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Deep Fusion Network for Splicing Forgery Localization

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

Abstract

Digital splicing is a common type of image forgery: some regions of an image are replaced with contents from other images. To locate altered regions in a tampered picture is a challenging work because the difference is unknown between the altered regions and the original regions and it is thus necessary to search the large hypothesis space for a convincing result. In this paper, we proposed a novel deep fusion network to locate tampered area by tracing its border. A group of deep convolutional neural networks called Base-Net were firstly trained to response the certain type of splicing forgery respectively. Then, some layers of the Base-Net are selected and combined as a deep fusion neural network (Fusion-Net). After fine-tuning by a very small number of pictures, Fusion-Net is able to discern whether an image block is synthesized from different origins. Experiments on the benchmark datasets show that our method is effective in various situations and outperform state-of-the-art methods.

Keywords

Image forensics Splicing forgery detection Forgery localization Deep convolutional network Fusion network 

Notes

Acknowledgement

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.
    Columbia image splicing detection evaluation dataset. http://www.ee.columbia.edu/ln/dvmm/downloads/AuthSplicedDataSet/AuthSplicedDataSet.htm. Accessed 28 June 2018
  2. 2.
    Amerini, I., Becarelli, R., Caldelli, R., Del Mastio, A.: Splicing forgeries localization through the use of first digit features. In: 2014 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 143–148. IEEE (2014)Google Scholar
  3. 3.
    Amerini, I., Uricchio, T., Ballan, L., Caldelli, R.: Localization of jpeg double compression through multi-domain convolutional neural networks. In: Proceedings of IEEE CVPR Workshop on Media Forensics (2017)Google Scholar
  4. 4.
    Bayar, B., Stamm, M.C.: A deep learning approach to universal image manipulation detection using a new convolutional layer. In: Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security, pp. 5–10. ACM (2016)Google Scholar
  5. 5.
    Bianchi, T., De Rosa, A., Piva, A.: Improved DCT coefficient analysis for forgery localization in JPEG images. In: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2444–2447. IEEE (2011)Google Scholar
  6. 6.
    Bianchi, T., Piva, A.: Image forgery localization via block-grained analysis of JPEG artifacts. IEEE Trans. Inf. Forensics Secur. 7(3), 1003–1017 (2012)CrossRefGoogle Scholar
  7. 7.
    Bondi, L., Lameri, S., Güera, D., Bestagini, P., Delp, E.J., Tubaro, S.: Tampering detection and localization through clustering of camera-based CNN features. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1855–1864 (2017)Google Scholar
  8. 8.
    Bunk, J., et al.: Detection and localization of image forgeries using resampling features and deep learning. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1881–1889. IEEE (2017)Google Scholar
  9. 9.
    Cozzolino, D., Gragnaniello, D., Verdoliva, L.: Image forgery localization through the fusion of camera-based, feature-based and pixel-based techniques. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 5302–5306, October 2014.  https://doi.org/10.1109/ICIP.2014.7026073
  10. 10.
    Dirik, A.E., Memon, N.: Image tamper detection based on demosaicing artifacts. In: 2009 16th IEEE International Conference on Image Processing (ICIP), pp. 1497–1500. IEEE (2009)Google Scholar
  11. 11.
    Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2012 (VOC 2012) Results. http://www.pascal-network.org/challenges/VOC/voc2012/workshop/index.html
  12. 12.
    Farid, H.: Exposing digital forgeries from JPEG ghosts. IEEE Trans. Inf. Forensics Secur. 4(1), 154–160 (2009)CrossRefGoogle Scholar
  13. 13.
    Ferrara, P., Bianchi, T., De Rosa, A., Piva, A.: Image forgery localization via fine-grained analysis of CFA artifacts. IEEE Trans. Inf. Forensics Secur. 7(5), 1566–1577 (2012)CrossRefGoogle Scholar
  14. 14.
    Flenner, A., Peterson, L., Bunk, J., Mohammed, T.M., Nataraj, L., Manjunath, B.: Resampling forgery detection using deep learning and a-contrario analysis. arXiv preprint arXiv:1803.01711 (2018)
  15. 15.
    Fontani, M., Bianchi, T., Rosa, A.D., Piva, A., Barni, M.: A framework for decision fusion in image forensics based on dempstershafer theory of evidence. IEEE Trans. Inf. Forensics Secur. 8(4), 593–607 (2013).  https://doi.org/10.1109/TIFS.2013.2248727CrossRefGoogle Scholar
  16. 16.
