Classification of JPEG Files by Using Extreme Learning Machine

  • Rabei Raad Ali
  • Kamaruddin Malik Mohamad
  • Sapiee Jamel
  • Shamsul Kamal Ahmad Khalid
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 700)


Recovery of data files when their system information missing is a challenging research issue. The recovery process entails methods that analyze the structure and contents of each individual file clusters. A primary and important process of files’ recovery is determining the files’ types including JPEG, DOC or HTML. This paper proposes an Extreme Learning Machine (ELM) algorithm to assign a class label of JPEG or Non-JPEG image for files in a continuous series of data clusters. The algorithm automatically classifies the files based on evaluation measures of three methods Entropy, Byte Frequency Distribution and Rate of Change. The ELM algorithm is applied to RABEI-2017 and DFRWS-2006 datasets. The experimental results show that the ELM algorithm is able to identify JPEG files of fragmented clusters with high accuracy rate. The classification accuracy of the RABEI-2017 dataset is 90.15% and the DFRWS-2006 is 93.46%. The DFRWS-2006 has more classes than the RABEI-2017 which improves the ELM classifier fitting.


Multimedia clusters JPEG image Classification Extreme learning machine (ELM) 



This work was supported by the Universiti Tun Hussein Onn Malaysia, Ministry of Higher Education Malaysia under Grant Vote No. U495.


  1. 1.
    Abdullah, N., Ibrahim, R., Mohamad, K.: Cluster size determination using JPEG files. In: 2012 Computational Science and Its Applications–ICCSA, pp. 353–363 (2012)Google Scholar
  2. 2.
    Mohammed, M.A., Gani, M.K. A., Hamed, R.I., Mostafa, S.A., Ahmad, M.S., Ibrahim, D.A.: Solving vehicle routing problem by using improved genetic algorithm for optimal solution. J. Comput. Sci. (2017)Google Scholar
  3. 3.
    Amirani, M.C., Toorani, M., Mihandoost, S.: Feature-based type identification of file fragments. Secur. Commun. Netw. 6(1), 115–128 (2013)CrossRefGoogle Scholar
  4. 4.
    Qiu, W., Zhu, R., Guo, J., Tang, X., Liu, B., Huang, Z.: A new approach to multimedia files carving. In: 2014 IEEE International Conference on Bioinformatics and Bioengineering (BIBE), pp. 105–110. IEEE, Nov 2014Google Scholar
  5. 5.
    Veenman, C.J.: Statistical disk cluster classification for file carving. In: 2007 Third International Symposium on  Information Assurance and Security, IAS 2007, pp. 393–398. IEEE, Aug 2007Google Scholar
  6. 6.
    McDaniel, M., Heydari, M.H.: Content based file type detection algorithms. In: 2003 Proceedings of the 36th Annual Hawaii International Conference on System Sciences, pp. 10-pp. IEEE, Jan 2003Google Scholar
  7. 7.
    Karresand, M., Shahmehri, N.: Oscar-file type identification of binary data in disk clusters and ram pages. Secur. Priv. Dyn. Environ. 413–424 (2006)Google Scholar
  8. 8.
    Li, Q., Ong, A., Suganthan, P., Thing, V.: A novel support vector machine approach to high entropy data fragment classification. In: Proceedings of the South African Information Security Multi-Conf (SAISMC), pp. 236–247 (2011)Google Scholar
  9. 9.
    Mehra, N., Gupta, S.: Survey on multiclass classification methods. Int. J. Comput. Sci. Inf. Technol. 4(4), 572–576 (2013)Google Scholar
  10. 10.
    Zhang, L., Zhang, D., Tian, F.: SVM and ELM: Who Wins? Object recognition with deep convolutional features from ImageNet. In Proceedings of ELM-2015, vol. 1, pp. 249–263. Springer International Publishing (2016)Google Scholar
  11. 11.
  12. 12.
    Shannon, M.: Forensic relative strength scoring: ASCII and entropy scoring. Int. J. Digit. Evid. 2(4), 1–19 (2004)Google Scholar
  13. 13.
    Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1), 489–501 (2006)CrossRefGoogle Scholar
  14. 14.
    Mohammed, M.A., Ghani, M. K.A., Hamed, R.I., Mostafa, S.A., Ibrahim, D.A., Jameel, H.K., Alallah, A.H.: Solving vehicle routing problem by using improved K-nearest neighbor algorithm for best solution. J. Comput. Sci. (2017).Google Scholar
  15. 15.
    Khaleefah, S.H., Nasrudin, M.F., Mostafa, S.A.: Fingerprinting of deformed paper images acquired by scanners. In: 2015 IEEE Student Conference on Research and Development (SCOReD), pp. 393–397. IEEE, Dec 2015Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Rabei Raad Ali
    • 1
  • Kamaruddin Malik Mohamad
    • 1
  • Sapiee Jamel
    • 1
  • Shamsul Kamal Ahmad Khalid
    • 1
  1. 1.Faculty of Computer Science and Information Technology, Information Security Interest Group (ISIG)Universiti Tun Hussein Onn MalaysiaParit RajaMalaysia

Personalised recommendations