More Secured Steganography Model with High Concealing Capacity by using Genetic Algorithm, Integer Wavelet Transform and OPAP

Steganography is an art of writing for conveying message inside another media in a secret way that can only be detected by its intended recipient. There are security agents who would like to fight these data hiding systems by steganalysis, i.e. discovering covered secret messages and rendering them useless. Steganalysis is the art of detecting the message's existence, message length or place of message where it is to be hidden in covered media and blockading the covert communication. There is currently no more secured steganography system which can resist all steganalysis attacks such as visual attack, statistical attack (active and passive) or structural attack. The most notable steganalysis algorithm is the Reversible Statistical attack which detects the embedded message by the statistic analysis of pixel values. To maintain the security against the Reversible Statistical analysis, the proposed work presents a new steganography model based on Genetic Algorithm using Integer Wavelet Transform. We present a novel approach to resolve such problems of substitution technique of image steganography. Using the proposed Genetic Algorithm and Reversible Statistical analysis Algorithm, the system is more secured against attacks and increases robustness. The robustness would be increased against those attacks which try to reveal the hidden message and also some unintentional attacks like noise addition as well. In this proposed work, we studied the steganographic paradigm of data hiding in standard digital images. In recent literature, some algorithms have been proposed where marginal statistics are preserved for achieving more capacity and more security. This proposed system presents a novel technique to increase the data hiding capacity and the imperceptibility of the image after embedding the secret message. In proposed work Optimal Pixel Adjustment Process also applied to minimize the error difference between the cover and stego image. By this work best results have been obtained as compared to existing works. The proposed steganography model reduces the embedding error and provides higher embedding capacity. Detection of message existence will be very hard for those stego images that produced using the proposed method. This work shows the highest embedding capacity and security against Reversible Statistical attack.