Skip to main content
SpringerLink
Log in
Book cover

International Workshop on Digital Watermarking

IWDW 2014: Digital-Forensics and Watermarking pp 47–59Cite as

Determination of Stop-Criterion for Incremental Methods Constructing Camera Sensor Fingerprint

  • Conference paper
  • First Online:

  • 1265 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 9023))

Abstract

This paper aims to find the minimum sample size of the camera reference image set that is needed to build a sensor fingerprint of a high performance. Today’s methods for building sensor fingerprints do rely on having a sufficient number of camera reference images. But, there is no clear answer to the question of how many camera reference images are really needed? In this paper, we will analyze and find out how to determine the minimum needed number of reference images to remove the mentioned uncertainty. We will introduce a quantitative measure (a stop-criterion) stating how many photos should be used to create a high-performance sensor fingerprint. This stop-criterion will directly reflect the confidence level that we would like to achieve. By considering that the number of digital images used to construct the camera sensor fingerprint can have a direct impact on performance of the sensor fingerprint, it is apparent that this, so far underestimated, topic is of major importance.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Sencar, H.T., Ramkumar, M., Akansu, A.N.: Data Hiding Fundamentals and Applications: Content Security in Digital Multimedia. Academic Press Inc., Orlando (2004)

    Google Scholar 

  2. Arnold, M., Schmucker, M., Wolthusen, S.D.: Techniques and Applications of Digital Watermarking and Content Protection. Artech House Inc., Norwood (2003)

    Google Scholar 

  3. Nikolaidis, N., Pitas, I.: Robust image watermarking in the spatial domain. Signal Process. 66(3), 385–403 (1998)

    Article  MATH  Google Scholar 

  4. Mahdian, B., Saic, S.: A bibliography on blind methods for identifying image forgery. Image Commun. 25(6), 389–399 (2010)

    Google Scholar 

  5. Ng, T.-T., Tsui, M.-P.: Camera response function signature for digital forensics - part i: theory and data selection. In: IEEE Workshop on Information Forensics and Security, December 2009, pp. 156–160 (2009)

    Google Scholar 

  6. Lint, Z., Wang, R., Tang, X., Shum, H.-Y.: Detecting doctored images using camera response normality and consistency. In: CVPR 2005: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005) - Volume 1, Washington, pp. 1087–1092. IEEE Computer Society (2005)

    Google Scholar 

  7. Popescu, A., Farid, H.: Exposing digital forgeries in color filter array interpolated images. IEEE Trans. Signal Process. 53(10), 3948–3959 (2005). www.cs.dartmouth.edu/farid/publications/sp05a.html. [Online]

  8. Mahdian, B., Saic, S.: Blind authentication using periodic properties of interpolation. IEEE Trans. Inf. Forensics Secur. 3(3), 529–538 (2008)

    Article  Google Scholar 

  9. Mahdian, B., Saic, S.: Detection of copy-move forgery using a method based on blur moment invariants. Forensic Sci. Int. 17(2–3), 180–189 (2007)

    Article  Google Scholar 

  10. Dirik, A.E., Bayram, S., Sencar, H.T., Memon, N.: New features to identify computer generated images. In: IEEE International Conference on Image Processing, ICIP 2007, vol. 4, pp. 433–436 (2007)

    Google Scholar 

  11. Fridrich, J., Pevny, T.: Detection of double-compression for applications in steganography. IEEE Trans. Inf. Sec. Forensics 3(2), 247–258 (2008)

    Article  Google Scholar 

  12. Chen, M., Goljan, M., Lukas, J.: Determining image origin and integrity using sensor noise. IEEE Trans. Inf. Forensics Secur. 3(1), 74–90 (2008)

    Article  Google Scholar 

  13. Lukas, J., Fridrich, J., Goljan, M.: Digital camera identification from sensor pattern noise. IEEE Trans. Inf. Forensics Secur. 1(2), 205–214 (2006)

