An Optimized Model for Quantized Steganalysis in Spatial Domain

Abstract

newline Steganography the art of hiding information in a stealthy manner has come of age newlineand matured with the ever expanding communication modes. Digital images, being so newlinepervasive in modern socially connected world, have emerged as the preferred cover newlineobject choice. Owing to simplicity and higher embedding capacity, steganographic newlineembedding in spatial domain has retained the favorite title. Though, steganalysis, the newlinecounter attack to steganography, has grown equivalently, quantitative steganalysis has newlinereceived comparatively lesser attention than binary steganalysis. This disparity is even newlinewider in case of spatial domain steganalysis. The present thesis aims to address this newlineshortfall. Optimized models for all the three primary objectives of quantitative newlinesteganalysis viz. payload estimation, identification of steganographic algorithm and newlinestego key recovery, that help extract and reconstruct the hidden message, have been newlineproposed.