TY - GEN
T1 - Detecting wireless spy cameras via stimulating and probing
AU - Liu, Tian
AU - Liu, Ziyu
AU - Huang, Jun
AU - Tan, Rui
AU - Tan, Zhen
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2018/6/10
Y1 - 2018/6/10
N2 - The rapid proliferation of wireless video cameras has raised serious privacy concerns. In this paper, we propose a stimulating-and-probing approach to detecting wireless spy cameras. The core idea is to actively alter the light condition of a private space to manipulate the spy camera’s video scene, and then investigates the responsive variations of a packet flow to determine if it is produced by a wireless camera. Following this approach, we develop Blink and Flicker – two practical systems for detecting wireless spy cameras. Blink is a lightweight app that can be deployed on off-the-shelf mobile devices. It asks the user to turn on/off the light of her private space, and then uses the light sensor and the wireless radio of the mobile device to identify the response of wireless cameras. Flicker is a robust and automated system that augments Blink to detect wireless cameras in both live and offline streaming modes. Flicker employs a cheap and portable circuit, which harnesses daily used LEDs to stimulate wireless cameras using human-invisible flickering. The time series of stimuli is further encoded using FEC to combat ambient light and uncontrollable packet flow variations that may degrade detection performance. Extensive experiments show that Blink and Flicker can accurately detect wireless cameras under a wide range of network and environmental conditions.
AB - The rapid proliferation of wireless video cameras has raised serious privacy concerns. In this paper, we propose a stimulating-and-probing approach to detecting wireless spy cameras. The core idea is to actively alter the light condition of a private space to manipulate the spy camera’s video scene, and then investigates the responsive variations of a packet flow to determine if it is produced by a wireless camera. Following this approach, we develop Blink and Flicker – two practical systems for detecting wireless spy cameras. Blink is a lightweight app that can be deployed on off-the-shelf mobile devices. It asks the user to turn on/off the light of her private space, and then uses the light sensor and the wireless radio of the mobile device to identify the response of wireless cameras. Flicker is a robust and automated system that augments Blink to detect wireless cameras in both live and offline streaming modes. Flicker employs a cheap and portable circuit, which harnesses daily used LEDs to stimulate wireless cameras using human-invisible flickering. The time series of stimuli is further encoded using FEC to combat ambient light and uncontrollable packet flow variations that may degrade detection performance. Extensive experiments show that Blink and Flicker can accurately detect wireless cameras under a wide range of network and environmental conditions.
UR - http://www.scopus.com/inward/record.url?scp=85051535502&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85051535502&origin=recordpage
U2 - 10.1145/3210240.3210332
DO - 10.1145/3210240.3210332
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781450357203
T3 - MobiSys 2018 - Proceedings of the 16th ACM International Conference on Mobile Systems, Applications, and Services
SP - 243
EP - 255
BT - MobiSys 2018 - Proceedings of the 16th ACM International Conference on Mobile Systems, Applications, and Services
PB - Association for Computing Machinery
T2 - 16th ACM International Conference on Mobile Systems, Applications, and Services,MobiSys 2018
Y2 - 10 June 2018 through 15 June 2018
ER -