基于图像纹理的空域富模型隐写分析研究
Research on Steganalysis of Spatial Rich Model Based on Image Texture

DOI: 10.12677/CSA.2020.105086, PP. 832-840

Keywords: 隐写术，隐写分析，空域富模型，空纹理复杂度
Steganography, Steganalysis, Spatial Rich Model, Texture Complexity

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Abstract:

图像纹理复杂度是隐写分析在攻击自适应隐写时常常关注的问题，而空域富模型(Spatial Rich Model)常用于针对传统非自适应图像空域隐写的攻击，但空域富模型却没有考虑图像纹理的纹理特征。考虑到空域富模型的准确率及图像纹理复杂度对自适应算法的作用，兼顾效率与准确率，本文结合两种思路，将图像纹理复杂度引入到空域富模型中，提出了一种基于图像纹理的空域富模型隐写分析方法。用MATLAB搭建了测试环境进行测试，通过实验结果可以看出，该方法能提高对于自适应隐写算法的检测准确率。
The complexity of image texture is often concerned when steganalysis is used for attacking adaptive steganography, while spatial rich model is often used to attack the traditional image steganalysis. But the spatial rich model does not consider the texture features of image texture. Considering the accuracy of the spatial rich model and the effect of the image texture complexity on the adaptive algorithm, considering both efficiency and accuracy, this paper combines two ideas, introduces the image texture complexity into the spatial rich model, and proposes a steganalysis method based on the image texture. The test environment is built by MATLAB. The experimental results show that this method can improve the detection accuracy of the adaptive steganography algorithm.

References

[1]	Johnson, N.F. and Jajodia, S. (1998) Exploring Steganography: Seeingthe Unseen. IEEE Computer, 31, 26-34. https://doi.org/10.1109/MC.1998.4655281
[2]	Westfeld, A. (2001) High Capacity despite Better Steganalysis. Proceedings of 4th International Workshop on Information Hiding, No. 2137, 289-302. https://doi.org/10.1007/3-540-45496-9_21
[3]	Fridrich, J. and Pevny, T. (2007) Statistically Undetectable JPEG Steganography: Dead Ends Challenges, and Opportunities. In: Proceedings of 9th Workshop on Multimedia & Security, 3-14. https://doi.org/10.1145/1288869.1288872
[4]	Fridrich, J. and Kodovsky, J. (2012) Rich Models for Ste-ganalysis of Digital Images. IEEE Transactions on Information Forensics and Security, 7, 868-882. https://doi.org/10.1109/TIFS.2012.2190402
[5]	Holub, V. and Fridrich, J. (2015) Low Complexity Features for JPEG Steganalysis Using Undecimated DCT. IEEE Transactions on Information Forensics and Security, 10, 219-228. https://doi.org/10.1109/TIFS.2014.2364918
[6]	Filler, T., Fridrich, J. and Judas, J. (2010) Minimizing Embedding Impact in Steganography Using Trellis-Coded Quantization. Proceedings of SPIE Electronic Imaging, Media Forensics and Security XII5, San Jose, CA, 18-20 January 2010, 1-14. https://doi.org/10.1117/12.838002
[7]	Holub, V. and Fridrich, J. (2013) Digital Image Steganography Using Universal Distortion. Proceedings of 1st ACM Workshop on In-formation Hiding and Multimedia Security, France, Montpellier, 59-68. https://doi.org/10.1145/2482513.2482514
[8]	Filler, T. and Fridrich, J. (2011) Gibbs Construction Insteganogra-phy. IEEE Transactions on Information Forensics and Security, 5, 705-720. https://doi.org/10.1109/TIFS.2010.2077629
[9]	Holub, V. and Fridrich, J. (2012) Designing Steganographic Distortion Using Directional Filters. Fourth IEEE International Workshop on Information Forensics and Security, Tene-rife, 234-239. https://doi.org/10.1109/WIFS.2012.6412655
[10]	Holub, V., Fridrich, J. and Denemark, T. (2014) Universal Dis-tortion Function for Steganography in an Arbitrary Domain. EURASIP Journal on Information Security, 2014, 1-13. https://doi.org/10.1186/1687-417X-2014-1
[11]	Chen, M., Sedighi, V., Boroumand, M. and Fridrich, J. (2017) JPEG-Phase-Aware Convolutional Neural Network for Steganalysis of JPEG Images. Proceedings of IH & MMSec 17, Philadelphia, PA, 20-22 June 2017. https://doi.org/10.1145/3082031.3083248
[12]	高程程. 基于灰度共生矩阵的纹理特征提取[J]. 计算机系统应用, 2010, 19(6): 195-198.
[13]	Haralick, R.M. and Shanmugam, K. (1973) Texture Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3, 610-621. https://doi.org/10.1109/TSMC.1973.4309314
[14]	Kodovsky, J. and Fridrich, J. (2011) Steganalysis in High Di-mensions: Fusing Classifiers Built on Random Subspaces. Proceedings of SPIE, Electronic Imaging, Security and Fo-rensics of Multimedia XIII, San Francisco, CA, 23-26 January 2011, 1-13. https://doi.org/10.1117/12.872279
[15]	Kodovsky, J., Fridrich, J. and Holub, V. (2012) Ensemble Classifier for Steganalysis of Digital Media. IEEE Transactions on Information Forensics and Security, 7, 432-444. https://doi.org/10.1109/TIFS.2011.2175919

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