A Novel Robust Image Forensics Algorithm Based on L1-Norm Estimation

  • Xin He
  • Qingxiao GuanEmail author
  • Yanfei Tong
  • Xianfeng Zhao
  • Haibo Yu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10082)


To improve the robustness of the typical image forensics with the noise variance, we propose a novel image forensics approach that based on L1-norm estimation. First, we estimate the kurtosis and the noise variance of the high-pass image. Then, we build a minimum error objective function based on L1-norm estimation to compute the kurtosis and the noise variance of overlapping blocks of the image by an iterative solution. Finally, the spliced regions are exposed through K-means cluster analysis. Since the noise variance of adjacent blocks are similar, our approach can accelerate the iterative process by setting the noise variance of the previous block as the initial value of the current block. According to analytics and experiments, our approach can effectively solve the inaccurate locating problem caused by outliers. It also performs better than reference algorithm in locating spliced regions, especially for those with realistic appearances, and improves the robustness effectively.


Image splicing L1-norm estimation Noise variance Image forensics 



This work was supported by the NSFC under U1536105 and 61303259, National Key Technology R&D Program under 2014BAH41B01, Strategic Priority Research Program of CAS under XDA06030600, and Key Project of Institute of Information Engineering, CAS, under Y5Z0131201.


  1. 1.
    Dirik, A.E., Memon, N.D.: Image tamper detection based on demosaicing artifacts. IEEE Trans. Image Process., 1497–1500 (2009)Google Scholar
  2. 2.
    Cao, H., Kot, A.C.: Manipulation detection on image patches using FusionBoost. IEEE Trans. Inf. Forensics Secur. 7(3), 992–1002 (2012)CrossRefGoogle Scholar
  3. 3.
    Johnson, M.K., Farid, H.: Exposing digital forgeries by detecting inconsistencies in lighting. In: Proceedings of the ACM 7th Workshop on Multimedia and Security, pp. 1–10 (2005)Google Scholar
  4. 4.
    Johnson, M, K., Farid, H.: Exposing digital forgeries through specular highlights on the eye. In: International Workshop on Information Hiding, pp. 311–325 (2007)Google Scholar
  5. 5.
    Gou, H., Swaminathan, A., Wu, M.: Intrinsic sensor noise features for forensic analysis on scanners and scanned images. IEEE Trans. Inf. Forensics Secur. 4(3), 476–491 (2009)CrossRefGoogle Scholar
  6. 6.
    Amer, A., Dubois, E.: Fast and reliable structure-oriented video noise estimation. IEEE Trans. Circ. Syst. Video Technol. 15(1), 113–118 (2005)CrossRefGoogle Scholar
  7. 7.
    Pan, X., Zhang, X., Lyu, S.: Exposing image splicing with inconsistent local noise variances. In: IEEE International Conference on Computational Photography, pp. 1–10 (2012)Google Scholar
  8. 8.
    Bethge, M.: Factorial coding of natural images: how effective are linear models in removing higher-order dependencies. J. Opt. Soc. Am. A 23(6), 1253–1268 (2006)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Lyu, S., Simoncelli, E.P.: Nonlinear extraction of independent components of natural images using radial Gaussianization. Neural Comput. 21(6), 1485–1519 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Zoran, D., Weiss, Y.: Scale invariance and noise in natural images. In: IEEE International Conference on Computer Vision, pp. 2209–2216 (2009)Google Scholar
  11. 11.
    Viola, P., Jones, M.: Robust real-time object detection. Int. J. Comput. Vision 21 (2001)Google Scholar
  12. 12.
    Chen, X.: Least absolute linear regression. Appl. Stat. Manage. 5, 48 (1989)Google Scholar
  13. 13.
    Li, Z.: Introduction of least absolute deviation method. Bull. Maths 2, 40 (1992)Google Scholar
  14. 14.
    Hsu, Y.F., Chang, S.F.: Detecting image splicing using geometry invariants and camera characteristics consistency. In: IEEE International Conference on Multimedia and Expo, pp. 549–552 (2006)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Xin He
    • 1
    • 2
  • Qingxiao Guan
    • 1
    • 2
    Email author
  • Yanfei Tong
    • 1
    • 2
  • Xianfeng Zhao
    • 1
    • 2
  • Haibo Yu
    • 1
    • 2
  1. 1.State Key Laboratory of Information SecurityInstitute of Information Engineering, Chinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

Personalised recommendations