  Surveillance cameras, which is a form of Cyber Physical System, are deployed
extensively to provide visual surveillance monitoring of activities of interest
or anomalies. However, these cameras are at risks of physical security attacks
against their physical attributes or configuration like tampering of their
recording coverage, camera positions or recording configurations like focus and
zoom factors. Such adversarial alteration of physical configuration could also
be invoked through cyber security attacks against the camera's software
vulnerabilities to administratively change the camera's physical configuration
settings. When such Cyber Physical attacks occur, they affect the integrity of
the targeted cameras that would in turn render these cameras ineffective in
fulfilling the intended security functions. There is a significant measure of
research work in detection mechanisms of cyber-attacks against these Cyber
Physical devices, however it is understudied area with such mechanisms against
integrity attacks on physical configuration. This research proposes the use of
the novel use of deep learning algorithms to detect such physical attacks
originating from cyber or physical spaces. Additionally, we proposed the novel
use of deep learning-based video frame interpolation for such detection that
has comparatively better performance to other anomaly detectors in
spatiotemporal environments.
