Multimedia Tools and Applications

, Volume 77, Issue 14, pp 17913–17935 | Cite as

Dither modulation based adaptive steganography resisting jpeg compression and statistic detection

  • Yi Zhang
  • Xiaodong Zhu
  • Chuan Qin
  • Chunfang Yang
  • Xiangyang LuoEmail author


In order to improve the JPEG compression resistant performance of the current steganogrpahy algorithms resisting statistic detection, an adaptive steganography algorithm resisting JPEG compression and detection based on dither modulation is proposed. Utilizing the adaptive dither modulation algorithm based on the quantization tables, the embedding domains resisting JPEG compression for spatial images and JPEG images are determined separately. Then the embedding cost function is constructed by the embedding costs calculation algorithm based on side information. Finally, the RS coding is combined with the STCs to realize the minimum costs messages embedding while improving the correct rates of the extracted messages after JPEG compression. The experimental results demonstrate that the algorithm can be applied to both spatial images and JPEG images. Compared with the current S-UNIWARD steganography, the message extraction error rates of the proposed algorithm after JPEG compression decrease from about 50 % to nearly 0; compared with the current JPEG compression and detection resistant steganography algorithms, the proposed algorithm not only possesses the comparable JPEG compression resistant ability, but also has a stronger detection resistant performance and a higher operation efficiency.


Steganography JPEG compression resistant Detection resistant Dither modulation Side information 



This work was supported by the National Nature Science Foundation of China (Grant No. U1636219, 61379151, 61401512, and 61572052), the Excellent Youth Foundation of Henan Province of China (No. 144100510001).


