Skip to main content
SpringerLink
Log in

Feature learning for steganalysis using convolutional neural networks

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

Abstract

Traditional steganalysis methods usually rely on handcrafted features. However, with the rapid development of advanced steganography, manual design of complex features has become increasingly difficult. In this paper, we propose a new paradigm for steganalysis based on the concept of feature learning. In our method, Convolutional Neural Network (CNN) is used to automatically learn features for steganalysis. To make CNN work better for steganalysis, we incorporate domain knowledge of steganalysis (i.e. enhancing stego noise and exploiting nearby dependencies) when designing the CNN architectures. We further propose to use model combination to boost the performance of CNN based method. Additionally, a cropping strategy is proposed to enable the CNN based model to deal with arbitrary input image sizes. We demonstrate the effectiveness of the proposed method against state-of-the-art spatial domain steganographic algorithms such as HUGO, WOW, S-UNIWARD, MiPOD, and HILL-CMD. To help understand the learned features from CNN, we provide visualizations of the learned filters and feature maps. Finally, we also provide quantitative analysis of the learned features from convolutional layers.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Atawneh S, Almomani A, Al Bazar H, Sumari P, Gupta B (2016) Secure and imperceptible digital image steganographic algorithm based on diamond encoding in dwt domain. Multimed Tools Appl 76(18):18451–18472

  2. Avcibas I, Memon N, Sankur B (2003) Steganalysis using image quality metrics. IEEE Trans Image Process 12(2):221–229

    Article  MathSciNet  Google Scholar 

  3. Bas P, Filler T, Pevnỳ T (2011) Break our steganographic system: the ins and outs of organizing boss. In: Information hiding. Springer, Berlin, pp 59–70

  4. Boureau YL, Ponce J, LeCun Y (2010) A theoretical analysis of feature pooling in visual recognition. In: Proceedings of the 27th international conference on machine learning (ICML-10), pp 111–118

  5. Browne M, Ghidary SS (2003) Convolutional neural networks for image processing: an application in robot vision. In: AI 2003: advances in artificial intelligence. Springer, Berlin, pp 641–652

  6. Cancelli G, Doërr G., Cox IJ, Barni M (2008) Detection of± 1 lsb steganography based on the amplitude of histogram local extrema. In: 15Th IEEE international conference on image processing. IEEE, Piscataway, pp 1288–1291

  7. Chen C, Shi YQ (2008) Jpeg image steganalysis utilizing both intrablock and interblock correlations. In: IEEE International symposium on circuits and systems. IEEE, Piscataway, pp 3029–3032

  8. Couchot JF, Couturier R, Guyeux C, Salomon M (2016) Steganalysis via a convolutional neural network using large convolution filters for embedding process with same stego key. arXiv:1605.07946

  9. Denemark T, Sedighi V, Holub V, Cogranne R, Fridrich J (2014) Selection-channel-aware rich model for steganalysis of digital images. In: 2014 IEEE international workshop on information forensics and security (WIFS). IEEE, Piscataway, pp 48–53

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

    Article  Google Scholar 

  11. Fridrich J, Kodovskỳ J, Holub V, Goljan M (2011) Steganalysis of content-adaptive steganography in spatial domain. In: Information hiding. Springer, Berlin, pp 102–117

  12. Geetha S, Sindhu SSS, Kamaraj N (2009) Blind image steganalysis based on content independent statistical measures maximizing the specificity and sensitivity of the system. Comput Secur 28(7):683– 697

    Article  Google Scholar 

  13. Girshick R (2015) Fast r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp 1440–1448

  14. Goljan M, Fridrich J, Holotyak T (2006) New blind steganalysis and its implications. In: Electronic imaging 2006. International society for optics and photonics, Bellingham, pp 607,201–607,201

  15. Gul G, Kurugollu F (2011) A new methodology in steganalysis: breaking highly undetectable steganograpy (hugo). In: Information hiding. Springer, Berlin, pp 71–84

  16. He FY, Chen TS, Zhong SP (2015) A classifier ensemble algorithm based on improved rsm for high dimensional steganalysis. Journal of Information Hiding and Multimedia Signal Processing 6(2):198–210

    Google Scholar 

  17. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  18. Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov RR (2012) Improving neural networks by preventing co-adaptation of feature detectors. arXiv:1207.0580

  19. Holotyak T, Fridrich J, Voloshynovskiy S (2005) Blind statistical steganalysis of additive steganography using wavelet higher order statistics. In: Communications and multimedia security, vol. 3677, pp. 273–274

  20. Holub V, Fridrich J (2012) Designing steganographic distortion using directional filters. In: The IEEE international workshop on information forensics and security (WIFS), pp 234–239

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

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

    Article  Google Scholar 

  23. Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv:1502.03167

  24. Jarrett K, Kavukcuoglu K, Lecun Y et al. (2009) What is the best multi-stage architecture for object recognition?. In: 2009 IEEE 12Th international conference on computer vision. IEEE, Piscataway, pp 2146–2153

  25. Karpathy A, Toderici G, Shetty S, Leung T, Sukthankar R, Fei-Fei L (2014) Large-scale video classification with convolutional neural networks. In: IEEE Conference on computer vision and pattern recognition, pp 1725–1732

  26. Ker AD, Böhme R. (2008) Revisiting weighted stego-image steganalysis. In: Electronic imaging 2008. International society for optics and photonics, Bellingham, pp 681,905–681,905

