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Multimedia Tools and Applications

, Volume 78, Issue 16, pp 22223–22247 | Cite as

Image splicing localization using noise distribution characteristic

  • Depeng Zhang
  • Xiaofeng WangEmail author
  • Meng Zhang
  • Jiaojiao Hu
Article
  • 205 Downloads

Abstract

Image splicing/compositing is common content tampering operation. In this work, we devote to improve the detection accuracy of the splicing/compositing attack for image, and propose an effective image splicing localization method based on the noise distribution characteristic in image. Firstly, the test image is divided into non-overlapping blocks by using an improved simple linear iterative clustering (SLIC) algorithm. Then block-wise local noise level estimation and noise distribution characteristic estimation are performed to generate distinguishing features. Utilizing the fact that image regions from different sources tend to have larger inter-class difference, the fuzzy c-means clustering is used to identify spliced regions. Compared to existing noise-based image splicing detection methods, experimental results on different datasets have shown that the proposed method has superior performance, especially when the noise difference between the spliced region and the original region is small. Moreover, the proposed method is robust for content-preserving manipulations.

Keywords

Image splicing detection Image splicing localization Simple linear iterative clustering Noise distribution characteristic Fuzzy c-means clustering 

Notes

Acknowledgements

This work was supported by the National Major Research and Development Plan Program of China under Grant No.2016YFB1001004; the National Natural Science Foundation of China under Grant No.61772416 and No. 91646108; Shaanxi province technology innovation guiding fund project, No.2018XNCG-G-02. The foundation of the State Key Laboratory of Astronautic Dynamics.

