基于高维特征正则化的JPEG图像隐写分析

doi:10.3969/j.issn.0255-8297.2016.05.008

Abstract

Abstract:

Steganalysis plays a vital role in information security. To improve recognition accuracy of JPEG stego-image, we establish a model of scatter matrix eigenspectrum, and purpose a new method to transfer training features of cover and stego images. The eigenspectrum is first modeled by its distribution and divided into three non-overlapping partitions: areas in which the eigenvalue drops rapidly, areas with stable eigenvalues, and areas of zero eigenvalue. The eigenvector is whitened and regularized in the three partitions with adaptive regularization. The result of these steps is a transfer matrix that transfers the input features. The features are now processed and extracted by picking the first t eigenvectors. The processed feature data are then trained with a Fisher linear discriminant (FLD) ensemble classifier. The result shows that recognition accuracy of JPEG stegoimages with the FLD ensemble is improved after the JPEG stego-image features are sorted, regularized and extracted.

Key words: JPEG steganalysis, eigenspectrum, Fisher linear discriminant (FLD), feature extraction, regularization

CLC Number:

TP391.41

SUN Wu-yi, FENG Guo-rui. Steganalysis of JPEG Image Based on High-Dimensional Feature Regularization[J]. Journal of Applied Sciences, 2016, 34(5): 555-563.

References

[1] Sallee P. Model-based steganography—Digital watermarking [M]. Berlin Heidelberg: Springer, 2003: 154-167.
[2] Fridrich J, Pevný T, Kodovský J. Statistically undetectable JPEG steganography: dead ends challenges, and opportunities [C]//Proceedings of the 9th workshop on Multimedia & security. ACM, 2007: 3-14.
[3] Westfeld A. F5-a steganographic algorithm[C]//Information hiding. Springer Berlin Heidelberg, 2001: 289-302.
[4] Holub V, Fridrich J, Denemark T. Universal distortion function for steganography in an arbitrary domain [J]. EURASIP Journal on Information Security, 2014, 2014(1): 1-13.
[5] Guo L, Ni J, Su W. Using statistical image model for JPEG steganography: uniform embedding revisited [J]. IEEE Transactions on Information Forensics and Security, 2015, 10(12): 2669-2680.
[6] Guo L, Ni J, Shi Y Q. Uniform embedding for efficient JPEG steganography [J]. IEEE Transactions on Information Forensics and Security, 2014, 9(5): 814-825.
[7] Fridrich J, Goljan M, Hogea D. Steganalysis of JPEG images: breaking the F5 algorithm[C]//Information hiding. Springer Berlin Heidelberg, 2002: 310-323.
[8] Kodovský J, Fridrich J. Quantitative steganalysis of LSB embedding in JPEG domain[C]//Proceedings of the 12th ACM workshop on Multimedia and security. ACM, 2010: 187-198.
[9] Kodovský J, Fridrich J. Steganalysis of JPEG images using rich models [C]//Proceeding of the International Society of Optical Engineering, 2012, 8303: 83030A.
[10] Holub V, Fridrich J. Low-complexity features for JPEG steganalysis using undecimated DCT[J]. IEEE Transactions on Information Forensics and Security, 2015, 10(2): 219-228.
[11] Song X, Liu F, Yang C. Steganalysis of adaptive JPEG steganography using 2D Gabor filters[C]//Proceedings of the 3rd ACM Workshop on Information Hiding and Multimedia Security, 2015: 15-23.
[12] Pevný T, Fridrich J. Novelty detection in blind steganalysis [C]//Proceedings of the 10th ACM workshop on Multimedia and security, 2008: 167-176.
[13] Kodovský J, Fridrich J, Holub V. Ensemble classifiers for steganalysis of digital media [J]. IEEE Transactions on Information Forensics and Security, 2012, 7(2): 432-444.
[14] Kodovský J, Fridrich J. Steganalysis in high dimensions: fusing classifiers built on random subspaces 2011: 78800L-78800L-13. [C]//Proceedings of the International Society for Optical Engineering, 2011, 7880: 7880L.
[15] Zhu M, Martinez A M. Subclass discriminant analysis [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(8): 1274-1286.
[16] Jiang X, Mandal B, Kot A. Eigenfeature regularization and extraction in face recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(3): 383-394.
[17] Cogranne R, Sedighi V, Fridrich J. Is ensemble classifier needed for steganalysis in highdimensional feature spaces? [C]//2015 IEEE International Workshop on Information Forensics and Security, 2015: 1-6.