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

    Article  Google Scholar 

  28. Krizhevsky A (2012) cuda-convnet. Http://code.google.com/p/cuda-convnet/

  29. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  30. LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    Article  Google Scholar 

  31. Li B, Wang M, Li X, Tan S, Huang J (2015) A strategy of clustering modification directions in spatial image steganography. IEEE Trans Inf Forensics Secur 10(9):1905–1917

    Article  Google Scholar 

  32. Lin M, Chen Q, Yan S (2013) Network in network. arXiv:1312.4400

  33. Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) Ssd: Single shot multibox detector. In: European conference on computer vision. Springer, Berlin, pp 21–37

  34. Lyu S, Farid H (2003) Detecting hidden messages using higher-order statistics and support vector machines. In: Information hiding. Springer, Berlin, pp 340–354

  35. Ngiam J, Khosla A, Kim M, Nam J, Lee H, Ng AY (2011) Multimodal deep learning. In: Proceedings of the 28th international conference on machine learning (ICML-11), pp 689–696

  36. Pevny T, Bas P, Fridrich J (2010) Steganalysis by subtractive pixel adjacency matrix. IEEE Trans Inf Forensics Secur 5(2):215–224

    Article  Google Scholar 

  37. Pevnỳ T, Filler T, Bas P (2010) Using high-dimensional image models to perform highly undetectable steganography. In: Information hiding. Springer, Berlin, pp 161–177

  38. 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 source-mismatch. In: EI: electronic imaging

  39. Qian Y, Dong J, Wang W, Tan T (2015) Deep learning for steganalysis via convolutional neural networks. In: ISAndamp;t/SPIE electronic imaging, pp 94,090j–94,090j

  40. Qian Y, Dong J, Wang W, Tan T (2016) Learning and transferring representations for image steganalysis using convolutional neural network. In: 2016 IEEE international conference on Image processing (ICIP). IEEE, Piscataway, pp 2752–2756

  41. Qian Y, Dong J, Wang W, Tan T (2016) Learning representations for steganalysis from regularized cnn model with auxiliary tasks. In: Proceedings of the 2015 international conference on communications, signal processing, and systems. Springer, Berlin, pp 629–637

  42. Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. In: Advances in neural information processing systems, pp 91–99

  43. Sedighi V, Cogranne R, Fridrich J (2016) Content-adaptive steganography by minimizing statistical detectability. IEEE Trans Inf Forensics Secur 11(2):221–234

    Article  Google Scholar 

  44. Shi YQ, Chen C, Chen W (2007) A markov process based approach to effective attacking jpeg steganography. In: Information hiding. Springer, Berlin, pp 249–264

  45. Shi YQ, Sutthiwan P, Chen L (2013) Textural features for steganalysis. In: Information hiding. Springer, Berlin, pp 63–77

  46. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556

  47. Tan S, Li B (2014) Stacked convolutional auto-encoders for steganalysis of digital images. In: Signal and information processing association annual summit and conference (APSIPA), 2014 asia-pacific. IEEE, Piscataway, pp 1–4

  48. Tang W, Li H, Luo W, Huang J (2014) Adaptive steganalysis against wow embedding algorithm. In: Proceedings of the 2nd ACM workshop on information hiding and multimedia security, pp 91– 96

  49. Wu S, Zhong SH, Liu Y (2016) Steganalysis via deep residual network. In: 2016 IEEE 22nd international conference on Parallel and distributed systems (ICPADS). IEEE, Piscataway, pp 1233–1236

  50. Xia Z, Wang X, Sun X, Wang B (2014) Steganalysis of least significant bit matching using multi-order differences. Security and Communication Networks 7 (8):1283–1291

    Article  Google Scholar 

  51. Xia Z, Wang X, Sun X, Liu Q, Xiong N (2016) Steganalysis of lsb matching using differences between nonadjacent pixels. Multimed Tools Appl 75 (4):1947–1962

    Article  Google Scholar 

  52. Xu G, Wu HZ, Shi YQ (2016) Ensemble of cnns for steganalysis: an empirical study. In: Proceedings of the 4th ACM workshop on information hiding and multimedia security. ACM, New York, pp 103– 107

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

    Article  Google Scholar 

  54. Xuan G, Shi YQ, Gao J, Zou D, Yang C, Zhang Z, Chai P, Chen C, Chen W (2005) Steganalysis based on multiple features formed by statistical moments of wavelet characteristic functions. In: Information hiding. Springer, Berlin, pp 262–277

  55. Yuan C, Xia Z, Sun X (2017) Coverless image steganography based on sift and bof. Journal of Internet Technology 18(2):435–442

    Google Scholar 

Download references

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant 61772529, 61303262, 61502496, U1536120, and U1636201, and in part by the National Key Research and Development Program of China under Grant 2016YFB1001003.

Author information

Authors and Affiliations

  1. Department of Automation, University of Science and Technology of China, 96 JinZhai Road, 230026, Hefei, China

    Yinlong Qian

  2. Center for Research on Intelligent Perception and Computing, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, 100190, Beijing, China

    Yinlong Qian, Jing Dong, Wei Wang & Tieniu Tan

  3. State Key Laboratory of Cryptology, P.O. Box 5159, Beijing, 100878, China

    Wei Wang

Authors
  1. Yinlong Qian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jing Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tieniu Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jing Dong.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Qian, Y., Dong, J., Wang, W. et al. Feature learning for steganalysis using convolutional neural networks. Multimed Tools Appl 77, 19633–19657 (2018). https://doi.org/10.1007/s11042-017-5326-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-017-5326-1

Keywords

Search

Navigation