    Fu, H., Cao, X.: Forgery authentication in extreme wide-angle lens using distortion cue and fake saliency map. IEEE Trans. Inf. Forensics Secur. 7(4), 1301–1314 (2012).  https://doi.org/10.1109/TIFS.2012.2195492MathSciNetCrossRefGoogle Scholar
  17. 17.
    Girshick, R., Donahue, J., Darrell, T., Malik, J.: Region-based convolutional networks for accurate object detection and segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 38(1), 142–158 (2016).  https://doi.org/10.1109/TPAMI.2015.2437384CrossRefGoogle Scholar
  18. 18.
    Hsu, Y.F., Chang, S.F.: Statistical fusion of multiple cues for image tampering detection. In: 2008 42nd Asilomar Conference on Signals, Systems and Computers, pp. 1386–1390, October 2008.  https://doi.org/10.1109/ACSSC.2008.5074646
  19. 19.
    Johnson, M.K., Farid, H.: Exposing digital forgeries in complex lighting environments. IEEE Trans. Inf. Forensics Secur. 2(3), 450–461 (2007).  https://doi.org/10.1109/TIFS.2007.903848CrossRefGoogle Scholar
  20. 20.
    Kim, D.H., Lee, H.Y.: Image manipulation detection using convolutional neural network. Int. J. Appl. Eng. Res. 12(21), 11640–11646 (2017)Google Scholar
  21. 21.
    Korus, P., Huang, J.: Multi-scale fusion for improved localization of malicious tampering in digital images. IEEE Trans. Image Process. 25(3), 1312–1326 (2016).  https://doi.org/10.1109/TIP.2016.2518870MathSciNetCrossRefGoogle Scholar
  22. 22.
    Krawetz, N., Solutions, H.F.: A picture’s worth... Hacker Factor Solutions, pp. 1–31 (2007)Google Scholar
  23. 23.
    Li, B., Luo, H., Zhang, H., Tan, S., Ji, Z.: A multi-branch convolutional neural network for detecting double JPEG compression. arXiv preprint arXiv:1710.05477 (2017)
  24. 24.
    Li, G., Yu, Y.: Visual saliency detection based on multiscale deep CNN features. IEEE Trans. Image Process. 25(11), 5012–5024 (2016).  https://doi.org/10.1109/TIP.2016.2602079MathSciNetCrossRefGoogle Scholar
  25. 25.
    Li, H., Luo, W., Qiu, X., Huang, J.: Image forgery localization via integrating tampering possibility maps. IEEE Trans. Inf. Forensics Secur. 12(5), 1240–1252 (2017)CrossRefGoogle Scholar
  26. 26.
    Li, W., Yuan, Y., Yu, N.: Passive detection of doctored JPEG image via block artifact grid extraction. Sig. Process. 89(9), 1821–1829 (2009)CrossRefGoogle Scholar
  27. 27.
    Lin, Z., He, J., Tang, X., Tang, C.K.: Fast, automatic and fine-grained tampered JPEG image detection via dct coefficient analysis. Pattern Recogn. 42(11), 2492–2501 (2009)CrossRefGoogle Scholar
  28. 28.
    Liu, F., Shen, C., Lin, G.: Deep convolutional neural fields for depth estimation from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5162–5170 (2015)Google Scholar
  29. 29.
    Liu, Q., Cao, X., Deng, C., Guo, X.: Identifying image composites through shadow matte consistency. IEEE Trans. Inf. Forensics Secur. 6(3), 1111–1122 (2011).  https://doi.org/10.1109/TIFS.2011.2139209CrossRefGoogle Scholar
  30. 30.
    Lyu, S., Pan, X., Zhang, X.: Exposing region splicing forgeries with blindlocal noise estimation. Int. J. Comput. Vis. 110(2), 202–221 (2013).  https://doi.org/10.1007/s11263-013-0688-yCrossRefGoogle Scholar
  31. 31.
    Mahdian, B., Saic, S.: Blind authentication using periodic properties of interpolation. IEEE Trans. Inf. Forensics Secur. 3(3), 529–538 (2008).  https://doi.org/10.1109/TIFS.2004.924603CrossRefGoogle Scholar
  32. 32.
    Mahdian, B., Saic, S.: Using noise inconsistencies for blind image forensics. Image Vis. Comput. 27(10), 1497–1503 (2009)CrossRefGoogle Scholar
  33. 33.
    Podilchuk, C.I., Delp, E.J.: Digital watermarking: algorithms and applications. IEEE Sig. Process. Mag. 18(4), 33–46 (2001).  https://doi.org/10.1109/79.939835CrossRefGoogle Scholar
  34. 34.
    Popescu, A.C., Farid, H.: Exposing digital forgeries by detecting traces of resampling. IEEE Trans. Sig. Process. 53(2), 758–767 (2005).  https://doi.org/10.1109/TSP.2004.839932MathSciNetCrossRefzbMATHGoogle Scholar
  35. 35.
    Rao, Y., Ni, J.: A deep learning approach to detection of splicing and copy-move forgeries in images. In: 2016 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1–6. IEEE (2016)Google Scholar
  36. 36.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  37. 37.
    Wang, Q., Zhang, R.: Double JPEG compression forensics based on a convolutional neural network. EURASIP J. Inf. Secur. 2016(1), 23 (2016)CrossRefGoogle Scholar
  38. 38.
    Ye, S., Sun, Q., Chang, E.C.: Detecting digital image forgeries by measuring inconsistencies of blocking artifact. In: 2007 IEEE International Conference on Multimedia and Expo, pp. 12–15. IEEE (2007)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of MacauTaipaChina

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