Identification of Source Camera by Amalgamation of PRNU and Noise Print Using Dimensionality Expansive Residual Network

##plugins.themes.academic_pro.article.sidebar##

Published Nov 26, 2022
https://doi.org/10.47164/ijngc.v13i5.919
Download
Requires Subscription PDF
Statistic

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...
Volume 13, Special Issue 5, November 2022

##plugins.themes.academic_pro.article.main##

Shubham Anjankar
Department of Electronics Engineering, Shri Ramdeobaba College of Engineering and Management, Nagpur, India
Somesh Telang
Department of Electronics Engineering, Shri Ramdeobaba College of Engineering and Management, Nagpur, India
Khushalsingh Bharadwaj
Department of Electronics Engineering, Shri Ramdeobaba College of Engineering and Management, Nagpur, India
Richa Khandelwal
Department of Electronics Engineering, Shri Ramdeobaba College of Engineering and Management, Nagpur, India

Abstract

It might be challenging in the field of image forensics to identify the source camera of a picture. This research
proposes a noise adaptable convolutional neural network-based technique for camera identification. For camera
identification, the suggested solution combines Photo Response Non-Uniformity (PRNU) noise and Noiseprint.
Three parallel dimensionality expanded residual networks with convolutional layers of kernel size 1x1 were put
together for enhanced feature extraction. The experiment mentioned above uses pictures from the ”Vision Dataset”
as its subject matter. The experimental findings demonstrate the effectiveness of the suggested methodology in
identifying the source camera at the brand, model, and device levels. When two of the three networks were fed
with PRNU and one with noiseprint, the best performance was obtained.

##plugins.themes.academic_pro.article.details##

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
Anjankar, S., Telang, S., Bharadwaj, K., & Khandelwal, R. (2022). Identification of Source Camera by Amalgamation of PRNU and Noise Print Using Dimensionality Expansive Residual Network. International Journal of Next-Generation Computing, 13(5). https://doi.org/10.47164/ijngc.v13i5.919
Crossref
Scopus
Google Scholar
Europe PMC

References

  1. Akshatha, K. R., Anitha, H., Karunakar, A. K., Raghavendra, U., and Shetty, D. 2016. Source camera identification using noise residual. 2016 IEEE International Conference on Recent Trends in Electronics, Information Communication Technology (RTEICT). DOI: https://doi.org/10.1109/RTEICT.2016.7807997
  2. Bayram, S., Sencar, H., Memon, N., and Avcibas, I. 2005. Source camera identification based on CFA interpolation. IEEE International Conference on Image Processing 2005 . DOI: https://doi.org/10.1109/ICIP.2005.1530330
  3. Chan, L.-H., Law, N.-F., and Siu, W.-C. 2013. A confidence map and pixel-based weighted correlation for PRNU-based camera identification. Digital Investigation 10, 3 (10), 215–225. DOI: https://doi.org/10.1016/j.diin.2013.04.001
  4. Cozzolino, D., Marra, F., Gragnaniello, D., Poggi, G., and Verdoliva, L. 2020. Combining PRNU and noiseprint for robust and efficient device source identification. EURASIP Journal on Information Security 2020, 1 (feb 12). DOI: https://doi.org/10.1186/s13635-020-0101-7
  5. Cozzolino, D. and Verdoliva, L. 2020. Noiseprint: A CNN-Based Camera Model Finger-print. IEEE Transactions on Information Forensics and Security 15, 144–159. DOI: https://doi.org/10.1109/TIFS.2019.2916364
  6. Gisolf, F., Malgoezar, A., Baar, T., and Geradts, Z. 2013. Improving source camera identification using a simplified total variation based noise removal algorithm. Digital Investigation 10, 3 (10), 207–214. DOI: https://doi.org/10.1016/j.diin.2013.08.002
  7. Hong Cao and Kot, A. 2009. Accurate Detection of Demosaicing Regularity for Digital Image Forensics. IEEE Transactions on Information Forensics and Security 4, 4 (12), 899–910. DOI: https://doi.org/10.1109/TIFS.2009.2033749
  8. Kee, E., Johnson, M. K., and Farid, H. 2011. Digital Image Authentication From JPEG Headers. IEEE Transactions on Information Forensics and Security 6, 3 (9), 1066–1075. DOI: https://doi.org/10.1109/TIFS.2011.2128309
  9. Luka, J., Fridrich, J., and Goljan, M. 2006. Digital Camera Identification From Sensor Pattern Noise. IEEE Transactions on Information Forensics and Security 1, 2 (6), 205–214. DOI: https://doi.org/10.1109/TIFS.2006.873602
  10. International Journal of Next-Generation Computing - Special Issue, Vol. 13, No. 2, April 2022.
  11. Makinen, Y., Azzari, L., and Foi, A. 2019. Exact Transform-Domain Noise Variance for Collaborative Filtering of Stationary Correlated Noise. 2019 IEEE International Conference on Image Processing (ICIP). DOI: https://doi.org/10.1109/ICIP.2019.8802964
  12. Milani, S., Bestagini, P., Tagliasacchi, M., and Tubaro, S. 2014. Demosaicing strategy identification via eigenalgorithms. 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). DOI: https://doi.org/10.1109/ICASSP.2014.6854082
  13. Seshadri, S., Akshatha, K. R., Karunakar, A. K., and Paul, K. H. 2019. A Preliminary Approach to Using PRNU Based Transfer Learning for Camera Identification. Advances in Intelligent Systems and Computing, 246–255. DOI: https://doi.org/10.1007/978-3-030-17798-0_21
  14. Tuama, A., Comby, F., and Chaumont, M. 2016. Camera model identification with the use of deep convolutional neural networks. 2016 IEEE International Workshop on Information DOI: https://doi.org/10.1109/WIFS.2016.7823908
  15. Forensics and Security (WIFS).
  16. Wang, B., Guo, Y., Kong, X., and Meng, F. 2009. Source Camera Identification Forensics Based on Wavelet Features. 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing. DOI: https://doi.org/10.1109/IIH-MSP.2009.244
  17. Wolthusen, S. D. 2001. On the limitations of digital watermarks : A cautionary note.
  18. Yang, P., Ni, R., Zhao, Y., and Zhao, W. 2019. Source camera identification based on content-adaptive fusion residual networks. Pattern Recognition Letters 119, 195–204. DOI: https://doi.org/10.1016/j.patrec.2017.10.016