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Proceedings Paper

Steganalysis using logistic regression
Author(s): Ivans Lubenko; Andrew D. Ker
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Paper Abstract

We advocate Logistic Regression (LR) as an alternative to the Support Vector Machine (SVM) classifiers commonly used in steganalysis. LR offers more information than traditional SVM methods - it estimates class probabilities as well as providing a simple classification - and can be adapted more easily and efficiently for multiclass problems. Like SVM, LR can be kernelised for nonlinear classification, and it shows comparable classification accuracy to SVM methods. This work is a case study, comparing accuracy and speed of SVM and LR classifiers in detection of LSB Matching and other related spatial-domain image steganography, through the state-of-art 686-dimensional SPAM feature set, in three image sets.

Paper Details

Date Published: 10 February 2011
PDF: 11 pages
Proc. SPIE 7880, Media Watermarking, Security, and Forensics III, 78800K (10 February 2011); doi: 10.1117/12.872245
Show Author Affiliations
Ivans Lubenko, Univ. of Oxford (United Kingdom)
Andrew D. Ker, Univ. of Oxford (United Kingdom)


Published in SPIE Proceedings Vol. 7880:
Media Watermarking, Security, and Forensics III
Nasir D. Memon; Jana Dittmann; Adnan M. Alattar; Edward J. Delp, Editor(s)

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