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This paper introduces a streaming integrity monitor designed to detect lens occlusion and photometric transitions in surveillance cameras, addressing the critical need for reliable tamper detection in low-cost sensors. By employing a clean-only sliding reference and structured-light rejection techniques, the system achieves a high balance of accuracy and low false-alarm rates, crucial for real-time monitoring. The results demonstrate a significant improvement in detection performance, with a recall of 0.925 at a 0.025 false-positive rate, showcasing its effectiveness in maintaining sensor integrity under varying conditions.
A novel streaming integrity monitor achieves 0.925 recall with minimal false alarms, redefining reliability standards for surveillance camera tamper detection.
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.
