Skip to main content
SpringerLink
Log in

Forensic Technology for Source Camera Identification

  • Conference paper
  • First Online:
Artificial Intelligence and Security (ICAIS 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1254))

Included in the following conference series:

Abstract

Source camera identification is a major branch of forensic source identification. It’s purpose is to determine which camera was used to capture the image of unknown provenance only by using the image itself. We study the recent developments in the field of source camera identification and divide the techniques described in the literature into six categories: EXIF metadata, lens aberration, CFA and demosaicing, sensor imperfections, image statistical features and convolutional neural network. We describe in detail the general ideas of the approaches used in each category. We summarize the six techniques at the end of the article and point out the challenges for future forensic.

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

Access this chapter

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

Institutional subscriptions

References

  1. Avcibas, I., Kharrazi, M., Memon, N., Sankur, B.: Image steganalysis with binary similarity measures. EURASIP J. Appl. Sig. Process. 2005, 27492757 (2005). https://doi.org/10.1155/ASP.2005.2749

    Article  MATH  Google Scholar 

  2. Avcibas, I., Sankur, B., Sayood, K.: Statistical evaluation of image quality measures. J. Electron. Imaging 11(2), 206–224 (2002)

    Article  Google Scholar 

  3. Baroffio, L., Bondi, L., Bestagini, P., Tubaro, S.: Camera identification with deep convolutional networks. arXiv preprint arXiv:1603.01068 (2016)

  4. Bayar, B., Stamm, M.C.: Augmented convolutional feature maps for robust CNN-based camera model identification. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 4098–4102. IEEE (2017)

    Google Scholar 

  5. Bayram, S., Sencar, H., Memon, N., Avcibas, I.: Source camera identification based on CFA interpolation. In: IEEE International Conference on Image Processing (2005)

    Google Scholar 

  6. Bondi, L., Baroffio, L., Güera, D., Bestagini, P., Delp, E.J., Tubaro, S.: First steps toward camera model identification with convolutional neural networks. IEEE Sig. Process. Lett. 24(3), 259–263 (2016)

    Article  Google Scholar 

  7. Cao, H., Kot, A.C.: Accurate detection of demosaicing regularity for digital image forensics. IEEE Trans. Inf. Forensics Secur. 4(4), 899–910 (2009)

    Article  Google Scholar 

  8. Celiktutan, O., Sankur, B., Avcibas, I.: Blind identification of source cell-phone model. IEEE Trans. Inf. Forensics Secur. 3(3), 553–566 (2008)

    Article  Google Scholar 

  9. Choi, C.H., Choi, J.H., Lee, H.K.: Cfa pattern identification of digital cameras using intermediate value counting. In: Proceedings of the Thirteenth ACM Multimedia Workshop on Multimedia and Security, pp. 21–26. ACM (2011)

    Google Scholar 

  10. Choi, K.S., Lam, E.Y., Wong, K.K.: Source camera identification using footprints from lens aberration. In: Digital Photography II, vol. 6069, pp. 172–179 (2006)

    Google Scholar 

  11. Costa, F.O., Eckmann, M., Scheirer, W.J., Rocha, A.: Open set source camera attribution. In: 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images, pp. 71–78. IEEE (2012)

    Google Scholar 

  12. Devernay, F., Faugeras, O.D.: Automatic calibration and removal of distortion from scenes of structured environments. In: Investigative and Trial Image Processing, vol. 2567, pp. 62–72. International Society for Optics and Photonics (1995)

    Google Scholar 

  13. Fang, W., Zhang, F., Sheng, V.S., Ding, Y.: A method for improving CNN-based image recognition using DCGAN. CMC: Comput. Mater. Continua 57(1), 167–178 (2018)

    Google Scholar 

  14. Geradts, Z.J., Bijhold, J., Kieft, M., Kurosawa, K., Kuroki, K., Saitoh, N.: Methods for identification of images acquired with digital cameras. In: Enabling Technologies for Law Enforcement and Security, vol. 4232, pp. 505–512. International Society for Optics and Photonics (2001)

