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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)

Abstract

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.

Keywords

Multimedia clusters JPEG image Classification Extreme learning machine (ELM) 

Notes

Acknowledgements

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

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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

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