    Article  Google Scholar 

  14. Amerini, I., Caldelli, R., Cappellini, V., Picchioni, F., Piva, A.: Analysis of denoising filters for photo response non uniformity noise extraction in source camera identification. In: Proceedings of the 16th International Conference on Digital Signal Processing, Series DSP 2009, Piscataway, pp. 511–517. IEEE Press (2009). Available: http://dl.acm.org/citation.cfm?id=1700307.1700392. [Online]

  15. Alles, E.J., Geradts, Z.J.M.H., Veenman, C.J.: Source camera identification for heavily JPEG compressed low resolution still images. J. Forensic Sci. 54(3), pp. 628–638 (2009). Available: http://www.science.uva.nl/research/publications/2009/AllesJFS2009. [Online]

  16. Yongjian Hu, C.J., Yu, B.: Source camera identification using large components of sensor pattern noise. In: 2nd International Conference on Computer Science and Its Applications, CSA 2009, Jeju Island, Korea (2009)

    Google Scholar 

  17. Li, Y., Li, C.-T.: Decomposed photo response non-uniformity for digital forensic analysis. In: Sorell, M. (ed.) e-Forensics 2009. LNICST, vol. 8, pp. 166–172. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  18. Hu, Y., Jian, C., Li, C.-T.: Using improved imaging sensor pattern noise for source camera identification. In: ICME, pp. 1481–1486 (2010)

    Google Scholar 

  19. Lukas, J., Fridrich, J., Goljan, M.: Detecting digital image forgeries using sensor pattern noise. In: Proceedings of the SPIE, West, p. 2006 (2006)

    Google Scholar 

  20. Chen, M., Fridrich, J., Goljan, M., Luk, J.: Source digital camcorder identification using sensor photo-response nonuniformity. In: Proceedings of SPIE Electronic Imaging, Photonics West (2007)

    Google Scholar 

  21. Chen, M., Fridrich, J., Goljan, M.: Digital imaging sensor identification (further study). In: Delp, E.J., III; Wong, P. W. (ed.) Security, Steganography, and Watermarking of Multimedia Contents IX. Proceedings of the SPIE, vol. 6505 (2007)

    Google Scholar 

  22. Williams, D., Codreanu, V., Yang, P., Liu, B., Dong, F., Yasar, B., Mahdian, B., Chiarini, A., Zhao, X., Roerdink, J.: Evaluation of autoparallelization toolkits for commodity graphics hardware. In: 10th International Conference on Parallel Processing and Applied Mathematics. Springer, Warsaw, Poland (2013)(to appear)

    Google Scholar 

  23. Williams, D., Codreanu, V., Roerdink, J.B., Yang, P., Liu, B., Dong, F., Chiarini, A.: Accelerating colonic polyp detection using commodity graphics hardware. In: Proceedings of the International Conference on Computer Medical Applications, Sousse, Tunisia, pp. 1–6 (2013)

    Google Scholar 

Download references

Acknowledgments

This work has been supported by the Czech Science Foundation under Project no. GACR 13-28462S.

Author information

Authors and Affiliations

  1. Institute of Information Theory and Automation of ASCR, Prague, Czech Republic

    Babak Mahdian, Adam Novozámský & Stanislav Saic

Authors
  1. Babak Mahdian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Adam Novozámský
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Stanislav Saic
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Babak Mahdian .

Editor information

Editors and Affiliations

  1. NJIT, Newark, New Jersey, USA

    Yun-Qing Shi

  2. Korea University, Seoul, Korea, Republic of (South Korea)

    Hyoung Joong Kim

  3. Universidad de La Laguna, La Laguna, Spain

    Fernando Pérez-González

  4. Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien, Taiwan

    Ching-Nung Yang

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Mahdian, B., Novozámský, A., Saic, S. (2015). Determination of Stop-Criterion for Incremental Methods Constructing Camera Sensor Fingerprint. In: Shi, YQ., Kim, H., Pérez-González, F., Yang, CN. (eds) Digital-Forensics and Watermarking. IWDW 2014. Lecture Notes in Computer Science(), vol 9023. Springer, Cham. https://doi.org/10.1007/978-3-319-19321-2_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-19321-2_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19320-5

  • Online ISBN: 978-3-319-19321-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation