Improving Robustness of Image Tampering Detection for Compression

  • Boubacar DialloEmail author
  • Thierry Urruty
  • Pascal Bourdon
  • Christine Fernandez-Maloigne
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11295)


The task of verifying the originality and authenticity of images puts numerous constraints on tampering detection algorithms. Since most images are acquired on the internet, there is a significant probability that they have undergone transformations such as compression, noising, resizing and/or filtering, both before and after the possible alteration. Therefore, it is essential to improve the robustness of tampered image detection algorithms for such manipulations. As compression is the most common type of post-processing, we propose in our work a robust framework against this particular transformation. Our experiments on benchmark datasets show the contribution of our proposal for camera model identification and image tampering detection compared to recent literature approaches.


Image forensics Lossy compression Camera model identification Convolutional neural networks 


  1. 1.
    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, vol. 3 (2017)Google Scholar
  2. 2.
    Barni, M., et al.: Aligned and non-aligned double JPEG detection using convolutional neural networks. J. Vis. Commun. Image Represent. 49, 153–163 (2017)CrossRefGoogle Scholar
  3. 3.
    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
  4. 4.
    Bayar, B., Stamm, M.C.: Design principles of convolutional neural networks for multimedia forensics. Electron. Imaging 2017(7), 77–86 (2017)CrossRefGoogle Scholar
  5. 5.
    Bayar, B., Stamm, M.C.: Towards open set camera model identification using a deep learning framework. In: The 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE (2018)Google Scholar
  6. 6.
    Bengio, Y., et al.: Learning deep architectures for AI. Found. Trends® Mach. Learn. 2(1), 1–127 (2009)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Bondi, L., Baroffio, L., Güera, D., Bestagini, P., Delp, E.J., Tubaro, S.: First steps toward camera model identification with convolutional neural networks. IEEE Sig. Process. Lett. 24(3), 259–263 (2017)CrossRefGoogle Scholar
  8. 8.
    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
  9. 9.
    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
  10. 10.
    Cao, H., Kot, A.C.: Accurate detection of demosaicing regularity for digital image forensics. IEEE Trans. Inf. Forensics Secur. 4(4), 899–910 (2009)CrossRefGoogle Scholar
  11. 11.
    Chen, C., Zhao, X., Stamm, M.C.: Detecting anti-forensic attacks on demosaicing-based camera model identification. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 1512–1516. IEEE (2017)Google Scholar
  12. 12.
    Farid, H.: Photo Forensics. MIT Press, Cambridge (2016)Google Scholar
  13. 13.
    Gloe, T., Böhme, R.: The ‘Dresden image Database’ for benchmarking digital image forensics. In: Proceedings of the 2010 ACM Symposium on Applied Computing, pp. 1584–1590. ACM (2010)Google Scholar
  14. 14.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
  15. 15.
    Huh, M., Liu, A., Owens, A., Efros, A.A.: Fighting fake news: image splice detection via learned self-consistency. arXiv preprint arXiv:1805.04096 (2018)
  16. 16.
    Kee, E., Johnson, M.K., Farid, H.: Digital image authentication from JPEG headers. IEEE Trans. Inf. Forensics Secur. 6(3–2), 1066–1075 (2011)CrossRefGoogle Scholar
  17. 17.
    Kharrazi, M., Sencar, H.T., Memon, N.: Blind source camera identification. In: 2004 International Conference on Image Processing, ICIP 2004, vol. 1, pp. 709–712. IEEE (2004)Google Scholar
  18. 18.
    Kirchner, M., Gloe, T.: Forensic camera model identification. In: Handbook of Digital Forensics of Multimedia Data and Devices, pp. 329–374 (2015)CrossRefGoogle Scholar
  19. 19.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  20. 20.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)CrossRefGoogle Scholar
  21. 21.
    LeCun, Y., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989)CrossRefGoogle Scholar
  22. 22.
    Marra, F., Poggi, G., Sansone, C., Verdoliva, L.: Evaluation of residual-based local features for camera model identification. In: Murino, V., Puppo, E., Sona, D., Cristani, M., Sansone, C. (eds.) ICIAP 2015. LNCS, vol. 9281, pp. 11–18. Springer, Cham (2015). Scholar
  23. 23.
    Marra, F., Poggi, G., Sansone, C., Verdoliva, L.: A study of co-occurrence based local features for camera model identification. Multimedia Tools Appl. 76(4), 4765–4781 (2017)CrossRefGoogle Scholar
  24. 24.
    Rössler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., Nießner, M.: FaceForensics: a large-scale video dataset for forgery detection in human faces. arXiv preprint arXiv:1803.09179 (2018)
  25. 25.
    Stamm, M.C., Wu, M., Liu, K.R.: Information forensics: an overview of the first decade. IEEE Access 1, 167–200 (2013)CrossRefGoogle Scholar
  26. 26.
    Swaminathan, A., Wu, M., Liu, K.R.: Nonintrusive component forensics of visual sensors using output images. IEEE Trans. Inf. Forensics Secur. 2(1), 91–106 (2007)CrossRefGoogle Scholar
  27. 27.
    Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)Google Scholar
  28. 28.
    Thai, T.H., Cogranne, R., Retraint, F.: Camera model identification based on the heteroscedastic noise model. IEEE Trans. Image Process. 23(1), 250–263 (2014)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Tuama, A., Comby, F., Chaumont, M.: Camera model identification with the use of deep convolutional neural networks. In: 2016 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1–6. IEEE (2016)Google Scholar
  30. 30.
    Wen, L., Qi, H., Lyu, S.: Contrast enhancement estimation for digital image forensics. ACM Trans. Multimedia Comput. Commun. Appl. (TOMM) 14(2), 49 (2018)Google Scholar
  31. 31.
    Xu, G., Shi, Y.Q.: Camera model identification using local binary patterns. In: 2012 IEEE International Conference on Multimedia and Expo (ICME), pp. 392–397. IEEE (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Boubacar Diallo
    • 1
    Email author
  • Thierry Urruty
    • 1
  • Pascal Bourdon
    • 1
  • Christine Fernandez-Maloigne
    • 1
  1. 1.XLIM Research Institute (UMR CNRS 7252), University of PoitiersPoitiersFrance

Personalised recommendations