Sensor Pattern Noise Matching Based on Reliability Map for Source Camera Identification

Riccardo Satta

Abstract

Source camera identification using the residual noise pattern left by the sensor, or Sensor Pattern Noise, has received much attention by the digital image forensics community in recent years. One notable issue in this regard is that high-frequency components of an image (textures, edges) can be easily mistaken as being part of the SPN itself, due to the procedure used to extract SPN, which is based on adaptive low-pass filtering. In this paper, a method to cope with this problem is presented, which estimates a SPN reliability map associating a degree of reliability to each pixel, based on the amount of high-frequency content in its neighbourhood. The reliability map is then used to weight SPN pixels during matching. The technique is tested using a data set of images coming from 27 different cameras; results show a notable improvement with respect to standard, non-weighted matching.

References

  1. Caldelli, R., Amerini, I., Picchioni, F., and Innocenti, M. (2010). Fast image clustering of unknown source images. In Information Forensics and Security (WIFS), 2010 IEEE International Workshop on, pages 1-5.
  2. 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.
  3. Chen, M., Fridrich, J., Goljan, M., and Lukas, J. (2008). Determining image origin and integrity using sensor noise. IEEE Transactions on Information Forensics and Security, 3(1):74-90.
  4. 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.
  5. 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.
  6. Fridrich, J. (2009). Digital image forensic using sensor noise. IEEE Signal Processing Magazine, 26(2):26- 37.
  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. 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.
  9. 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.
  10. 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).
  11. 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.
  12. 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.
  13. Oskoei, M. A. and Hu, H. (2010). A survey on edge detection methods. Technical Report CES-506, University of Essex, UK.
  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. Satta, R., Galbally, J., and Beslay, L. (2013). State-of-theart review: Video analytics for fight against on-line child abuse. Technical Report JRC85864, European Commission - Joint Research Centre.
  16. Satta, R. and Stirparo, P. (2014). On the usage of sensor pattern noise for picture-to-identity linking through social network accounts. In Proceeding of the International Conference on Computer Vision Theory and Applications Lisbon (VISAPP), Portugal.
  17. Sorrell, M. J. (2009). Digital camera source identification through jpeg quantisation. In Li, C.-T., editor, Multimedia forensics and security. Information Science Reference.
  18. 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.
Download


Paper Citation


in Harvard Style

Satta R. (2015). Sensor Pattern Noise Matching Based on Reliability Map for Source Camera Identification . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-089-5, pages 222-226. DOI: 10.5220/0005354202220226


in Bibtex Style

@conference{visapp15,
author={Riccardo Satta},
title={Sensor Pattern Noise Matching Based on Reliability Map for Source Camera Identification},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={222-226},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005354202220226},
isbn={978-989-758-089-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)
TI - Sensor Pattern Noise Matching Based on Reliability Map for Source Camera Identification
SN - 978-989-758-089-5
AU - Satta R.
PY - 2015
SP - 222
EP - 226
DO - 10.5220/0005354202220226