  Camera tamper detection is the ability to detect unauthorized and
unintentional alterations in surveillance cameras by analyzing the video.
Camera tampering can occur due to natural events or it can be caused
intentionally to disrupt surveillance. We cast tampering detection as a change
detection problem, and perform a review of the existing literature with
emphasis on feature types. We formulate tampering detection as a time series
analysis problem, and design experiments to study the robustness and capability
of various feature types. We compute ten features on real-world surveillance
video and apply time series analysis to ascertain their predictability, and
their capability to detect tampering. Finally, we quantify the performance of
various time series models using each feature type to detect tampering.
