Revealing Image Splicing Forgery Using Local Binary Patterns of DCT Coefficients

  • Yujin Zhang
  • Chenglin Zhao
  • Yiming Pi
  • Shenghong Li
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 202)


The wide use of powerful image processing software has made it easy to tamper images for malicious purposes. Image splicing, which has constituted a menace to integrity and authenticity of images, is a very common and simple trick in image tampering. Therefore, image splicing detection is of great importance in digital forensics. In this chapter, an effective framework for revealing image splicing forgery is proposed. The local binary pattern (LBP) operator is used to model magnitude components of 2-D arrays obtained by applying multi-size block discrete cosine transform (MBDCT) to the test images, all of bins of histograms computed from LBP codes are served as discriminative features for image splicing detection. To avoid the high computational complexity and possible overfitting for support vector machine (SVM) classifier, principal component analysis (PCA) is utilized to reduce the dimensionality of the proposed features. Our experiment results demonstrate the efficiency of the proposed method over the Columbia image splicing detection evaluation dataset.


Image splicing detection Local binary pattern DCT PCA 



This work is supported by National Science Foundation of China (61071152, 60702043), 973 Program (2010CB731403, 2010CB731406) of China and National “Twelfth Five-Year” Plan for Science & Technology Support (2012BAH38 B04). Credits for the use of the Columbia Image Splicing Detection Evaluation Dataset are given to the DVMM Laboratory of Columbia University. CalPhotos Digital Library and the photographers listed in


  1. 1.
    Ng T-T, Chang S-F, Sun Q (2004) Blind detection of photomontage using higher order statistics. In: Proceedings of the IEEE international symposium on circuits and systems, Vancouver, Canada, vol 5, pp V688–V691Google Scholar
  2. 2.
    Ng T-T, Chang S-F (2004) A dataset of authentic and spliced image blocks. ADVENT Technical Report, #203-2004-3, Columbia UniversityGoogle Scholar
  3. 3.
    Johnson MK, Farid H (2005) Exposing digital forgeries by detecting inconsistencies in lighting. ACM multimedia and security workshop, New York, pp 1–9Google Scholar
  4. 4.
    Fu D, Shi YQ, Su W (2006) Detection of image splicing based on Hilbert-Huang transform and moments of characteristic functions with wavelet decomposition. In: International workshop on digital watermarking, LNCS, Springer, Heidelberg, vol 4283, pp 177–187Google Scholar
  5. 5.
    Chen W, Shi YQ, Su W (2007) Image splicing detection using 2-D phase congruency and statistical moments of characteristic function. Society of photo-optical instrumentation engineers conference series, SPIE, Washington, vol 6505, pp 65050R.1-65050R.8Google Scholar
  6. 6.
    Shi YQ, Chen C, Chen W (2007) A natural image model approach to splicing detection. In: Proceedings of the 9th workshop on multimedia and security, Dallas, Texas, USA, pp 51–62Google Scholar
  7. 7.
    Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987CrossRefGoogle Scholar
  8. 8.
    Chang CC, Lin CJ (2001) LIBSVM: a library for support vector machines [EB/OL].
  9. 9.
    Theodoridis S, Koutroumbas K (2009) Pattern recognition. Academic, BurlingtonGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Yujin Zhang
    • 1
  • Chenglin Zhao
    • 2
  • Yiming Pi
    • 3
  • Shenghong Li
    • 1
  1. 1.Department of Electronic EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.School of Information and Communication EngineeringBeijing University of Posts and TelecommunicationsBeijingChina
  3. 3.School of Electronic EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina

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