Quantitative steganalyzers are important in forensic steganalysis
as they can estimate the payload, or, more precisely, the number of
embedding changes in the stego image. This paper proposes a general
method for constructing quantitative steganalyzers from features used
in blind detectors. The method is based on support vector regression,
which is used to learn the mapping between a feature vector extracted
from the image and the relative embedding change rate. The performance is evaluated by constructing quantitative steganalyzers for eight steganographic methods for JPEG files, using a 275-dimensional feature set. Error distributions of within- and between-image errors are empirically estimated for Jsteg and nsF5. For Jsteg, the accuracy is compared to state-of-the-art quantitative steganalyzers.