Mahmudur Rahman
ABSTRACT
The visual information captured with camera-equipped mobile devices has greatly appreciated in value and importance as a result of their ubiquitous and connected nature. Today, banking customers expect to be able to deposit checks using mobile devices, and broadcasting videos from camera phones uploaded by unknown users is admissible on news networks. We present Movee, a system that addresses the fundamental question of whether the visual stream coming into a mobile app from the camera of the device can be trusted to be un-tampered with, live data, before it can be used for a variety of purposes.
Movee is a novel approach to video liveness analysis for mobile devices. It is based on measuring the consistency between the data from the accelerometer sensor and the inferred motion from the captured video. Contrary to existing algorithms, Movee has the unique strength of not depending on the audio track. Our experiments on real user data have shown that Movee achieves 8% Equal Error Rate.
- Arxan: Protecting the App Economy. http://www.arxan.com/.Google Scholar
- Chicago Works. http://www.chicagoworksapp.com/.Google Scholar
- Kickstarter. http://www.kickstarter.com/.Google Scholar
- NYC 311: Pothole or Other Street Surface Complaint. http://www.nyc.gov/apps/311/allServices.htm?requestType=topService&serviceName=Pothole+or+Other+Street+Surface+Complaint.Google Scholar
- Open Source Computer Vision. http://opencv.org/.Google Scholar
- Optical mouse. https://en.wikipedia.org/wiki/Optical_mouse.Google Scholar
- Root and Me. https://play.google.com/store/apps/details?id=com.iamjake.root&hl=en.Google Scholar
- Sensor Delay. http://developer.android.com/reference/android/hardware/SensorManager.html.Google Scholar
- Unlock Root. http://www.unlockroot.com/.Google Scholar
- Vine. http://vine.co/.Google Scholar
- Weka. http://www.cs.waikato.ac.nz/ml/weka/.Google Scholar
- XPrivacy 1.9.5: The ultimate privacy manager. http://forum.xda-developers.com/showthread.php?t=2320783.Google Scholar
- YouTube. http://www.youtube.com.Google Scholar
- A. Ali, F. Deravi, and S. Hoque. Liveness detection using gaze collinearity. In Emerging Security Technologies (EST), pages 62--65, 2012. Google ScholarDigital Library
- A. Anjos and S. Marcel. Counter-measures to photo attacks in face recognition: A public database and a baseline. In Biometrics (IJCB), pages 1--7, 2011. Google ScholarDigital Library
- L. Breiman. Random forests. Machine Learning, 45:5--32, 2001. Google ScholarDigital Library
- S. Capkun, K. B. Rasmussen, M. Cagalj, and M. B. Srivastava. Secure location verification with hidden and mobile base stations. IEEE Trans. Mob. Comput., 7(4):470--483, 2008. Google ScholarDigital Library
- G. Chetty. Biometric liveness detection based on cross modal fusion. In Information Fusion, 2009. FUSION '09. 12th International Conference on, pages 2255--2262, July.Google Scholar
- G. Chetty and M. Wagner. Multi-level liveness verification for face-voice biometric authentication. In Biometric Symposium, 2006.Google ScholarCross Ref
- D. Clifford and G. Stone. Variable penalty dynamic time warping code for aligning mass spectrometry chromatograms in r. Journal of Statistical Software, 47(8):1--17, 4 2012.Google ScholarCross Ref
- I. Coope. Circle fitting by linear and nonlinear least squares. Journal of Optimization Theory and Applications, 76:381--388, 1993. Google ScholarDigital Library
- A. J. Davison, I. D. Reid, N. D. Molton, and O. Stasse. Monoslam: Real-time single camera slam. IEEE Trans. Pattern Anal. Mach. Intell., 29(6):1052--1067, June 2007. Google ScholarDigital Library
- E. De Castro and C. Morandi. Registration of translated and rotated images using finite fourier transforms. IEEE Trans. Pattern Anal. Mach. Intell., 9(5):700--703, May 1987. Google ScholarDigital Library
- J. B. J. Fourier and A. Freeman. The Analytical Theory of Heat. Cambridge University Press, 2009.Google ScholarCross Ref
- R. Frischholz and U. Dieckmann. Bioid: A multimodal biometric identification system. IEEE Computer, 33(2):64--68, 2000. Google ScholarDigital Library
