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Authentication of splicing manipulation by exposing inconsistency in color shift

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Abstract

Splicing is one of the most common tampering techniques for image manipulation in many forensic cases. In this paper, a novel detection method which can expose splicing manipulation by discerning the inconsistencies of color shift in splicing images is presented. Color temperature varies dramatically in formation of different images due to photographic light sources, and always leads to color shift in an image, though it sometimes is imperceptible for human visualization. Therefore color shift can be taken as one kind of optical fingerprint of images for authentication. In this study, we proposed a color shift estimation based method to authenticate the presented images by localizing splicing manipulations automatically. These quantitative evidences contribute a lot to forensic investigators. This method comprises three steps: firstly it estimates the color shift of local partitions and the whole image; secondly exposes inconsistency in color shift by calculating distances between them as an indicator; and finally classifies these partitions into different classes by an optimized threshold. In our proposed method, color shift is estimated by using color constancy algorithm, which has been widely applied in cameras as AWB (automated white balance) to correct color shift in captured images. The following experiments exhibits the effectiveness of the proposed method with visual and quantitative evaluation.

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References

  1. Farid H (2009) A survey of image forgery detection. IEEE Signal Process Mag 26(2):16–25

    Article  Google Scholar 

  2. Farid H (2010) Image forensic analyses that elude the human visual system. Proc SPIE 7541:754106

    Article  Google Scholar 

  3. Johnson MK, Farid H (2005) Exposing digital forgeries by detecting inconsistencies in lighting. In: Proceeding of the Workshop on Multimedia & Security, DBLP, pp 1–10.

  4. O'Brien JF, Farid H (2012) Exposing photo manipulation with inconsistent reflections. ACM Trans Graph 31(1):1–11

    Article  Google Scholar 

  5. Carvalho TJD, Riess C, Angelopoulou E, Pedrini H, Rocha ADR (2013) Exposing digital image forgeries by illumination color classification. IEEE Transactions on Information Forensics and Security 8(7):1182–1194

  6. Kee E, O'Brien JF, Farid H (2012) Exposing photo manipulation with inconsistent shadows. ACM Trans Graph 32(3):1–11

    Article  Google Scholar 

  7. Kee E, O'Brien JF, Farid H (2014) Exposing photo manipulation from shading and shadows. ACM Trans Graph 33(5):1935–1946

    Article  Google Scholar 

  8. Xiao Q, Wang H, Li F, Gao Y (2011) 3D object retrieval based on a graph model descriptor. Neurocomputing 74(17):3486–3493

    Article  Google Scholar 

  9. Xiao Q, Yichuang L, Wang H (2014) Motion retrieval based on switching Kalman filters model. Multimed Tools Appl 72(1):951–966

    Article  Google Scholar 

  10. Lee MH, Seo DK, Seo BK, Park JI (2009) Optimal illumination for discriminating objects with different spectra. Opt Lett 34(17):2664–2666

    Article  Google Scholar 

  11. Li CJ, Cui GH, Melgosa M, Ruan XK, Zhang YJ, Ma L, Xiao KD, Luo M (2016) Accurate method for computing correlated color temperature. Opt Express 24(13):14066–14078

    Article  Google Scholar 

  12. Huo JY, Chang YL, Wang J, Wei XX (2006) Robust automatic white balance algorithm using gray color points in images. IEEE Trans Consum Electron 52(2):541–546

    Article  Google Scholar 

  13. Zhang XS, Gao SB, Li RX, Du XY, Li CY, Li YJ (2016) A retinal mechanism inspired color constancy codel. IEEE Trans Image Process 25(3):1219–1232

    Article  MathSciNet  Google Scholar 

  14. Gao SB, Zhang M, Li CY, Li YJ (2017) Improving color constancy by discounting the variation of camera spectral sensitivity. Journal of the Optical Society of America A-Optics Image Science and Vision 34(8):1448–1462

  15. Qinkun X, Ren S (2018) Action recognition based on hierarchical dynamic Bayesian network. Multimed Tools Appl 77(6):6955–6968

    Article  Google Scholar 

  16. Gijsenij A, Lu R, Gevers T (2012) Color constancy for multiple light sources. IEEE Trans Image Process 21(2):697–709

    Article  MathSciNet  Google Scholar 

  17. Nakano N, Nishimura R, Sai H, Nishizawa A, Komatsu H (1998) Digital still camera system for mega pixel CCD. IEEE Trans Consum Electron 44(3):581–586

    Article  Google Scholar 

  18. Lee J, Jung Y, Kim B, Ko SJ (2001) An advanced video camera system with robust AF, AE and AWB control. IEEE Trans Consum Electron 47(3):694–699

    Article  Google Scholar 

  19. Zhou RZ, He J, Hong ZL (2005) Adaptive algorithm of auto white balance for digital camera. Journal of Computer-Aided Design and Computer Graphics 17(3):529–533

  20. Danielsson PE (1980) Euclidean distance mapping. Computer Graphics and Image Processing 14(3):227–248

  21. Nobuyuki O (2007) A Threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 9(1):62–66

  22. Ng TT, Hsu J, Chang SF. Columbia image splicing detection evaluation dataset. http://www.ee.columbia.edu/ln/dvmm/downloads/AuthSplicedDataSet/photographers.htm

  23. Christlein V, Riess C, Jordan J, Riess C, Angelopoulou E (2012) An evaluation of popular copy-move forgery detection approaches[J]. IEEE Transactions on Information Forensicsand Security 7(6):1841–1854

  24. Chang X, Ma Z, Lin M et al (2017) Feature interaction augmented sparse learning for fast Kinect motion detection[J]. IEEE Trans Image Process:3911–3920

  25. Chang X, Nie F, Wang S, Yang Y, Zhou X, Zhang C (2016) Compound rank-k projections for bilinear analysis[J]. IEEE Transactions on Neural Networks and Learning Systems 27(7):1502–1513

  26. Sun P, Lang Y, Fan S, Shen Z, Liu L, Shan D, Peng S (2018) Exposing splicing forgery based on color temperature estimation[J]. Forensic Sci Int 289:1–11

    Article  Google Scholar 

Download references

Acknowledgements

“This work was supported in part by Natural National Science Foundation of China (NSFC) (61307016). National Key R&D Program (2017YFC0822204). National Engineering Laboratory of Evidence Traceability Technology (2017NELKFKT09).”

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Authors and Affiliations

  1. Civil aviation college, Shenyang Aerospace University, Shenyang, 110136, China

    Shen Zhe

  2. School of Information and Control Engineering, Liaoning Shihua University, Fushun, 113001, China

    Shen Zhe

  3. Department of Criminal Science and Technique, Criminal Investigation Police University of China, Shenyang, 110035, China

    Sun Peng

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  1. Shen Zhe
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  2. Sun Peng
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Correspondence to Sun Peng.

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Zhe, S., Peng, S. Authentication of splicing manipulation by exposing inconsistency in color shift. Multimed Tools Appl 79, 8235–8248 (2020). https://doi.org/10.1007/s11042-019-08565-2

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