Preprint Article Version 1 This version is not peer-reviewed

Detecting Malicious False Frame Injection Attacks on Video Surveillance at the Edge Using Electrical Network Frequency Signals

Version 1 : Received: 29 March 2019 / Approved: 1 April 2019 / Online: 1 April 2019 (09:50:05 CEST)

How to cite: Nagothu, D.; Chen, Y.; Blasch, E.; Aved, A.; Zhu, S. Detecting Malicious False Frame Injection Attacks on Video Surveillance at the Edge Using Electrical Network Frequency Signals. Preprints 2019, 2019040004 Nagothu, D.; Chen, Y.; Blasch, E.; Aved, A.; Zhu, S. Detecting Malicious False Frame Injection Attacks on Video Surveillance at the Edge Using Electrical Network Frequency Signals. Preprints 2019, 2019040004

Abstract

Over the past few years, the importance of video surveillance in securing the national critical infrastructure has significantly increased, whose applications include detecting failures and anomalies. Accompanied by video proliferation is the increasing number of attacks against surveillance systems. Among the attacks, false frame injection (FFI) attacks that replay video frames from a previous recording to mask the live feed has the highest impact. While many attempts have been made to detect FFI frames using features from the video feeds, video analysis is computationally too intensive to be deployed on-site for real-time false frame detection. In this paper, we investigate the feasibility of FFI attacks on compromised surveillance systems at the edge and propose an effective technique to detect the injected false video and audio frames by monitoring the surveillance feed using the embedded Electrical Network Frequency (ENF) signals. An ENF operates at a nominal frequency of 60 Hz/50 Hz based on its geographical location and maintains a stable value across the entire power grid interconnection with minor fluctuations. For surveillance system video/audio recordings connected to the power grid, the ENF signals are embedded. The time-varying nature of the ENF component is used as a forensic application for authenticating the surveillance feed. The paper highlights the ENF signal collection from a power grid creating a reference database and ENF extraction from the recordings using conventional short-time Fourier Transform and spectrum detection for robust ENF signal analysis in the presence of noise and interference caused in different harmonics. The experimental results demonstrate the effectiveness of ENF signal detection and/or abnormalities for FFI attacks.

Subject Areas

video surveillance; visual layer attack; electrical network frequency (ENF) signal; false frame injection (FFI) attack

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