journal article
Open Access
Dec 03, 2018
FacePET: Enhancing Bystanders’ Facial Privacy with Smart Wearables/Internet of Things
Abstract
Given the availability of cameras in mobile phones, drones and Internet-connected devices, facial privacy has become an area of major interest in the last few years, especially when photos are captured and can be used to identify bystanders’ faces who may have not given consent for these photos to be taken and be identified. Some solutions to protect facial privacy in photos currently exist. However, many of these solutions do not give a choice to bystanders because they rely on algorithms that de-identify photos or protocols to deactivate devices and systems not controlled by bystanders, thereby being dependent on the bystanders’ trust in these systems to protect his/her facial privacy. To address these limitations, we propose FacePET (Facial Privacy Enhancing Technology), a wearable system worn by bystanders and designed to enhance facial privacy. We present the design, implementation, and evaluation of the FacePET and discuss some open research issues.
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References
49
[1]
(2018, August 30). The Ericcson Mobility Report. Available online: https://www.ericsson.com/en/mobility-report.
[2]
Perez "Bystanders’ Privacy" IT Prof. (2017) 10.1109/mitp.2017.42
[3]
Perez "Privacy Issues and Solutions for Consumer Wearables" IT Prof. (2018) 10.1109/mitp.2017.265105905
[4]
(2018, September 18). This Russian Technology Can Identify You with Just a Picture of Your Face. Available online: http://www.businessinsider.com/findface-facial-recognition-can-identify-you-with-just-a-picture-of-your-face-2016-6.
[5]
Mitchell, R. (2015, January 7–11). Sensing mine, yours, theirs, and ours: Interpersonal ubiquitous interactions. Proceedings of the 2015 ACM Int’l. Symposium Wearable Computers (ISWC 2015), Osaka, Japan.
10.1145/2800835.2806203
[6]
Denning, T., Dehlawi, Z., and Kohno, T. (May, January 26). In situ with bystanders of augmented reality glasses: Perspectives on recording and privacy-mediating technologies. Proceedings of the 32nd SIGCHI Conference on Human Factors in Computing Systems, Toronto, ON, Canada.
[7]
Flammer "Genteel Wearables: Bystander-Centered Design" IEEE Secur. Priv. (2016) 10.1109/msp.2016.91
[8]
Hatuka "Being visible in public space: The normalisation of asymmetrical visibility" Urban Stud. (2017) 10.1177/0042098015624384
[9]
Palen, L., Salzman, M., and Youngs, E. (2000, January 2–6). Going wireless: Behavior & practice of new mobile phone users. Proceedings of the 2000 ACM Conf. on Computer Supported Cooperative Work (CSCW’00), Philadelphia, PA, USA.
10.1145/358916.358991
[10]
Motti, V.G., and Caine, K. (2015, January 26–30). Users’ privacy concerns about wearables. Proceedings of the International Conference on Financial Cryptography and Data Security, San Juan, Puerto Rico.
10.1007/978-3-662-48051-9_17
[11]
Jarvis, J. (2011). Public Parts: How Sharing in the Digital Age Improves the Way We Work and Live, Simon & Schuster. [1st ed.].
[12]
Truong, K.N., Patel, S.N., Summet, J.W., and Abowd, G.D. (2005, January 11–14). Preventing camera recording by designing a capture-resistant environment. Proceedings of the International Conference on Ubiquitous Computing, Tokio, Japan.
10.1007/11551201_5
[13]
Tiscareno, V., Johnson, K., and Lawrence, C. (2014). Systems and Methods for Receiving Infrared Data with a Camera Designed to Detect Images Based on Visible Light. (8,848,059), U.S. Patent.
[14]
Wagstaff, J. (2018, September 21). Using Bluetooth to Disable Camera Phones. Available online: http://www.loosewireblog.com/2004/09/using_bluetooth.html.
[15]
Kapadia, A., Henderson, T., Fielding, J.J., and Kotz, D. (2007, January 13–16). Virtual walls: Protecting digital privacy in pervasive environments. Proceedings of the International Conference Pervasive Computing, LNCS 4480, Toronto, ON, Canada.
