Multimedia Tools and Applications

, Volume 76, Issue 4, pp 4765–4781 | Cite as

A study of co-occurrence based local features for camera model identification

  • Francesco Marra
  • Giovanni Poggi
  • Carlo Sansone
  • Luisa Verdoliva


Camera model identification has great relevance for many forensic applications, and is receiving growing attention in the literature. Virtually all techniques rely on the traces left in the image by the long sequence of in-camera processes which are specific of each model. They differ in the prior assumptions, if any, and in how such evidence is gathered in expressive features. In this work we study a class of blind features, based on the analysis of the image residuals of all color bands. They are extracted locally, based on co-occurrence matrices of selected neighbors, and then used to train a classifier. A number of experiments are carried out on the well-known Dresden Image Database. Besides the full-knowledge case, where all models of interest are known in advance, other scenarios with more limited knowledge and partially corrupted images are also investigated. Experimental results show these features to provide a state-of-the-art performance.


Camera model identification Local features Residuals Co-occurrences. 


  1. 1.
    Amerini I, Becarelli R, Bertini B, Caldelli R (2015) Acquisition source identification through a blind image classification. IET Image Process 9(4):329–337CrossRefGoogle Scholar
  2. 2.
    Avcibaş I, Memon N, Sankur B (2003) Steganalysis using image quality metrics. IEEE Trans Image Process 12(2):221–229MathSciNetCrossRefGoogle Scholar
  3. 3.
    Bayram S, Sencar H, Memon N (2006) Improvements on source camera-model identification based on CFA. In: Advances in Digital Forensics II, IFIP international conference on digital Forensics, pp 289–299Google Scholar
  4. 4.
    Bayram S, Sencar H, Memon N, Avcibas I (2005) Source camera identification based on CFA interpolation. In: IEEE Int. conference on image processing, pp 69–72Google Scholar
  5. 5.
    Cao H, Kot A (2009) Accurate detection of demosaicing regularity for digital image forensics. IEEE Trans Inf Forensics Secur 4(4):899–910CrossRefGoogle Scholar
  6. 6.
    Çeliktutan O, Sankur B, Avcibaş I (2008) Blind identification of source cell-phone model. IEEE Trans Inf Forensics Secur 3(3):553–566CrossRefGoogle Scholar
  7. 7.
    Chang CC, Lin CJ (2011) LIBSVM: A library for support vector machines. ACM Trans Intell Syst Technol 2:27:1–27:27. Software available at CrossRefGoogle Scholar
  8. 8.
    Chen C, Stamm M (2015) Camera model identification framework using an ensemble of demosaicing features. In: IEEE workshop on information forensics and security, pp 1–6Google Scholar
  9. 9.
    Chen M, Fridrich J, Goljan M, Lukás J (2008) Determining image origin and integrity using sensor noise. IEEE Trans Inf Forensics Secur 3(1):74–90CrossRefGoogle Scholar
  10. 10.
    Chierchia G, Parrilli S, Poggi G, Sansone C, Verdoliva L (2010) On the influence of denoising in PRNU based forgery detection. In: 2nd ACM workshop on multimedia in forensics, security and intelligence, pp 117–122Google Scholar
  11. 11.
    Chierchia G, Poggi G, Sansone C, Verdoliva L (2014) A Bayesian-MRF approach for PRNU-based image forgery detection. IEEE Trans Inf Forensics Secur 9 (4):554–567CrossRefGoogle Scholar
  12. 12.
    Cogranne R, Fridrich JJ (2015) Modeling and extending the ensemble classifier for steganalysis of digital images using hypothesis testing theory. IEEE Trans Inf Forensics Secur 10(12):2627–2642CrossRefGoogle Scholar
  13. 13.
    Costa F, Silva E, Eckmann M, Scheirer W, Rocha A (2014) Open set source camera attribution and device linking. Pattern Recogn Lett 39:92–101CrossRefGoogle Scholar
  14. 14.
    Cozzolino D, Gragnaniello D, Verdoliva L (2014) Image forgery detection through residual-based local descriptors and block-matching. In: IEEE conference on image processing (ICIP), pp 5297–5301Google Scholar
  15. 15.
    Duda RO, Hart PE, Stork D (2001) Pattern Classification, 2nd edn. Wiley, New YorkMATHGoogle Scholar
  16. 16.
    Fan N, Jin C, Huang Y (2006) Source camera identification by JPEG compression statistics for image forensics. In: TENCON, pp 1–4Google Scholar
  17. 17.
    Filler T, Fridrich J, Goljan M (2008) Using sensor pattern noise for camera model identification. In: IEEE international conference on image processing, pp 1296–1299Google Scholar
  18. 18.
    Fridrich J, Kodovsky J (2012) Rich models for steganalysis of digital images. IEEE Trans Inf Forensics Secur 7:868–882CrossRefGoogle Scholar
  19. 19.
    Galdi C, Nappi M, Dugelay JL (2015) Multimodal authentication on Smartphones: combining iris and sensor recognition for a double check of user identity. Pattern Recogn LettGoogle Scholar
  20. 20.
    Gloe T (2012) Feature-based forensic camera model identification. In: LNCS transactions on data hiding and multimedia security VIII, vol. 7228, pp 42–62Google Scholar
  21. 21.
    Gloe T, Böhme R (2010) The Dresden image database for benchmarking digital image forensics. J Digital Forensic Practice 3(2–4):150–159CrossRefGoogle Scholar
  22. 22.
    Goljan M, Cogranne R, Fridrich J (2014) Rich model for steganalysis of color images. In: Sixth IEEE international workshop on information forensics and securityGoogle Scholar
  23. 23.
