Skip to main content
Springer Nature Link
Log in

An Effective Training Strategy for Enhanced Source Camera Device Identification

  • Conference paper
  • First Online:
Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges (ICPR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13646))

Included in the following conference series:

  • 300 Accesses

Abstract

Source camera identification is a forensic problem of linking an image in question to the camera used to capture it. This could be a useful tool in forensic applications to identify potential suspects of cybercrime. Over the last decade, several successful attempts have been made to identify the source camera using deep learning. However, existing techniques that provide effective solutions for camera model identification fail to distinguish between different devices of the same model. This is because cameras of different brands and models were used to train the data-driven system when dealing with exact device identification. We show that training the data-driven system on different camera models opens side-channel information on model-specific features, which acts as interference for identifying individual devices of the same model. Thus, we provide an effective training strategy that involves a way to construct the dataset for enhanced source camera device identification. The experimental results suggest that involving only cameras of the same model for training improves the discriminative capability of the data-driven system by eliminating the threat of interfering model-specific features.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

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

    Article  Google Scholar 

  2. Chen, Y., Huang, Y., Ding, X.: Camera model identification with residual neural network. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 4337–4341. IEEE (2017)

    Google Scholar 

  3. Ding, X., Chen, Y., Tang, Z., Huang, Y.: Camera identification based on domain knowledge-driven deep multi-task learning. IEEE Access 7, 25878–25890 (2019)

    Article  Google Scholar 

  4. Dirik, A.E., Karaküçük, A.: Forensic use of photo response non-uniformity of imaging sensors and a counter method. Opt. Express 22(1), 470–482 (2014)

    Article  Google Scholar 

  5. Dirik, A.E., Sencar, H.T., Memon, N.: Analysis of seam-carving-based anonymization of images against PRNU noise pattern-based source attribution. IEEE Trans. Inf. Forensics Secur. 9(12), 2277–2290 (2014)

    Article  Google Scholar 

  6. Freire-Obregón, D., Narducci, F., Barra, S., Castrillón-Santana, M.: Deep learning for source camera identification on mobile devices. Pattern Recogn. Lett. 126, 86–91 (2019)

    Article  Google Scholar 

  7. Goljan, M., Fridrich, J., Filler, T.: Large scale test of sensor fingerprint camera identification. In: Media Forensics and Security, vol. 7254, p. 72540I. International Society for Optics and Photonics (2009)

    Google Scholar 

  8. Hadwiger, B., Riess, C.: The Forchheim image database for camera identification in the wild. In: Del Bimbo, A., et al. (eds.) ICPR 2021. LNCS, vol. 12666, pp. 500–515. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68780-9_40

    Chapter  Google Scholar 

  9. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  10. Huang, N., He, J., Zhu, N., Xuan, X., Liu, G., Chang, C.: Identification of the source camera of images based on convolutional neural network. Digit. Investig. 26, 72–80 (2018)

    Article  Google Scholar 

  11. Karaküçük, A., Dirik, A.E.: Adaptive photo-response non-uniformity noise removal against image source attribution. Digit. Investig. 12, 66–76 (2015)

    Article  Google Scholar 

  12. Akshatha, K.R., Karunakar, A.K., Anitha, H., Raghavendra, U., Shetty, D.: Digital camera identification using PRNU: a feature based approach. Digit. Investig. 19, 69–77 (2016)

    Article  Google Scholar 

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

    Google Scholar 

  14. Li, C.-T., Chang, C.-Y., Li, Y.: On the repudiability of device identification and image integrity verification using sensor pattern noise. In: Weerasinghe, D. (ed.) ISDF 2009. LNICST, vol. 41, pp. 19–25. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-11530-1_3

    Chapter  Google Scholar 

  15. Lin, X., Li, C.-T.: Enhancing sensor pattern noise via filtering distortion removal. IEEE Signal Process. Lett. 23(3), 381–385 (2016)

    Google Scholar 

  16. 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 

  17. Manisha, A.K., Li, C.-T.: Identification of source social network of digital images using deep neural network. Pattern Recogn. Lett. 150, 17–25 (2021)

    Google Scholar 

  18. Quan, Y., Li, C.-T., Zhou, Y., Li, L.: Warwick image forensics dataset for device fingerprinting in multimedia forensics. In: 2020 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6. IEEE (2020)

    Google Scholar 

  19. Samaras, S., Mygdalis, V., Pitas, I.: Robustness in blind camera identification. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 3874–3879. IEEE (2016)

    Google Scholar 

  20. Sameer, V.U., Dali, I., Naskar, R.: A deep learning based digital forensic solution to blind source identification of Facebook images. In: Ganapathy, V., Jaeger, T., Shyamasundar, R.K. (eds.) ICISS 2018. LNCS, vol. 11281, pp. 291–303. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-05171-6_15

    Chapter  Google Scholar 

  21. Sameer, V.U., Naskar, R.: Deep siamese network for limited labels classification in source camera identification. Multimedia Tools Appl. 79(37), 28079–28104 (2020)

    Article  Google Scholar 

  22. Shullani, D., Fontani, M., Iuliani, M., Al Shaya, O., Piva, A.: Vision: a video and image dataset for source identification. EURASIP J. Inf. Secur. 2017(1), 1–16 (2017)

    Google Scholar 

  23. Tian, H., Xiao, Y., Cao, G., Zhang, Y., Xu, Z., Zhao, Y.: Daxing smartphone identification dataset. IEEE Access 7, 101046–101053 (2019)

    Article  Google Scholar 

  24. 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 

  25. Wang, B., Yin, J., Tan, S., Li, Y., Li, M.: Source camera model identification based on convolutional neural networks with local binary patterns coding. Signal Process. Image Commun. 68, 162–168 (2018)

    Article  Google Scholar 

  26. Lin, X., Li, C.-T.: Preprocessing reference sensor pattern noise via spectrum equalization. IEEE Trans. Inf. Forensics Secur. 11(1), 126–140 (2015)

    Article  MathSciNet  Google Scholar 

  27. Yang, P., Ni, R., Zhao, Y., Zhao, W.: Source camera identification based on content-adaptive fusion residual networks. Pattern Recogn. Lett. 119, 195–204 (2019)

    Article  Google Scholar 

  28. Yao, H., Qiao, T., Xu, M., Zheng, N.: Robust multi-classifier for camera model identification based on convolution neural network. IEEE Access 6, 24973–24982 (2018)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Data Science and Computer Applications, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India

    Manisha & Karunakar A. Kotegar

  2. School of Information Technology, Deakin University, Geelong, 3216, Australia

    Chang-Tsun Li

Authors
  1. Manisha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chang-Tsun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Karunakar A. Kotegar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chang-Tsun Li .

Editor information

Editors and Affiliations

  1. York University, Toronto, ON, Canada

    Jean-Jacques Rousseau

  2. Ontario Tech University, Oshawa, ON, Canada

    Bill Kapralos

Rights and permissions

Reprints and permissions

Copyright information

© 2023 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Manisha, Li, CT., Kotegar, K.A. (2023). An Effective Training Strategy for Enhanced Source Camera Device Identification. In: Rousseau, JJ., Kapralos, B. (eds) Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges. ICPR 2022. Lecture Notes in Computer Science, vol 13646. Springer, Cham. https://doi.org/10.1007/978-3-031-37745-7_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-37745-7_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-37744-0

  • Online ISBN: 978-3-031-37745-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships