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Image resampling detection based on texture classification

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Abstract

This study presents a method for resampling detection. By combining texture analysis with resampling detection, the task of resampling detection is considered as a texture classification problem. In other words, the influence of resampling operations on a raw single-sampled image is viewed as an alteration of the image texture in a fine scale. First, local linear transform is used to obtain textural detail sub-bands. A 36-D feature vector is then extracted from the normalized characteristic function moments of textural detail sub-bands to train a support vector machine classifier. Finally, experimental results are reported on three databases, with each having almost 10,000 images. Comparison with the previous study reveals that the proposed method is effective for resampling detection. In addition, extensive experiments on cover and stego bitmap images illustrate that the proposed method is essential for constructing accurate targeted and blind steganalysis methods for heterogeneous images, raw single-sampled images, and images resampled at different scales.

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Abbreviations

BMP:

Bitmap

LLT:

Local linear transform

CF:

Characteristic function

1-D:

One dimensional

EM:

Expectation/maximization

SOD:

Second-order derivative

SVM:

Support vector machine

DFT:

Discrete Fourier transform

DCT:

Discrete cosine transform

PDF:

Probability density function

LSB:

Least significant bit

MB1:

Model-based

AC:

Alternating current

bpnc:

Bit per non-zero AC DCT coefficient

References

  1. Chang CC, Lin CJ. LIBSVM: a library for support vector machines [EB/OL]. http://www.csie.ntu.edu.tw/~cjlin/libsvm. Accessed 11 May 2011

  2. Doёrr G Image database for steganalysis studies [Online]. http://www.cs.ucl.ac.uk/staff/I.Cox/Content/Downloads.html. Accessed 11 May 2011

  3. Farid H (2009) Image forgery detection. IEEE Signal Process Mag 26(2):16–25. doi:10.1109/MSP.2008.931079

    Article  Google Scholar 

  4. Feng XY, Cox IJ, Doërr G (2012) Normalized energy density-based forensic detection of resampled images. IEEE Trans Multimed 14(3):536–545. doi:10.1109/TMM.2012.2191946

    Article  Google Scholar 

  5. Fridrich J (2004) Feature-based steganalysis for JPEG images and its implications for future design of steganographic schemes. In: Proc. 6th Information Hiding Workshop, Springer LNCS, 3200:67–81. doi:10.1007/978-3-540-30114-1_6

  6. Gallagher AC (2005) Detection of linear and cubic interpolation in JPEG compressed images. In: Proc. 2th Canadian Conf. on Computer and Robot Vision, Washington, DC, pp 65–72. doi:10.1109/CRV.2005.33

  7. Harmsen JJ, Pearlman WA (2003) Steganalysis of additive noise modelable information hiding. In: Proc. SPIE, Security and watermarking of Multimedia Contents V, Santa Clara, CA, 5020:131–142

  8. Kirchner M (2008) Fast and reliable resampling detection by spectral analysis of fixed linear predictor residue. In: Proc. 10th ACM Workshop on Multimedia and Security, New York, NY, USA, pp 11–20. doi:10.1145/1411328.1411333

  9. Li B, Huang JW, Shi YQ (2008) Textural features based universal steganalysis. In: Proc. SPIE Security, Forensics, Steganography and Watermarking of Multimedia Contents X, 6819:1201–1212. doi:10.1117/12.765817

  10. Li X, Zhang T, Zhang Y, Li WX, Li KD (2012) A novel blind detector for additive noise steganography in JPEG decompressed images. Multimed Tools Appl, published online. doi:10.1007/s11042-012-1112-2

  11. Lyu S, Farid H (2006) Steganalysis using higher-order image statistics. IEEE Trans Inf Forensics Secur 1(1):111–119. doi:10.1109/TIFS.2005.863485

    Article  Google Scholar 

  12. Mahdian B, Saic S (2008) Blind authentication using periodic properties of interpolation. IEEE Trans Inf Forensics Secur 3(3):529–538. doi:10.1109/TIFS.2004.924603

    Article  Google Scholar 

  13. Ng I, Tan T, Kittler J (1992) On local linear transform and Gabor filter representation of texture. In: Proc. 11th Int. Conf. on Pattern Recognition, Hague, III:627–631