  1. 1.
    Cheddad A, Condell J, Curran K et al (2010) Digital image steganography: Survey and analysis of current methods. Signal Process 90(3):727–752CrossRefzbMATHGoogle Scholar
  2. 2.
    Chen B, Wornell GW (2001) Quantization index Modulation: a class of provably good methods for digital watermarking and information embedding. IEEE Trans Inf Theory 47(4):1423–1443MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Denemark T, Fridrich J (2015) Side-informed steganography with additive distortion. In: Proceedings of International Workshop on Information Forensics and Security (WIFS), IEEE, pp 1–6Google Scholar
  4. 4.
    Filler T, Fridrich J (2011) Design of adaptive steganographic schemes for digital images. In: Proceedings of SPIE - The International Society for Optical Engineering, 7880:0F-0F-14Google Scholar
  5. 5.
    Filler T, Judas J, Fridrich J (2010) Minimizing Embedding Impact in Steganography Using Trellis-coded Quantization. In: Proceedings of SPIE - The International Society for Optical Engineering, (7541): 175-178Google Scholar
  6. 6.
    Filler T, Judas J, Fridrich J (2011) Minimizing additive distortion in steganography using syndrome-trellis codes. IEEE Trans Inf Forensics Secur 6(3):920–935CrossRefGoogle Scholar
  7. 7.
    Holub V, Fridrich J (2012) Designing steganographic distortion using directional filters. In: Proceedings of IEEE Workshop on Information Forensic and Security, pp 234–239Google Scholar
  8. 8.
    Holub V, Fridrich J (2013) Digital steganography using universal distortion function. In: Proceedings of the 15th ACM International Workshop on Information Hiding, pp 59–68Google Scholar
  9. 9.
    Kodovský J, Fridrich J (2009) Calibration revisited. In: Proceedings of the 11th Workshop on Multimedia and Security. ACM, pp 63–74Google Scholar
  10. 10.
    Katzenbeisser S, Petitcolas FA (2000) Information hiding techniques for steganography and digital watermarking. Artech House, IncGoogle Scholar
  11. 11.
    Luo W, Heileman GL, Pizano CE (2002) Fast and robust watermarking of JPEG files. In: Proceedings of the Fifth IEEE Southwest Symposium, pp 158–162Google Scholar
  12. 12.
    Liu W, Liu G, Dai Y (2015) Damage-resistance matrix embedding framework: the contradiction between robustness and embedding efficiency. Security and Communication Networks 8(9):1636–1647CrossRefGoogle Scholar
  13. 13.
    Li B, Wang M, Huang J et al (2014) A new cost function for spatial image steganography. In: Proceedings of International Conference on Image Processing (ICIP), IEEE, pp 4206–4210Google Scholar
  14. 14.
    Li C, Zhang Z, Wang Y et al (2015) Dither modulation of significant amplitude difference for wavelet based robust watermarking. Neurocomputing 166(C):404–415CrossRefGoogle Scholar
  15. 15.
    Maity SP, Maity S, Sil J et al (2013) Collusion Resilient Spread Spectrum Watermarking in M-band Wavelets Using GA-fuzzy Hybridization. J Syst Softw 86 (1):47–59CrossRefGoogle Scholar
  16. 16.
    Miyazaki A, Okamoto A (2002) Analysis of watermarking systems in the frequency domain and its application to design of robust watermarking systems. IEICE Trans Fundam Electron Commun Comput Sci 85(1):117–124Google Scholar
  17. 17.
    Pevný T, Bas P, Fridrich J (2010) Steganalysis by subtractive pixel adjacency matrix. IEEE Trans Inf Forensics Secur 5(2):215–224CrossRefGoogle Scholar
  18. 18.
    Pevný T, Fridrich J (2008) Multiclass detector of current steganographic methods for JPEG format. IEEE Trans Inf Forensics Secur 3(4):635–650CrossRefGoogle Scholar
  19. 19.
    Pevný T, Filler T, Bas P (2010) Using High-dimensional Image Models to Perform Highly Undetectable Steganography. In: Proceedings of the 12th ACM International Workshop on Information Hiding, pp 161–177Google Scholar
  20. 20.
    Sedighi V, Cogranne R, Fridrich J (2016) Content-Adaptive Steganography by minimizing statistical detectability. IEEE Trans Inf Forensics Secur 11(2):221–234CrossRefGoogle Scholar
  21. 21.
    Shen C, Zhang H, Feng D et al (2007) Survey of information security. Science in China Series F: Information Sciences 50(3):273–298CrossRefzbMATHGoogle Scholar
  22. 22.
    Tsai JS, Huang WB, Kuo YH (2011) On the selection of optimal feature region set for robust digital image watermarking. IEEE Trans Image Process 20(3):735–743MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Tsai J, Huang W, Kuo Y et al (2012) Joint Robustness and Security Enhancement for Feature-based Image Watermarking Using Invariant Feature Regions. Signal Process. 92(6):1431–1445CrossRefGoogle Scholar
  24. 24.
    Wang X, Niu P, Yang H et al (2014) A new robust color image watermarking using local quaternion exponent moments. Inf Sci 277:731–754CrossRefGoogle Scholar
  25. 25.
    Xiao J, Wang Y (2009) Adaptive dither modulation image watermarking algorithm. J Electron Inf Technol 31(3):552–555MathSciNetGoogle Scholar
  26. 26.
    Zhang Y, Luo X, Yang C et al (2015) A JPEG-compression Resistant Adaptive Steganography Based on Relative Relationship between DCT Coefficients. In: Proceedings of the IEEE 10th International Conference on Availability, Reliability and Security (ARES), pp 461–466Google Scholar
  27. 27.
    Zhang Y, Luo X, Yang C et al (2016) Joint JPEG compression and detection resistant performance enhancement for adaptive steganography using feature regions selection. Multimedia Tools and Applications, 1–20Google Scholar
  28. 28.
    Zhang Y, Luo X, Yang C et al (2016) A framework of adaptive steganography resisting JPEG compression and detection. Security and Communication Networks 9 (15):2957–2971CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Yi Zhang
    • 1
    • 2
  • Xiaodong Zhu
    • 1
    • 2
  • Chuan Qin
    • 3
  • Chunfang Yang
    • 1
    • 2
  • Xiangyang Luo
    • 1
    • 2
    Email author
  1. 1.State Key Laboratory of Mathematical Engineering and Advanced ComputingZhengzhouChina
  2. 2.Zhengzhou Science and Technology InstituteZhengzhouChina
  3. 3.School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghaiChina

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