Skip to main content
SpringerLink
Log in

JPEG steganalysis with combined dense connected CNNs and SCA-GFR

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

Abstract

The detection of weakly hidden information in a JPEG compressed image is challenging. In this paper, we propose a 32-layer convolutional neural network (CNN) involving feature reuse by concatenating all features from previous layers. The proposed method can improve the flow of gradient, and the sharing of features and bottleneck layers can also dramatically reduce the number of parameters in the proposed CNN model. To further improve the detection accuracy and combine the directional features from the selection-channel-aware Gabor filtering residual (SCA-GFR) method with Gabor filtering and non-directional feature maps from the CNN model, an ensemble architecture called CNN-SCA-GFR is used, which combines the proposed CNN method with the conventional SCA-GFR method to detect J-UNIWARD and UERD. This can significantly reduce the detection error rate to below that of the existing JPEG steganalysis methods. For example, in the detection of J-UNIWARD at 0.1 bpnzAC, the detection error rate using our proposed method is 5.67% lower than that achieved by XuNet, and 7.89% lower than that achieved by the conventional SCA-GFR method. When detecting UERD at 0.1 bpnzAC, the detection error rate using our proposed method is 5.94% lower than that achieved by XuNet, and 10.28% lower than that achieved by the conventional SCA-GFR method.

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

Similar content being viewed by others

References

  1. Bas P, Furon T (2007) Bows-2. http://bows2.ec-lille.fr. Accessed 20 Nov 2018

  2. Bas P, Filler T, Pevný T (2011) Break our steganographic system: the ins and outs of organizing BOSS. In: Proceedings of the 2011 International Workshop on Information Hiding, pp 59–70

    Chapter  Google Scholar 

  3. Chen M, Sedighi V, Boroumand M, Fridrich J (2017) JPEG-phase-aware convolutional neural network for steganalysis of JPEG images. In: Proceedings of the 5th ACM Workshop on Information Hiding and Multimedia Security, pp 75–84

  4. Chen Y, Lyu Z, Kang X, Wang ZJ (2018) A rotation invariant convolutional neural network for image enhancement forensics. In: Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2108), pp 2111–2115

  5. Denemark TD, Boroumand M, Fridrich J (2016) Steganalysis features for content-adaptive JPEG steganography. IEEE Transactions on Information Forensics and Security 11(8):1736–1746

    Article  Google Scholar 

  6. Guo L, Ni J, Shi YQ (2014) Uniform embedding for efficient JPEG steganography. IEEE Transactions on Information Forensics and Security 9(5):814–825

    Article  Google Scholar 

  7. Guo L, Ni J, Su W, Tang C, Shi YQ (2015) Using statistical image model for JPEG steganography: uniform embedding revisited. IEEE Transactions on Information Forensics and Security 10(12):2669–2680

    Article  Google Scholar 

  8. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp 770–778

  9. Holub V, Fridrich J (2015) Low-complexity features for JPEG steganalysis using undecimated DCT. IEEE Transactions on Information Forensics and Security 10(2):219–228

    Article  Google Scholar 

  10. Holub V, Fridrich J (2015) Phase-aware projection model for steganalysis of JPEG images. In: Proceedings of 2015 Media Watermarking, Security, and Forensics. International Society for Optics and Photonics, 9409: 94090T

  11. Holub V, Fridrich J, Denemark T (2014) Universal distortion function for steganography in an arbitrary domain. EURASIP J Inf Secur 2014(1):1–16

    Article  Google Scholar 

  12. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 1(2), pp 3

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

    Google Scholar 

  14. Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R et al (2014) Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp 675–678

  15. Kodovsky J, Fridrich J (2012) Steganalysis of JPEG images using rich models. In: Proc. SPIE Int. Society for Optics and Photonics Engineering, Burlingame, 8303A, pp 1–13

  16. Kodovský J, Fridrich JJ, Holub V (2012) Ensemble classifiers for steganalysis of digital media. IEEE Transactions on Information Forensics and Security 7(2):432–444

    Article  Google Scholar 

  17. Kuncheva LI, Whitaker CJ (2003) Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Mach Learn 51(2):181–207

    Article  Google Scholar 

  18. Liu K, Yang J, Kang X (2017) Ensemble of CNN and rich model for steganalysis. In: Systems, Signals and Image Processing (IWSSIP), 2017 International Conference on, pp 1–5

  19. Ma Y, Luo X, Li X, Bao Z, Zhang Y (2018) Selection of rich model steganalysis features based on decision rough set α-positive region reduction. IEEE Transactions on Circuits and Systems for Video Technology. Accepted, early access:. https://doi.org/10.1109/TCSVT.2018.2799243

  20. Qian Y, Dong J, Wang W, Tan T (2016) Learning and transferring representations for image steganalysis using convolutional neural network. In: Proceedings of Image Processing (ICIP), 2016 IEEE International Conference, pp 2752–2756

  21. Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Berg AC (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115(3):211–252

    Article  MathSciNet  Google Scholar 

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

  23. Xu G (2017) Deep convolutional neural network to detect J-UNIWARD. In: Proceedings of the 5th ACM Workshop on Information Hiding and Multimedia Security, pp 67–73

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

    Article  Google Scholar 

  25. 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, pp 103–107

  26. Yang J, Liu K, Kang X, Wong E, Shi Y (2017) Steganalysis based on awareness of selection-channel and deep learning. In: Proceedings of 2017 International Workshop on Digital Watermarking, pp 263–272

    Chapter  Google Scholar 

  27. Ye J, Ni J, Yi Y (2017) Deep learning hierarchical representations for image steganalysis. IEEE Transactions on Information Forensics and Security 12(11):2545–2557

    Article  Google Scholar 

  28. Zeng J, Tan S, Li B, Huang J (2016) Large-scale JPEG steganalysis using hybrid deep-learning framework. arXiv preprint arXiv:1611.03233

    Google Scholar 

Download references

Acknowledgments

This work was supported by NSFC (Grant Nos. U1536204, NSFC 61772571) and special funding for basic scientific research from Sun Yat-sen University (Grant No.6177060230).

Author information

Authors and Affiliations

  1. Guangdong Key Lab of Information Security, School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, 510006, China

    Jianhua Yang & Xiangui Kang

  2. Computer Science and Engineering Department, New York University, New York, NY, USA

    Edward K. Wong

  3. Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ, 07102, USA

    Yun-Qing Shi

Authors
  1. Jianhua Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiangui Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Edward K. Wong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yun-Qing Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiangui Kang.

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

Yang, J., Kang, X., Wong, E.K. et al. JPEG steganalysis with combined dense connected CNNs and SCA-GFR. Multimed Tools Appl 78, 8481–8495 (2019). https://doi.org/10.1007/s11042-018-6878-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6878-4

Keywords

Search

Navigation