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

Tao Zhang; Hao Zhang; Ran Wang; Yunda Wu; Tao Zhang; Hao Zhang; Ran Wang; Yunda Wu

doi:10.3934/mbe.2019201

Mathematical Biosciences and Engineering

2019, Volume 16, Issue 5: 4069-4081. doi: 10.3934/mbe.2019201

Previous Article Next Article

Research article Special Issues

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

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: 21 January 2019 Accepted: 29 April 2019 Published: 07 May 2019

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.
- Steganalysis,
- rich model,
- ensemble classifier,
- convolutional neural networks
Citation: Tao Zhang, Hao Zhang, Ran Wang, Yunda Wu. A new JPEG image steganalysis technique combining rich model features and convolutional neural networks[J]. Mathematical Biosciences and Engineering, 2019, 16(5): 4069-4081. doi: 10.3934/mbe.2019201

Related Papers:

Abstract

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.

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.

Reader Comments

Your name:*

Email:*
© 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)