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.

通过结合监控与 Wi-Fi 基础设施等技术，提出了一种实时、细粒度的视频防伪方法，其可通过监控与 Wi-Fi 信号中所携带的人体姿态信息来追踪视频篡改攻击，定位攻击的异常物体，并取得了 98.7% 的检测准确率。