Steganographic coding scheme based on dither convolutional trellis under resampling mechanism

Pengcheng Cao; Weiwei Liu; Guangjie Liu; Jiangtao Zhai; Xiaopeng Ji; Yuewei Dai; Pengcheng Cao; Weiwei Liu; Guangjie Liu; Jiangtao Zhai; Xiaopeng Ji; Yuewei Dai

doi:10.3934/mbe.2019301

Mathematical Biosciences and Engineering

2019, Volume 16, Issue 5: 6015-6033. doi: 10.3934/mbe.2019301

Previous Article Next Article

Research article Special Issues

Steganographic coding scheme based on dither convolutional trellis under resampling mechanism

1.
The School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China
2.
The School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210094, China

Received: 25 January 2019 Accepted: 09 June 2019 Published: 29 June 2019

With the rapid development of mobile internet and cloud computing, numerous digital me-dia files in mobile social networking and media sharing software have become the important carriers of steganography. However, these digital media files may be resampled by the media server when being pushed to the intelligent mobile terminals. The resampling of digital media files is a transfor-mation which enlarges or shrinks objects by a scale factor that is the same in all dimensions. In order to reduce embedding distortion while ensuring the correct extraction of secret messages under resam-pling mechanism, a steganographic coding scheme based on dither convolutional trellis is proposed in this paper. The resampling mapping is estimated with finite sample pairs. The resampling stego media files with secret messages embedded are generated from the estimated resampling cover media files by syndrome-trellis codes (STCs). According to the estimated resampling mapping, the dither convolutional trellis for one dimensional resampling is constructed to generate the source stego me-dia files from source cover media files and resampling stego media files. The steganographic coding scheme is also extended to the circumstance of two dimensional resampling such as image scaling. The experimental results show that the proposed steganographic scheme can achieve less embedding dis-tortion while ensuring the accuracy of secret messages extraction under multi-dimensional resampling mechanism.
- steganographic coding,
- multi-dimensional resampling,
- resampling mapping estimation,
- dither convolution trellis
Citation: Pengcheng Cao, Weiwei Liu, Guangjie Liu, Jiangtao Zhai, Xiaopeng Ji, Yuewei Dai. Steganographic coding scheme based on dither convolutional trellis under resampling mechanism[J]. Mathematical Biosciences and Engineering, 2019, 16(5): 6015-6033. doi: 10.3934/mbe.2019301

Related Papers:

Abstract

With the rapid development of mobile internet and cloud computing, numerous digital me-dia files in mobile social networking and media sharing software have become the important carriers of steganography. However, these digital media files may be resampled by the media server when being pushed to the intelligent mobile terminals. The resampling of digital media files is a transfor-mation which enlarges or shrinks objects by a scale factor that is the same in all dimensions. In order to reduce embedding distortion while ensuring the correct extraction of secret messages under resam-pling mechanism, a steganographic coding scheme based on dither convolutional trellis is proposed in this paper. The resampling mapping is estimated with finite sample pairs. The resampling stego media files with secret messages embedded are generated from the estimated resampling cover media files by syndrome-trellis codes (STCs). According to the estimated resampling mapping, the dither convolutional trellis for one dimensional resampling is constructed to generate the source stego me-dia files from source cover media files and resampling stego media files. The steganographic coding scheme is also extended to the circumstance of two dimensional resampling such as image scaling. The experimental results show that the proposed steganographic scheme can achieve less embedding dis-tortion while ensuring the accuracy of secret messages extraction under multi-dimensional resampling mechanism.

