Mathematical Biosciences and Engineering, 2019, 16(5): 4069-4081. doi: 10.3934/mbe.2019201 Research article Special Issues

Export file:

Format

RIS(for EndNote,Reference Manager,ProCite)
BibTex
Text

Content

Citation Only
Citation and Abstract

A new JPEG image steganalysis technique combining rich model features and convolutional neural networks

Tao Zhang, Hao Zhang, Ran Wang, Yunda Wu

1 School of Computer Science and Engineering, Changshu Institute of Technology, No.99, Hushan Road, Changshu 215500, Jiangsu Province, China
2 National Digital Switching System Engineering and Technology Research Center, No.62, Kexue Avenue, Zhengzhou 450001, Henan Province, China

Received: , Accepted: , Published:

Special Issues: Security and Privacy Protection for Multimedia Information Processing and communication

Abstract Full Text(HTML) Figure/Table Related pages

The best traditional steganalysis methods aiming at adaptive steganography are the combination of rich models and ensemble classifier. In this study, a new steganalysis method for JPEG images based on convolutional neural networks is proposed to solve the high dimension problem in steganalysis from another aspect. On the basis of the original rich model, the algorithm adds different sizes of discrete cosine transform (DCT) basis functions to extract different detection features. Different features are combined at the fully connected layer through inputting 2-D feature values to the neural network convolutional layer for predictive classification. Experimental results show that convolutional neural networks as classifiers do not require a large number of training samples, and the final classification performance is better than that of the original ensemble classifier.

Figure/Table

Supplementary

Article Metrics

References

1. V. Holub and J. Fridrich, Low-complexity features for jpeg steganalysis using undecimated DCT, IEEE Trans. Inf. Fore. Secu., 10(2015), 219–228.

2. J. Fridrich and J. Kodovsky, Rich models for steganalysis of digital images, IEEE Trans. Inf. Fore. Secu., 7(2012), 868–882.

3. V. Holub and J. Fridrich, Phase-aware projection model for steganalysis of JPEG images, Proc. SPIE, 9409(2015), 1–11.

4. J. Kodovsky, J. Fridrich and V. Holub, Ensemble classifiers for steganalysis of digital media, IEEE Trans. Inf. Fore. Secu., 7(2012), 432–444.

5. J. Fridrich, Steganalysis in high dimensions: fusing classifiers built on random subspaces, Proc. SPIE, 7880(2011), 181–197.

6. V. Holub and J. Fridrich, Random projections of residuals for digital image steganalysis, IEEE Trans. Inf. Fore. Secu., 8(2013), 1996–2006.

7. J. Kodovský and J. Fridrich, Steganalysis in high dimensions: fusing classifiers built on random subspaces, Proc. SPIE, 7880(2011), 1–13.

8. S. Tan and B. Li, Stacked convolutional auto-encoders for steganalysis of digital images, Proc. IEEE APSIPA, Siem Reap, Cambodia, (2014), 1–4.

9. T. Pevný, T. Filler and P. Bas, Using high-dimensional image models to perform highly undetectable steganography, Proc. IHW, (2010), 161–177.

10. Y. Qian, J. Dong and W. Wang, Deep learning for steganalysis via convolutional neural networks, Proc. SPIE, 9409(2015), 1–10.

11. V. Holub and J. Fridrich, Designing steganographic distortion using directional filters, Proc. IEEE IFS, (2012), 234–239.

12. V. Holub, J. Fridrich and T. Denemark, Universal distortion function for steganography in an arbitrary domain, EURASIP J. Info. Sec., 2014(2014), 1–13.

13. L. Pibre L, J. Pasquet and D. Ienco, Deep learning is a good steganalysis tool when embedding key is reused for different images, even if there is a cover sourcemismatch, Proc. EI'2016, (2016), 79–95.

14. Mo Chen, V. Sedighi, M. Boroumand, et al., JPEG-phase-aware convolutional neural network for steganalysis of JPEG images, Proc. IH MMSec, (2017), 1–10.

15. J. Zeng, S. Tan, B. Li, et al., Large-scale JPEG image steganalysis using hybrid deep-learning framework, IEEE Trans. Info. Forens. Secu., 13(2018), 1200–1214.

16. G. Xu, Deep convolutional neural network to detect JUNIWARD, Proc. IH MMSec, (2017), 1–6.

17. M. Boroumand, M. Chen and J. Fridrich, Deep residual network for steganalysis of digital images. IEEE Trans. Info. Fore. Secu., 14(2019), 1181–1193.

18. L. Chang, X. Den and M. Zhou, Convolution neural networks in image understanding, Acta Automat. Sinica, 42(2016), 1300–1312.

19. X. Glorot and Y. Bengio, Understanding the difficulty of training deep feedforward neural networks, J. Mach. Lear. Rese., 9(2010), 249–256.

20. S. Ioffe and C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift, Proc. ICML, (2015), 448–456.

21. N. Srivastava, G. Hinton and A. Krizhevsky, Dropout: a simple way to prevent neural networks from overfitting, J. Mach. Lear. Rese., 15(2014), 1929–1958.

22. L. Guo, J. Ni and Y. Shi, Uniform embedding for efficient JPEG steganography, IEEE Trans. Info. Fore. Secu., 9(2014), 814–825.

23. J. Bergstra, O. Breuleux and F. Bastien, Theano: A CPU and GPU math compiler in Python, Proc. PSC, (2010), 1–7.

Download full text in PDF

Export Citation

Article outline

Show full outline