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

Steganalysis of YASS using Huffman Length statistics

This work proposes two main contributions to statistical steganalysis of Yet Another Steganographic Scheme (YASS) in JPEG images. Firstly, this work presents a reliable blind steganalysis technique to predict YASS which is one of recent and least statistically detectable embedding scheme using only five features, four Huffman length statistics (H) and the ratio of file size to resolution (FR Index). Secondly these features are shown to be unique, accurate and monotonic over a wide range of settings for YASS and several supervised classifiers with the accuracy of prediction superior to most blind steganalyzers in vogue. Overall, the proposed model having Huffman Length Statistics as its linchpin predicts YASS with an average accuracy of over 94 percent

JPEG steganalysis using HBCL statistics and FR index

This paper introduces a new statistical model for blind steganalysis of JPEG images. The functionals used to build this model are based on Huffman Bit Code Lengths (HBCL statistics) and the file size to image resolution ratio (FR Index). JPEG images spanning a wide range of resolutions were used to create a 'stego-image' database employing three embedding schemes - the advanced Least Significant Bit encoding technique, JPEG Hide-and-Seek and Model Based Steganography. Existing blind steganalysis techniques mostly involve the analyses of generalized category attacks and the higher order statistics. This work builds an effective neural network prediction model using HBCL statistics and FR Index, which are not yet explored by steganalysts. The experimental results proved to be efficient over a diverse image database and several payloads. © 2010 Springer-Verlag

Steganalysis of Perturbed Quantization Using HBCL Statistics and FR Index

Targeted steganalysis aims at detecting hidden data embedded by a particular algorithm without any knowledge of the ‘cover’ image. In this paper we propose a novel approach for detecting Perturbed Quantization Steganography (PQ) by HFS (Huffman FR index Steganalysis) algorithm using a combination of Huffman Bit Code Length (HBCL) Statistics and File size to Resolution ratio (FR Index) which is not yet explored by steganalysts. JPEG images spanning a wide range of sizes, resolutions, textures and quality are used to test the performance of the model. In this work we evaluate the model against several classifiers like Artificial Neural Networks (ANN), k-Nearest Neighbors (k-NN), Random Forests (RF) and Support Vector Machines (SVM) for steganalysis. Experiments conducted prove that the proposed HFS algorithm can detect PQ of several embedding rates with a better accuracy compared to the existing attacks