[16]
Blank "Privacy-Aware Restricted Areas for Unmanned Aerial Systems" IEEE Secur. Priv. (2018) 10.1109/msp.2018.1870868
[17]
Pidcock, S., Smits, R., Hengartner, U., and Goldberg, I. (2011, January 12–15). Notisense: An urban sensing notification system to improve bystander privacy. Proceedings of the 2nd International Workshop on Sensing Applications on Mobile Phones (PhoneSense), Seattle, WA, USA.
[18]
Yamada, T., Gohshi, S., and Echizen, I. (2012, January 29). Use of invisible noise signals to prevent privacy invasion through face recognition from camera images. Proceedings of the ACM Multimedia 2012 (ACM MM 2012), Nara, Japan.
10.1145/2393347.2396460
[19]
Yamada, T., Gohshi, S., and Echizen, I. (2013, January 13–16). Privacy visor: Method based on light absorbing and reflecting properties for preventing face image detection. Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Manchester, UK.
10.1109/smc.2013.271
[20]
Sharif, M., Bhagavatula, S., Bauer, L., and Reiter, M.K. (2016, January 24–28). Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition. Proceedings of the 2016 ACM SIGSAC Conf. Computer and Communications Security (CCS 2016), Vienna, Austria.
10.1145/2976749.2978392
[21]
(2018, September 30). ObscuraCam: Secure Smart Camera. Available online: https://guardianproject.info/apps/obscuracam/.
[22]
Aditya, P., Sen, R., Druschel, P., Joon Oh, S., Benenson, R., Fritz, M., Schiele, B., Bhattacharjee, B., and Wu, T.T. (2016, January 25–30). I-pic: A platform for privacy-compliant image capture. Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys), Singapore.
10.1145/2906388.2906412
[23]
Nielsen, J. (1993). Usability Engineering, Academic Press. [1st ed.].
10.1016/b978-0-08-052029-2.50007-3
[24]
Zeadally "Energy-efficient Networking: Past, present, and future" J. Supercomput. (2012) 10.1007/s11227-011-0632-2
[25]
Face Detection: A Survey
Erik Hjelmås, Boon Kee Low
Computer Vision and Image Understanding
2001
10.1006/cviu.2001.0921
[26]
Yang "Detecting faces in images: A survey" IEEE Trans. Pattern Anal. Mach. Intell. (2002) 10.1109/34.982883
[27]
(2018, October 05). Facebook’s Push for Facial Recognition Prompts Privacy Alarms. Available online: https://www.nytimes.com/2018/07/09/technology/facebook-facial-recognition-privacy.html.
[28]
(2018, October 05). Inside China’s Dystopian Dreams: A.I., Shame and Lots of Cameras. Available online: https://www.nytimes.com/2018/07/08/business/china-surveillance-technology.html.
[29]
(2018, October 05). Face Recognition, Available online: https://www.fbi.gov/file-repository/about-us-cjis-fingerprints_biometrics-biometric-center-of-excellences-face-recognition.pdf/view.
[30]
Roesner, F., Molnar, D., Moshchuk, A., Kohno, T., and Wang, H.J. (2014, January 3–7). World-driven access control for continuous sensing. Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security (CCS 2014), Scottsdale, AZ, USA.
10.1145/2660267.2660319
[31]
Maganis, G., Jung, J., Kohno, T., Sheth, A., and Wetherall, D. Sensor Tricorder: What does that sensor know about me? In Proceedings of the 12th Workshop on Mobile Computing Systems and Applications (HotMobile ‘11), Phoenix, AZ, USA, 1–3 March 2011; pp.
10.1145/2184489.2184510
[32]
Templeman, R., Korayem, M., Crandall, D.J., and Kapadia, A. (2014, January 23–26). PlaceAvoider: Steering First-Person Cameras away from Sensitive Spaces. Proceedings of the 2014 Network and Distributed System Security (NDSS) Symposium, San Diego, CA, USA.
10.14722/ndss.2014.23014
[33]
(2018, September 30). AVG Reveals Invisibility Glasses at Pepcom Barcelona. Available online: http://now.avg.com/avg-reveals-invisibility-glasses-at-pepcom-barcelona.
