A Novel Adaptive Steganographic Technique using Kohonen Neural Network based on Integer Wavelet Transform

Abstract

Steganography allows us to hide a desired confidential information in a covered or host image while image encryption is decomposing the desired information to a nonreadable, non-comprehended manner. Steganography gained importance in the past few years due to the increasing need for providing secrecy in an open environment like the internet. This is made possible by hiding the very existence of the message and make communication undetectable. Steganography is the science of secret message delivery using cover media and has many technical challenges such as high hiding capacity and security. In this paper, a method is proposed to optimize these two requirements by suggesting a novel technique for hiding data in digital images. For this, an adaptive hiding capacity function along with a Kohonen neural network has been employed. This method hides secret data in Integer wavelet coefficients. The Optimal pixel adjustment (OPA) algorithm is applied after embedding the message. A new high capacity image steganographic method based on Kohonen neural network is also introduced. Kohonen network is trained according to the absolute contrast sensitivity of pixels present in cover image. On the receiving side, the original image is not needed for extracting the embedded data. It is observed that the capacity and security is increased with acceptable PSNR and hiding capacity.