Using Sensor Dirt for Toolmark Analysis of Digital Photographs

  • Martin Olivier
Part of the IFIP — The International Federation for Information Processing book series (IFIPAICT, volume 285)

Abstract

Dust particles that collect on the image sensors of digital cameras often leave marks on the pictures taken with these cameras. The question therefore arises whether these marks may be used for forensic identification of the camera used to take a specific picture. This paper considers the question by investigating the impact of various camera and lens factors, such as focal length and recording format. A matching technique involving grid overlay is proposed and the probability of false positive matches is quantified. Initial results indicate that toolmark analysis based on sensor dirt has potential as a forensic technique for camera identification.

Keywords

Digital cameras sensor dirt toolmark analysis 

References

  1. [1]
    Adobe Systems, Digital Negative (DNG) Specification (version 1.1.0.0), San Jose, California (www.adobe.com/products/dng/pdfs/dng spec.pdf ), 2005.
  2. [2]
    P. Alvarez, Using extended file information (EXIF) file headers in digital evidence analysis, International Journal of Digital Evidence, vol. 2(3), pp. 1-5, 2004.Google Scholar
  3. [3]
    Anonymous, Pentax K10D, Practical Photography, pp. 110-113, February 2007.Google Scholar
  4. [4]
    Delkin, SensorScope and Digital Duster System Cleaning Guide, In- glewood, California, 2006.Google Scholar
  5. [5]
    A. Dirik, H. Sencar and N. Memon, Source camera identification based on sensor dust characteristics, Proceedings of the IEEE Workshop on Signal Processing Applications for Public Security and Forensics, pp. 1-6, 2007.Google Scholar
  6. [6]
    J. Freeman, Collins Digital SLR Handbook, Collins, London, United Kingdom, 2007.Google Scholar
  7. [7]
    N. Genge, The Forensic Casebook: The Science of Crime Scene In- vestigation, Ballantine Books, New York, 2002.Google Scholar
  8. [8]
    J. Hedgecoe, The New Manual of Photography, DK Publishing, New York, 2003.Google Scholar
  9. [9]
    S. James and J. Nordby (Eds.), Forensic Science: An Introduction to Scientific and Investigative Techniques, CRC Press, Boca Raton, Florida, 2005.Google Scholar
  10. [10]
    J. Lukas, J. Fridrich and M. Goljan, Digital camera identifica- tion from sensor pattern noise, IEEE Transactions on Information Forensics and Security, vol. 1(2), pp. 205-214, 2006.CrossRefGoogle Scholar
  11. [11]
    Pentax Corporation, K10D Operating Manual, Tokyo, Japan (www.pentaxslr.com/files/scmsdocs/K10D Manual.pdf ), 2006.
  12. [12]
    R. Saferstein, Criminalistics - An Introduction to Forensic Science, Prentice-Hall, Englewood Cliffs, New Jersey, 2007.Google Scholar
  13. [13]
    A. Zamfir, A. Drimbarean, M. Zamfir, V. Buzuloiu, E. Steinberg and D. Ursu, An optical model of the appearance of blemishes in digital photographs, in Proceedings of SPIE (Volume 6502) - Digital Photography III, R. Martin, J. DiCarlo and N. Sampat (Eds.), SPIE, Bellingham, Washington, 2007.Google Scholar
  14. [14]
    C. Zhou and S. Lin, Removal of image artifacts due to sensor dust, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2007.Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2008

Authors and Affiliations

  • Martin Olivier
    • 1
  1. 1.The University of PretoriaPretoriaSouth Africa

Personalised recommendations