JPEG based steganography methods using Cohort Intelligence with Cognitive Computing and modified Multi Random Start Local Search optimization algorithms

Abstract

The JPEG image is a very popular digital image format used in various steganography techniques to hide a large capacity of secret data into a cover file with minimum distortion, visually undetectable, of picture quality. Cohort Intelligence (CI), an optimization algorithm inspired from the natural and social tendency of learning from one another is being tested and shown very encouraging results for solving unconstrained, constrained and NP-hard combinatorial problems. Cognitive computing (CC), an emerging area of research and have various applications in the field of machine learning. Another interesting optimization technique Multi Random Start Local Search (MRSLS) optimization algorithm has already been studied for NP-hard combinatorial problems and shown good results. Considering two important aspects of steganography techniques – picture quality and high data hiding capacity, a research effort has been made to propose two novel image based steganography techniques based on JPEG compression i.e., a modified CI optimization algorithm combined with CC technique referred to as CICC and modified MRSLS optimization algorithm referred to as M-MRSLS.