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Image Splicing Localization via Semi-global Network and Fully Connected Conditional Random Fields

  • Xiaodong Cun
  • Chi-Man PunEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11130)

Abstract

We address the problem of image splicing localization: given an input image, localizing the spliced region which is cut from another image. We formulate this as a classification task but, critically, instead of classifying the spliced region by local patch, we leverage the features from whole image and local patch together to classify patch. We call this structure Semi-Global Network. Our approach exploits the observation that the spliced region should not only highly relate to local features (spliced edges), but also global features (semantic information, illumination, etc.) from the whole image. Furthermore, we first integrate Fully Connected Conditional Random Fields as post-processing technique in image splicing to improve the consistency between the input image and the output of the network. We show that our method outperforms other state-of-the-art methods in three popular datasets.

Keywords

Image splicing localization Image forgery localization Multimedia security 

Notes

Acknowledgements

This work was supported in part by the Research Committee of the University of Macau under Grant MYRG2018-00035-FST, and the Science and Technology Development Fund of Macau SAR under Grant 041/2017/A1.

References

  1. 1.
    de Carvalho, T.J., Riess, C., Angelopoulou, E., Pedrini, H., de Rezende Rocha, A.: Exposing digital image forgeries by illumination color classification. IEEE Trans. Inf. Forensics Secur. 8, 1182–1194 (2013)CrossRefGoogle Scholar
  2. 2.
    Bappy, J.H., Roy-Chowdhury, A.K., Bunk, J., Nataraj, L., Manjunath, B.: Exploiting spatial structure for localizing manipulated image regions. In: International Conference on Computer Vision (ICCV) (2017)Google Scholar
  3. 3.
    Hsu, Y.-F., Chang, S.-F.: Image splicing detection using camera response function consistency and automatic segmentation. In: ICME, pp. 28–31 (2007)Google Scholar
  4. 4.
    Chen, W., Shi, Y.Q., Su, W.: Image splicing detection using 2-D phase congruency and statistical moments of characteristic function. In: Security, Steganography, and Watermarking of Multimedia Contents, vol. 6505, p. 65050R (2007)Google Scholar
  5. 5.
    Hsu, Y.-F., Chang, S.-F.: Detecting image splicing using geometry invariants and camera characteristics consistency. In: ICME, pp. 549–552 (2006)Google Scholar
  6. 6.
    He, Z., Lu, W., Sun, W., Huang, J.: Digital image splicing detection based on Markov features in DCT and DWT domain. Pattern Recogn. 45(12), 4292–4299 (2012)CrossRefGoogle Scholar
  7. 7.
    Salloum, R., Ren, Y., Kuo, C.C.J.: Image splicing localization using a multi-task fully convolutional network (MFCN). arXiv preprint arXiv:1709.02016 (2017)
  8. 8.
    Liu, Y., Guan, Q., Zhao, X., Cao, Y.: Image Forgery Localization Based on Multi-Scale Convolutional Neural Networks. CoRR cs.CV (2017)Google Scholar
  9. 9.
    Pun, C.M., Liu, B., Yuan, X.C.: Multi-scale noise estimation for image splicing forgery detection. J. Vis. Commun. Image Represent. 38, 195–206 (2016)CrossRefGoogle Scholar
  10. 10.
    Lyu, S., Pan, X., Zhang, X.: Exposing region splicing forgeries with blind local noise estimation. Int. J. Comput. Vis. 110, 202–221 (2014)CrossRefGoogle Scholar
  11. 11.
    Mahdian, B., Saic, S.: Using noise inconsistencies for blind image forensics. Image Vis. Comput. 27, 1497–1503 (2009)CrossRefGoogle Scholar
  12. 12.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)Google Scholar
  13. 13.
    He, K., Gkioxari, G., Dollár, P., Girshick, R.B.: Mask R-CNN. In: ICCV (2017)Google Scholar
  14. 14.
    Bondi, L., Lameri, S., Guera, D., Bestagini, P., Delp, E.J., Tubaro, S.: Tampering detection and localization through clustering of camera-based CNN features. In: CVPR Workshops, pp. 1–10, November 2017Google Scholar
  15. 15.
    Chen, C., McCloskey, S., Yu, J.: Image splicing detection via camera response function analysis. In: CVPR, pp. 1876–1885 (2017)Google Scholar
  16. 16.
    Long, J., Shelhamer, E., Darrell, T.: Fully Convolutional Networks for Semantic Segmentation. CoRR cs.CV (2014)Google Scholar
  17. 17.
    Ye, S., Sun, Q., Chang, E.C.: Detecting digital image forgeries by measuring inconsistencies of blocking artifact. In: ICME (2007)Google Scholar
  18. 18.
    Popescu, A.C., Farid, H.: Exposing digital forgeries in color filter array interpolated images. IEEE Trans. Sig. Process. 53(10), 3948–3959 (2005)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Dirik, A.E., Memon, N.D.: Image tamper detection based on demosaicing artifacts. In: ICIP (2009)Google Scholar
  20. 20.
    Hsu, Y.F., Chang, S.F.: Camera response functions for image forensics - an automatic algorithm for splicing detection. IEEE Trans. Inf. Forensics Secur. 5, 816–825 (2010)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Farid, H.: Exposing digital forgeries from JPEG ghosts. IEEE Trans. Inf. Forensics Secur. 4, 154–160 (2009)CrossRefGoogle Scholar
  22. 22.
    Li, W., Yuan, Y., Yu, N.: Passive detection of doctored JPEG image via block artifact grid extraction. Sig. Process. 89, 1821–1829 (2009)CrossRefGoogle Scholar
  23. 23.
    Wu, Y., AbdAlmageed, W., Natarajan, P.: Deep Matching and Validation Network - An End-to-End Solution to Constrained Image Splicing Localization and Detection. \({\rm arXiv}{\rm .}{\rm org}\) (2017)Google Scholar
  24. 24.
    Johnson, M.K., Farid, H.: Exposing digital forgeries in complex lighting environments. IEEE Trans. Inf. Forensics Secur. 2, 450–461 (2007)CrossRefGoogle Scholar
  25. 25.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  26. 26.
    Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Li, F.F.: ImageNet - a large-scale hierarchical image database. In: CVPR (2009)Google Scholar
  27. 27.
    Krähenbühl, P., Koltun, V.: Efficient inference in fully connected CRFs with Gaussian edge potentials. In: NIPS (2011)Google Scholar
  28. 28.
    Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab - Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. CoRR (2016)Google Scholar
  29. 29.
    Zheng, S., et al.: Conditional random fields as recurrent neural networks. In: ICCV (2015)Google Scholar
  30. 30.
    Paszke, A., et al.: Automatic differentiation in pytorch (2017)Google Scholar
  31. 31.
    Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
  32. 32.
    NIST: Nimble Media Forensics Challenge Datasets (2016). https://www.nist.gov/itl/iad/mig/media-forensics-challenge
  33. 33.
    Ng, T.T.: Columbia image splicing detection evaluation dataset (2004)Google Scholar
  34. 34.
    Zampoglou, M., Papadopoulos, S., Kompatsiaris, Y.: A large-scale evaluation of splicing localization algorithms for web images. Multimedia Tools Appl. 76, 4801–4834 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of MacauTaipaMacau

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