کاهش ابعاد روش پنهان‌شکنی CDF با استفاده از یک روش انتخاب ویژگی مبتنی بر تئوری گراف
الموضوعات :
سعید آزادی‌فر
1
,
سیدحسین خواسته
2
,
محمدهادی ادریسی
3
1 - دانشگاه صنعتی خواجه نصیرالدین طوسی
2 - دانشگاه صنعتی خواجه نصیرالدین طوسی
3 - دانشگاه اصفهان
الکلمات المفتاحية: پنهان‌شکنیپنهان‌نگاریانتخاب ویژگیکاهش بعد,
ملخص المقالة :
پنهان‌شکنی دانش کشف حضور داده پنهان در یک رسانه پوششی است. هدف پنهان‌شکنی جلوگیری از رسیدن روش‌های پنهان‌نگاری به اهداف خود می‌باشد. یکی از معروف‌ترین روش‌های پنهان‌شکنی روش CDF است که در این پژوهش استفاده شده است. یکی از چالش‌های عمده در مسئله پنهان‌شکنی تصاویر تعداد زیاد ویژگی‌های استخراج‌شده برای این کار است. مجموعه‌های داده‌ای با ابعاد بالا از دو جهت باعث کاهش عملکرد پنهان‌شکنی می‌شود. از یک طرف با افزایش ابعاد داده‌‌ها، حجم محاسبات افزایش پیدا می‌کند و از طرف دیگر مدلی که بر اساس داده‌های با ابعاد بالا ساخته می‌شود دارای قابلیت تعمیم پایینی است و احتمال بیش‌برازش افزایش می‌یابد. در نتیجه، کاهش ابعاد مسئله می‌تواند هم پیچیدگی محاسباتی را کاهش داده و هم باعث بهبود عملکرد پنهان‌شکنی شود. در این مقاله تلاش شده با ترکیب مفهوم زیرگراف کامل بیشینه وزن‌دار و معیار مرکزیت یال و در نظر گرفتن مناسب‌بودن هر ویژگی، ویژگی‌های تأثیرگذار و دارای حداقل افزونگی به‌عنوان ویژگی‌های نهایی انتخاب شوند. نتایج شبیه‌سازی بر روی مجموعه داده‌های SPAM و CC-PEV نشان داد روش پیشنهادی دارای عملکرد مناسبی است و به دقت حدود 96% در تشخیص جاسازی داده در تصاویر دست پیدا کرده و همچنین این روش در مقایسه با روش‌های شناخته‌شده قبلی دارای دقت بالاتری است.
[1] S. M. Badr, G. Ismaial, and A. H. Khalil, "A review on steganalysis techniques: from image format point of view," International J. of Computer Applications, vol. 102, no. 4, pp. 11-19, Sep. 2014.
[2] V. Bhasin, P. Bedi, and A. Singhal, "Feature selection for steganalysis based on modified stochastic diffusion search using Fisher score," in Proc. Int. Conf. on Advances in Computing, Communications and Informatics, ICACCI'14, pp. 2323-2330, New Delhi, India, 24-27 Sept. 2014.
[3] Y. Miche, B. Roue, A. Lendasse, and P. Bas, "A feature selection methodology for steganalysis," in B. Gunsel, A. K. Jain, A. M. Tekalp, B. Sankur (Eds.) Multimedia Content Representation, Classification and Security: Int. Workshop, MRCS 2006, Springer, Berlin, Heidelberg, pp. 49-56, Sep. 2006.
[4] T. Pevn, P. Bas, and J. Fridrich, "Steganalysis by subtractive pixel adjacency matrix," Trans. Info. For. Sec., vol. 5, no. 2, pp. 215-224, Jun. 2010.
[5] A. Westfeld and A. Pfitzmann, Attacks on Steganographic Systems, Information Hiding, 2000.
[6] J. Fridrich, M. Goljan, and R. Du, "Reliable detection of LSB steganography in color and grayscale images," in Proc. of the Workshop on Multimedia and Security, MM&Sec'01, pp. 27-30, Ottawa, ON, Canada, 5-5 Oct 2001.
[7] S. Dumitrescu, X. Wu, and Z. Wang, "Detection of LSB steganography via sample pair analysis," Information Hiding, vol. 51, no. 7, pp. 1995-2007, Jul. 2003.
[8] A. Westfeld, Detecting Low Embedding Rates," in: F. A. P. Petitcolas (Ed.) Information Hiding: 5th International Workshop, IH 2002, Springer Berlin Heidelberg, Berlin, 2003.
[9] S. M. S. Tarzjani and S. Ghaemmaghami, "Detection of LSB replacement and LSB matching steganography using Gray level run length matrix," in Proc. of 5th Int. Conf. on Intelligent Information Hiding and Multimedia Signal Processing, pp. 787-790, Kyoto, Japan, 12-14 Sept. 2009.
[10] M. Goljan, J. Fridrich, and T. Holotyak, "New blind steganalysis and its implications," Proc. SPIE 6072, Security, Steganography, and Watermarking of Multimedia Contents VIII, pp. 1-13, 2006.
[11] J. Harmsen and W. Pearlman, “Steganalysis of additive-noise modelable information hiding,” in Proc. SPIE Security Watermarking Multimedia Contents, vol. 5020, pp. 131-142, 2003.
[12] Z. Zhang and E. R. Hancock, "Hypergraph based information-theoretic feature selection," Pattern Recognition Letters, vol. 33, no. 15, pp. 1991-1999, 1 Nov. 2012.
[13] P. Zhu, W. Zhu, Q. Hu, C. Zhang, and W. Zuo, "Subspace clustering guided unsupervised feature selection," Pattern Recognition, vol. 66, pp. 364-374, Jun. 2017.
[14] H. Peng, F. Long, and C. Ding, "Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 8, pp. 1226-1238, Aug. 2005.
[15] M. Belkin and P. Niyogi, "Laplacian eigenmaps and spectral techniques for embedding and clustering," Proc. of the 14th Int. Conf. on Neural Information Processing Systems: Natural and Synthetic, NIPS'01, pp. 585-592, Vancouver, BC, Canada, 3-8 Dec. 2002.
[16] J. Shi and J. Malik, "Normalized cuts and image segmentation," IEEE Trans. Pattern Anal. Machine Intell, vol. 22, no. 8, pp. 888-905, Aug. 2000.
[17] F. Chung, "Spectral graph theory," in Regional Conf. Series in Mathematics American Mathematical Society, vol. 92, pp. 1-212, 1997.
[18] Q. Gu, Z. Li, and J. Han, "Generalized fisher score for feature selection," in Proc. of the Int. Conf. on Uncertainty in Artificial Intelligence, pp. 266-273, Barcelona, Spain, 14-17 Jul. 2011.
[19] X. He, D. Cai, and P. Niyogi1, "Laplacian score for feature selection," in Proc. of the 18th Int. Conf. on Neural Information Processing System, NIPS'05, pp. 507-514, Vancouver, BC, Canada, 5-8 Dec. 2005.
[20] H. Cheng, W. Deng, C. Fu, Y. Wang, and Z. Qin, "Graph-based semi-supervised feature selection with application to automatic spam image identification," In: Yu Y., Yu Z., Zhao J. (eds) Computer Science for Environmental Engineering and EcoInformatics. CSEEE 2011. Communications in Computer and Information Science, vol. 159. Springer, Berlin, pp. 259-264, 2011.
[21] Q. Song, J. Ni, and G. Wang, "A fast clustering-based feature subset selection algorithm for high-dimensional data," IEEE Trans. on Knowledge and Data Engineering, vol. 25, no. 1, pp. 1-14, Jan. 2013.
[22] S. Bandyopadhyay, T. Bhadra, P. Mitra, and U. Maulik, "Integration of dense subgraph finding with feature clustering for unsupervised feature selection," Pattern Recognition Letters, vol. 40, pp. 104-112, 15 Apr. 2014.
[23] S. Tabakhi and P. Moradi, "Relevance-redundancy feature selection based on ant colony optimization," Pattern Recognition, vol. 48, no. 9, pp. 2798-2811, Sept. 2015.
[24] P. Moradi and M. Rostami, "A graph theoretic approach for unsupervised feature selection," Engineering Applications of Artificial Intelligence, vol. 44, pp. 33-45, Sept. 2015.
[25] J. Che, Y. Yang, L. Li, X. Bai, S. Zhang, and C. Deng, "Maximum relevance minimum common redundancy feature selection for nonlinear data," Information Sciences, vol. 409-410, pp. 68-86, Oct. 2017.
[26] M. Ghaemi and M. R. Feizi-Derakhshi, "Feature selection using forest optimization algorithm," Pattern Recognition, vol. 60, pp. 121-129, Dec. 2016.
[27] A. K. Farahat, A. Ghodsi, and M. S. Kamel, "Efficient greedy feature selection for unsupervised learning," Knowledge and Information Systems, vol. 35, no. 2, pp. 285-310, May 2013.
[28] A. J. Ferreira and M. A. T. Figueiredo, "An unsupervised approach to feature discretization and selection," Pattern Recognition, vol. 45, no. 9, pp. 3048-3060, Sept. 2012.
[29] Q. Wu, J. K. Hao, and F. Glover, "Multi-neighborhood tabu search for the maximum weight clique problem," Ann Oper Res, vol. 196, no. 1, pp. 611-634, Jul. 2012.
[30] U. Brandes, "On variants of shortest-path betweenness centrality and their generic computation," Social Networks, vol. 30, no. 2, pp. 136-145, May 2008.
[31] M. Mandal and A. Mukhopadhyay, "Unsupervised non-redundant feature selection: a graph-theoretic approach," in Proc. of the Int. Conf. on Frontiers of Intelligent Computing: Theory and Applications, FICTA'13, pp. 373-380, 2013.
[32] M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. Witten, The WEKA data mining software, Available from: <http://www.cs.waikato.ac.nz/ml/weka>.
[33] J. H. J. Xiao, K. Ehinger, A. Oliva, and A. Torralba, "SUN database: large-scale scene recognition from abbey to zoo," in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 3485 -3492, San Francisco, CA, USA , 13-18 Jun. 2010.
[34] M. Friedman, "A comparison of alternative tests of significance for the problem of m rankings," Annals of Math. Statistics, vol. 11, no. 1, pp. 86-92, 1940.
