Digital Forensic Source Camera Identification with Efficient Feature Selection Using Filter, Wrapper and Hybrid Approaches

  • Venkata Udaya Sameer
  • S. Sugumaran
  • Ruchira Naskar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10063)

Abstract

Digital Forensics is the branch of science dealing with investigation of evidences recovered from digital devices, to safeguard against rapidly increasing cyber crimes in today’s digital world. The Source Camera Identification (SCI) problem is to map an image under question correctly to its source device. Following a Digital Forensic approach, the source of an image is detected by post–priori investigation of traces left behind in the image, by the camera. Such traces are generated due to the post–processing operations an image undergoes inside a digital camera, after being captured. In this paper, we model the SCI problem as a machine learning classification problem and focus on the most crucial component of a learning model, i.e. feature selection. We propose three different techniques for feature selection: Filter based approach, Wrapper based approach using Genetic Algorithm (GA), and also a hybrid approach with both Filter and Wrapper methods combined together. We investigate the source detection accuracy that each technique succeeds to achieve. Our experimental results suggest that the proposed methods produced a much compact feature set, hence considerably improve the source detection accuracy and minimize the training time of the learning model, as compared to the state–of–the–art.

Keywords

Classification Cybercrime Digital forensics Feature extraction Feature selection Genetic Algorithm Source camera identification 

References

  1. 1.
    Celiktutan, O., Sankur, B., Avcibas, I.: Blind identification of source cell-phone model. IEEE Trans. Inf. Forensics Secur. 3(3), 553–566 (2008)CrossRefGoogle Scholar
  2. 2.
    Bayram, S., Sencar, H.T., Memon, N.: Improvements on source camera-model identification based on CFA interpolation. In: Proceeding of WG (2006)Google Scholar
  3. 3.
    Kharrazi, M., Sencar, H.T., Memon, N.: Blind source camera identification. In: International Conference on Image Processing (ICIP) (2004)Google Scholar
  4. 4.
    Tsai, M.-J.: Adaptive feature selection for digital camera source identification. In: IEEE International Symposium on Circuits, Systems, pp. 412–415 (2008)Google Scholar
  5. 5.
    Tsai, M.-J.: A Hybrid model for digital camera source identification. IEEE International Conference on Image Processing (ICIP), pp. 2901–2904 (2009)Google Scholar
  6. 6.
    Lukas, J.: Digital camera identification from sensor pattern noise. IEEE Trans. Inf. Forensics Secur. 1(2), 205–214 (2006)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Li, C.-T.: Digital camera identification from sensor pattern noise. IEEE Trans. Inf. Forensics Secur. 5(2), 280–287 (2010)CrossRefGoogle Scholar
  8. 8.
    Lin, X., Li, C.-T.: Preprocessing reference sensor pattern noise via spectrum equalization. IEEE Trans. Inf. Forensics Secur. 11(1), 126–140 (2016)CrossRefGoogle Scholar
  9. 9.
    Biney, A.G., Sellahewa, H.: Analysis of smartphone model identification using digital images. In: International Conference on Image Processing (ICIP) (2013)Google Scholar
  10. 10.
    Bayram, S., Avcibas, I., Sankur, B., Memon, N.: Image manipulation detection. J. Electronic Imaging 15(4), 041102 (2006). International Society for Optics and PhotonicsCrossRefGoogle Scholar
  11. 11.
    Avcibas, I., Sankur, B., Memon, N.: Image steganalysis with binary similarity measures. In: International Conference on Image Processing (ICIP), vol. 3 (2002)Google Scholar
  12. 12.
    Avcibas, I., Memon, N., Sankur, B.: Steganalysis using image quality metrics. IEEE Trans. Image Process. 12(2), 221–229 (2003)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Lyu, S., Farid, H.: Steganalysis using higher-order image statistics. IEEE Trans. Inf. Forensics Secur. 1(1), 111–119 (2006)CrossRefGoogle Scholar
  14. 14.
    Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)Google Scholar
  15. 15.
    Schaffernicht, E., Gross, H.M.: Weighted mutual information for feature selection. In: International Conference on Artificial Neural Networks (2011)Google Scholar
  16. 16.
    Van Hulse, J., Khoshgoftaar, T.M., Napolitano, A., Wald, R.: Threshold-based feature selection techniques for high-dimensional bioinformatics data. Network Modeling Anal. Health Inform. Bioinform. 1(1), 47–61 (2012)CrossRefGoogle Scholar
  17. 17.
    Liu, D., Cho, S.Y., Sun, D.M., Qiu, Z.D.: A spearman correlation coefficient ranking for matching-score fusion on speaker recognition. In: TENCON (2010)Google Scholar
  18. 18.
    Yuan, C., Sun, D., Liu, D., Cho, S. Y., Zhang, Y.: A research on feature selection and fusion in palmprint recognition. In: International Workshop on Emerging Techniques and Challenges for Hand-Based Biometrics (ETCHB) (2010)Google Scholar
  19. 19.
    Onpans, J., Rasmequan, S., Jantarakongkul, B., Chinnasarn, K., Rodtook, A.: Intrusion feature selection using mmodified heuristic greedy algorithm of itemset. In: International Symposium on Communications and Information Technologies (ISCIT) (2013)Google Scholar
  20. 20.
    Rachburee, N., Punlumjeak, W.: A comparision of feature selection approach between Greedy, IG-ratio, Chi-square, and mRMR in educational mining. In: International Conference on Information Technology and Electrical Engineering (ICITEE) (2015)Google Scholar
  21. 21.
    Bhasin, V., Bedi, P., Singhal, A.: Feature selection for steganalysis based on modified stochastic diffusion search using fisher score. In: International Conference on Advances in Computing, Communications and Informatics (ICACCI), September 2014Google Scholar
  22. 22.
    Singh, B., Sankhwar, J.S., Vyas, O.P.: Optimization of feature selection method for high dimensional data using fisher score and minimum spanning tree. In: INDICON, December 2014Google Scholar
  23. 23.
    Xu, J., Yin, Y., Man, H., He, H.: Feature selection based on sparse imputation. In: International Joint Conference on Neural Networks (IJCNN), June 2012Google Scholar
  24. 24.
    Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artif. Intell. 97(1), 273–324 (1997)CrossRefMATHGoogle Scholar
  25. 25.
    Chen, Y.-H., Lin, T.-C.: Dimension reduction techniques for accessing chinese readability. In: International Conference on Machine Learning and Cybernetics, July 2014Google Scholar
  26. 26.
    Packianather, M.S., kapoor, B.: A wrapper-based feature selection approach using bees algorithm for a wood defect classification system. In: System of Systems Engineering Conference (2015)Google Scholar
  27. 27.
    Yu, E., Cho, S.: GA-SVM wrapper approach for feature subset selection in keystroke dynamics identity verification. In: Proceedings of the International Joint Conference on Neural Networks (2003)Google Scholar
  28. 28.
    Talukder, K.H., Harada, K.: Haar wavelet based approach for image compression and quality assessment of compressed image. Int. J. Appl. Math. 36(1) (2007)Google Scholar
  29. 29.
    Gunawan, I.P., Halim, A.: Haar wavelet decomposition based blockiness detector and picture quality assessment method for JPEG images. In: International Conference on Advanced Computer Science and Information System (2011)Google Scholar
  30. 30.
    Gloe, T., Bhme, R.: Dresden image database’ for benchmarking digital image forensics. In: IEEE International Conference on Acoustics, Speech and Signal Processing (2007)Google Scholar
  31. 31.
    Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 27:1–27:27 (2011)CrossRefGoogle Scholar
  32. 32.
    Ng, A.: “CS229 Lecture Notes”, CS229 Lecture notes, Stanford (2000)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Venkata Udaya Sameer
    • 1
  • S. Sugumaran
    • 1
  • Ruchira Naskar
    • 1
  1. 1.Department of Computer Science and EngineeringNational Institute of TechnologyRourkelaIndia

Personalised recommendations