- S. I. Gallant. Perceptron-based learning algorithms. Trans. Neur. Netw., 1(2):179--191, June 1990. Google ScholarDigital Library
- B. F. Hildebrand. Introduction to numerical analysis: 2nd edition. Dover Publications, Inc., 1987. Google ScholarDigital Library
- G. Hong, A. Rahmati, Y. Wang, and L. Zhong. Sensecoding: accelerometer-assisted motion estimation for efficient video encoding. MM '08, pages 749--752. ACM, 2008. Google ScholarDigital Library
- P. Indyk, G. Iyengar, and N. Shivakumar. Finding pirated video sequences on the internet. Technical report, Stanford University, 1999.Google Scholar
- R. Kohavi. A study of cross-validation and bootstrap for accuracy estimation and model selection. pages 1137--1143, 1995. Google ScholarDigital Library
- K. Kollreider, H. Fronthaler, and J. Bigün. Non-intrusive liveness detection by face images. Image Vision Comput., 27(3):233--244, 2009. Google ScholarDigital Library
- J. Mantyjarvi, M. Lindholm, E. Vildjiounaite, S.-M. Makela, and H. Ailisto. Identifying users of portable devices from gait pattern with accelerometers. In ICASSP '05, volume 2, pages ii/973--ii/976 Vol. 2, March.Google Scholar
- F. Moiz, D. Leon-Salas, and Y. Lee. A wearable motion tracker. BodyNets '10, pages 214--219. Google ScholarDigital Library
- M. Mãijller. Dynamic time warping. In Information Retrieval for Music and Motion, pages 69--84. Springer Berlin Heidelberg, 2007.Google ScholarCross Ref
- G. Pan, L. Sun, Z. Wu, and S. Lao. Eyeblink-based anti-spoofing in face recognition from a generic webcamera. In ICCV 2007., pages 1--8.Google ScholarCross Ref
- G.-t. Park, H. Wang, and Y.-s. Moon. Liveness detection method and apparatus of video image, August 2007.Google Scholar
- T. Pylvänäinen. Accelerometer based gesture recognition using continuous hmms. IbPRIA'05, pages 639--646. Springer-Verlag, 2005. Google ScholarDigital Library
- A. Shabtai, Y. Fledel, and Y. Elovici. Securing android-powered mobile devices using selinux. Security Privacy, IEEE, 8(3):36--44, 2010. Google ScholarDigital Library
- J. Six. Application Security for the Android Platform: Processes, Permissions, and Other Safeguards. O'Reilly, 2011.Google Scholar
- J. O. Smith. Spectral Audio Signal Processing. W3K Publishing, 2011. online book.Google Scholar
- N. P. H. Thian and S. Bengio. Evidences of equal error rate reduction in biometric authentication fusion, 2004.Google Scholar
- L. von Ahn, M. Blum, N. J. Hopper, and J. Langford. Captcha: Using hard ai problems for security. In In Proceedings of EUROCRYPT, pages 294--311. Springer-Verlag, 2003. Google ScholarDigital Library
- Wikipedia. http://en.wikipedia.org/wiki/Receiver_operating_characteristic.Google Scholar
Index Terms
- Seeing is not believing: visual verifications through liveness analysis using mobile devices
Recommendations
Seeing-Is-Believing: Using Camera Phones for Human-Verifiable Authentication
SP '05: Proceedings of the 2005 IEEE Symposium on Security and PrivacyCurrent mechanisms for authenticating communication between devices that share no prior context are inconvenient for ordinary users, without the assistance of a trusted authority. We present and analyze Seeing-Is-Believing, a system that utilizes 2D ...
Seeing-Is-Believing: using camera phones for human-verifiable authentication
Current mechanisms for authenticating communication between devices that share no prior context are inconvenient for ordinary users, without the assistance of a trusted authority. We present and analyse Seeing-Is-Believing (SiB), a system that utilises ...
Seeing is Believing?: Effects of Visualization on Smart Device Privacy Perceptions
MM '21: Proceedings of the 29th ACM International Conference on MultimediaResearch on smart device privacy has consistently highlighted how privacy is an important concern for users, but they fail to act on their concerns. While this discrepancy between user perceptions and actions has been consistently reported, currently ...
Comments