[34]
Frome, A., Cheung, G., Abdulkader, A., Zennaro, M., Wu, B., Bissacco, A., Adam, H., Neven, H., and Vincent, L. (October, January 29). Large-scale privacy protection in Google Street View. Proceedings of the 2009 12th International Conference on Computer Vision, Kyoto, Japan.
10.1109/iccv.2009.5459413
[35]
Li, A., Li, Q., and Gao, W. (2016, January 27–30). Privacycamera: Cooperative privacy-aware photographing with mobile phones. Proceedings of the 2016 13th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), London, UK.
10.1109/sahcn.2016.7733008
[36]
Schiff, J., Meingast, M., Mulligan, D.K., Sastry, S., and Goldberg, K. (November, January 29). Respectful cameras: Detecting visual markers in real-time to address privacy concerns. Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, San Diego, CA, USA.
10.1109/iros.2007.4399122
[37]
Ra "Do Not Capture: Automated Obscurity for Pervasive Imaging" IEEE Internet Comput. (2017) 10.1109/mic.2017.67
[38]
Ashok, A., Nguyen, V., Gruteser, M., Mandayam, N., Yuan, W., and Dana, K. (2014, January 7–11). Do not share!: Invisible light beacons for signaling preferences to privacy-respecting cameras. Proceedings of the 1st ACM MobiCom Workshop on Visible Light Communication Systems, Maui, HI, USA.
10.1145/2643164.2643168
[39]
Ye, T., Moynagh, B., Albatal, R., and Gurrin, C. (2014, January 3–7). Negative face blurring: A privacy-by-design approach to visual lifelogging with google glass. Proceedings of the 23rd ACM International Conference on Information and Knowledge Management, Shanghai, China.
10.1145/2661829.2661841
[40]
Zhang, C., and Zhengyou, Z. (2010). A Survey of Recent Advances in Face Detection, Microsoft Corporation. Microsoft Technical Report.
[41]
Robust Real-Time Face Detection
Paul Viola, Michael J. Jones
International Journal of Computer Vision
2004
10.1023/b:visi.0000013087.49260.fb
[42]
(2018, October 08). Arduino Uno Rev3. Available online: https://store.arduino.cc/usa/arduino-uno-rev3.
[43]
(2018, October 08). Seedstudio Bluetooth Low Energy Shield Version 2.1. Available online: https://www.seeedstudio.com/Bluetooth-4.0-Low-Energy-BLE-Shield-v2.1-p-1995.html.
[44]
(2018, October 08). Face Detection using Haar Cascades. Available online: https://docs.opencv.org/3.4.2/d7/d8b/tutorial_py_face_detection.html.
[45]
Lienhart, R., Kuranov, A., and Pisarevsky, V. (2003, January 10–12). Empirical analysis of detection cascades of boosted classifiers for rapid object detection. Proceedings of the Joint Pattern Recognition Symposium, Magdeburg, Germany.
10.1007/978-3-540-45243-0_39
[46]
Bayer, B.E. (1976). Color Imaging Array. (3,971,065), U.S. Patent.
[47]
(2018, November 19). KentOptronics e-TransFlector™. Available online: http://www.kentoptronics.com/solutions.html.
[48]
Sun "Face detection using deep learning: An improved faster RCNN approach" Neurocomputing (2018) 10.1016/j.neucom.2018.03.030
[49]
Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7–12). Faster R-CNN: Towards real-time object detection with region proposal networks. Proceedings of the Neural Information Processing Systems Conference, Montreal, QC, Canada.
Metrics
15
Citations
49
References
Details
- Published
- Dec 03, 2018
- Vol/Issue
- 7(12)
- Pages
- 379
- License
- View
Authors
Funding
National Science Foundation
Award: 1560214
U.S. Department of Defense
Award: 1560214
Cite This Article
Alfredo Pérez, Sherali Zeadally, Luis Matos Garcia, et al. (2018). FacePET: Enhancing Bystanders’ Facial Privacy with Smart Wearables/Internet of Things. Electronics, 7(12), 379. https://doi.org/10.3390/electronics7120379
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