Skip to main content
SpringerLink
Log in

Noisy Smoothing Image Source Identification

  • Conference paper
  • First Online:
Cyberspace Safety and Security (CSS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 10581))

Included in the following conference series:

Abstract

Feature based image source identification plays an important role in the toolbox for forensics investigations on images. Conventional feature based identification schemes suffer from the problem of noise, that is, the training dataset contains noisy samples. To address this problem, we propose a new Noisy Smoothing Image Source Identification (NS-ISI) method. NS-ISI address the noise problem in two steps. In step 1, we employ a classifier ensemble approach for noise level evaluation for each training sample. The noise level indicates the probability of being noisy. In step 2, a noise sensitive sampling method is employed to sample training samples from original training set according to the noise level, producing a new training dataset. The experiments carried out on the Dresden image collection confirms the effectiveness of the proposed NS-ISI. When the noisy samples present, the identification accuracy of NS-ISI is significantly better than traditional methods.

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 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

Institutional subscriptions

Notes

  1. 1.

    http://www.facebook.com/.

  2. 2.

    http://instagram.com/.

References

  1. Stamm, M.C., Liu, K.J.R.: Forensic detection of image manipulation using statistical intrinsic fingerprints. IEEE Trans. Inf. Forensics Secur. 5(3), 492–506 (2010)

    Article  Google Scholar 

  2. 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 

  3. Jain, L.C., Pan, J.S.: [1]. In: Cox, I.J., Miller, M.L., Bloom, J.A. (eds.) Digital Watermarking and Fundamentals. Morgan Kaufmann, San Francisco (2002)

    Google Scholar 

  4. Schyndel, R.G.V., Tirkel, A.Z., Osborne, C.F.: A digital watermark. In: Proceedings of IEEE International Conference on Image Processing, ICIP-94, vol. 2, pp. 86–90 (2002)

    Google Scholar 

  5. Committee, C.: Exchangeable image file format for digital still cameras: Exif version 2.3. (2010)

    Google Scholar 

  6. Bayram, S., Sencar, H., Memon, N., Avcibas, I.: Source camera identification based on CFA interpolation. In: IEEE International Conference on Image Processing, vol. 3, pp. 69–72 (2009)

    Google Scholar 

  7. Kharrazi, M., Sencar, H.T., Memon, N.: Blind source camera identification (2004)

    Google Scholar 

  8. Peng, F., Shi, J., Long, M.: Comparison and analysis of the performance of PRNU extraction methods in source camera identification. J. Comput. Inf. Syst. 9(14), 5585–5592 (2013)

    Google Scholar 

  9. Castiglione, A., Cattaneo, G., Cembalo, M., Petrillo, U.F.: Experimentations with source camera identification and online social networks. J. Ambient Intell. Humanized Comput. 4(2), 265–274 (2013)

    Article  Google Scholar 

  10. Cortes, C., Vapnik, V.: Support-Vector Networks. Kluwer Academic Publishers, Boston (1995)

    MATH  Google Scholar 

  11. Lyu, S., Farid, H.: Detecting hidden messages using higher-order statistics and support vector machines. In: Revised Papers from the International Workshop on Information Hiding, pp. 340–354 (2002)

    Google Scholar 

  12. Wahid, A., Wahab, A., Ho, A.T.S., Li, S.: Inter-camera model image source identification with conditional probability features. In: Proceedings of IIEEJ Image Electronics & Visual Computing Workshop (2012)

    Google Scholar 

  13. Wang, B., Kong, X., You, X.: Source camera identification using support vector machines. In: Peterson, G., Shenoi, S. (eds.) DigitalForensics 2009. IAICT, vol. 306, pp. 107–118. Springer, Heidelberg (2009). doi:10.1007/978-3-642-04155-6_8

    Chapter  Google Scholar 

  14. Roychowdhury, A., Lin, T.Y., Maji, S., Learnedmiller, E.: Face identification with bilinear CNNs, pp. 1–9 (2015)

    Google Scholar 

  15. 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 

  16. Sutcu, Y., Bayram, S., Sencar, H.T., Memon, N.: Improvements on sensor noise based source camera identification. In: IEEE International Conference on Multimedia and Expo, pp. 24–27 (2007)

    Google Scholar 

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

    Article  Google Scholar 

  18. Li, C.T., Li, Y.: Color-decoupled photo response non-uniformity for digital image forensics. IEEE Trans. Circ. Syst. Video Technol. 22(2), 260–271 (2012)

    Article  MathSciNet  Google Scholar 

  19. Kang, X., Li, Y., Qu, Z., Huang, J.: Enhancing source camera identification performance with a camera reference phase sensor pattern noise. IEEE Trans. Inf. Forensics Secur. 7(2), 393–402 (2012)

    Article  Google Scholar 

  20. Kang, X., Chen, J., Lin, K., Peng, A.: A context-adaptive spn predictor for trustworthy source camera identification. EURASIP J. Image Video Process. 2014(1), 19 (2014)

    Article  Google Scholar 

  21. Tsai, M.J., Wang, C.S., Liu, J., Yin, J.S.: Using decision fusion of feature selection in digital forensics for camera source model identification. Comput. Stand. Interfaces 34(3), 292–304 (2012)

