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2D Gabor filters-based steganalysis of content-adaptive JPEG steganography

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Abstract

To improve the detection accuracy for content-adaptive JPEG steganography, which often constrains the embedding changes to complex texture regions, a new steganalysis feature called the GRF (Gabor Rich Feature) based on two-dimensional (2D) Gabor filters is proposed. First, the diverse 2D Gabor filters are generated and used to filter the decompressed JPEG image. Second, five types of statistical features are extracted from the filtered images and these features are merged according to their respective symmetry. Third, all the features are combined and feature selection is performed to reduce dimensionality. Last, an ensemble classifier is used to assemble the steganalysis feature as well as the final steganalyzer. The experimental results show that the proposed steganalysis feature can achieve a performance that is competitive with the state-of-the-art steganalysis features when used for the detection of the latest content-adaptive JPEG steganography algorithms.

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Notes

  1. Lena image [EB/OL]. http://en.wikipedia.org/wiki/lenna

References

  1. Bas P, Filler T, Pevnỳ T (2011) Break our steganographic system: The ins and outs of organizing Boss. In: Proceedings of the 13th International workshop on Information Hiding. Springer, pp 59–70

  2. Chai Z, Sun Z, Mendez-Vazquez H, He R, Tan T (2014) Gabor ordinal measures for face recognition. IEEE Trans Inf Forensic Secur 9(1):14–26

    Article  Google Scholar 

  3. Chen C, Shi YQ (2008) JPEG image steganalysis utilizing both intrablock and interblock correlations. In: IEEE International Symposium on Circuits and Systems. IEEE, pp 3029–3032

  4. Daugman JG (1985) Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. J Opt Soc Amer Opt Image Sci 2(7):1160–1169

    Article  Google Scholar 

  5. Denemark T, Sedighi V, Holub V, Cogranne R, Fridrich J (2014) Selection-channel-aware rich model for steganalysis of digital images. In: Proceedings of 2014 IEEE Workshop on Information Forensic and Security, pp 48–53

  6. Filler T, Fridrich J (2011) Design of adaptive steganographic schemes for digital images. In: Proceedings of SPIE, Electronic Imaging, Media Watermarking, Security and Forensics of Multimedia XIII, International Society for Optics and Photonics, pp 0F1–14

  7. Filler T, Judas J, Fridrich J (2011) Minimizing additive distortion in steganography using syndrome-trellis codes. IEEE Trans Inf Forensic Secur 6(3):920–935

    Article  Google Scholar 

  8. Fridrich J, Kodovsky J (2012) Rich models for steganalysis of digital images. IEEE Trans Inf Forensic Secur 7(3):868–882

    Article  Google Scholar 

  9. Fridrich J, Goljan M, Lisonek P, Soukal D (2005a) Writing on wet paper. IEEE Trans Signal Process 53(10):3923–3935

  10. Fridrich J, Goljan M, Soukal D (2005b) Perturbed quantization steganography. Multimed Syst 11(2):98–107

  11. Fridrich J, Pevnỳ T, Kodovskỳ J (2007) Statistically undetectable JPEG steganography: dead ends challenges, and opportunities. In: Proceedings of the 9th Workshop on Multimedia & Security. ACM, pp 3–14

  12. Grigorescu SE, Petkov N, Kruizinga P (2002) Comparison of texture features based on Gabor filters. IEEE Trans Image Process 11(10):1160–1167

    Article  MathSciNet  Google Scholar 

  13. Guo L, Ni J, Shi YQ (2014) Uniform embedding for efficient JPEG steganography. IEEE Trans Inf Forensic Secur 9(5):814–825

    Article  Google Scholar 

  14. Holub V, Fridrich J (2013a) Digital image steganography using universal distortion. In: Proceedings of the first ACM workshop on Information hiding and multimedia security, ACM, pp 59–68

  15. Holub V, Fridrich J (2013b) Random projections of residuals for digital image steganalysis. IEEE Trans Inf Forensic Secur 8(12):1996–2006

  16. Holub V, Fridrich J (2015a) Low complexity features for JPEG steganalysis using undecimated DCT. IEEE Trans Inf Forensic Secur 10(2):219–228

  17. Holub V, Fridrich J (2015b) Phase-aware projection model for steganalysis of JPEG images. Proceedings SPIE, Electronic Imaging, Media Watermarking, Security, and Forensics XVII pp 0T 1–11

  18. Huang F, Huang J, Shi YQ (2012) New channel selection rule for JPEG steganography. IEEE Trans Inf Forensic Secur 7(4):1181–1191

    Article  Google Scholar 

  19. Idrissa M, Acheroy M (2002) Texture classification using Gabor filters. Pattern Recogn Lett 23(9):1095–1102

    Article  MATH  Google Scholar 

  20. Jan K, Jessica F (2009) Calibration revisited. In: Proceedings of ACM Multimedia and Security Workshop, pp 63–74

  21. Jones JP, Palmer LA (1987) An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. J neurophysiol 58(6):1233–1258

    Google Scholar 

  22. Kim Y, Duric Z, Richards D (2007) Modified matrix encoding technique for minimal distortion steganography. In: Proceedings of 8th International Workshop Information Hiding. Springer, pp 314–327

  23. Kodovskỳ J, Fridrich J (2012) Steganalysis of JPEG images using rich models. In: Proceedings of SPIE, Electronic Imaging, Media Watermarking, Security, and Forensics of Multimedia XIV, International Society for Optics and Photonics, pp 0A1-13

  24. Kodovsky J, Fridrich J, Holub V (2011) On dangers of overtraining steganography to incomplete cover model. In: Proceedings of the thirteenth ACM multimedia workshop on Multimedia and security. ACM, pp 69–76

  25. Kodovsky J, Fridrich J, Holub V (2012) Ensemble classifiers for steganalysis of digital media. IEEE Trans Inf Forensic Secur 7(2):432–444

    Article  Google Scholar 

  26. Lee TS (1996) Image representation using 2D Gabor wavelets. IEEE Trans Pattern Anal Mach Intell 18(10):959–971

    Article  Google Scholar 

  27. Leng L, Zhang J, Khan MK, Chen X, Alghathbar K (2010a) Dynamic weighted discrimination power analysis: a novel approach for face and palmprint recognition in DCT domain. Int J Phys Sci 5(17):2543–2554

  28. Leng L, Zhang J, Xu J, Khan MK, Alghathbar K (2010b) Dynamic weighted discrimination power analysis in DCT domain for face and palmprint recognition. In: Proceedings of 2010 International Conference on Information and Communication Technology Convergence. IEEE, pp 467–471

  29. Leng L, Zhang J, Chen G, Khan MK, Alghathbar K (2011) Two-directional two-dimensional random projection and its variations for face and palmprint recognition. In: International Conference on Computational Science and Its Applications. Springer, pp 458–470

  30. Leng L, Teoh ABJ, Li M, Khan MK (2014) Analysis of correlation of 2DPalmHash code and orientation range suitable for transposition. Neurocomputing 131:377–387

    Article  Google Scholar 

  31. Li F, Wu K, Lei J, Wen M, Bi Z, Gu C (2016) Steganalysis over large-scale social networks with high-order joint features and clustering ensembles. IEEE Trans Inf Forensic Secur 11(2):344–357

    Article  Google Scholar 

  32. Li M, Staunton RC (2008) Optimum Gabor filter design and local binary patterns for texture segmentation. Pattern Recogn Lett 29(5):664–672

    Article  Google Scholar 

  33. Liu Q (2011) Steganalysis of DCT-embedding based adaptive steganography and yass. In: Proceedings of the thirteenth ACM multimedia workshop on Multimedia and security. ACM, pp 77–86

  34. Liu Q, Chen Z (2014) Improved approaches with calibrated neighboring joint density to steganalysis and seam-carved forgery detection in JPEG images. ACM Trans Intell Syst Technol 5(4):1–28

