Identifying Image Splicing Based on Local Statistical Features in DCT and DWT Domain

  • Yujin Zhang
  • Shenghong Li
  • Shilin Wang
  • Xudong Zhao
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 322)

Abstract

In this paper, an effective image splicing detection algorithm based on the local statistical features in DCT and DWT domain is proposed. The local ternary pattern (LTP) operator is introduced to characterize the statistical changes of DCT and DWT coefficients caused by image splicing. The LTP histograms are generated from the magnitude components of the block DCT coefficients with varying block sizes and the DWT coefficients in three detail subbands, respectively. All these LTP histograms are concatenated together to form the discriminative feature set for splicing detection. The effectiveness of the proposed detector is evaluated on the Columbia image splicing detection evaluation dataset. Simulation results have shown that the proposed method can perform better than several state-of-the-art methods investigated.

Keywords

Passive image forensics Splicing detection Local ternary pattern DCT DWT 

References

  1. 1.
    Paquet H, Ward RK, Pitas I (2003) Wavelet packets-based digital watermarking for image verification and authentication. Signal Process 83(3):2117–2132MATHCrossRefGoogle Scholar
  2. 2.
    Ng T-T, Chang S-F, Sun Q (2004) Blind detection of photomontage using higher order statistics. In: IEEE international symposium on circuits and systems, Vancouver, Canada, pp. V688–V691Google Scholar
  3. 3.
    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, vol 4283. Springer, Heidelberg, pp 177–187CrossRefGoogle Scholar
  4. 4.
    Chen W, Shi YQ, Su W (2007) Image splicing detection using 2-D phase congruency and statistical moments of characteristic function. In: Society of photo-optical instrumentation engineers conference series, vol 6505. SPIE, Washington, DC, pp 65050R.1–65050R.8Google Scholar
  5. 5.
    Shi YQ, Chen C, Chen W (2007) A natural image model approach to splicing detection. In: The 9th workshop on multimedia & security, Dallas, Texas, USA, pp. 51–62Google Scholar
  6. 6.
    He Z, Lu W, Sun W, Huang J (2012) Digital image splicing detection based on Markov features in DCT and DWT domain. Pattern Recogn 45(12):4292–4299CrossRefGoogle Scholar
  7. 7.
    Tan X, Triggs B (2010) Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans Image Process 19(6):1635–1650MathSciNetCrossRefGoogle Scholar
  8. 8.
    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
  9. 9.
    Ng T-T, Chang S-F (2004) A dataset of authentic and spliced image blocks. ADVENT technical report, #203-2004-3, Columbia University, New York, USAGoogle Scholar
  10. 10.
    Chang CC, Lin CJ (2001) LIBSVM: a library for support vector machines. http://www.csie.ntu.edu.tw/cjlin/libsvm
  11. 11.
    Fawcett, T. Roc graphs: notes and practical considerations for researchers. http://home.comcast.net/~tom.fawcett/public_html/papers/ROC101.pdf

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Yujin Zhang
    • 1
  • Shenghong Li
    • 1
  • Shilin Wang
    • 2
  • Xudong Zhao
    • 1
  1. 1.Department of Electronic EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.School of Information Security EngineeringShanghai Jiao Tong UniversityShanghaiChina

Personalised recommendations