access icon free Splicing image forgery detection using textural features based on the grey level co-occurrence matrices

© The Institution of Engineering and Technology
Received 11/04/2016, Accepted 25/08/2016, Revised 12/08/2016, Published 08/09/2016

To further improve the detection rate with relatively low dimension feature vector, a novel passive splicing detection method using textural features based on the grey level co-occurrence matrices, namely TF-GLCM, is proposed in this study. In the TF-GLCM, the GLCM are calculated based on the difference block discrete cosine transform arrays to capture the textural information and the spatial relationship between image pixels sufficiently. The discriminable properties contained in the GLCM are described by six textural features, which include two new introduced ones and four independent ones. In addition, the statistical moments mean Me and standard deviation SD of textural features are used instead of themselves as elements in feature vector to reduce the dimensionality of feature vector and computational complexity. A support vector machine is employed for classification purpose. Experimental results show that the TF-GLCM achieves the detection rates of 98% on CASIA v1.0, and 97% on CASIA v2.0 with 96-D feature vector. The detection rates benefit from the two new textural features. Meanwhile, the TF-GLCM is superior to some state-of-the-art methods with lower dimension feature vector.

Inspec keywords: support vector machines; image texture; grey systems; computational complexity; matrix algebra

Other keywords: grey level co-occurrence matrices; discrete cosine transform arrays; support vector machine; image pixels; passive splicing detection method; computational complexity; splicing image forgery detection; CASIA; textural information; 96-D feature vector; textural features; statistical moments; TF-GLCM

Subjects: Algebra; Algebra; Optical, image and video signal processing; Computational complexity; Combinatorial mathematics; Combinatorial mathematics; Knowledge engineering techniques; Computer vision and image processing techniques

