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.