On the Usage of Sensor Pattern Noise for Picture-to-Identity Linking through Social Network Accounts

Riccardo Satta, Pasquale Stirparo

Abstract

Digital imaging devices have gained an important role in everyone’s life, due to a continuously decreasing price, and of the growing interest on photo sharing through social networks. As a result of the above facts, everyone continuously leaves visual “traces” of his/her presence and life on the Internet, that can constitute precious data for forensic investigators. Digital Image Forensics is the task of analysing such digital images for collecting evidences. In this field, the recent introduction of techniques able to extract a unique “fingerprint” of the source camera of a picture, e.g. based on the Sensor Pattern Noise (SPN), has set the way for a series of useful tools for the forensic investigator. In this paper, we propose a novel usage of SPN, to find social network accounts belonging to a certain person of interest, who has shot a given photo. This task, that we name Picture-to-Identity linking, can be useful in a variety of forensic cases, e.g., finding stolen camera devices, cyber-bullying, or on-line child abuse. We experimentally test a method for Picture-to-Identity linking on a benchmark data set of publicly accessible social network accounts collected from the Internet. We report promising result, which show that such technique has a practical value for forensic practitioners.

References

  1. Cao, H. and Kot, A. C. (2009). Accurate detection of demosaicing regularity for digital image forensics. IEEE Transactions on Information Forensics and Security, 4(4):899-910.
  2. Casey, E. (2009). Handbook of Digital Forensics and Investigation. Academic Press.
  3. Choi, K. S., Lam, E. Y., and Wong, K. K. Y. (2006). Automatic source camera identification using the intrinsic lens radial distortion. Optics Express, 14(24):11551- 11565.
  4. Dirik, A. E., Sencar, H. T., and Memon, N. (2008). Digital single lens reflex camera identification from traces of sensor dust. IEEE Transactions on Information Forensics and Security, 3(3):539-552.
  5. Fridrich, J. (2009). Digital image forensic using sensor noise. IEEE Signal Processing Magazine, 26(2):26- 37.
  6. Gray, D., Brennan, S., and Tao, H. (2007). Evaluating appearance models for recognition, reacquisition, and tracking. In 10th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS).
  7. Kang, X., Li, Y., Qu, Z., and Huang, J. (2012). Enhancing source camera identification performance with a camera reference phase sensor pattern noise. IEEE Transactions on Information Forensics and Security, 7(2):393-402.
  8. Li, C.-T. (2010). Source camera identification using enhanced sensor pattern noise. IEEE Transactions on Information Forensics and Security, 5(2):280-287.
  9. Li, C.-T. and Li, Y. (2012). Color-decoupled photo response non-uniformity for digital image forensics. IEEE Transactions on Circuits and Systems for Video Technology, 22(2):260-271.
  10. Li, C.-T. and Satta, R. (2011). On the location-dependent quality of the sensor pattern noise and its implication in multimedia forensics. In Proceedings of the 4th International Conference on Imaging for Crime Detection and Prevention 2011 (ICDP 2011), page 37.
  11. Li, C.-T. and Satta, R. (2012). Empirical investigation into the correlation between vignetting effect and the quality of sensor pattern noise. IET Computer Vision, 6:560-566(6).
  12. Long, Y. and Huang, Y. (2006). Image based source camera identification using demosaicking. In 2006 IEEE 8th Workshop on Multimedia Signal Processing, pages 419-424.
  13. Lukas, J., Fridrich, J., and Goljan, M. (2006). Digital camera identification from sensor pattern noise. IEEE Transactions on Information Forensics and Security, 1(2):205-214.
  14. Popescu, A. and Farid, H. (2005). Exposing digital forgeries in color filter array interpolated images. IEEE Transactions on Signal Processing, 53(10):3948-3959.
  15. Sorrell, M. J. (2009). Digital camera source identification through jpeg quantisation. In Li, C.-T., editor, Multimedia forensics and security. Information Science Reference.
  16. Van, L. T., Emmanuel, S., and Kankanhalli, M. (2007). Identifying source cell phone using chromatic aberration. In 2007 IEEE International Conference on Multimedia and Expo, pages 883-886.
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Paper Citation


in Harvard Style

Satta R. and Stirparo P. (2014). On the Usage of Sensor Pattern Noise for Picture-to-Identity Linking through Social Network Accounts . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-009-3, pages 5-11. DOI: 10.5220/0004682200050011


in Bibtex Style

@conference{visapp14,
author={Riccardo Satta and Pasquale Stirparo},
title={On the Usage of Sensor Pattern Noise for Picture-to-Identity Linking through Social Network Accounts},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={5-11},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004682200050011},
isbn={978-989-758-009-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014)
TI - On the Usage of Sensor Pattern Noise for Picture-to-Identity Linking through Social Network Accounts
SN - 978-989-758-009-3
AU - Satta R.
AU - Stirparo P.
PY - 2014
SP - 5
EP - 11
DO - 10.5220/0004682200050011