HUBFIRE - A MULTI-CLASS SVM BASED JPEG STEGANALYSIS USING HBCL STATISTICS AND FR INDEX

Blind Steganalysis attempts to detect steganographic data without prior knowledge of either the embedding algorithm or the 'cover' image. This paper proposes new features for JPEG blind steganalysis using a combination of Huffman Bit Code Length (HBCL) Statistics and File size to Resolution ratio (FR Index); the Huffman Bit File Index Resolution (HUBFIRE) algorithm proposed uses these functionals to build the classifier using a multi-class Support Vector Machine (SVM). JPEG images spanning a wide range of resolutions are used to create a 'stego-image' database employing three embedding schemes - the advanced Least Significant Bit encoding technique, that embeds in the spatial domain, a transform-domain embedding scheme: JPEG Hide-and-Seek and Model Based Steganography which employs an adaptive embedding technique. This work employs a multi-class SVM over the proposed 'HUBFIRE' algorithm for statistical steganalysis, which is not yet explored by steganalysts. Experiments conducted prove the model's accuracy over a wide range of payloads and embedding schemes