    Google Scholar 

  35. Liu Q, Sung AH, Chen Z, Xu J (2008) Feature mining and pattern classification for steganalysis of lsb matching steganography in grayscale images. Pattern Recogn 41(1):56–66

    Article  MATH  Google Scholar 

  36. Luo XY, Wang DS, Wang P, Liu FL (2008) A review on blind detection for image steganography. Signal Process 88(9):2138–2157

    Article  MATH  Google Scholar 

  37. Lyu S, Farid H (2006) Steganalysis using higher-order image statistics. IEEE Trans Inf Forensic Secur 1(1):111–119

    Article  Google Scholar 

  38. Pevny T, Fridrich J (2007) Merging markov and DCT features for multi-class JPEG steganalysis. In: Proceedings SPIE, Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents IX, volume 650503, International Society for Optics and Photonics, pp 1–14

  39. Provos N (2001) Defending against statistical steganalysis. In: Proceeding of 10th Usenix Security Symposium, vol 10, pp 323–336

  40. Sallee P (2004) Model-based steganography. In: Proceeding of 2nd International Workshop on Digital Watermarking. Springer, pp 154–167

  41. Sallee P (2005) Model-based methods for steganography and steganalysis. Int J Image Graph 5(1):167–189

    Article  Google Scholar 

  42. Solanki K, Sarkar A, Manjunath B (2007) Yass: Yet another steganographic scheme that resists blind steganalysis. In: Proceedings of 9th International Workshop on Information Hiding. Springer, pp 16–31

  43. Song X, Liu F, Luo X, Lu J, Zhang Y (2015a) Steganalysis of perturbed quantization steganography based on the enhanced histogram features. Multimed Tools Appl 74(24):11,045–11,071

  44. Song X, Liu F, Yang C, Luo X, Zhang Y (2015b) Steganalysis of adaptive JPEG steganography using 2D Gabor filters. In: Proceedings of the 3rd ACM Workshop on Information Hiding and Multimedia Security. ACM, pp 15–23

  45. Sun Z, Tan T (2009) Ordinal measures for iris recognition. IEEE Trans Pattern Anal Mach Intell 31(12):2211–2226

    Article  Google Scholar 

  46. Upham D (1993) Steganographic algorithm JSteg. Software available at http://zooidorg/~paul/crypto/jsteg

  47. Wang C, Ni J (2012) An efficient JPEG steganographic scheme based on the block entropy of DCT coefficients. In: Proceedings of 2012 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, pp 1785–1788

  48. Westfeld A (2001) F5a steganographic algorithm. In: Proceedings of 4th International Workshop on Information Hiding. Springer, pp 289–302

  49. Zong H, Fl L, Xy L (2012) Blind image steganalysis based on wavelet coefficient correlation. Digit Investig 9(1):58–68

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China (No.61272489, 61379151, 61302159 and 61602508), the Excellent Youth Foundation of Henan Province of China (No.144100510001) and the Natural Science Basic Research Plan in Shaanxi Province of China(No.2014JM2-6103).

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Authors and Affiliations

  1. Zhengzhou Science and Technology Institute, and the State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, 450002, China

    Xiaofeng Song, Fenlin Liu, Chunfang Yang & Xiangyang Luo

  2. Xi’an Communication Institute, Xi’an, 710106, China

    Xiaofeng Song & Liju Chen

  3. School of Foreign Languages, Xi’an University of Posts and Telecommunications, Xi’an, 710121, China

    Zhengui Zhang

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  1. Xiaofeng Song
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  2. Fenlin Liu
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  3. Zhengui Zhang
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  4. Chunfang Yang
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  5. Xiangyang Luo
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  6. Liju Chen
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Correspondence to Xiaofeng Song.

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Song, X., Liu, F., Zhang, Z. et al. 2D Gabor filters-based steganalysis of content-adaptive JPEG steganography. Multimed Tools Appl 76, 26391–26419 (2017). https://doi.org/10.1007/s11042-016-4157-9

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