Revealing Image Splicing Forgery Using Local Binary Patterns of DCT Coefficients
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.
KeywordsImage 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 http://www.ee.columbia.edu/ln/dvmm/downloads/AuthSplicedDataSet/photographers.htm.
- 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.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.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.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.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.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
- 8.Chang CC, Lin CJ (2001) LIBSVM: a library for support vector machines [EB/OL]. http://www.csie.ntu.edu.tw/cjlin/libsvm
- 9.Theodoridis S, Koutroumbas K (2009) Pattern recognition. Academic, BurlingtonGoogle Scholar