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.