Research article Special Issues

Locating secret messages based on quantitative steganalysis

  • Received: 26 January 2019 Accepted: 25 April 2019 Published: 29 May 2019
  • Steganography poses a serious challenge to forensics because investigators cannot identify even traces of secret messages embedded using a steganographer. Contrarily, the objective of locating steganalysis is to locate the embedded message, which should help extract the secret message. In this paper, a methodology of locating steganalysis using quantitative steganalysis is presented for multiple stego images with embedded messages along the same embedding path. Three typical quantitative steganalysis methods are applied to the methodology to locate the messages embedded using LSB re-placement. Experimental results show that the presented methods can reliably estimate the embedding positions, which verifies the validity of the presented methodology. The presented methodology points out a new use of quantitative steganalysis, and further demonstrates that it is necessary to design more precise quantitative steganalysis methods.

    Citation: Chunfang Yang, Fenlin Liu, Shuangkui Ge, Jicang Lu, Junwei Huang. Locating secret messages based on quantitative steganalysis[J]. Mathematical Biosciences and Engineering, 2019, 16(5): 4908-4922. doi: 10.3934/mbe.2019247

    Related Papers:

  • Steganography poses a serious challenge to forensics because investigators cannot identify even traces of secret messages embedded using a steganographer. Contrarily, the objective of locating steganalysis is to locate the embedded message, which should help extract the secret message. In this paper, a methodology of locating steganalysis using quantitative steganalysis is presented for multiple stego images with embedded messages along the same embedding path. Three typical quantitative steganalysis methods are applied to the methodology to locate the messages embedded using LSB re-placement. Experimental results show that the presented methods can reliably estimate the embedding positions, which verifies the validity of the presented methodology. The presented methodology points out a new use of quantitative steganalysis, and further demonstrates that it is necessary to design more precise quantitative steganalysis methods.


