Steganalysis using logistic regression

Ivans Lubenko; Andrew D. Ker

doi:10.1117/12.872245

10 February 2011 Steganalysis using logistic regression

Ivans Lubenko, Andrew D. Ker

Proceedings Volume 7880, Media Watermarking, Security, and Forensics III; 78800K (2011) https://doi.org/10.1117/12.872245
Event: IS&T/SPIE Electronic Imaging, 2011, San Francisco Airport, California, United States

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.

Citation Download Citation

Ivans Lubenko and Andrew D. Ker "Steganalysis using logistic regression", Proc. SPIE 7880, Media Watermarking, Security, and Forensics III, 78800K (10 February 2011); https://doi.org/10.1117/12.872245

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