    Google Scholar 

  15. Gloe, T.: Feature-based forensic camera model identification. In: Shi, Yun Q., Katzenbeisser, S. (eds.) Transactions on Data Hiding and Multimedia Security VIII. LNCS, vol. 7228, pp. 42–62. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31971-6_3

    Chapter  Google Scholar 

  16. Gloe, T., Borowka, K., Winkler, A.: Feature-based camera model identification works in practice. In: Katzenbeisser, S., Sadeghi, A.-R. (eds.) IH 2009. LNCS, vol. 5806, pp. 262–276. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04431-1_19

    Chapter  Google Scholar 

  17. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and pattern Recognition, pp. 4700–4708 (2017)

    Google Scholar 

  18. Johnson, M.K., Farid, H.: Exposing digital forgeries through chromatic aberration. In: Proceedings of the 8th Workshop on Multimedia and Security, pp. 48–55. ACM (2006)

    Google Scholar 

  19. Kharrazi, M., Sencar, H.T., Memon, N.: Blind source camera identification. In: 2004 International Conference on Image Processing, ICIP 2004, vol. 1, pp. 709–712. IEEE (2004)

    Google Scholar 

  20. Kuzin, A., Fattakhov, A., Kibardin, I., Iglovikov, V.I., Dautov, R.: Camera model identification using convolutional neural networks. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 3107–3110. IEEE (2018)

    Google Scholar 

  21. Li, C.T.: Source camera identification using enhanced sensor pattern noise. IEEE Trans. Inf. Forensics Secur. 5(2), 280–287 (2010)

    Article  Google Scholar 

  22. Li, C.T., Satta, R.: On the location-dependent quality of the sensor pattern noise and its implication in multimedia forensics (2011)

    Google Scholar 

  23. 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 

  24. Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 7, 674–693 (1989)

    Article  Google Scholar 

  25. Takamatsu, J., Matsushita, Y., Ogasawara, T., Ikeuchi, K.: Estimating demosaicing algorithms using image noise variance. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 279–286. IEEE (2010)

    Google Scholar 

  26. Tuama, A., Comby, F., Chaumont, M.: Camera model identification with the use of deep convolutional neural networks. In: 2016 IEEE International workshop on information forensics and security (WIFS), pp. 1–6. IEEE (2016)

    Google Scholar 

  27. Van, L.T., Emmanuel, S., Kankanhalli, M.S.: Identifying source cell phone using chromatic aberration. In: IEEE International Conference on Multimedia Expo (2007)

    Google Scholar 

  28. Van Lanh, T., Chong, K.S., Emmanuel, S., Kankanhalli, M.S.: A survey on digital camera image forensic methods. In: 2007 IEEE International Conference on Multimedia and Expo, pp. 16–19. IEEE (2007)

    Google Scholar 

  29. Wu, H., Liu, Q., Liu, X.: A review on deep learning approaches to image classification and object segmentation. TSP 1(1), 1–5 (2018)

    Google Scholar 

  30. Yu, J., Craver, S., Li, E.: Toward the identification of DSLR lenses by chromatic aberration. In: Media Watermarking, Security, and Forensics III, vol. 7880, p. 788010. International Society for Optics and Photonics (2011)

    Google Scholar 

  31. Zhang, J., Li, Y., Niu, S., Cao, Z., Wang, X.: Improved fully convolutional network for digital image region forgery detection (2019)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, University of Science and Technology of China, Huangshan Road 443, Hefei, 230026, Anhui, China

    Lan Chen & Anran Li

  2. School of Medical Information Technology, Anhui University of Chinese Medicine, Hefei, 230012, Anhui, China

    Lei Yu

Authors
  1. Lan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anran Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lei Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lan Chen .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Jinwei Wang

  3. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chen, L., Li, A., Yu, L. (2020). Forensic Technology for Source Camera Identification. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Communications in Computer and Information Science, vol 1254. Springer, Singapore. https://doi.org/10.1007/978-981-15-8101-4_42

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-8101-4_42

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-8100-7

  • Online ISBN: 978-981-15-8101-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation