Contributions in Mathematical Approaches to Steganalysis

Current steganalysis techniques focus on detecting the secret message or estimating some parameters of secret message such as its length. In this work we develop a steganalysis technique that estimates the secret message itself. Most steganalysis algorithms are histogram based and such techniques fail if the cover distribution is preserved after embedding. One such distribution preserving steganographic technique is proposed. To cater to such hiding strategies a steganalysis algorithm is developed which unlike its counterparts is not training based. Instead it is a deterministic algorithm formulated along the lines of Bayesian inference techniques. The knowledge about the embedding process and the cover medium is sufficiently exploited as apriori information. We develop such a technique for spatial bit plane embedding. For spread spectrum type embedding a source separation formulation is used in separating the message carrying signal from the host using certain prior information about the embedding process and the host medium