发表机构
Auckland University of Technology; Guilin University of Electronic Technology（奥克兰理工大学; 桂林电子科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对相机镜头遮挡和光度转换的监测问题，提出通过比较采样数据与清洁参考、应用多种抑制方法来检测，在多组实验中取得较好效果，能保持低误报率，可作为可审计的传感器健康子系统。
AI 中文摘要
监控摄像头作为图像传感器，其物理性能的无声退化会使下游数据使用者的数据无效。此类视觉传感器的原位完整性警报需要低误报率、有限的计算量以及在有害光照变化下具有可诊断行为。本文研究了一种针对两种低成本传感器故障特征的窄流完整性监测器，即纹理坍塌镜头遮挡和突然的光度场景转换。该检测器将采样的亮度和局部梯度统计数据与仅包含清洁数据的滑动参考进行比较，应用粗网格结构光抑制和模式/快速亮度抑制，并且每个篡改事件最多发出一次通知。我们形式化了决策谓词，并推导了快速亮度抑制使场景转换路径无法到达时的一致性规则。在320个范围内的受控序列上，默认状态机的F1值为0.800，平衡准确率为0.822；在幅度扫描的公共审计中，在5%误报预算下达到最高的部分AUC，在单独的扩展压力FPR受限扫描中，在0.025误报率下召回率达到0.925。公共Xiph、不来梅物联网和UHCTD诊断表明，固定谓词在保持低误报的同时，召回率集中在声明的范围内。该方法最好被解释为一个可审计的传感器健康子系统，而不是一个通用的相机篡改分类器。
英文摘要
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.
