Multimedia Tools and Applications

, Volume 76, Issue 4, pp 4783–4799 | Cite as

Image splicing localization using PCA-based noise level estimation

Article

Abstract

Image splicing is one of the most common image tampering operations, where the content of the tampered image usually significantly differs from that of the original one. As a consequence, forensic methods aiming to locate the spliced areas are of great realistic significance. Among these methods, the noise based ones, which utilize the fact that images from different sources tend to have various noise levels, have drawn much attention due to their convenience to implement and the relaxation of some operation specific assumptions. However, the performances of the existing noise based image splicing localization methods are unsatisfactory when the noise difference between the original and spliced regions is relatively small. In this paper, through incorporation of a recent developed noise level estimation algorithm, we propose an effective image splicing localization method. The proposed method performs blockwise noise level estimation of a test image with principal component analysis (PCA)-based algorithm, and segments the tampered region from the original region by k-means clustering. The experimental results demonstrate the superiority of the proposed method over several state-of-the-art methods, especially for practical image splicing, where the noise difference between the original and spliced regions is typically small.

Keywords

Image splicing Image splicing localization Noise level Principal component analysis (PCA) K-means clustering 

References

  1. 1.
    Al-Qershi OM, Khoo BE (2013) Passive detection of copy-move forgery in digital images: state-of-the-art. Forensic Sci Int 231:284–295CrossRefGoogle Scholar
  2. 2.
    Bahrami K, Kot AC, Li L, Li H (2015) Blurred image splicing localization by exposing blur type inconsistency. IEEE Trans Inf Forensics Secur 10(5):999–1009CrossRefGoogle Scholar
  3. 3.
    Bas P, Filler T, Pevny T (2011) 'Break our Steganographic System': the ins and outs of organizing BOSS. Filler T et al. (Eds.): IH 2011. LNCS 6958:59–70Google Scholar
  4. 4.
    Bianchi T, Piva A (2012) Image forgery localization via block-grained analysis of JPEG artifacts. IEEE Trans Inf Forensics Secur 7(3):1003–1017CrossRefGoogle Scholar
  5. 5.
    Chen YL, Hsu CT (2011) Detecting recompression of jpeg images via periodicity analysis of compression artifacts for tampering detection. IEEE Trans Inf Forensics Secur 6(2):396–406CrossRefGoogle Scholar
  6. 6.
    Chen M, Fridrich J, Goljan M, Lukas J (2008) Determining image origin and integrity using sensor noise. IEEE Trans Inf Forensics Secur 3(1):74–90CrossRefGoogle Scholar
  7. 7.
    Chierchia G, Parrilli S, Poggi G, Sansone C, Verdoliva L (2010) On the influence of denoising in PRNU based forgery detection. In Proc. of the 2nd ACM workshop on Multimedia in Forensics, Security and Intelligence, pp. 117–122Google Scholar
  8. 8.
    Chierchia G, Cozzolino D, Poggi G, Sansone C, Verdoliva L (2014a) Guided filtering for PRNU-based localization of small-size image forgeries, in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Process., pp. 6272–6276Google Scholar
  9. 9.
    Chierchia G, Poggi G, Sansone C, Verdoliva L (2014b) A bayesian-MRF approach for PRNU-based image forgery detection. IEEE Trans Inf Forensics Secur 9(4):554–567CrossRefGoogle Scholar
  10. 10.
    Cozzolino D, Gragnaniello D, Verdoliva L (2014) Image forgery localization through the fusion of camera-based, feature-based and pixel-based techniques, In Proc. of IEEE Int. Conf. Image Processing, pp. 5302–5306Google Scholar
  11. 11.
    Dabov K, Foi A, Katkovnik V, Egiazarian K (2007) Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans Image Process 16(8):2080–2095MathSciNetCrossRefGoogle Scholar
