；在幅度扫描公开审计中，在5%误报率预算下取得最高部分AUC；独立扩展压力测试在0.025假阳性率下实现0.925召回率。公开数据集Xiph、Bremen IoT和UHCTD的诊断表明，固定谓词保持低误报，召回率集中于声明区间内（UHCTD在范围内的召回率为0.667，范围外仅为0.016）；9.09相机小时验证负样本审计记录零误报。该方法更适合作为可审计的传感器健康子系统，而非通用摄像头篡改分类器。
原文摘要 · Abstract (English)
A surveillance camera is an image sensor whose silent physical degradation invalidates every downstream consumer of its data. In-situ integrity alarms for such vision sensors require low false-alarm rates, bounded computation, and diagnosable behavior under nuisance illumination changes. This paper studies a deliberately narrow streaming integrity monitor for two low-cost sensor-fault signatures: texture-collapsing lens occlusion and abrupt photometric scene transition. The detector compares sampled luminance and local-gradient statistics with a clean-only sliding reference, applies coarse-grid structured-light rejection and mode/rapid-brightness suppression, and emits at most one notification per tamper episode. We formalize the decision predicates and derive a consistency rule for when rapid-brightness suppression makes the scene-transition path unreachable. On 320 in-scope controlled sequences, the default state machine attains 0.800 F1 and 0.822 balanced accuracy (significantly better paired correctness than the strongest baseline, though the F1 margin is not statistically resolved); on a magnitude-swept public audit it attains the highest partial AUC under a 5\% false-alarm budget, and a separate extended-stress FPR-constrained sweep reaches 0.925 recall at 0.025 false-positive rate. Public Xiph, Bremen IoT, and UHCTD diagnostics show the fixed predicates preserve low false alarms while recall concentrates inside the declared envelope (UHCTD in-scope covered recall 0.667 versus 0.016 out of scope), and a 9.09-camera-hour verified-negative public audit records zero false alarms. The method is best interpreted as an auditable sensor-health subsystem rather than a universal camera-tamper classifier.
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