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Multimedia Tools and Applications

, Volume 77, Issue 10, pp 12805–12835 | Cite as

JPEG image classification in digital forensic via DCT coefficient analysis

  • Nadeem Alherbawi
  • Zarina Shukur
  • Rossilawati Sulaiman
Article
  • 147 Downloads

Abstract

From the digital forensics point of view, image forgery is considered as evidence that could provide a major breakthrough in the investigation process. Additionally, the development of storage device technologies has increased storage space significantly. Thus a digital investigator can be overwhelmed by the amount of data on storage devices that needs to be analysed. In this paper, we propose a model for classifying bulk JPEG images produced by the data carving process or other means into three different classes to solve the problem of identifying forgery quickly and effectively. The first class is JPEG images that contain errors or corrupted data, the second class is JPEG images that contain forged regions, and the third is JPEG images that have no signs of corruption or forgery. To test the proposed model, some experiments were conducted on our own dataset in addition to CASIA V2 image forgery dataset. The experiments covered different types of forgery technique. The results yielded around 88% accuracy rate in the classification process using five different machine learning methods on CASIA V2 dataset. It can be concluded that the proposed model can help investigators to automatically classify JPEG images, which reduce the time needed in the overall digital investigation process.

Keywords

Digital investigation Data carving JPEG image forgery DCT coefficient analysis JPEG image classification 

Notes

Acknowledgements

Credits for the use of the CASIA Image Tempering Detection Evaluation Database (CAISA TIDE) V2.0 are given to the National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Science, Corel Image Database and the photographers. http://forensics.idealtest.org

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Research Center for Software Technology and Management, Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiMalaysia

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