收稿日期: 2016-08-02
修回日期: 2016-08-17
网络出版日期: 2016-09-30
基金资助
国家自然科学基金（No.61173147，No.61471122）资助
Universal Steganalysis against Adaptive Steganographic Algorithms
Received date: 2016-08-02
Revised date: 2016-08-17
Online published: 2016-09-30
刘格, 黄方军, 李中华 . 针对自适应隐写的通用隐写分析研究[J]. 应用科学学报, 2016 , 34(5) : 598 -604 . DOI: 10.3969/j.issn.0255-8297.2016.05.012
Digital image is a popular carrier in steganography. In reality, the specific steganographic algorithm used to hide secrete data is generally unknown. Therefore, universal steganalytic techniques capable of detecting hidden information with unknown steganographic algorithms are important. This paper proposes a universal steganalysis technique against adaptive steganographic algorithms for images. With feature extraction and training, the influence of various adaptive steganographic algorithms on statistical characteristics of the carrier image are captured so that unknown steganographic algorithms can be accurately detected. Experimental results show that high detection accuracy can be obtained even for previously unknown steganographic algorithms.
Key words： steganography; feature extraction; steganalysis
[1] Filler T, Fridrich J. Gibbs construction in steganography [J]. IEEE Transactions on Information Forensics and Security, 2010, 5: 705-720.
[2] Holub V, Fridrich J. Designing steganographic distortion using directional filters [J]. IEEE Workshop on Information Forensic and Security, Tenerife, Canary Islands, 2012: 234-239.
[3] Holub V, Fridrich J, Denemark T. Universal distortion function for steganography in an arbitrary domain [J]. Eurasip Journal on Information Security, 2014, 2014(1): 1-3.
[4] Sedighi V, Fridrich J, Cogranne R. Content-adaptive pentary steganography using the multivariate generalized gaussian cover model [C]//Proceedings of SPIE-The International Society for Optical Engineering, 2015, 9409: 94090H-94090H-13.
[5] Fridrich J, Kodovsky J. Multivariate Gaussian model for designing additive distortion for steganography [C]//IEEE International Conference on Acoustics, Speech and Signal Processing, 2013: 2949-2953.
[6] Sedighi V, Cogranne R, Fridrich J. Content-adaptive steganography by minimizing statistical detectability [J]. IEEE Transactions on Information Forensics and Security, 2015, 11: 221-234.
[7] Avcibas I, Memon N, Sankur B. Steganalysis using image quality metrics [J]. IEEE Transactions on Image Processing, 2003, 12(2): 221-229.
[8] Farid H. Detecting hidden messages using higher-order statistical models [C] //Proceedings of IEEE International Conference on Image Processing, 2002: 905-908.
[9] Lyu S, Farid H. Detecting hidden messages using higher-order statistics and support vector machines [J]. Springer Berlin Heidelberg, 2002, 2578: 340-354.
[10] Filler T, Fridrich J. Design of adaptive steganographic schemes for digital images[C]//Proceedings of SPIE, Electronic Imaging, Security and Watermarking of Multimedia Contents III, 2011, 7880(1): 181-197.
[11] Pevny T, Filler T, Bas P. Using high-dimensional image models to perform highly undetectable steganography [J]. Proceedings of 12th International Workshop on Information Hiding, 2010, 6387: 161-177.
[12] Fridrich J, Kodovský J. Rich models for steganalysis of digital images [J]. IEEE Transaction on Information Forensics and Security, 2012, 7(3): 868-882.
[13] Denemark T, Sedighi V, Holub V, Cogranne R, Fridrich J. Selection-channel-aware rich model for steganalysis of digital images [J]. IEEE Workshop on Information Forensic and Security, 2014: 48-53.
[14] 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.
[15] Cogranne R. Modeling and extending the ensemble classifier for steganalysis of digital images using hypothesis testing theory [J]. IEEE Transactions on Information Forensics and Security, 2015, 10(2): 2627-2642.
[16] Bas P, Pevný T. Break our steganographic system: the ins and outs of organizing BOSS[C]//Information Hiding, springer-Verlag, 2011: 59-70.
/
| 〈 |
|
〉 |
