Multimedia Tools and Applications

, Volume 75, Issue 17, pp 10201–10211 | Cite as

Image tamper detection based on noise estimation and lacunarity texture



Aiming at the problem of image tampering, a novel detection method is proposed based on the image noise and lacunarity. As there exist differences in image sensor pattern noise and image lacunarity between real image and tampered image, standard deviation of noise, relative frequency lacunarity (RFL), relative frequency mean (RFM) and relative frequency variance (RFV) are extracted from the suspected image to construct feature space. By using LIBSVM classifier, the image is detected if it is tampered or not. Experimental results and analysis show that it can effectively be used for the detection of real image and tampered image, natural image and computer generated graphics. Furthermore, it can be implemented for the detection of artificial blurring in the image with high precision.


Digital image forensics Sensor pattern noise Image tamper Lacunarity 


  1. 1.
    Ardizzone E, Bruno A (2010) Copy-move forgery detection via texture description, vol 10. ACM, Firenze, Italy, pp 59–64Google Scholar
  2. 2.
    Bayram S, Avcıbas I, Sankur B, Memon N (2005) Image manipulation detection with binary similarity measures. In: Proc. of 13th European Signal Processing Conference, vol. 1. pp 752–755Google Scholar
  3. 3.
    Chang C-C, Lin C-J (2001) LIBSVM: a library for support vector machinesGoogle Scholar
  4. 4.
    Chen M, Fridrich J, Goljan M, Lukáš J (2008) Determining image origin and integrity using sensor noise. IEEE Trans Inf Forensics Secur 3:74–90CrossRefGoogle Scholar
  5. 5.
    Dirik AE, Memon N (2009) Image tamper detection based on demosaicing artifacts. IEEE International Conference Image Processing (ICIP), pp 1497–1500Google Scholar
  6. 6.
    Gilmore S, Wellenhof RH, Muir J, Soyer HP (2009) Lacunarity analysis: a promising method for the automated assessment of melanocytic naevi and melanoma. PLoS ONE 4:e7449,1–e7449,10CrossRefGoogle Scholar
  7. 7.
    Gou H, Swaminathan A, Wu M (2007) Noise features for image tampering detection and steganalysis. In IEEE International Conference on Image ProcessingGoogle Scholar
  8. 8.
    Guo K, Wang R (2010) An effective method for identifying natural images and computer graphics. J Comput Inf Syst pp 3303–3308Google Scholar
  9. 9.
    Hsu Y-F, Chang S-F (2007) Image splicing detection using camera response function consistency and automatic segmentation. In: International Conference on Multimedia and ExpoGoogle Scholar
  10. 10.
    Kaye BH (1989) A random walk through fractal dimensions. VCH Publishers, New YorkMATHGoogle Scholar
  11. 11.
    Kemal IK, Rahib HA (2011) Exploiting the synergy between fractal dimension and lacunarity for improved texture recognition. Signal Process 91:2332–2344CrossRefMATHGoogle Scholar
  12. 12.
    Lin WS, Tjoa SK, Zhao HV, Ray Liu KJ (2009) Digital image source coder forensics via intrinsic fingerprints. IEEE Trans Inf Forensics Secur 4:460–475CrossRefGoogle Scholar
  13. 13.
    Mahdian B, Saic S (2009) Using noise inconsistencies for blind image forensice. Image Vis Comput 27:1497–1503CrossRefGoogle Scholar
  14. 14.
    Muhammad G, Hussain M, Khawaji K, Bebis G (2011) Blind copy move image forgery detection using dyadic undecimated wavelet transform. Browse Conference Publications Digital Signal Processing, 17th International Conference onGoogle Scholar
  15. 15.
    Plotnick RE, Gardner RH, Hargrove WW, Prestegaard K, Perlmutter M (1996) Lacunarity analysis: a general technique for the analysis of spatial patterns. Phys Rev E 53:5461–5468CrossRefGoogle Scholar
  16. 16.
    Plotnick RE, Gardner RH, O’Neill RV (1993) Lacunarity indices as measures of landscape texture. Landsc Ecol 8:201–211CrossRefGoogle Scholar
  17. 17.
    Pospescu AC, Farid H (2004) Exposing digital forgeries by detecting dublicated image regionsGoogle Scholar
  18. 18.
    Roy A, Perfect E, Dunne WM, Odling N, Kim JW (2010) Lacunarity analysis of fracture networks: evidence for scale-dependent clustering. J Struct Geol 32:1444–1449CrossRefGoogle Scholar
  19. 19.
    Valous NA, Mendoza F, Sun DW, Allen P (2009) Texture appearance characterization of pre-sliced pork ham images using fractal metrics: fourier analysis dimension and lacunarity. Food Res Int 42:353–362CrossRefGoogle Scholar
  20. 20.
    Ye S, Sun Q, Chang E (2007) Detecting digital image forgeries by measuring inconsistencies of blocking artifact. In: IEEE International Conference on Multimedia and Expo, pp 12–15Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Qiuwei Yang
    • 1
  • Fei Peng
    • 1
    • 2
  • Jiao-Ting Li
    • 1
  • Min Long
    • 3
  1. 1.School of Computer Science and Electronic EngineeringHunan UniversityChangshaChina
  2. 2.School of Computer and SoftwareNanjing University of Information Science & TechnologyNanjingChina
  3. 3.College of Computer and Communication EngineeringChangsha University of Science and TechnologyChangshaChina

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