Last ned (Gratis)

Abstrakt

Steganografi er teknikkar for hemmeleg kommunikasjon, der sjølve eksistensen
av den hemmelege meldinga må haldast løynd. Steganalyse dreier seg om
teknikkar for å påvisa slike meldingar. Ei rekkje moderne steganografiteknikkar
finst for å modulera digital informasjon i digitale bilete. Dei mest lovande
steganalyseteknikkane byggjer på maskinlæring. Literaturen omfattar ei lang
rekkje feature vectors som kan brukast til å påvisa steganografi saman med
vanlege maskinlæringsalgoritmar som t.d. support vector machines (SVM).
I denne artikkelen går me systematisk gjennom yteevna til kjende
features, både dei kjende feature-vektorane og delvektorar. Ved å ha
reimplementert ca. 15 kjende teknikkar, kan me gjera ei langt meir
omfattande samanlikning enn tidlegare forfattarar. Me har òg brukt tre ulike
feature selection–teknikkar for å identifisera lovande einskild-features.

Referanser

[1] I. Ahmad and Pi-Erh Lin. A nonparametric estimation of the entropy for absolutely continuous distributions. IEEE Transactions on Information Theory, 22:372–375, 1976.

[2] Ismail Avciba¸s, Mehdi Kharrazi, Nasir Memon, and Bulent Sankur. Image steganalysis with binary similarity measures. EURASIP Journal on Applied Signal Processing, 17:2749––2757, 2005. [3] Johann Briffa, Anthony TS Ho, Hans Georg Schaathun, and Ainuddin Wahid Abdul Wahab. Conditional probability based steganalysis for JPEG steganography. In International Conference on Signal Processing Systems (ICSPS 2009), May 2009.

[4] Gavin Brown. A new perspective for information theoretic feature selection. In Twelfth International Conference on Artificial Intelligence and Statistics, June 2009. Florida.

[5] G. Cancelli, G. Doërr, I. Cox, and M. Barni. Detection of ±1 steganography based on the amplitude of histogram local extrema. In Proceedings IEEE, International Conference on Image Processing (ICIP), October 2008.

[6] Chih-Chung Chang and Chih-Jen Lin. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.

[7] Chunhua Chen, Yun Q. Shi, Wen Chen, and Guorong Xuan. Statistical moments based universal steganalysis using JPEG 2-d array and 2-d characteristic function. In Proceedings of the International Conference on Image Processing, ICIP 2006, Atlanta, Georgia, USA, pages 105–108, October 2006. [8] Xiaochuan Chen, Yunhong Wang, Tieniu Tan, and Lei Guo1. Blind image steganalysis based on statistical analysis of empirical matrix. In Proc. 18th Int. Conf. on Pattern Recognition, volume 3, pages 1107–1110, 2006.

[9] Jing Dong, Xiaochuan Chen, Lei Guo, and Tieniu Tan. Fusion based blind image steganalysis by boosting feature selection. In IWDW ’07: Proceedings of the 6th International Workshop on Digital Watermarking, pages 87–98, Berlin, Heidelberg, 2008. Springer-Verlag.

[10] Tomas Filler, Tomas Pevny, and Patrick Bas. Break our steganographic system, 2008. Includes a 10000-image database intended for Information Hiding experimentation.

[11] Jessica Fridrich. Feature-based steganalysis for JPEG images and its implications for future design of steganographic schemes. In Information Hiding, volume 3200 of Lecture Notes in Computer Science, pages 67–81. Springer Berlin / Heidelberg, 2005.

[12] M. Goljan, J. Fridrich, and T. Holotyak. New blind steganalysis and its implications. In E. J. Delp and P. W. Wong, editors, Proc. SPIE, Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents VIII, volume 6072, pages 1–13, January 2006.

[13] Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen Lin. A practical guide to support vector classification. Technical report, Department of Computer Science, National Taiwan University, 2003. [14] Jan Kodovský and Jessica Fridrich. Calibration revisited. In MM&Sec ’09: Proceedings of the 11th ACM workshop on Multimedia and security, pages 63–74, New York, NY, USA, 2009. ACM. [15] Siwei Lyu and Hany Farid. Detecting hidden messages using higher-order statistics and support vector machines. In IH ’02: Revised Papers from the 5th International Workshop on Information Hiding, Lecture Notes in Computer Science, pages 340–354, London, UK, 2003. Springer-Verlag.

[16] Tomáš Pevný, Patrick Bas, and Jessica Fridrich. Steganalysis by subtractive pixel adjacency matrix. In MM&Sec ’09: Proceedings of the 11th ACM workshop on Multimedia and security, pages 75–84, New York, NY, USA, 2009. ACM.

[17] Tomáš Pevný and Jessica Fridrich. Merging Markov and DCT features for multi-class JPEG steganalysis. In Proc. SPIE Electronic Imaging, pages 3–4, January 2007.

[18] Hans Georg Schaathun. pysteg – a python library for steganography and steganalysis, 2011. http: //www.ifs.schaathun.net/pysteg/.

[19] G. Schaefer and M. Stich. UCID - an uncompressed colour image database. In Proc. SPIE, Storage and Retrieval Methods and Applications for Multimedia 2004, pages 472–480, 2004.

[20] Yun Q. Shi, Chunhua Chen, and Wen Chen. A Markov process based approach to effective attacking JPEG steganography. In Information Hiding, Lecture Notes in Computer Science, 2006.

[21] Gustavus J. Simmons. The prisoners’ problem and the subliminal channel. In CRYPTO, pages 51–67, 1983.

[22] Kenneth Sullivan, Upamanyu Madhow, Shivkumar Chandrasekaran, and B. S. Manjunath. Steganalysis of spread spectrum data hiding exploiting cover memory. In Edward J. Delp and Ping Wah

Wong, editors, Security, Steganography, and Watermarking of Multimedia Contents, volume 5681 of Proceedings of SPIE, pages 38–46. SPIE, 2005.

[23] Kinh Tieu and Paul Viola. Boosting image retrieval. Int. J. Comput. Vision, 56(1-2):17–36, 2004. [24] AndreasWestfeld. F5—a steganographic algorithm. In IHW ’01: Proceedings of the 4th International Workshop on Information Hiding, pages 289–302, London, UK, 2001. Springer-Verlag.

[25] Guorong Xuan, Jianjiong Gao, Y.Q. Shi, and D. Zou. Image steganalysis based on statistical moments of wavelet subband histograms in DFT domain. In Multimedia Signal Processing, 2005 IEEE 7th Workshop on, pages 1–4, 30 2005-Nov. 2 2005.

[26] Arezoo Yadollahpour and Hossein Miar Naimi. Attack on LSB steganography in color and grayscale images using autocorrelation coefficients. European Journal of Scientific Research, 31(2):172–183, 2009.




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