NDLI: How to find relevant training data: A paired bootstrapping approach to blind steganalysis

Content Provider	IEEE Xplore Digital Library
Author	Pham Hai Dang Le Franz, M.O.
Copyright Year	2012
Description	Author affiliation: Institute for Optical Systems, HTWG Konstanz, Brauneggerstr. 55, 78462 Konstanz, Germany (Pham Hai Dang Le; Franz, M.O.)
Abstract	Today, support vector machines (SVMs) seem to be the classifier of choice in blind steganalysis. This approach needs two steps: first, a training phase determines a separating hyperplane that distinguishes between cover and stego images; second, in a test phase the class membership of an unknown input image is detected using this hyperplane. As in all statistical classifiers, the number of training images is a critical factor: the more images that are used in the training phase, the better the steganalysis performance will be in the test phase, however at the price of a greatly increased training time of the SVM algorithm. Interestingly, only a few training data, the support vectors, determine the separating hyperplane of the SVM. In this paper, we introduce a paired bootstrapping approach specifically developed for the steganalysis scenario that selects likely candidates for support vectors. The resulting training set is considerably smaller, without a significant loss of steganalysis performance.
Starting Page	228
Ending Page	233
File Size	1551028
Page Count	6
File Format	PDF
ISBN	9781467322850
e-ISBN	9781467322874
e-ISBN	9781467322867
DOI	10.1109/WIFS.2012.6412654
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-02
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Support vector machines Unsolicited electronic mail Training data Markov processes Feature extraction Erbium
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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