Steganalysis of JPEG Image Based on High-Dimensional Feature Regularization

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	ZHANG Tongtong, DONG Junyu, ZHAO Haoran, LI Qiong, SUN Xin. Lightweight Phytoplankton Detection Network Based on Knowledge Distillation [J]. Journal of Applied Sciences, 2020, 38(3): 367-376.
[2]	ZHENG Guo-hua, FENG Guo-rui, YU Jiang, CHENG Hang, ZHANG Xin-peng. JPEG Steganalysis Based on LSB Detection and Enhanced Features [J]. Journal of Applied Sciences, 2016, 34(6): 670-676.
[3]	LIU Ge, HUANG Fang-jun, LI Zhong-hua. Universal Steganalysis against Adaptive Steganographic Algorithms [J]. Journal of Applied Sciences, 2016, 34(5): 598-604.
[4]	WANG Jian-hong1, XU Ying1, XIONG Zhao-hua1, XU Bo2. Dual Decomposition in Subspace Predictive Control under Fault Condition [J]. Journal of Applied Sciences, 2014, 32(6): 652-660.
[5]	JIANG En-Yu, Zan-Feng, Shu-Xiao-Jin, Shao-Yong, Wang-Xiao-Hua. Rectal Perception Function Rebuilding Based on Wavelet Packet Analysis and Support Vector Machine [J]. Journal of Applied Sciences, 2012, 30(5): 538-544.
[6]	. Seabed Classification Based on Principal Component Analysis of Multiple Features Combined with Sonar Image [J]. Journal of Applied Sciences, 2010, 28(4): 374-380.
[7]	HU Xiao, YU Wang-xin, YAO Jing. Face Recognition Using Binary Structure-Based Feature Selection [J]. Journal of Applied Sciences, 2010, 28(3): 271-276.
[8]	MA Xiu-li1, JIAO Li-cheng2, WAN Wang-gen1. Two-Stage Texture Image Segmentation [J]. Journal of Applied Sciences, 2009, 27(3): 260-265.
[9]	NI Xue;LI Qing-wu;MENG Fan;CAI Yan-mei. Face Feature Extraction and Face Recognition Using Curvelet Transform [J]. Journal of Applied Sciences, 2009, 27(1): 34-38 .
[10]	ZHANG Jian-jun;WANG Qin. Nonlinear Regularization for Bi-level Image Restoration [J]. Journal of Applied Sciences, 2009, 27(1): 74-78 .
[11]	LIU Hai-feng;YAO Ze-qing;LIU Shou-sheng;WANG Qian . Kernel-Based Nonlinear Discriminant Method in Text Classification [J]. Journal of Applied Sciences, 2008, 26(6): 627-631 .
[12]	HAN Yu-bing, WU Le-nan. Super Resolution Reconstruction of the Video Image Sequence Based on PDE Diffusion [J]. Journal of Applied Sciences, 2005, 23(4): 331-335.
[13]	HAN Yu-bing, WU Le-nan. Image Restoration Based on the Modified Paik-Hopfield Neural Network [J]. Journal of Applied Sciences, 2005, 23(2): 126-130.
[14]	SUN Da-rui, WU Le-nan. Feature-Based Face Recognition by Elastic Graph Matching [J]. Journal of Applied Sciences, 2002, 20(4): 377-381.
[15]	HE ZHENYA, GU MINGLIANG, WANG TAIJUN, SHI XIAOXING. Principal Component Feature for Speech Recognition [J]. Journal of Applied Sciences, 1999, 17(4): 427-432.