Skip to main content
SpringerLink
Log in

A novel steganalysis method with deep learning for different texture complexity images

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Most steganalysis methods based on deep learning don’t distinguish the image texture complexity, but rather to mix all images for training. As a result, the differences of image content between images with different texture complexities are larger than the differences caused by steganographic signal, which is unfavourable for extracting the effective stegananalysis features. In order to reduce the influence of image content difference on steganalysis performance, this paper propose a steganalyais framework adopting the method of training model separately on dividing data sets. Firstly, according to the image texture feature, some correlated statistical features of gray level co-occurrence matrix are used as a measure index of texture complexity, and the data set is divided into several subsets of different complexity according to the index. Secondly, for images with different texture complexities, the corresponding steganalysis models are constructed and trained by using Most Effective Region (MER) and Inception ideas, respectively. For an image to be detected, its texture complexity is calculated first and the corresponding model is used for detection. Finally, an ensemble learning method is used to further improve the framework precision. The experimental results show that our proposed steganalysis method outperforms the handcrafted-features-based steganalysis methods and several CNN-based methods in detection accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Amirkhani H, Rahmati M (2011) New framework for using image contents in blind steganalysis systems. J Electron Imag 20(1):013016

    Article  Google Scholar 

  2. Cho S, Cha BH, Wang J et al (2013) Block-based image steganalysis: algorithm and performance evaluation. J Vis Commun Image Represent 24(7):846–856

    Article  Google Scholar 

  3. Deng G, Zhao XF, Huang W, Sheng RN (2012) Image texture complexity estimation approach for steganography evaluation. Comput Eng 38(14):116–118

    Google Scholar 

  4. Dhanawe S, Hanchate DB (2014) Tamper detection in database using forensic analysis. Int J Adv Trends Comput Sci Eng 3(6):2319–7242

    Google Scholar 

  5. Dixit A, Dixit R (2017) A review on digital image watermarking techniques. Int J Image Graph Sign Process 9(4):56–66

    Article  Google Scholar 

  6. Filler T, Judas J, Fridrich J (2012) Minimizing additive distortion in steganography using syndrome-trellis codes. IEEE Trans Inform Foren Sec 6(3):920–935

    Article  Google Scholar 

  7. Fridrich J, Filler T (2007) Practical methods for minimizing embedding impact in steganography. Proc SPIE - Int Soc Optic Eng 6505:650502–650502-15

    Article  Google Scholar 

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

    Article  Google Scholar 

  9. He K, Zhang X, Ren S, Sun, J (2016) Deep residual learning for image recognition. Proc IEEE Conf Comput Vision Pattern Recogn Las Vegas: 770–778

  10. Holub V, Fridrich J (2013) Digital image steganography using universal distortion. ACM workshop on information hiding and multimedia security (pp.59-68). ACM

  11. Holub V, Fridrich J (2012) Designing steganographic distortion using directional filters. IEEE Int Workshop Inform Foren Sec 2(4):234–239

    Google Scholar 

  12. Hu D, Shen Q, Zhou S, Liu X, Fan Y, Wang L (2017) Adaptive steganalysis based on selection region and combined convolutional neural networks. Sec Commun Netw 2017(4):1–9

    Article  Google Scholar 

  13. Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. Proc 32nd Int Conf Mach Learn Lille: 448–456

  14. Juarez-Sandoval O, Cedillo-Hernandez M, Nakano-Miyatake M et al. (2016) Image-adaptive steganalysis for LSB matching steganography. Proc 39th Int Conf Telecommun Sign Process, Vienna: 478–483

  15. Liu T, Wang S (2014) A steganalyis scheme based on improved local texture features. Inform Sec Commun Privacy 4:96–102

    Google Scholar 

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

    Article  Google Scholar 

  17. Liu X, Deng R, Choo K K R, et al (2018) Privacy-preserving outsourced calculation toolkit in the cloud. IEEE Trans Depend Secure Comput (99):1–1

  18. Luo X, Song X, Li X et al (2016) Steganalysis of HUGO steganography based on parameter recognition of syndrome-trellis-codes. Multimed Tools Appl 75(21):13557–13583