  14. Popescu AC, Farid H (2004) Statistical tools for digital forensics. In: Proc. 6th Information Hiding Workshop, Springer LNCS, 3200:128–147. doi:10.1007/978-3-540-30114-1_10

  15. Popescu AC, Farid H (2005) Exposing digital forgeries by detecting traces of re-sampling. IEEE Trans Signal Process 53(2):758–767. doi:10.1109/TSP.2004.839932(410) 53

    Article  MathSciNet  Google Scholar 

  16. Prasad S, Ramakrishnan KR (2006) On resampling detection and its application to image tampering. In Proc. of the IEEE Int. Conference on Multimedia and Expo, Toronto, Canada, pp 1325–1328. doi:10.1109/ICME.2006.262783

  17. Sallee P (2004) Model-based steganography. In: Proc. IWDW, Springer LNCS, 2939:154–167. doi:10.1007/978-3-540-24624-4_12

  18. Sharp T (2001) An implementation of key-based digital signal steganography. In: Proc. 4th Int. Workshop on Information Hiding, Springer LNCS, 2137:13–26. doi:10.1007/3-540-45496-9_2

  19. Shi YQ, Xuan GR, Yang CY et al (2005) Effective steganalysis based on statistics moments of wavelet characteristic function. In: Proc. IEEE Int. Conference on Information Technology: Coding and Computing, 1:768–773. doi:10.1109/ITCC.2005.138

  20. Siwei L, Farid H (2003) Detecting hidden message using higer-order statistics and support vector machines. In: Proc. 5th Information Hiding workshop, Springer LNCS, 2578:340–354. doi:10.1007/3-540-36415-3_22

  21. Uccheddu F, De Rosa A, Piva A, Barni M (2010) Detection of resampled images: performance analysis and practical challenges. In: Proc. 18th European Signal Processing Conference, Aalborg, Denmark, pp 1675–1679

  22. Unser M (1986) Local linear transforms for texture measurements. Signal Process 11(1):61–79. doi:10.1016/0165-1684(86)90095-2

    Article  MathSciNet  Google Scholar 

  23. Wang Y, Moulin P (2007) Optimized feature extraction for learning-based image steganalysis. IEEE Trans Inf Forensics Secur 2(1):31–45. doi:10.1109/TIFS.2006.890517

    Article  Google Scholar 

  24. Xiong G, Ping XJ, Zhang T, Hou XD (2012) Image textural features for steganalysis of spatial domain steganography. J Electron Imaging 21(3):033015-1–033015-15. doi:10.1117/1.JEI.21.3.033015

    Article  Google Scholar 

  25. Xuan GR, Shi YQ, Gao JJ et al (2005) Steganalysis based on multiple features formed by statistics moments of wavelet characteristic functions. In: Proc. 7th Information Hiding Workshop, Springer LNCS, 3727:262–277. doi:10.1007/11558859_20

  26. Zheng EG, Ping XJ, Zhang T (2011) Local linear transform and new features of histogram characteristic functions for steganalysis of least significant bit matching steganography. KSII Trans Int Inform Syst 5(4):840–855. doi:10.3837/tiis.2011.04.012

    Google Scholar 

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 61272490 and 60903221). The authors would like to thank the reviewers for their insightful comments and helpful suggestions.

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Authors and Affiliations

  1. Zhengzhou Information Science and Technology Institute, Zhengzhou, 450002, Henan, China

    Xiaodan Hou, Tao Zhang & Gang Xiong

  2. Zhengzhou University of Light Industry, Zhengzhou, 450002, Henan, China

    Yan Zhang & Xin Ping

Authors
  1. Xiaodan Hou
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  2. Tao Zhang
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  3. Gang Xiong
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  4. Yan Zhang
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  5. Xin Ping
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Correspondence to Xiaodan Hou.

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Hou, X., Zhang, T., Xiong, G. et al. Image resampling detection based on texture classification. Multimed Tools Appl 72, 1681–1708 (2014). https://doi.org/10.1007/s11042-013-1466-0

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