    加载中


    [1] J. Fridrich and M. Golijan, Practical steganalysis of digital images-state of the art, Proc. SPIE,4675 (2002), 1–13.
    [2] L. Xiang, Y. Li, W. Hao, et al., Reversible natural language watermarking using synonym substi-tution and arithmetic coding, Comput. Mater. Con., 55 (2018), 541–559.
    [3] Y. Zhang, C. Qin, W. Zhang, et al., On the fault-tolerant performance for a class of robust image steganography, Signal Process., 146 (2018), 99–111.
    [4] Y. Zhang, D. Ye, J. Gan, et al., An image steganography algorithm based on quantization index modulation resisting scaling attacks and statistical detection, Comput. Mater. Con., 56 (2018), 151–167.
    [5] J. Chen, W. Lu, Y. Yeung, et al., Binary image steganalysis based on distortion level co-occurrence matrix, Comput. Mater. Con., 55 (2018), 201–211.
    [6] Y. Ma, X. Luo, X. Li, et al., Selection of rich model steganalysis features based on decision rough set α-positive region reduction, IEEE Trans. Circ. Syst Vid. Technol., 29 (2019), 336–350.
    [7] J. Fridrich, M. Goljan and D. Soukal, Searching for the stego-key, Proc. SPIE, (2004), 70–82.
    [8] J. Fridrich, M. Goljan, D. Soukal, et al., Forensic steganalysis: determining the stego key in spatial domain steganography, Proc. SPIE, (2005), 631–642.
    [9] J. Fridrich, M. Goljan, D. Hogea, et al., Quantitative steganalysis of digital images: estimating the secret message length, ACM Mult. Syst. J., 9 (2003), 288–302.
    [10] S. Dumitrescu, X. Wu and Z. Wang, Detection of LSB steganography via sample pair analysis, IEEE Trans. Signal Proces., 51 (2003), 1995–2007.
    [11] J. Fridrich, M. Goljan and R. Du, Detecting LSB steganography in color and gray-scale images, IEEE Mult., 8 (2001), 22–28.
    [12] A. D. Ker, A general framework for the structural steganalysis of LSB replacement, in Proc. Information Hiding (eds. M. Barni, J. Herrera-Joancomart$\acute{i}$, S. Katzenbeisser, F. Pérez-González), Springer-Verlag, (2005), 296–311.
    [13] C. Yang, F. Liu, X. Luo, et al., Steganalysis frameworks of embedding in multiple least-significant bits, IEEE. Trans. Inf. Foren. Sec., 3 (2008), 662–672.
    [14] J. Kodovský and J. Fridrich, Quantitative structural steganalysis of JSteg, IEEE. Trans. Inf. Foren. Sec., 5 (2010), 681–693.
    [15] C. Yang, F. Liu, X. Luo, et al., Pixel group trace model-based quantitative steganalysis for multiple least-significant bits steganography, IEEE. Trans. Inf. Foren. Sec., 8 (2013), 216–228.
    [16] J. Fridrich and M. Goljan, On estimation of secret message length in LSB steganography in spatial domain, Proc. SPIE, (2004), 23–34.
    [17] T. Pevný, J. Fridrich and A. D. Ker, From blind to quantitative steganalysis, Proc. SPIE, (2009), 72540C.
    [18] Q. Guan, J. Dong and T. Tan, Blind quantitative steganalysis based on feature fusion and gradient boosting, in Proc. Int. Workshop Digit. Watermark (eds. H. J. Kim, Y. Q. Shi and M. Barni), Springer-Verlag, (2011), 266–279.
    [19] T. T. Quach, Locating payload embedded by group-parity steganography, Digit. Invest., 9 (2012), 160–166.
    [20] T. T. Quach, Extracting hidden messages in steganographic images, Digit. Invest., 11 (2014), S40–S45.
    [21] J. Liu, Y. Tian, T. Han, et al., Stego key searching for LSB steganography on JPEG decompressed image, Sci. China Inform. Sci., 59 (2016), 32105.
    [22] A. Westfeld and A. Pfitzmann, Attacks on steganographic systems, in Proc. Inform. Hid. (eds. A. Pfitzmann), Springer-Verlag, (2000), 61–75.
    [23] S. Trivedi and R. Chandramouli, Secret key estimation in sequential steganography, IEEE. Trans. Singal Proces., 53 (2005), 746–757.
    [24] A. D. Ker and R. Böhme, Revisiting weighted stego-image steganalysis, in Proc. SPIE (eds. E. J. Delp, P. W. Wong, J. Dittmann and N. D. Memon), SPIE, (2008), 681905.
    [25] A. D. Ker, Locating steganographic payload via WS residual, in Proc. ACM Mult. Sec. (eds. A. D. Ker, J. Dittmann and J. Fridrich), ACM, (2008), 27–32.
    [26] A. D. Ker and I. Lubenko, Feature reduction and payload location with wam steganalysis, in Proc. SPIE (eds. E. J. Delp, J. Dittmann, N. D. Memon and P. W. Wong), SPIE, (2009), 72540A.
    [27] Y. Luo, X. Li and B. Yang, Locating steganographic payload for LSB matching embedding, in Proc. ICME (eds. I. Cheng, G. Fernandez and H. Wang), IEEE, (2011), 1–6.
    [28] T. T. Quach, On locating steganographic payload using residuals, in Proc. SPIE (eds. N. D. Memon, J. Dittmann, A. M. Alattar and E. J. Delp), SPIE, 2011, 0J1–0J7.
    [29] T. T. Quach, Optimal cover estimation methods and steganographic payload location, IEEE Trans. Inf. Foren. Sec., 6 (2011), 1214–1222.
    [30] X. Gui, X. Li and B. Yang, Improved payload location for LSB matching steganography, in IEEE ICIP (eds. E. Saber, S. Hemami and G. Sharma), IEEE, (2012), 1125–1128.
    [31] T. T. Quach, Cover estimation and payload location using Markov random fields, in Proc. SPIE (eds. A. M. Alattar, N. D. Memon and C. D. Heitzenrater), SPIE, (2014), 90280H.
    [32] J. Liu, Y. Tian, T. Han, et al., LSB steganographic payload location for JPEG-decompressed images, Digit. Signal Process, 38 (2015), 66–76.
    [33] C. Yang, X. Luo, J. Lu, et al., Extracting hidden messages of MLSB steganography based on optimal stego subset, Sci. China Inform. Sci., 61 (2018), 119103:1–119103:3.
    [34] X. Luo, C. Yang and F. Liu, Equivalence analysis among DIH, SPA, and RS steganalysis methods, in Proc. IFIP CMS (eds. H. Leitold and E. P. Markatos), Springer-Verlag, (2006), 161–172.
    [35] L. Xiang, G. Zhao, Q. Li, et al., TUMK-ELM: a fast unsupervised heterogeneous data learning approach, IEEE Access, 6 (2018), 35305–35315.
    [36] L. Xiang, X. Shen, J. Qin, et al.,Discrete multi-graph hashing for large-scale visual search, Neural Process Lett., (2018).
    [37] L. Liu, Z. Wang, Z. Qian, et al.,Steganography in beautified images, Math. Biosci. Engineer., 16 (2019), 2333–2333.
  • Reader Comments
  • © 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(2899) PDF downloads(548) Cited by(3)

Article outline

Figures and Tables

Figures(7)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog