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Tytuł artykułu

Current trends in the field of steganalysis and guidelines for constructions of new steganalysis schemes

Autorzy
Identyfikatory
Warianty tytułu
PL
Aktualne trendy w dziedzinie steganalizy oraz zalecenia dla konstrukcji nowych systemów steganalitycznych
Języki publikacji
EN
Abstrakty
EN
The paper concerns blind steganalysis techniques in the passive steganalysis scenario designed to detect the steganographic cover modification schemes. The goal is to investigate the state-of-art in the field of steganalysis, and, above all, to recognize current trends existing in this field and determine guidelines for constructions of new steganalysis schemes. The intended effects are to examine the possibilities for the development of knowledge in the field of steganography and to set directions for future research.
PL
Artykuł dotyczy niekoherentnych technik steganalizy w scenariuszu pasywnej steganalizy przeznaczonych do detekcji systemów steganograficznych stosujących metodę modyfikacji obrazów cover. Celem jest zbadanie aktualnego stanu wiedzy w dziedzinie steganalizy, a przede wszystkim rozpoznanie aktualnych kierunków w tej dziedzinie i ustalenie wytycznych dla konstrukcji nowych systemów steganalitycznych. Zamierzonymi efektami są zbadanie możliwości rozwoju wiedzy w dziedzinie steganografii i wyznaczenie celów dla przyszłych badań.
Rocznik
Tom
Strony
1121--1125
Opis fizyczny
Bibliogr. 44 poz., tab.
Twórcy
  • Gdańsk University of Technology, Faculty of Electronics, Telecommunications and Informatics, Department of Teleinformation Networks
Bibliografia
  • [1] Fridrich J., "Steganography in Digital Media: Principles, Algorithms, and Applications", Cambridge, UK: Cambridge University Press, ISBN: 978-0-521-19019-0, 2010.
  • [2] Westfeld A., High capacity despite better steganalysis (F5 - a steganographic algorithm), Information Hiding, 4th Int. Workshop, vol. 2137 of Lecture Notes in Computer Science, pp. 289-302, 2001.
  • [3] Kodovsky J., J. Fridrich, Influence of embedding strategies on security of steganographic methods in the JPEG domain, Proc. SPIE, Electronic Imaging, Security, Forensics, Steganography, and Watermarking of Multimedia Contents X, vol. 6819, pp. 02-1-02-13, 2008.
  • [4] Fridrich J., M. Goljan, D. Soukal, Efficient Wet Paper Codes, Information Hiding. 7th International Workshop, UMCS vol. 3727, pp. 204-218,2005.
  • [5] Li Z., K. Lu, X. Zeng, X. Pan, Feature-Based Steganalysis for JPEG Images, Int. Conf. Digital Image Processing, pp. 76-80,2009.
  • [6] Deng Q.L, J.J. Lin, A Universal Steganalysis Using Features Derived from the Differential Image Histogram in Frequency Domain, CISP '09. 2nd Int. Congress on Image and Signal Processing, pp. 1-4, 2009.
  • [7] Yu W., Z. Li, L. Ping, Blind detection for JPEG Steganography, Int. Conf. on Networking and Information Technology (ICNIT), pp. 128-132, 2010.
  • [8] Deng Q.L, The blind detection of information hiding in color image, 2nd Int. Conf. on Computer Engineering and Technology (ICCET), vol. 7, pp. V7-346-V7-348, 2010.
  • [9] Joo J.C., TW. On, J.H. Choi, H.K. Lee, Steganalysis Scheme Using the Difference Image of Calibrated Sub-sampling, 6th Int. Conf. on Intelligent Information Hiding and Multimedia Signal Processing, pp. 51-54, 2010.
  • [10] Fridrich J., "Rich Models for Steganalysis of Digital Images", IEEE Transactions on Information Forensics and Security, vol. 7, no. 3,2012.
  • [11] Ge H., H. Liu and Z. Jin, Key Technical Analysis on Steganography and Steganalysis, 3rd Int. Conf. Multimedia Technology ICMT, 2013.
  • [12] Lin J.O.; S.P Zhong, JPEG image steganalysis method based on binary similarity measures, Int. Conf. on Machine Learning and Cybernetics, vol. 4, pp. 2238-2243, 2009.
  • [13] Yang G., H. Zhang, Using Higher Order DCT Difference to Effective improve Markov Process Based JPEG Steganalysis Detection Rate, Asia-Pacific Conf. Information Processing, vol. 2, pp. 47-50, 2009.
  • [14] Liu S., L. Ma, H. Yao, D. Zhao, Universal Steganalysis Based on Statistical Models Using Reorganization of Block- based DCT Coefficients, 5th Int. Conf. Information Assurance and Security IAS '09, vol. 1, pp. 778-781,2009.
  • [15] He Z.M.; W.W.Y Ng, RRK. Chan, D.S. Yeung, Steganography detection using localized generalization error model, IEEE Int. Conf. Systems Man and Cybernetics (SMC), pp. 1544-1549, 2010.
  • [16] Chen Q., S. Zhong, Universal Steganographic Detection Algorithmin in JPEG Image Using the Data-Dependent Kernel, 3th Int. Symposium on Electronic Commerce and Security (ISECS), pp. 232-236, 2010.
  • [17] Bhat VH., S. Krishna, RD. Shenoy, K.R. Venugopal, L.M. Patnaik, HUBFIRE - A multi-class SVM based JPEG steganalysis using HBCL statistics and Fr index, Int. Conf. Security and Cryptography, pp. 1-6, 2010.
  • [18] Ping Q., C. LJ-ya, W. Meng, A universal steganalysis to steganographic images on frequency domain, Int. Conference E-Business and E -Government (ICEE), pp. 1-5, 2011.
  • [19] Anitha R I, M. Rajaram, S. N. Sivanandham, "Neural Network Based Steganalysis Framework to Detect Stego-Contents in Corporate Emails", International Journal of Emerging Technology and Advanced Engineering, vol. 2, issue 3, 2012.
  • [20] Geetha S., S.S. Sindhu, N. Ishwarya, A. Mohan, R Amuthayazhini, N. Kamaraj, Intelligent detection of LSB stego anomalies in images using soft computing paradigms; Int. Conf. Methods and Models in Computer Science, pp. 1-5, 2009.
  • [21] Sun Z., H. Li, Z. Wu, Z. Zhou, An Image Steganalysis Method Based on Characteristic Function Moments of Wavelet Subbands, Int. Conf. Artificial Intelligence and Computational Intelligence, vol. 1, pp. 291-295, 2009.
  • [22] Yang X., S. Wang, J. Liu, Universal Steganalysis to Images with WBMC Model, 5lh Int. Conf. Information Assurance and Security, vol. 2, pp 627-630, 2009.
  • [23] Ramezani M., S. Ghaemmaghami, Towards Genetic Feature Selection in Image Steganalysis, 7th IEEE Consumer Communications and Networking Conference, pp. 1 -4, 2010.
  • [24] Gireesh Kumar I, R. Jithin, D.D. Shankar, Int. Conf. Feature Based Steganalysis Using Wavelet Decomposition and Magnitude Statistics Advances in Computer Engineering, pp. 298-300.
  • [25] LJ H.; Z. Sun, Z. Zhou, An image steganalysis method based on characteristic function moments and PCA, 30th Chinese Control Conference (CCC), pp. 3005-3008, 2011.
  • [26] Yang X., Y Lei, X. Pan, J. Liu, Universal Image Steganalysis Based on Wavelet Packet Decomposition and Empirical Transition Matrix in Wavelet Domain; Int. Forum on Computer Science, Technology and Applications IFCSTA '09, vol. 2 pp. 179-182, 2009.
  • [27] He F, S. Zhong, K. Chen. “An Effective Ensemble-based Classification Algorithm for High-Dimensional Steganalysis", Journal of Software, vol. 9, no. 7, 2014.
  • [28] Tan S., B. LJ, "Stacked Convolutional Auto-Enooders for Steganalysis of Digital Images", Asia-Pacific Signal and Information Processing Association", 2014 Annual Summit and Conference (APSIPA), 2014.
  • [29] Pibre L., J. Pasquet, D. lenco, M. Chaumont, Deep learning is a good steganalysis tool when embedding key is reused for different images, even if there is a cower sourcemismatch, IS&T. Media Watermarking, Security and Forensics, Part of IS&T Int. Symp. On Electronic Imaging, 2016.
  • [30] Xu G., H. Wu, YQ. Shi, "Ensemble of CNNs for Steganalysis: An Empirical Study", Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security, pp. 103-107, 2016.
  • [31] Couchot J., R. Couturier, C. Guyeux, M. Salomon, Steganalysis via a Convolutional Neural Network using Large Convolution Filters, CoRR abs/1605.0794, 2016.
  • [32] Yu X.Y, A. Wang, An Investigation of Genetic Algorithm on Steganalysis Techniques, 5th Int. Conf. Intelligent Information Hiding and Multimedia Signal Processing, pp. 1118-1121, 2009.
  • [33] Yu X. Y, A. Wang, Steganalysis Based on Regression Model and Bayesion Network, Int. Conf. Multimedia Information Networking and Security, vol. 1, pp. 41-44, 2009.
  • [34] Yu X. Y, A. Wang, Steganalysis Based on Bayesion Network and Genetic Algorithm, 2nd Int. Congress on Image and Signal Processing CISP '09, pp. 1-4, 2009.
  • [35] Sheikhan M., M.S. Moin, M. Pezhmanpour, Blind image steganalysis via joint co-occurrence matrix and statistical moments of contourlet transform; 10th Int. Conf. Intelligent Systems Design and Applications (ISDA),pp. 368-372, 2010.
  • [36] Veena H.B., S. Krishna, RD. Shenoy, SURF: Steganalysis using random forests, 10th Int. Conf. Intelligent Systems Design and Applications (ISDA), pp. 373-378, 2010.
  • [37] Ke K., T. Zhao, O. Li, Bhattacharyya Distance for Blind Image Steganalysis; Int. Conf. Multimedia Information Networking and Security (MINES), pp. 658-661, 2010.
  • [38] Holoska J., Z. Oplatkova, l. Zelinka, R. Senkerik, Comparison between Neural Network Steganalysis and Linear Classification Method Stegdetect, 2nd Int. Conf. Computational Intelligence, Modelling and Simulation, pp. 15-20, 2010.
  • [39] Zhang F., Quaternion and Matrices of Quaternions, Linear Algebra and its Applications, Elsevier Science Inc., pp. 21-57,1997.
  • [40] Baker M.J., Maths - Quaternions, http://www.euclideanspace.com/maths/algebra/realNormedAlgebra/quaternions/index.htm, date: 18.06.2014.
  • [41] Dzwonkowski M., M. Papaj, R. Rykaczewski, "A New Quaternion-Based Encryption Method for DICOM Images", IEEE Transactions on Image Processing, vol. 24, issue 11, pp. 4614-4622, 2015.
  • [42] Dzwonkowski M., R. Rykaczewski, "Quaternion Feistel Cipher with an infinite key space based on quaternion Julia sets", Journal of Telecommunications and Information Technology, vol. 4, pp. 15-21, 2015.
  • [43] Czaplewski B., R. Rykaczewski, "Receiver-side fingerprinting method for color images based on a series of quaternion rotations", Przegląd Telekomunikacyjny i Wiadomości Telekomunikacyjne / Telecommunication Review + Telecommunication News, vol. 8-9, pp. 1127-1134,2015.
  • [44] Czaplewski B., "Joint fingerprinting and decryption method for color images based on quaternion rotation with cipher quaternion chaining", Journal of Visual Communication and Image Representation, vol. 40, part A, pp. 1-13,2016.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-03cb4860-99df-4f79-a941-9374f8a14b87
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