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Multimedia Tools and Applications

, Volume 77, Issue 11, pp 14153–14175 | Cite as

Blind image forensics using reciprocal singular value curve based local statistical features

  • Gajanan K. Birajdar
  • Vijay H. Mankar
Article
  • 176 Downloads

Abstract

In this article, passive contrast enhancement detection technique is presented using block based reciprocal singular value curve features. Contrast enhancement operation changes the natural statistics of the image and variation in singular value curve is exploited for constructing the feature vector for forgery detection. Various statistical features using reciprocal singular value curve are extracted after multilevel 2-Dimensional wavelet decomposition. Fisher criterion is employed to choose the most discriminating and to discard the redundant features. Experimental results are presented using gray scale, G component and C b image database and support vector machine classifier. Robustness against anti-forensic algorithm and JPEG compression is also presented. The algorithm outperforms all the existing feature based blind contrast enhancement detection methods in terms of detection accuracy.

Keywords

Image forgery detection Passive contrast enhancement detection SVD DWT Reciprocal singular value curve 

References

  1. 1.
    Abu-Marie W, Gutub A, Abu-Mansour H (2010) Image based steganography using truth table based and determinate array on rgb indicator. Int J Signal & Image Process 1(3):196–204Google Scholar
  2. 2.
    Al-Otaibi N, Gutub A (2014) 2-layer security system for hiding sensitive text data on personal computers. Lect Notes Inf Theory 2(2):151–157Google Scholar
  3. 3.
    Al-Otaibi N A, Gutub A A (2014) Flexible stego-system for hiding text in images of personal computers based on user security priority. In: Proceedings of international conference on advanced engineering technologies (AET-2014), pp 250–256Google Scholar
  4. 4.
    Avcibas I, Bayram S, Memon N, Ramkumar M, Sankur B (2004) A classifier design for detecting image manipulations. In: Proceedings of international conference on image processing, pp 2645–2648Google Scholar
  5. 5.
    Bayram S, Avcibas I, Sankur B, Memon N (2005) Image manipulation detection with binary similarity measures. In: Proceedings of European signal processing conference, pp 1–4Google Scholar
  6. 6.
    Bayram S, Avcibas I, Sankur B, Memon N (2006) Image manipulation detection. J Electron Imag 15(4):1–17CrossRefGoogle Scholar
  7. 7.
    Birajdar G K, Mankar V H (2013) Digital image forgery detection using passive techniques: a survey. Digit Investig 10(3):226–245CrossRefGoogle Scholar
  8. 8.
    Birajdar G K, Mankar V H (2016) Passive image manipulation detection using wavelet transform and support vector machine classifier. In: Proceedings of international conference on ICT for sustainable development. Springer, Singapore, pp 447–455Google Scholar
  9. 9.
    Blum A, Langley P (1997) Selection of relevant features and examples in machine learning. Artif Intell 97(1–2):245–271MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Boato G, Natale F, Zontone P (2010) How digital forensics may help assessing the perceptual impact of image formation and manipulation. In: International workshop on video processing and quality metrics for consumer electronics, pps 1–6Google Scholar
  11. 11.
    Cao G, Zhao Y, Ni R, Li X (2014) Contrast enhancement-based forensics in digital images. IEEE Trans Inf Foren Secur 9(3):515–525CrossRefGoogle Scholar
  12. 12.
    Chandra D (2002) Digital image watermarking using singular value decomposition. In: Proceedings of 45th IEEE midwest symposium on circuits and systems, pp 264–267Google Scholar
  13. 13.
    Chang C C, Lin CJ (2001) LIBSVM: a library for support vector machines http://www.csie.ntu.edu.tw/cjlin/libsvm
  14. 14.
    Chun-Wing K, C AO, Sung-Him C (2012) Alternative anti-forensics method for contrast enhancement. Springer, Berlin Heidelberg, pp 398–410Google Scholar
  15. 15.
    Cortes C, Vapnik V (1995) Support vector networks. Mach Learn 20(3):273–297zbMATHGoogle Scholar
  16. 16.
    Farid H (2009) A survey of image forgery detection. IEEE Signal Process Mag 26(2):16–25CrossRefGoogle Scholar
  17. 17.
    Gul G, Avcibas I, Kurugollu F (2010) SVD based image manipulation detection. In: Proceedings of 17th IEEE international conference on image processing, pp 1765–1768Google Scholar
  18. 18.
    Gul G, Kurugollu F (2010) SVD-based universal spatial domain image steganalysis. IEEE Trans Inf Foren Secur 5(2):349–353CrossRefGoogle Scholar
  19. 19.
    Gutub A (2010) Pixel indicator technique for rgb image steganography. J Emerg Technolgoies Web Intell 2(1):56–63Google Scholar
  20. 20.