References

[1]	L. Li, X. Yuan, Z. Lu, et al., Rotation invariant watermark embedding based on scale-adapted characteristic regions, Inform. Sci., 180(2010), 2875–2888.
[2]	M. Amirmazlaghani, M. Rezghi and H. Amindavar, A novel robust scaling image watermarking scheme based on Gaussian Mixture Model, Expert Systems Appl., 42(2014), 1960–1971.
[3]	B. K. Vivekananda, I. Sengupta and A. Das, An adaptive audio watermarking based on the singular value decomposition in the wavelet domain, Digit. Signal Process., 20(2010), 1547–1558.
[4]	L. Li, J. Qian and J. S. Pan, Characteristic region based watermark embedding with RST invariance and high capacity, AEUE - Int. J. Electron. C., 65(2011), 435–442.
[5]	X. Wang, P. Wang, Z. Peng, et al., A norm-space, adaptive, and blind audio watermarking algorithm by discrete wavelet transform, Signal Process., 93(2013), 913–922.
[6]	D. Avci, T. Tuncer and E. Avci, A new information hiding method for audio signals, Digit. Forens. Secur., (2018), 1–4.
[7]	M. A. Nematollahi, S. A. R. Al-Haddad and F. Zarafshan, Blind digital speech watermarking based on Eigen-value quantization in DWT, J. King Saud University - Computer and Inform. Sci., 27(2015), 58–67.
[8]	B. Lei, I. Song and S. A. Rahman, Robust and secure watermarking scheme for breath sound, J. Systems Software, 86(2015), 80–94.
[9]	B. Lei, Z. Feng, E. L. Tan, et al., Optimal and secure audio watermarking scheme based on self-adaptive particle swarm optimization and quaternion wavelet transform, Signal Process., 113(2015), 80–94.
[10]	B. Han and J. Li, Medical image watermarking in sub-block three-dimensional discrete cosine transform domain, Int. J. Bioautomat., (2016).
[11]	X. Jia, Y. Qi, L. Shao, et al., A watermark algorithm based on SVD and image geometric correc-tion, Int. Conference Systems Inform., (2012).
[12]	M. Nutzinger and J. Wurzer, A novel phase coding technique for steganography in auditive media, ARES, 6(2011), 91–98.
[13]	K. U. Singh, A survey on audio steganography approaches, Int. J. Computer Appl., 95(2014), 7–14.
[14]	J. Zhao, Robust image watermarking algorithm based on radon and analytic Fourier-Mellin transforms, Open Automat. Control Systems J., 7(2015), 1071–1074.
[15]	Y. Zhang, X. Luo, Y. Guo, et al., Zernike moment-Based spatial image steganography resisting scaling attack and statistic detection, IEEE Access, (2019).
[16]	J. Wang, T. Li, X. Luo, et al., Identifying computer generated images based on quaternion central moments in color quaternion wavelet domain, IEEE Transact. Circu. Syst. Video Technol., DOI: 10.1109/TCSVT.2018.2867786.
[17]	T. Filler, J. Judas and J. Fridrich, Minimizing embedding impact in steganography using trellis-coded quantization, IS&T/SPIE Electron. Imag., (2010).
[18]	T. Filler, J. Judas and J. Fridrich, Minimizing additive distortion in steganography using syndrome-trellis codes, IEEE Transact. Inform. Forens. Secur., 6(2011), 920–935.
[19]	R. G. Keys, Cubic convolution interpolation for digital image processing, IEEE Transact. Acoust. Speech Signal Process., 29(1981), 1153–1160.
[20]	T. M. Lehmann, C. Gnner and K. Spitzer, Survey: Interpolation methods in medical image processing, IEEE Transact. Med. Imag., 18(1999), 1049–1075.
[21]	J. Shi and S. E. Reichenbach, Image interpolation by two-dimensional parametric cubic convolu-tion, IEEE Transact. Image Process., 15(2006), 1857–1870.
[22]	J. A. Parker, R. V. Kenyon and D. Troxel, Comparison of Interpolating Methods for Image Resampling, IEEE Transact. Med. Imag., 2(1983), 31–39.
[23]	W. Liu, G. Liu and Y. Dai, Damageresistance matrix embedding framework: the contradiction between robustness and embedding efficiency, Secur. Commun. Networks, 8(2015), 1636–1647.
[24]	Y. Long and Y. Huang, Image based source camera identification using demosaicking, IEEE Workshop Mult. Signal Process., (2016), 419–424.
[25]	A. Swaminathan, M. Wu and K. J. R. Liu, Nonintrusive component forensics of visual sensors using output images, IEEE Transact. Inform. Forens. Secur., 2(2017), 91–106.
[26]	P. Bas, T. Filler and T. Pevn, Break Our Steganographic System: The Ins and Outs of Organizing BOSS, J. Am. Statist. Associat., 96(2011), 488–499.
[27]	V. Holub, J. Fridrich and T. Denemark, Universal distortion function for steganography in an arbitrary domain, EURASIP J. Inform. Secur., 2014(2014), 1–13.
[28]	T. Denemark, V. Sedighi, V. Holub, et al., Selection-channel-aware rich model for steganalysis of digital images, IEEE Int. Workshop Inform. Forens. Secur., (2014), 48–53.
[29]	J. Kodovsk, J. Fridrich and V. Holub, Ensemble classifiers for steganalysis of digital media, IEEE Transact. Inform.n Forens. Secur., 7(2012), 432–444.

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)