Image Splicing Forgery Detection Using DWT and Local Binary Pattern

Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 120)


The broad use of high-performance tools for image acquisition and strong image processing software has made it easy for malicious purposes to manipulate images. Image splicing, which posed a threat to image integrity and authenticity, is a very popular and easy image forgery trick. Therefore, detection of image splicing is one of the major problems in digital forensics. A new passive (non-intrusive) image tampering detection technique is proposed here to detect splicing forgery based on discrete wavelet transform (DWT) and local binary pattern (LBP). First, input image is converted into YCbCr channels, and then, chroma channels are used as input image for feature extraction using 5-bin histogram and 3-CF moments from DWT domain. Then, ensemble classifier is used for detection of spliced and authentic images.


Forgery detection Image splicing CF moments DWT LBP 


  1. 1.
    Mahdian, B., Saic, S.: A bibliography on blind methods for identifying image forgery. Signal Process. Image Commun. 25(6), 389–399 (2010)Google Scholar
  2. 2.
    Birajdar, G.K., Mankar, V.H.: Digital image forgery detection using passive techniques: a survey. Digit. Investig. 10(3), 226–245 (2013)Google Scholar
  3. 3.
    Peterson, G.: Forensic analysis of digital image tampering. In: IFIP international conference on digital forensics, pp. 259–270. Springer, New York (2005)Google Scholar
  4. 4.
    Mahale, V.H., Ali, M.M., Yannawar, P.L., Gaikwad, A.T.: Image inconsistency detection using local binary pattern (lbp). Procedia Comput. Sci. 115, 501–508 (2017)Google Scholar
  5. 5.
    Hakimi, F., Zanjan, I., Hariri, I.: Image-splicing forgery detection based on improved lbp and k-nearest neighbors algorithm. Electron. Inf. Plan. 3(0304–9876), 7 (2015)Google Scholar
  6. 6.
    Abrahim, A.R., Rahim, M.S.M., Sulong, G.B.: Splicing image forgery identification based on artificial neural network approach and texture features. Clust. Comput. 1–14 (2018)Google Scholar
  7. 7.
    Alahmadi, A., Hussain, M., Aboalsamh, H., Muhammad, G., Bebis, G., Mathkour, H.: Passive detection of image forgery using dct and local binary pattern. Signal Image Video Process. 11(1), 81–88 (2017)CrossRefGoogle Scholar
  8. 8.
    Hakimi, F., Hariri, M., GharehBaghi, F.: Image splicing forgery detection using local binary pattern and discrete wavelet transform. In: 2015 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI), pp. 1074–1077. (IEEE, 2015)Google Scholar
  9. 9.
    Shen, X., Shi, Z., Chen, H.: Splicing image forgery detection using textural features based on the grey level co-occurrence matrices. IET Image Process. 11(1), 44–53 (2016)CrossRefGoogle Scholar
  10. 10.
    Kodovsky, J., Fridrich, J., Holub, V.: Ensemble classifiers for steganalysis of digital media. IEEE Trans. Inf. Forensics Secur. 7(2), 432–444 (2012)CrossRefGoogle Scholar
  11. 11.
    Alahmadi, A.A., Hussain, M., Aboalsamh, H., Muhammad, G., Bebis, G.: Splicing image forgery detection based on dct and local binary pattern. In: 2013 IEEE Global Conference on Signal and Information Processing, pp. 253–256. (IEEE, 2013)Google Scholar
  12. 12.
    Park, T.H., Han, J.G., Moon, Y.H., Eom, I.K.: Image splicing detection based on inter-scale 2d joint characteristic function moments in wavelet domain. EURASIP J. Image Video Process. 2016(1), 30 (2016)Google Scholar
  13. 13.
    Shi, Y.Q., Xuan, G., Zou, D., Gao, J., Yang, C., Zhang, Z., Chai, P., Chen, W., Chen, C.: Image steganalysis based on moments of characteristic functions using wavelet decomposition, prediction-error image, and neural network. In: 2005 IEEE International Conference on Multimedia and Expo, pp. 4–pp. (IEEE, 2005)Google Scholar
  14. 14.
    Prakash, C.S., Kumar, A., Maheshkar, S., Maheshkar, V.: An integrated method of copy-move and splicing for image forgery detection. Multimed. Tools Appl. 1–25 (2018)Google Scholar
  15. 15.
    Mayer, O., Stamm, M.C.: Accurate and efficient image forgery detection using lateral chromatic aberration. IEEE Trans. Inf. Forensics Secur. 13(7), 1762–1777 (2018)Google Scholar
  16. 16.
    Kanwal, N., Girdhar, A., Kaur, L., Bhullar, J.S.: Detection of digital image forgery using fast fourier transform and local features. In: 2019 International Conference on Automation, Computational and Technology Management (ICACTM), pp. 262–267. (IEEE, 2019)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringCGPIT, BardoliSuratIndia
  2. 2.Department of Computer Science and EngineeringKoneru Lakshmaiah Education FoundationVaddeswaramIndia
  3. 3.Department of Computer Science and EngineeringR. N. G. Patel Institute of Technology (RNGPIT)BardoliIndia

Personalised recommendations