    Gutub A, Al-Qahtani A, Tabakh A (2009) Triple-a: secure rgb image steganography based on randomization. In: 2009 IEEE/ACS international conference on computer systems and applications, pp 400–403Google Scholar
  21. 21.
    Gutub A, Ankeer M, Abu-Ghalioun M, Shaheen A, Alvi A (2008) Pixel indicator high capacity technique for rgb image based steganography. In: WoSPA 2008 – 5th IEEE international workshop on signal processing and its applications, pp 1–4Google Scholar
  22. 22.
    Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mac Learn Res 3:1157–1182zbMATHGoogle Scholar
  23. 23.
    http://www.cio.com (2016). Last accessed on 17 July 2016
  24. 24.
    Khan F, Gutub A A A (2007) Message concealment techniques using image based steganography. In: 4th IEEE GCC conference and exhibition, gulf international convention centre. Manamah, pp 1–4Google Scholar
  25. 25.
    Lin X, Li C T, Hu Y (2013) Exposing image forgery through the detection of contrast enhancement. In: 2013 IEEE international conference on image processing, pp 4467–4471Google Scholar
  26. 26.
    Liu R, Tan T (2002) An SVD-based watermarking scheme for protecting rightful ownership. IEEE Trans Multimed 4(1):121–128CrossRefGoogle Scholar
  27. 27.
    Liu H, Yu L (2005) Toward integrating feature selection algorithms for classification and clustering. IEEE Trans Knowl Data Eng 17(4):491–502MathSciNetCrossRefGoogle Scholar
  28. 28.
    Lu CJ, Liu LF, Luo Y X (2014) Selection of image features for steganalysis based on the fisher criterion. Digit Investig 11(1):57–66CrossRefGoogle Scholar
  29. 29.
    Mahdian B, Saic S (2010) A bibliography on blind methods for identifying image forgery. Signal Process Image Commun 25(6):389–399CrossRefGoogle Scholar
  30. 30.
    Mallat S (1989) The theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(7):654–693CrossRefzbMATHGoogle Scholar
  31. 31.
    Nouri R, Mansour A (2016) Digital image steganalysis based on the reciprocal singular value curve. Multimed Tools Appl. 1–12. doi: 10.1007/s11042-016-3507-y
  32. 32.
    Pandey P, Kumar S, Singh S (2014) Rightful owenship through image adaptive DWT-SVD watermarking algorithm and perceptual tweaking. Multimed Tools Appl 72 (1):723–748CrossRefGoogle Scholar
  33. 33.
    Redi J A, Taktak W, Dugelay J L (2011) Digital image forensics: a booklet for beginners. Multimed Tools Appl 51(1):133–162CrossRefGoogle Scholar
  34. 34.
    Sacchi D L, Agnoli F, Loftus E (2007) Changing history: doctored photographs affect memory for past public events. Appl Cogn Psychol 21(8):1005–1022CrossRefGoogle Scholar
  35. 35.
    Sang Q, Wu X, Li C, Bovik A C (2014) Blind image quality assessment using a reciprocal singular value curve. Signal Process Image Commun 29(10):1149–1157CrossRefGoogle Scholar
  36. 36.
    Schaefer G, Stich M (2004) UCID - an uncompressed colour image database. In: Proceedings of SPIE, storage and retrieval methods and applications for multimedia, pp 472–480Google Scholar
  37. 37.
    Singh D, Singh S (2016) DWT-SVD and DCT based robust and blind watermarking scheme for copyright protection. Multimed Tools Appl 1–24. doi: 10.1007/s11042-016-3706-6
  38. 38.
    Stamm M, Liu K J R (2010) Forensic detection of image manipulation using statistical intrinsic fingerprints. IEEE Trans Inf Foren Secur 5(3):492–506CrossRefGoogle Scholar
  39. 39.
    Staroszczyk T, Osowski S, Markiewicz T (2012) Comparative analysis of feature selection methods for blood cell recognition in leukemia. In: Perner P (ed) Machine learning and data mining in pattern recognition, vol 7376. Springer Berlin, Heidelberg, LNCS, pp 467—481Google Scholar
  40. 40.
    Vapnik V (1998) Statistical learning theory, 1st edn. Adaptive and learning systems for signal processing, communications, and control. WileyGoogle Scholar
  41. 41.
    Wang R, Ping X (2009) Detection of resampling based on singular value decomposition. In: Proceedings of Fifth international conference on image and graphics, pp 879–884Google Scholar
  42. 42.
    Wang D, Liu S, Luo X, Li S (2013) A transcoding-resistant video watermarking algorithm based on corners and singular value decomposition. Telecommun Syst 54:359–371CrossRefGoogle Scholar
  43. 43.
    Zhang X, Lyu S (2014) Blind estimation of pixel brightness transform. In: 2014 IEEE International conference on image processing (ICIP), pp 4472–4476Google Scholar
  44. 44.
    Zontone P, Carli M, Boato G, Natale D (2010) Impact of contrast modification on human feeling: an objective and subjective assessment. In: International Conference on image processing, pp 1757–1760Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Electronics EngineeringRamrao Adik Institute of TechnologyMaharashtraIndia
  2. 2.Department of Electronics and Communication EngineeringGovernment Polytechnic AhmednagarMaharashtraIndia

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