References

  1. 1.
    Alahmadi A, Hussain M, Aboalsamh H, Muhamma G, Bebis G (2013) Splicing image forgery detection based on DCT and local binary pattern. Proc IEEE GLOBALSIP, Austin, TX, USA: 253-256Google Scholar
  2. 2.
    Al-Hannadi M H, Hussain M, Aboalsamh H, Muhamma G, Bebis G (2013) Curvelet transform and local texture based image forgery detection. International Symposium on Visual Computing, Crete, Greece: 503–512Google Scholar
  3. 3.
    Bahrami K, Kot A C, Fan J (2013) Splicing detection in out-of-focus blurred images. Proc The IEEE International workshop on information forensics and security, Guangzhou, China: 15-21Google Scholar
  4. 4.
    Bahrami K, Alex C, Li L, Li H (2015) Blurred image splicing localization by exposing blur type inconsistency. IEEE Trans Inform Forensics Sec 5(10):999–1009CrossRefGoogle Scholar
  5. 5.
    Cao G, Zhao Y, Ni R (2010) Edge-based blur metric for tamper detection. J Inform Hiding Multimed Signal Process 1(1):20–27Google Scholar
  6. 6.
    Chierchia G, Poggi G, Sansone C, Verdoliva L (2014) A Bayesian-MRF approach for PRNU-based image forgery detection. IEEE Trans Inf Forensics Sec 9(4):554–567CrossRefGoogle Scholar
  7. 7.
    Columbia DVMM Research Lab (2004). Columbia Image Splicing Detection Evaluation Dataset, http://www.ee.Columbia.edu/ln/dvmm/downloads/AuthSplicedDataSet/Auth Spliced DataSet.htm
  8. 8.
    Cozzolino D, Verdoliva L (2017) Single-image splicing localization through autoencoder-based anomaly detection. IEEE International Workshop on Information Forensics and Security: 1–6Google Scholar
  9. 9.
    Gallagher A, Chen T (2010) Image authentication by detecting traces of demosaicing. Proc IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Anchorage, AK: 1–8Google Scholar
  10. 10.
    Han J G, Park T H, Yong H M, et al (2018) Quantization-based Markov feature extraction method for image splicing detection, Machine Vision & Applications, (6):1-10Google Scholar
  11. 11.
    Hsu Y, Chang S (2006) Detecting image splicing using geometry invariants and camera characteristics consistency. Proc. 2006 IEEE international conference on multimedia and expo, Toronto, Ontario, Canada: 9-12Google Scholar
  12. 12.
    Hsu Y, Chang S (2010) Camera response functions for image forensics: an automatic algorithm for splicing detection. IEEE Trans Inform Forensics Sec 5(4):816–825CrossRefGoogle Scholar
  13. 13.
    Iakovidou C, Zampoglou M, Papadopoulos S et al (2018) Content-aware detection of JPEG grid inconsistencies for intuitive image forensics. J Visual Commun Image Represent 54:155–170CrossRefGoogle Scholar
  14. 14.
    Johnson M K, Farid H (2005) Exposing digital forgeries by detecting inconsistencies in lighting. Proc 7th workshop on Multimedia & Security, New York, USA: 1–10Google Scholar
  15. 15.
    Lakhani G (2008) Enhancing Poisson's equation-based approach for DCT prediction. IEEE Trans Image Process Public IEEE Signal Process Soc 17(3):427–430MathSciNetCrossRefGoogle Scholar
  16. 16.
    Lancaster P, Salkauskas K (1986) Curve and surface fitting. Academic PressGoogle Scholar
  17. 17.
    Liu Q, Sung A (2009) A new approach for JPEG resize and image splicing detection. Proc ACM Multimed Sec Workshop 23(4):716–744Google Scholar
  18. 18.
    Liu Q, Cao X, Deng C, Guo X (2011) Identifying image composites through shadow matte consistency. IEEE Trans Inf Forensics Sec 6(3):1111–1122CrossRefGoogle Scholar
  19. 19.
    Liu X, Tanaka M, Okutomi M (2014) Practical signal-dependent noise parameter estimation from a single noisy image. IEEE Trans Image Process Publ IEEE Signal Process Soc 23(10):4361–4371MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Lyu S, Pan X, Zhang X (2014) Exposing region splicing forgeries with blind local noise estimation. Int J Comput Vis 110(2):202–221CrossRefGoogle Scholar
  21. 21.
    Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on feature distributions. Pattern Recogn 29(1):51–59CrossRefGoogle Scholar
  22. 22.
    Pal NR, Bezdek JC (1995) On cluster validity for the fuzzy c- means model. IEEE Tran Fuzzy Syst 3(3):370–379CrossRefGoogle Scholar
  23. 23.
    Popescu A, Farid H (2005) Exposing digital forgeries in color filter array interpolated images. IEEE Trans Signal Process 10(53):3948–3959MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Pun C M, Liu B, Yuan X C (2016) Multi-scale noise estimation for image splicing forgery detection. Academic press, Inc 38 (C) :195-206Google Scholar
  25. 25.
    Pyatykh S, Hesser, J (2015) MMSE estimation for Poisson noise removal in images, Computer ScienceGoogle Scholar
  26. 26.
    Pyatykh S, Hesser J, Zheng L (2013) Image noise level estimation by principal component analysis. IEEE Trans Image Process A Public IEEE Signal Process Soc 22(2):687MathSciNetCrossRefzbMATHGoogle Scholar
  27. 27.
    Salloum R, Ren Y, Kuo C (2017) Image Splicing Localization Using A Multi-Task Fully Convolutional Network (MFCN)Google Scholar
  28. 28.
    Salmon J, Harmany Z, Deledalle CA et al (2012) Poisson noise reduction with non-local PCA. J Math Imag Vision 48(2):279–294MathSciNetCrossRefzbMATHGoogle Scholar
  29. 29.
    Shah A, El-Sayed M, El-Alfy (2018) Image splicing forgery detection using DCT coefficients with multi-scale LBP. Int. Conf. Computing Sciences and Engineering (ICCSE): 1–16Google Scholar
  30. 30.
    Song C, Lin X (2014) Natural image splicing detection based on defocus blur at edges. Proc 2014 symposium on privacy and security in commutations, Shanghai, China: 24-26Google Scholar
  31. 31.
    Wang B, Kong X (2012) Image splicing localization based on re-demosaicing. In: D. Zeng (eds) Advances in Information Technology and Industry Applications. Lecture Notes in Electrical Engineering, 136, Berlin, Heidelberg: 725-732Google Scholar
  32. 32.
    Wang W, Dong J, Tan T (2009) Effective image splicing detection based on image chroma. Proc. international conference on image processing, Cairo, Egypt, Nov. 7-10: 1249-1252Google Scholar
  33. 33.
    Wang W, Dong J, Tan T (2014) Image tampering detection based on stationary distribution of Markov chain. Proc International Conference on Image Processing, Hong Kong, China: 2101-2104Google Scholar
  34. 34.
    Wang P, Liu F, Yang C et al (2018) Blind forensics of image gamma transformation and its application in splicing Detectio. J Visual Commun Image Represent 55:80–90CrossRefGoogle Scholar
  35. 35.
    Wang P, Liu F, Yang C, et al (2018) Blind forensics of image gamma transformation and its application in splicing detection. Journal of Visual Communication & Image Representation: 81–90Google Scholar
  36. 36.
    Wattanachote K, Shih TK, Chang WL, Chang HH (2015) Tamper detection of jpeg image due to seam modifications. IEEE Trans Inf Forensics Sec 10(12):2477–2491CrossRefGoogle Scholar
  37. 37.
    Yao H, Wang S, Zhang X, Qin C, Wang J (2017) Detecting image splicing based on noise level inconsistency. Multimed Tools Appl 6(10):1–23Google Scholar
  38. 38.
    Zeng H, Zhan Y, Kang X, et al (2016) Image splicing localization using PCA-based noise level estimation. Multimedia Tools & Applic: 1–17Google Scholar
  39. 39.
    Zhang X, Fang Z, Wang S (2009) Image splicing detection using camera characteristic inconsistency, Proc International conference on multimedia information networking and security, Washington, DC, USA: 20-24Google Scholar
  40. 40.
    Zhang W, Cao X, Zhang J, Zhu J, Wang P (2009) Detecting photographic composites using shadows. Proc IEEE international conference on multimedia and expo, New York, USA: 1042–1045Google Scholar
  41. 41.
    Zhang W, Cao X, Qu Y, Hou Y, Zhao H, Zhang C (2010) Detecting and extracting the photo composites using planar homography and graph cut. IEEE Trans Inform Forensics Sec 5(3):544–555CrossRefGoogle Scholar
  42. 42.
    Zhang Y, Zhao C, Pi Y, Li S (2012) Revealing image splicing forgery using local binary patterns of DCT coefficients. In:proc. international conference on communications, signal processing, and systems, New York, NY, Jan.1-3. In: pp 181-189Google Scholar
  43. 43.
    Zhang Q, Lu W, Wang R et al (2018) Digital image splicing detection based on Markov features in block DWT domain. Multimed Tools Appl 7(23):31239–31260CrossRefGoogle Scholar
  44. 44.
    Zhao X, Li J, Li S, Wang S (2010) Detecting digital image splicing in Chroma spaces. Proc. the 9th international conference on digital watermarking, Seoul Korea: 12-22Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Depeng Zhang
    • 1
  • Xiaofeng Wang
    • 1
    Email author
  • Meng Zhang
    • 1
  • Jiaojiao Hu
    • 1
  1. 1.Xi’an University of TechnologyXi’anChina

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