Image Splicing Detection Using Electromagnetism-Like Based Descriptor

  • Hamid A. JalabEmail author
  • Ali M. Hasan
  • Zahra Moghaddasi
  • Zouhir Wakaf
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 16)


This study proposes a simple and powerful descriptor called Electromagnetism-like mechanism descriptor (EMag) for image splicing detection. EMag is based on the electrostatic mechanism that represents the image pixels as electrical charges. For a given tampered image, the EMag algorithm divides an image into blocks and then calculates the final attraction-repulsion force between the central pixel of the square image block and its neighbors. The experimental results using an image splicing dataset provided by Digital Video and Multimedia Lab at Columbia University (DVMM) confirm that EMag impressively outperforms the other widely used descriptors for the detection of image splicing. Support vector machine is used as a classifier that distinguishes between the authentic and spliced images. The experimental results presented demonstrate that the achieved improvements are compatible with other splicing detection methods.


Electromagnetism-like mechanism Image splicing detection Features reduction SVM 


  1. 1.
    He, Z., Sun, W., Lu, W., Lu, H.: Digital image splicing detection based on approximate run length. Pattern Recogn. Lett. 32, 1591–1597 (2011)CrossRefGoogle Scholar
  2. 2.
    Sadeghi, S., Jalab, H.A., Wong, K.S., Uliyan, D.M., Dadkhah, S.: Keypoint based authentication and localization of copy-move forgery in digital image. Malaysian J. Comput. Sci. 30(2), 117–133 (2017)Google Scholar
  3. 3.
    Shi, Y.Q., Chen, C., Chen, W.: A natural image model approach to splicing detection. In: Proceedings of the 9th Workshop on Multimedia & Security, pp. 51–62 (2007)Google Scholar
  4. 4.
    Zhang, J., Zhao, Y., Su, Y.: A new approach merging Markov and DCT features for image splicing detection. In: IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009, pp. 390–394 (2009)Google Scholar
  5. 5.
    Moghaddasi, Z., Jalab, H.A., Md Noor, R., Aghabozorgi, S.: Improving RLRN image splicing detection with the use of PCA and Kernel PCA. Sci. World J. 1–10 (2014)Google Scholar
  6. 6.
    Moghaddasi, Z., Jalab, H.A., Noor, R.M.: SVD-based image splicing detection. In: 2014 International Conference on Information Technology and Multimedia (ICIMU), pp. 27–30 (2014)Google Scholar
  7. 7.
    Birbil, Ş.İ., Fang, S.-C.: An electromagnetism-like mechanism for global optimization. J. Global Optim. 25, 263–282 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Wu, P., Yang, W.-H., Wei, N.-C.: An electromagnetism algorithm of neural network analysis—an application to textile retail operation. J. Chin. Inst. Indus. Eng. 21, 59–67 (2004)Google Scholar
  9. 9.
    Dadkhah, S., Koppen, M., Jalab, H.A., Sadeghi, S., Manaf, A.A., Uliyan, D.: Electromagnetismlike mechanism descriptor with fourier transform for a passive copy-move forgery detection in digital image forensics. In: Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2017, Porto - Portugal, pp. 612–619, 24 February 2017Google Scholar
  10. 10.
    Turabieh, H., Abdullah, S.: An integrated hybrid approach to the examination timetabling problem. Omega 39, 598–607 (2011)CrossRefGoogle Scholar
  11. 11.
    Jhang, J.-Y., Lee, K.-C.: Array pattern optimization using electromagnetism-like algorithm. AEU-Int. J. Electron. Commun. 63, 491–496 (2009)CrossRefGoogle Scholar
  12. 12.
    Jalab, H.A., Abdullah, N.A.: Content-based image retrieval based on electromagnetism-like mechanism. Math. Probl. Eng. 1–10 (2013)Google Scholar
  13. 13.
    Jalab, H.A., Shaker, K.: Training the neural networks by electromagnetism-like mechanism based algorithm. In: Proceedings of the 3rd International Conference on Quantitative Sciences and Its Applications (ICOQSIA 2014), pp. 582–586 (2014)Google Scholar
  14. 14.
    Wang, X.-J., Gao, L., Zhang, C.-Y.: Electromagnetism-like mechanism based algorithm for neural network training. In: Advanced Intelligent Computing Theories and Applications: With Aspects of Artificial Intelligence. Springer, pp. 40–45 (2008)Google Scholar
  15. 15.
    Anusudha, K., Koshie, S.A., Ganesh, S.S., Mohanaprasad, K.: Image splicing detection involving moment-based feature extraction and classification using artificial neural networks. ACEEE Int. J. Signal Image Process. 1, 9 (2010)Google Scholar
  16. 16.
    Ng, T.-T., Chang, S.-F., Sun, Q.: A data set of authentic and spliced image blocks, Columbia University, ADVENT Technical report, pp. 203–2004 (2004)Google Scholar
  17. 17.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011)CrossRefGoogle Scholar
  18. 18.
    He, Z., Lu, W., Sun, W., Huang, J.: Digital image splicing detection based on Markov features in DCT and DWT domain. Pattern Recogn. 45, 4292–4299 (2012)CrossRefGoogle Scholar
  19. 19.
    Fu, D., Shi, Y.Q., Su, W.: Detection of image splicing based on hilbert-huang transform and moments of characteristic functions with wavelet decomposition. In: Digital Watermarking, pp. 177–187. Springer (2006)Google Scholar
  20. 20.
    Dong, J., Wang, W., Tan, T., Shi, Y.Q.: Run-length and edge statistics based approach for image splicing detection. In: Digital Watermarking, pp. 76–87. Springer (2009)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Hamid A. Jalab
    • 1
    Email author
  • Ali M. Hasan
    • 2
  • Zahra Moghaddasi
    • 1
  • Zouhir Wakaf
    • 3
  1. 1.Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala LumpurMalaysia
  2. 2.School of Computing, Science and EngineeringUniversity of SalfordGreater ManchesterUK
  3. 3.Electronic and Electrical EngineeringUniversity of StrathclydeGlasgowUK

Personalised recommendations