Forgery Attack Detection in Surveillance Video Streams Using Wi-Fi Channel State Information

• URL: http://arxiv.org/abs/2201.09487v1
• Date: Mon, 24 Jan 2022 06:51:03 GMT
• Title: Forgery Attack Detection in Surveillance Video Streams Using Wi-Fi Channel State Information
• Authors: Yong Huang, Xiang Li, Wei Wang, Tao Jiang, Qian Zhang
• Abstract summary: cybersecurity breaches expose surveillance video streams to forgery attacks. Traditional video forensics approaches can localize traces using spatial-temporal analysis on relatively long video clips. We propose Secure-Pose, which exploits the pervasive coexistence of surveillance and Wi-Fi infrastructures. Secure-Pose achieves a high detection accuracy of 98.7% and localizes abnormal objects under playback and tampering attacks.
• Score: 20.815889839515087
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The cybersecurity breaches expose surveillance video streams to forgery attacks, under which authentic streams are falsified to hide unauthorized activities. Traditional video forensics approaches can localize forgery traces using spatial-temporal analysis on relatively long video clips, while falling short in real-time forgery detection. The recent work correlates time-series camera and wireless signals to detect looped videos but cannot realize fine-grained forgery localization. To overcome these limitations, we propose Secure-Pose, which exploits the pervasive coexistence of surveillance and Wi-Fi infrastructures to defend against video forgery attacks in a real-time and fine-grained manner. We observe that coexisting camera and Wi-Fi signals convey common human semantic information and forgery attacks on video streams will decouple such information correspondence. Particularly, retrievable human pose features are first extracted from concurrent video and Wi-Fi channel state information (CSI) streams. Then, a lightweight detection network is developed to accurately discover forgery attacks and an efficient localization algorithm is devised to seamlessly track forgery traces in video streams. We implement Secure-Pose using one Logitech camera and two Intel 5300 NICs and evaluate it in different environments. Secure-Pose achieves a high detection accuracy of 98.7% and localizes abnormal objects under playback and tampering attacks.

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