  The proliferation of surveillance cameras has greatly improved the physical
security of many security-critical properties including buildings, stores, and
homes. However, recent surveillance camera looping attacks demonstrate new
security threats - adversaries can replay a seemingly benign video feed of a
place of interest while trespassing or stealing valuables without getting
caught. Unfortunately, such attacks are extremely difficult to detect in
real-time due to cost and implementation constraints. In this paper, we propose
SurFi to detect these attacks in real-time by utilizing commonly available
Wi-Fi signals. In particular, we leverage that channel state information (CSI)
from Wi-Fi signals also perceives human activities in the place of interest in
addition to surveillance cameras. SurFi processes and correlates the live video
feeds and the Wi-Fi CSI signals to detect any mismatches that would identify
the presence of the surveillance camera looping attacks. SurFi does not require
the deployment of additional infrastructure because Wi-Fi transceivers are
easily found in the urban indoor environment. We design and implement the SurFi
system and evaluate its effectiveness in detecting surveillance camera looping
attacks. Our evaluation demonstrates that SurFi effectively identifies attacks
with up to an attack detection accuracy of 98.8% and 0.1% false positive rate