    Article  Google Scholar 

  22. Choi, K.S., Lam, E.Y., Wong, K.K.: Automatic source camera identification using the intrinsic lens radial distortion. Opt. Express 14(24), 11551–11565 (2006)

    Article  Google Scholar 

  23. Kai, S.C., Lam, E.Y., Wong, K.K.Y.: Source camera identification using footprints from lens aberration. In: Proceedings of SPIE - The International Society for Optical Engineering, vol. 6069, pp. 60690J–60690J-8 (2006)

    Google Scholar 

  24. Min, G.H., Har, D.H., Park, H.J.: Source camera identification based on interpolation via lens distortion correction. Aust. J. Forensic Sci. 46(1), 98–110 (2014)

    Article  Google Scholar 

  25. Van, L.T., Emmanuel, S., Kankanhalli, M.S.: Identifying source cell phone using chromatic aberration. In: IEEE International Conference on Multimedia and Expo, pp. 883–886 (2007)

    Google Scholar 

  26. Dirik, A.E., Sencar, H.T., Memon, N.: Digital single lens reflex camera identification from traces of sensor dust. IEEE Trans. Inf. Forensics Secur. 3(3), 539–552 (2008)

    Article  Google Scholar 

  27. Dirik, A.E., Sencar, H.T., Memon, N.: Source camera identification based on sensor dust characteristics. In: IEEE Workshop on Signal Processing Applications for Public Security and Forensics, SAFE 2007, pp. 1–6 (2007)

    Google Scholar 

  28. Li, C.T., Li, Y.: Digital camera identification using colour-decoupled photo response non-uniformity noise pattern. In: IEEE International Symposium on Circuits and Systems, pp. 3052–3055 (2010)

    Google Scholar 

  29. Liu, B.B., Lee, H.K., Hu, Y., Choi, C.H.: On classification of source cameras: a graph based approach. In: IEEE International Workshop on Information Forensics and Security, pp. 1–5 (2010)

    Google Scholar 

  30. Li, C.T.: Unsupervised classification of digital images using enhanced sensor pattern noise. In: IEEE International Symposium on Circuits and Systems, pp. 3429–3432 (2010)

    Google Scholar 

  31. Tuama, A., Comby, F., Chaumont, M.: Camera model identification with the use of deep convolutional neural networks. In: IEEE International Workshop on Information Forensics and Security (2016)

    Google Scholar 

  32. Chen, Y.N., Han, C.C., Wang, C.T., Jeng, B.S., Fan, K.C.: The application of a convolution neural network on face and license plate detection. In: International Conference on Pattern Recognition, pp. 552–555 (2006)

    Google Scholar 

  33. Wang, B., Zhang, J., Zhang, Z., Luo, W., Xia, D.: Robust traffic classification with mislabelled training samples. In: IEEE International Conference on Parallel and Distributed Systems, pp. 328–335 (2015)

    Google Scholar 

  34. Pyun, K., Lim, J., Gray, R.M.: A robust hidden Markov Gauss mixture vector quantizer for a noisy source. IEEE Trans. Image Process. 18(7), 1385–1394 (2009). IEEE Signal Processing Society

    Article  MATH  MathSciNet  Google Scholar 

  35. Breiman, L.: Random forest. Mach. Learn. 45, 5–32 (2001)

    Article  MATH  Google Scholar 

  36. Chatterjee, S., Dash, A., Bandopadhyay, S.: Ensemble support vector machine algorithm for reliability estimation of a mining machine. Qual. Reliab. Eng. Int. 31(8), 1503–1516 (2016)

    Article  Google Scholar 

  37. Gloe, T.: The ‘Dresden image database’ for benchmarking digital image forensics. In: ACM Symposium on Applied Computing, pp. 1584–1590 (2010)

    Google Scholar 

Download references

Acknowledgment

This work was supported by the National Natural Science Foundation of China (No. 61300077 & No. 61502319).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications, Beijing Institute of Technology, Beijing, 100081, China

    Yuying Liu, Yonggang Huang & Xu Liu

  2. School of Information Technology, Deakin University, Melbourne, VIC, 3125, Australia

    Jun Zhang

  3. College of Computer and Information Engineering, Henan Normal University, Xinxiang, 453007, China

    Hualei Shen

Authors
  1. Yuying Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yonggang Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hualei Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yonggang Huang .

Editor information

Editors and Affiliations

  1. Deakin University , Geelong, China

    Sheng Wen

  2. Fujian Normal University, Fuzhou Shi, China

    Wei Wu

  3. University of Salerno, Fisciano, Italy

    Aniello Castiglione

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Liu, Y., Huang, Y., Zhang, J., Liu, X., Shen, H. (2017). Noisy Smoothing Image Source Identification. In: Wen, S., Wu, W., Castiglione, A. (eds) Cyberspace Safety and Security. CSS 2017. Lecture Notes in Computer Science(), vol 10581. Springer, Cham. https://doi.org/10.1007/978-3-319-69471-9_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-69471-9_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69470-2

  • Online ISBN: 978-3-319-69471-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation