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Unconditional Steganalysis of JPEG and BMP Images and Its Performance Analysis Using Support Vector Machine

Publication Type : Journal Article

Thematic Areas : TIFAC-CORE in Cyber Security

Publisher : Communications in Computer and Information Science

Source : Communications in Computer and Information Science, Volume 101, Kochi, Kerala, p.638-640 (2010)

Url : http://www.scopus.com/inward/record.url?eid=2-s2.0-84880730682&partnerID=40&md5=b84605a2eccb602e7757cd25dc18bcae

ISBN : 9783642157653

Keywords : Algorithms, DCT, Embedding technique, Feature vectors, Hidden information, Information technology, Performance analysis, Principal component analysis, Spatial domains, Steganalysis, Steganographic algorithms, Steganography, Support vector machines

Campus : Coimbatore

School : Centre for Cybersecurity Systems and Networks, School of Engineering

Center : TIFAC CORE in Cyber Security

Department : Computer Science, cyber Security

Year : 2010

Abstract : A feature based steganalytic method used for detecting both transform and spatial domain embedding techniques was developed. We developed an unconditional steganalysis which will automatically classify an image as having hidden information or not using a powerful classifier Support Vector Machine which is independent of any embedding techniques. To select the most relevant features from the total 269 features extracted, they apply Principal Component Analysis. Experimental results showed that our steganalysis scheme blindly detect the images obtained from six steganographic algorithms- F5, Outguess, S-Tool, JP Hide & Seek, LSB flipping and PVD. This method is able to detect any new algorithms which are not used during the training step, even if the embedding rate is very low. We also analyzed embedding rate versus detectability performances. © Springer-Verlag Berlin Heidelberg 2010.

Cite this Research Publication : P. P. Amritha, Madathil, A., and Dr. Gireesh K. T., “Unconditional Steganalysis of JPEG and BMP Images and Its Performance Analysis Using Support Vector Machine”, Communications in Computer and Information Science, vol. 101, pp. 638-640, 2010.

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