LBP and GLCM Based Image Forgery Recognition

  • Authors

    • D. Vaishnavi
    • T. S. Subashini
    • G. N. Balaji
    • D. Mahalakshmi
    2018-09-25
    https://doi.org/10.14419/ijet.v7i4.6.20478
  • Image splicing forgery, local binary pattern, SVM, BPNN, combined k-NN.

  • Abstract

    The forgery of digital images became very easy and it’s very difficult to ascertain the authenticity of such images by naked eye. Among the various kinds of image forgeries, image splicing is a frequent and widely used technique. Even though various methods are available to detect image splicing forgery, authors have attempted to provide a novel hybrid method which can yield greater accuracy, sensitivity and specificity. In this method, gray level co-occurrence matrix (GLCM) features are extracted using local binary pattern (LBP) operator on the image and the detection of the splicing forged images among the authentic images is done using the popular pattern recognition algorithms such as combined k-NN (Comb-KNN), back propagation neural network (BPNN) and support vector machine (SVM). The recorded results are also compared with the existing results of the previous studies to ascertain the quality of the results.

     

     

  • References

    1. [1] Vaishnavi, D., and Subashini, T. (2015) Fragile Watermarking Scheme Based on Wavelet Edge Features, Journal of Electrical Engineering & Technology, KOREAN INST ELECTR ENG 901 KSTC, 635-4 YEOKSAM-DONG, GANGNAM-GU, SEOUL, 135-703, SOUTH KOREA 10, 2149–2154.

      [2] Wang, X., Xue, J., Zheng, Z., Liu, Z., and Li, N. (2012) Image forensic signature for content authenticity analysis, Journal of Visual Communication and Image Representation, Elsevier 23, 782–797.

      [3] Farid, H. (2009) Image forgery detection–A survey, Citeseer.

      [4] Vaishnavi, D., and Subashini, T. (2016) Recognizing image splicing forgeries using histogram features. In 2016 3rd MEC International Conference on Big Data and Smart City (ICBDSC), pp 1–4, IEEE.

      [5] Vaishnavi, D., and Subashini, T. (2015) A passive technique for image forgery detection using contrast context histogram features, International Journal of Electronic Security and Digital Forensics, Inderscience Publishers (IEL) 7, 278–289.

      [6] Alahmadi, A., Hussain, M., Aboalsamh, H., Muhammad, G., Bebis, G., and others. (2013) Splicing image forgery detection based on DCT and Local Binary Pattern. In Global Conference on Signal and Information Processing (GlobalSIP), 2013 IEEE, pp 253–256, IEEE.

      [7] Saleh, S. Q., Hussain, M., Muhammad, G., and Bebis, G. (2013) Evaluation of image forgery detection using multi-scale weber local descriptors. In Advances in Visual Computing, pp 416–424, Springer.

      [8] Hashmi, M. F., and Keskar, A. G. (2015) Image Forgery Authentication and Classification using Hybridization of HMM and SVM Classifier., International Journal of Security & Its Applications 9.

      [9] Al-Hammadi, M. H., Muhammad, G., Hussain, M., and Bebis, G. (2013) Curvelet transform and local texture based image forgery detection. In Advances in Visual Computing, pp 503–512, Springer.

      [10] Muhammad, G., Al-Hammadi, M. H., Hussain, M., and Bebis, G. (2014) Image forgery detection using steerable pyramid transform and local binary pattern, Machine Vision and Applications, Springer 25, 985–995.

      [11] Agarwal, S., and Chand, S. (2015) Image Forgery Detection using Multi Scale Entropy Filter and Local Phase Quantization.

      [12] He, Z., Lu, W., Sun, W., and Huang, J. (2012) Digital image splicing detection based on Markov features in DCT and DWT domain, Pattern Recognition, Elsevier 45, 4292–4299.

      [13] Zhao, X., Li, J., Li, S., and Wang, S. (2011) Detecting digital image splicing in chroma spaces. In Digital Watermarking, pp 12–22, Springer.

      [14] Pietikäinen, M., Hadid, A., Zhao, G., and Ahonen, T. (2011) Local binary patterns for still images. In Computer Vision Using Local Binary Patterns, pp 13–47, Springer.

      [15] Vapnik, V. N., and Vapnik, V. (1998) Statistical learning theory, Wiley New York.

      [16] Yegnanarayana, B. (2009) Artificial neural networks, PHI Learning Pvt. Ltd.

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  • How to Cite

    Vaishnavi, D., S. Subashini, T., N. Balaji, G., & Mahalakshmi, D. (2018). LBP and GLCM Based Image Forgery Recognition. International Journal of Engineering & Technology, 7(4.6), 217-219. https://doi.org/10.14419/ijet.v7i4.6.20478

    Received date: 2018-09-29

    Accepted date: 2018-09-29

    Published date: 2018-09-25