References

    1. 1)
      • 14. Haralick, R.M., Shanmugam, K., Dinstein, I.: ‘Textural features for image classification’, IEEE Trans Syst. Man Cybern., 2010, smc-3, (6), pp. 610–621.
    2. 2)
      • 24. ‘CASTA Tampered Image Detection Evaluation Database (CASIA TIDE v2.0)’. Available at http://forensics.idealtest.org:8080/idex_v2.html.
    3. 3)
      • 12. Siqueira, F.R.D., Schwartz, W.R., Pedrini, H.: ‘Multi-scale gray level co-occurrence matrices for texture description’, Neurocomputing, 2013, 120, (10), pp. 336–345.
    4. 4)
      • 28. Fawcett, T.: ‘ROC Graphs: Notes and practical considerations for researchers’, Mach. Learn., 2004, 31, (1), pp. 1–38.
    5. 5)
      • 9. Muhammad, G., Al-Hammadi, M.H., Hussain, M., et al: ‘Image forgery detection using steerable pyramid transform and local binary pattern’, Mach. Vis. Appl., 2014, 25, (4), pp. 985–995.
    6. 6)
      • 31. Wang, W., Dong, J., Tan, T.: ‘Effective image splicing detection based on image chroma’. Proc. IEEE Int. Conf. Image Processing (ICIP), Cairo, Egypt, January 2009, pp. 1257–1260.
    7. 7)
      • 25. Agarwal, S., Chand, S.: ‘Image forgery detection using multi scale entropy filter and local phase quantization’, Graph. Signal Process., 2015, 10, pp. 78–85.
    8. 8)
      • 26. Chang, C.C., Lin, C.J.: ‘LIBSVM: a library for support vector machines’, ACM Trans. Intell. Syst. Technol., 2011, 2, (3), pp. 389–396.
    9. 9)
      • 20. Nanni, L., Brahnam, S., Ghidoni, S., et al: ‘Improving the descriptors extracted from the co-occurrence matrix using preprocessing approaches’, Expert Syst. Appl., 2015, 42, (22), pp. 8989–9000.
    10. 10)
      • 21. Ulaby, F.T., Kouyate, F., Brisco, B., et al: ‘Textural information in SAR images’, IEEE Trans. Geosci. Remote Sens., 1986, 1986, (2), pp. 235–245.
    11. 11)
      • 2. Mahdian, B., Saic, S.: ‘A bibliography on blind methods for identifying image forgery’, Signal Process. Image Commun., 2010, 25, (6), pp. 389–399.
    12. 12)
      • 11. Sastry, S.S., Kumari, T.V., Rao, C.N., et al: ‘Transition temperatures of thermotropic liquid crystals from the local binary gray level cooccurrence matrix’, Adv. Condens. Matter Phys., 2012, 2012, (8), pp. 217–294.
    13. 13)
      • 23. ‘CASTA Tampered Image Detection Evaluation Database (CASIA TIDE v1.0)’. Available at http://forensics.idealtest.org:8080/idex_vl.html.
    14. 14)
      • 3. De Carvalho, T.J., Riess, C., Angelopoulou, E., et al: ‘Exposing digital image forgeries by illumination color classification’, IEEE Trans. Inf. Forensics Sec., 2013, 8, (7), pp. 1182–1194.
    15. 15)
      • 10. Saleh, S.Q., Hussain, M., Muhammad, G., et al: ‘Evaluation of image forgery detection using multi-scale weber local descriptors’, Int. J. Artif. Intell. Tools, 2015, 24, (4), pp. 416–424.
    16. 16)
      • 6. Shi, Y.Q., Chen, C., Chen, W.: ‘A natural image model approach to splicing detection’. Proc. ACM Multimedia and Security Workshop, Texas, USA, 2007, pp. 334–340.
    17. 17)
      • 27. Hsu, C.W., Chang, C.C., Lin, C.J.: ‘A practical guide to support vector classification’ (National Taiwan University, 2003), pp. 1–16.
    18. 18)
      • 19. Su, B., Yuan, Q., Wang, S., et al: ‘Enhanced state selection Markov model for image splicing detection’, Eurasip J. Wirel. Commun. Netw., 2014, 2014, (1), pp. 254–283.
    19. 19)
      • 30. Muhammad, G., Dewan, M.S., Moniruzzaman, M., et al: ‘Image forgery detection using Gabor filters and DCT’. Proc. IEEE Int. Conf. Electrical Engineering Information Communication Technology, Dhaka, Bangladesh, April 2014, pp. 1–5.
    20. 20)
      • 15. Yang, J., Guo, J.: ‘Image texture feature extraction method based on regional average binary gray level difference co-occurrence matrix’. Proc. IEEE Int. Conf. Virtual Reality and Visualization (ICVRV), Beijing, China, November 2011, pp. 239–242.
    21. 21)
      • 22. Baraldi, A., Parmiggiani, F.: ‘Investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters’, IEEE Trans. Geosci. Remote Sens., 1995, 33, (2), pp. 293–304.
    22. 22)
      • 7. He, Z., Lu, W., Sun, W., et al: ‘Digital image splicing detection based on markov features in DCT and DWT domain’, Pattern Recognit., 2012, 45, (12), pp. 4292–4299.
    23. 23)
      • 5. He, Z., Sun, W., Lu, W., et al: ‘Digital image splicing detection based on approximate run length’, Pattern Recognit. Lett., 2011, 32, (12), pp. 1591–1597.
    24. 24)
      • 18. Sutthiwan, P., Shi, Y.Q., Su, W., et al: ‘Rake transform and edge statistics for image forgery detection’. Proc. IEEE Int. Conf. Multimedia Expo (ICME), Singapore, July 2010, pp. 1463–1468.
    25. 25)
      • 13. Chen, S., Wu, C., Chen, D., et al: ‘Scene classification based on gray level-gradient co-occurrence matrix in the neighborhood of interest points’. Proc. IEEE Int. Conf. Intelligent Computing and Intelligent System, Shanghai, China, November 2009, pp. 482–485.
    26. 26)
      • 29. Hussain, M., Muhammad, G., Saleh, S.Q., et al: ‘Image forgery detection using multi-resolution weber local descriptors’. Proc. IEEE Eurocon, Zagreb, Croatia, July 2013, pp. 1570–1577.
    27. 27)
      • 16. Chen, G.C., Su, B., Wang, S.L.: ‘Blind detection of splicing imagebased on gray level co-occurrence matrix of image DCT domain’, J. Shanghai Jiaotong Univ., 2011, 45, (10), pp. 1547–1551.
    28. 28)
      • 17. Ibrahim, R.W., Moghaddasi, Z., Jalab, H.A., et al: ‘Fractional differential texture descriptors based on the machado entropy for image splicing detection’, Entropy, 2015, 17, (7), pp. 4775–4785.
    29. 29)
      • 4. El-Alfy, E.S.M., Qureshi, M.A.: ‘Combining spatial and DCT based Markov features for enhanced blind detection of image splicing’, Formal Pattern Anal. Applications, 2014, 18, (3), pp. 1–11.
    30. 30)
      • 1. Tsai, H.H., Liu, C.C.: ‘Wavelet-based image watermarking with visibility range estimation based on HVS and neural networks’, Pattern Recognit., 2011, 44, (4), pp. 751–763.
    31. 31)
      • 8. Alahmadi, A.A., Hussain, M., Aboalsamh, H., et al: ‘Splicing image forgery detection based on DCT and local binary pattern’. Proc. IEEE Global Conf. on Signal and Information Processing (GlobalSIP), Austin, TX, USA, December 2013, pp. 253–256.
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