    Article  Google Scholar 

  19. Ma Y, Luo X, Li X, et al (2018) Selection of rich model Steganalysis features based on decision rough set α-positive region reduction. IEEE Trans Circ Syst Video Technol (99): 1–1

  20. Mao JF (2010) Shangrao: A novel general Steganalysis technique based on cover image describing. Chin J Comput 33(33):569–579

    Article  Google Scholar 

  21. Pevný T, Filler T, Bas P (2010) Using high-dimensional image models to perform highly undetectable steganography. Lect Notes Comput Sci 6387:161–177

    Article  Google Scholar 

  22. Pibre L, Pasquet J, Ienco D, Chaumont M (2016) Deep learning is a good steganalysis tool when embedding key is reused for different images, even if there is a cover sourcemismatch. Electron Imag 4(8):1–11

    Google Scholar 

  23. Qian Y, Dong J, Wang W, Tan T (2015) Deep learning for steganalysis via convolutional neural networks. SPIE Med Watermark Sec Foren 9409

  24. Sedighi V, Cogranne R, Fridrich J (2015) Content-adaptive steganography by minimizing statistical detectability. IEEE Trans Inform Foren Sec 11(2):221–234

    Article  Google Scholar 

  25. Sedighi V, Cogranne R, Fridrich J (2016) Content-adaptive steganography by minimizing statistical detectability. IEEE Trans Inf Forensic Sec 1(2):221–234

    Article  Google Scholar 

  26. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S et al (2015) Going deeper with convolutions. Proceedings of the 28th IEEE conference on computer vision and pattern recognition, Boston 1–9

  27. Tang W, Li H, Luo W, Huang J (2014) Adaptive steganalysis against WOW embedding algorithm. Proc 2nd ACM Workshop Inform Hiding Multimed Sec. Salzburg, 91–96

  28. Wang R, Xu MK, Ping XJ, Zhang T (2014) Steganalysis of spatial images based on segmentation. Acta Automat Sin 40(12):2936–2943

    MATH  Google Scholar 

  29. Wang R, Xu M, Ping X et al (2015) Steganalysis of JPEG images by block texture based segmentation. Multimed Tools Appl 74(15):5725–5746

    Article  Google Scholar 

  30. Wang LN, Tan XZ, Xu YB, Zhai LM (2017) An adaptive JPEG image steganography based on texture complexity. J Wuhan Univ 63(5):421–426

    Google Scholar 

  31. Wu S, Zhong S, Liu Y (2018) Deep residual learning for image steganalysis. Multimed Tools Appl 77(9):10437–10453

    Article  Google Scholar 

  32. Wu S, Zhong S, Liu Y (2017) Residual convolution network based steganalysis with adaptive content suppression. IEEE International Conference on Multimedia and Expo. pp 241–246

  33. Xu G, Wu HZ, Shi YQ (2016) Structural design of convolutional neural networks for steganalysis. IEEE Sign Process Lett 23(5):708–712

    Article  Google Scholar 

  34. Ye J, Ni J, Yi Y (2017) Deep learning hierarchical representations for image Steganalysis. IEEE Trans Inform Foren Sec 12(11):2545–2557

    Article  Google Scholar 

  35. Yuan Y, Lu W, Feng B, Weng J (2017) Steganalysis with CNN using multi-channels filtered residuals. Proc 3rd Int Conf Cloud Comput Sec, Nanjing, 110–120

  36. Zhang Y, Qin C, Zhang W et al (2018) On the fault-tolerant performance for a class of robust image steganography. Signal Process 146:99–111

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China

    Shangping Zhong, Cunmin Jia, Kaizhi Chen & Peng Dai

  2. University Key Laboratory of Information Security of Network Systems (Fuzhou University), Fuzhou, 350116, Fujian Province, China

    Shangping Zhong, Cunmin Jia, Kaizhi Chen & Peng Dai

Authors
  1. Shangping Zhong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cunmin Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kaizhi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Peng Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shangping Zhong.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhong, S., Jia, C., Chen, K. et al. A novel steganalysis method with deep learning for different texture complexity images. Multimed Tools Appl 78, 8017–8039 (2019). https://doi.org/10.1007/s11042-018-6573-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6573-5

Keywords

Search

Navigation