Signal, Image and Video Processing

, Volume 11, Issue 1, pp 81–88 | Cite as

Passive detection of image forgery using DCT and local binary pattern

  • Amani Alahmadi
  • Muhammad Hussain
  • Hatim Aboalsamh
  • Ghulam Muhammad
  • George Bebis
  • Hassan Mathkour
Original Paper


With the development of easy-to-use and sophisticated image editing software, the alteration of the contents of digital images has become very easy to do and hard to detect. A digital image is a very rich source of information and can capture any event perfectly, but because of this reason, its authenticity is questionable. In this paper, a novel passive image forgery detection method is proposed based on local binary pattern (LBP) and discrete cosine transform (DCT) to detect copy–move and splicing forgeries. First, from the chrominance component of the input image, discriminative localized features are extracted by applying 2D DCT in LBP space. Then, support vector machine is used for detection. Experiments carried out on three image forgery benchmark datasets demonstrate the superiority of the method over recent methods in terms of detection accuracy.


Copy–move forgery Image splicing Forgery detection Image forensics LBP DCT SVM 



This project was supported by NSTIP strategic technologies programs, Grant No. 10-INF1140-02, in the Kingdom of Saudi Arabia.


  1. 1.
    Farid, H.: A survey of image forgery detection. IEEE Signal Process. Mag. 2(26), 16–25 (2009)CrossRefGoogle Scholar
  2. 2.
    Mahdian, B., Saic, S.: A bibliography on blind methods for identifying image forgery. Signal Process. Image Commun. 25(6), 389–399 (2010)CrossRefGoogle Scholar
  3. 3.
    Shivakumar, B.L., Baboo, S.S.: Detecting copy-move forgery in digital images: a survey and analysis of current methods. Glob. J. Comput. Sci. Technol. 10(7), 61–65 (2011)Google Scholar
  4. 4.
    Hussain, M., Saleh, S.Q., Aboalsamh, H., Muhammad, G., Bebis, G.: Comparison between WLD and LBP descriptors for non-intrusive image forgery detection. In: Proceedings of the IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA 2014) (2014)Google Scholar
  5. 5.
    Muhammad, G., Hussain, M., Bebis, G.: Passive copy move image forgery detection using undecimated dyadic wavelet transform. Digit. Investig. 9(1), 49–57 (2012)CrossRefGoogle Scholar
  6. 6.
    Jaberi, M., Bebis, G., Hussain, M., Muhammad, G.: Accurate and robust localization of duplicated region in copy–move image forgery. Mach. Vis. Appl. 25(2), 451–475 (2014)CrossRefGoogle Scholar
  7. 7.
    Shi, Y.Q., Chen, C.: A natural image model approach to splicing detection. In: Proceedings of the 9th workshop on Multimedia and Security, Dallas, TX, pp. 51–62 (2007)Google Scholar
  8. 8.
    Zhang, Y., Zhao, C.: Revealing image splicing forgery using local binary patterns of DCT coefficients. Commun. Signal Process. Syst. 202, 181–189 (2012)CrossRefGoogle Scholar
  9. 9.
    Johnson, M.K., Farid, H.: Exposing digital forgeries through chromatic aberration. In: Proceedings of the 8th Workshop on Multimedia and Security, Geneva, pp. 48–55 (2006)Google Scholar
  10. 10.
    Zhao, X., Li, J.: Detecting digital image splicing in chroma spaces. Digit. Watermarking 6526, 12–22 (2011)CrossRefGoogle Scholar
  11. 11.
    Alahmadi, A.A., Hussain, M., Aboalsamh, M., Muhammad, G., Bebis, G.: Splicing image forgery detection based on DCT and local binary pattern. In: IEEE Global Conference on Signal and Information Processing (GlobalSIP 2013) (2013)Google Scholar
  12. 12.
    Ng, T.-T., Chang, S.-F.: A Data Set of Authentic and Spliced Image Blocks. ADVENT Technical Report, #203-2004-3, Columbia University (2004)Google Scholar
  13. 13.
    Zhen, Z., Jiquan, K.: An effective algorithm of image splicing detection. In: Proceedings of the International Conference on Computer Science and Software Engineering, Wuhan, Hubei, pp. 1035–1039 (2008)Google Scholar
  14. 14.
    Wei, W., Jing, D.: Image tampering detection based on stationary distribution of Markov chain. In: 17th IEEE International Conference Image Processing (ICIP 2010), Hong Kong, pp. 2101–2104 (2010)Google Scholar
  15. 15.
    CASIA, Image Tampering Detection Evaluation Database,
  16. 16.
    He, Z., Lu, W.: Digital image splicing detection based on Markov features in DCT and DWT domain. Pattern Recognit. 45(12), 4292–4299 (2012)Google Scholar
  17. 17.
    Dong, J., Wang, W.: Run-length and edge statistics based approach for image splicing detection. Digit. Watermarking 5450, 76–87 (2009)CrossRefGoogle Scholar
  18. 18.
    Yu-Feng, H., Shih-Fu, C.: Detecting image splicing using geometry invariants and camera characteristics consistency. In: IEEE International Conference on Multimedia and Expo, Toronto, ONGoogle Scholar
  19. 19.
    Hussain, M., Muhammad, G., Saleh, S.Q., Mirza, A., M., Bebis, G.: Image forgery detection using multi-resolution Weber local descriptors. In: Proceedings of the IEEE EUROCON, Zagreb, pp. 1570–1577 (2013)Google Scholar
  20. 20.
    Muhammad, G., Al-Hammadi, M., Hussain, M., Bebis, G.: Image forgery detection using steerable pyramid transform and local binary pattern. Mach. Vis. Appl. 25(4), 985–995 (2014)CrossRefGoogle Scholar
  21. 21.
    Zhang, G., Huang, X.: Boosting local binary pattern (LBP)-based face recognition. Adv. Biometr. Person Authent. 3338, 179–186 (2005)CrossRefGoogle Scholar
  22. 22.
    Lee, B.B., Pokorny, J.: Luminance and chromatic modulation sensitivity of macaque ganglion cells and human observers. JOSA A 7, 2223–2236 (1990)CrossRefGoogle Scholar
  23. 23.
    Di, H., Caifeng, S.: Local binary patterns and its application to facial image analysis: a survey. IEEE Trans. Syst. Man Cybern. 41(6), 765–781 (2011)CrossRefGoogle Scholar
  24. 24.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)zbMATHGoogle Scholar
  25. 25.
    Hussain, M., Wajid, S.K., Elzaart, A., Berbar, M.: A comparison of SVM kernel functions for breast cancer detection. In: Proceedings of the 2011 Eighth International Conference on Computer Graphics, Imaging and Visualization (CGIV 2011), Singapore, pp. 145–150 (2011)Google Scholar
  26. 26.
    Hsu, C.-W., Chang, C.-C., Lin, C.-J.: A practical guide to support vector classification. (2010)
  27. 27.
    Sokolova, M., Japkowicz, N.: Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation. Adv. Artif. Intell. 4304, 1015–1021 (2006)Google Scholar

Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  • Amani Alahmadi
    • 1
  • Muhammad Hussain
    • 1
  • Hatim Aboalsamh
    • 1
  • Ghulam Muhammad
    • 1
  • George Bebis
    • 2
  • Hassan Mathkour
    • 1
  1. 1.College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia
  2. 2.Department of Computer Science and EngineeringUniversity of Nevada at RenoRenoUSA

Personalised recommendations