Merging Markov and DCT features for multi-class JPEG steganalysis

Abstract

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.