Blind steganalysis based on classifying feature vectors derived from images is becoming increasingly more powerful.
For steganalysis of JPEG images, features derived directly in the embedding domain from DCT coefficients
appear to achieve the best performance (e.g., the DCT features10 and Markov features21). The goal of this paper
is to construct a new multi-class JPEG steganalyzer with markedly improved performance. We do so first by extending
the 23 DCT feature set,10 then applying calibration to the Markov features described in21 and reducing
their dimension. The resulting feature sets are merged, producing a 274-dimensional feature vector. The new feature
set is then used to construct a Support Vector Machine multi-classifier capable of assigning stego images to
six popular steganographic algorithms-F5,22 OutGuess,18 Model Based Steganography without ,19 and with20
deblocking, JP Hide&Seek,1 and Steghide.14 Comparing to our previous work on multi-classification,11, 12 the
new feature set provides significantly more reliable results.