Physical Integrity Attack Detection of Surveillance Camera with Deep Learning Based Video Frame Interpolation

TL;DR

This paper introduces a novel deep learning-based method utilizing video frame interpolation to detect physical integrity attacks on surveillance cameras, addressing a gap in security measures for cyber-physical systems.

Contribution

It proposes a new deep learning approach using video frame interpolation for detecting physical tampering of surveillance cameras, outperforming existing anomaly detection methods.

Findings

Deep learning-based video frame interpolation improves detection accuracy.

The method effectively identifies physical tampering in surveillance systems.

Performance exceeds traditional anomaly detection techniques.

Abstract

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.