  12. 12.
    Donoho D, Johnstone I (1994) Ideal spatial adaption by wavelet shrinkage. Biometrika 8:425–455CrossRefMATHGoogle Scholar
  13. 13.
    Faraji H, MacLean WJ (2006) Ccd noise removal in digital images. IEEE Trans Image Process 15(9):2676–2685CrossRefGoogle Scholar
  14. 14.
    He K, Sun J, Tang X (2013) Guided image filtering. IEEE Trans Pattern Anal Mach Intell 35(6):1397–1409CrossRefGoogle Scholar
  15. 15.
    Hsu YF, Chang SF (2006) Detecting image splicing using geometry invariants and camera characteristics consistency. In International Conference on Multimedia and Expo, pp. 549–552Google Scholar
  16. 16.
    Huang DY, Huang CN, Hu WC, Chou CH (2015) Robustness of copy-move forgery detection under high JPEG compression artifacts, Multimed. Tools Appl., published onlineGoogle Scholar
  17. 17.
    Kang X, Stamm MC, Peng A, Liu KJR (2013) Robust median filtering forensics using an autoregressive model. IEEE Trans Inf Forensics Secur 8(9):1456–1468CrossRefGoogle Scholar
  18. 18.
    Lin X, Li CT, Hu Y (2013) Exposing image forgery through the detection of contrast enhancement, In Proc. of IEEE Int. Conf. Image Processing, pp. 4467–4471Google Scholar
  19. 19.
    Liu X, Tanaka M, Okutomi M (2012) Noise level estimation using weak textured patches of a single noisy image. In Proc. of IEEE Int. Conf. Image Processing, pp. 665–668Google Scholar
  20. 20.
    Lukas J, Fridrich J, Goljan M (2006) Detecting digital image forgeries using sensor pattern noise. In SPIE Electronic Imaging, Forensics, Security, Steganography, and Watermarking of Multimedia Contents VIII, 6072: 362–372Google Scholar
  21. 21.
    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
  22. 22.
    Mahdian B, Saic S (2009) Using noise inconsistencies for blind image forensics. Image Vis Comput 27(10):1497–1503CrossRefGoogle Scholar
  23. 23.
    Pan X, Zhang X, Lyu S (2011) Exposing image forgery with blind noise estimation. In the 13th ACM Workshop on Multimedia and Security, pp. 15–20Google Scholar
  24. 24.
    Pan X, Zhang X, Lyu S (2012) Exposing image splicing with inconsistent local noise variances. In IEEE International Conference on Computational Photography (ICCP), pp. 1–10Google Scholar
  25. 25.
    Petteri M (2008) Dependence of the parameters of digital image noise model on ISO number, temperature and shutter time. Project work report, Dec. 2008, Tampere, Finland. [online] http://www.cs.tut.fi/~foi/MobileImagingReport_PetteriOjala_Dec2008.pdf
  26. 26.
    Popescu C, Farid H (2004) Statistical tools for digital forensics, in 6th International Workshop on Information Hiding. Toronto, Canada, pp. 128–147Google Scholar
  27. 27.
    Popescu C, Farid H (2005) Exposing digital forgeries by detecting traces of resampling. IEEE Trans Signal Process 53(2):758–767MathSciNetCrossRefGoogle Scholar
  28. 28.
    Pyatykh S, Hesser J, Zheng L (2013) Image noise level estimation by principal component analysis. IEEE Trans Image Process 22(2):687–699MathSciNetCrossRefGoogle Scholar
  29. 29.
    Stamm MC, Liu KJR (2011) Anti-forensics of digital image compression. IEEE Trans Inf Forensics Secur 6(3):1050–1065CrossRefGoogle Scholar
  30. 30.
    Zhu Y, Shen X, Chen H (2016) Copy-move forgery detection based on scaled ORB. Multimed Tools Appl 75:3221–3233CrossRefGoogle Scholar
  31. 31.
    Zoran D, Weiss Y (2009) Scale invariance and noise in nature image. In Proc. of IEEE International Conference on Computer Vision, pp. 2209–2216Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Hui Zeng
    • 1
  • Yifeng Zhan
    • 1
  • Xiangui Kang
    • 1
  • Xiaodan Lin
    • 1
  1. 1.Guangdong key laboratory of information security, School of Data and Computer ScienceSun Yat-sen UniversityGuangzhouChina

Personalised recommendations