    Goljan M, Fridrich J (2015) CFA-aware features for steganalysis of color images. In: SPIE, electronic imaging, media watermarking, security and Forensics XVII, vol. 9409Google Scholar
  24. 24.
    Goyal K, Panwar R, Khanna N (2014) Evaluation of iqms effectiveness for cell phone identification using captured videos and images. In: International conference on power, control and embedded systems, pp 1–6Google Scholar
  25. 25.
    Gragnaniello D, Poggi G, Sansone C, Verdoliva L (2015) An investigation of local descriptors for biometric spoofing detection. IEEE Transactions on Information Forensics and Security 10(4):849–863CrossRefGoogle Scholar
  26. 26.
    Huang Y, Zhang J, Huang H (2015) Camera model identification with unknown models. IEEE Trans Inf Forensics Secur 10(12):2692–2704CrossRefGoogle Scholar
  27. 27.
    Kharrazi M, Sencar H, Memon N (2004) Blind source camera identification. In: IEEE international conference on image processing, pp 709–712Google Scholar
  28. 28.
    Kirchner M, Fridrich J (2010) On detection of median filtering in images. In: SPIE, Electronic Imaging, Media Forensics and Security XII, pp 101–112Google Scholar
  29. 29.
    Kirchner M, Gloe T (2015) Forensic camera model identification. In: Ho T, Li S (eds) Handbook of digital forensics of multimedia data and devices. Wiley-IEEE PressGoogle Scholar
  30. 30.
    Kodovsky J, Fridrich J, Holub V (2012) Ensemble classifiers for steganalysis of digital media. IEEE Trans Inf Forensics Secur 7(2):432–444CrossRefGoogle Scholar
  31. 31.
    Lukàš J, Fridrich J, Goljan M (2006) Digital camera identification from sensor pattern noise. IEEE Trans Inf Forensics Secur 1(2):205–214CrossRefGoogle Scholar
  32. 32.
    Lyu S, Farid H (2006) Steganalysis using higher-order image statistics. IEEE Trans Inf Forensics Secur 1(1):111–119CrossRefGoogle Scholar
  33. 33.
    Marra F, Poggi G, Sansone C, Verdoliva L (2015) Evaluation of residual-based local features for camera model identification. In: New trends in image analysis and processing – ICIAP 2015 Workshops, vol. 9281, pp 11–18Google Scholar
  34. 34.
    Mihcak M, Kozintsev I, Ramchandran K (1999) Spatially adaptive statistical modeling of wavelet image coefficients and its application to denoising. In: IEEE international conference on acoustics, speech and signal processing, pp 3253–3256Google Scholar
  35. 35.
    Ojala T, Pietikäinen M, Mäenpää T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987CrossRefMATHGoogle Scholar
  36. 36.
    Pevnỳ T, Bas P, Fridrich J (2010) Steganalysis by subtractive pixel adjacency matrix. IEEE Trans Inf Forensics Secur 5(2):215–224CrossRefGoogle Scholar
  37. 37.
    Popescu A, Farid H (2005) Exposing digital forgeries by detecting traces of resampling. IEEE Trans Signal Process 53(2):758–767MathSciNetCrossRefGoogle Scholar
  38. 38.
    Qiu X, Li H, Luo W, Huang J (2014) A universal image forensic strategy based on steganalytic model. In: ACM Workshop on Information Hiding and Multimedia Security, pp 165–170Google Scholar
  39. 39.
    Razzazi F, Seyedabadi A (2014) A robust feature for single image camera identification using local binary patterns. In: IEEE International Symposium on Signal Processing and Information Technology, pp 462–467Google Scholar
  40. 40.
    Sandoval Orozco AL, Corripio JR, Garcia Villalba LJ, Hernandez Castro JC (2015) Image source acquisition identification of mobile devices based on the use of features. Multi Tools ApplGoogle Scholar
  41. 41.
    Shi Y, Chen C, Xuan G, Su W (2008) Steganalysis versus splicing detection. In: International workshop on digital-forensics and watermarking, pp 158–172Google Scholar
  42. 42.
    Swaminathan A, Wu M, Liu KJR (2007) Rich models for steganalysis of digital images. IEEE Trans Inf Forensics Secur 2(1):91–105CrossRefGoogle Scholar
  43. 43.
    Thai T, Cogranne R, Retraint F (2014) Camera model identification based on the heteroscedastic noise model. IEEE Trans Image Process 23(1):250–263MathSciNetCrossRefGoogle Scholar
  44. 44.
    Thai T, Retraint F, Cogranne R (2015) Camera model identification based on DCT coefficient statistics. Digital Signal Process 4:88–100MathSciNetCrossRefGoogle Scholar
  45. 45.
    Tuama A, Comby F, Chaumont M (2015) Source camera model identification using features from contaminated sensor noise. In: International Workshop on Digital-forensics and WatermarkingGoogle Scholar
  46. 46.
    Van L, Emmanuel S, Kankanhalli M (2007) Identifying source cell phone using chromatic aberration. In: IEEE international conference on multimedia and expo, pp 883–886Google Scholar
  47. 47.
    Verdoliva L, Cozzolino D, Poggi G (2014) A feature-based approach for image tampering detection and localization. In: IEEE Workshop on Information Forensics and Security, pp 149–154Google Scholar
  48. 48.
    Xu G, Gaon S, Shi Y, Hu R, Su W (2009) Camera-model identification using markovian transition probability matrix. In: Digital watermarking, LNCS, vol. 5703, pp 294–307Google Scholar
  49. 49.
    Xu G, Shi Y (2012) Camera model identification using local binary patterns. In: IEEE international conference on multimedia and expo, pp 392–397Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Francesco Marra
    • 1
  • Giovanni Poggi
    • 1
  • Carlo Sansone
    • 1
  • Luisa Verdoliva
    • 1
  1. 1.University Federico II of NaplesNaplesItaly

Personalised recommendations