DWT Feature based Blind Image Steganalysis using Neural Network Classifier
Manisha Saini, Rita Rana Chhikara · International Journal of Engineering Research and · 2015
Abstract- The objective of forensic steganalysis is to identify the existence of embedded message and to ultimately retrieve the secret message. In this paper, we have extracted Histogram, Markov and Co-occurrence features from wavelet domain and compared with existing farid 72 DWT features. The performance metrics are MSE (Mean square error) and classification accuracy for blind image steganalysis using two steganography algorithms outguess and nsF5. Neural network back propagation classifier has been used to classify images into stego images and clean images. Experimental results show that the proposed features considerably outperform existing farid 72 DWT features. Keywords — Discrete wavelet transformation, Neural network, Outguess, Steganography, Steganalysis, nsF5. I. INTRODUCTION Steganography[1] is a process in which the message is embedded in a cover medium to create a stego medium with the help of steganography tool that results in changes in the statistical properties of cover medium where medium could be video, text, image, audio etc. Now a day’s wide variety of steganographic tools are freely and widely available on the internet such as StegHide [2], Outguess [3] etc. Statistical un-detectability [4] is the necessity of steganography process, which means that it is difficult or impossible for attacker to judge whether an image is the stego or cover based on the statistics. Steganography hides data in [5] Spatial domain and Transform domain or Frequency domain. Transform Domain is further divided into three categories (a) DCT (Discrete Cosine Transformation technique), (b) DWT (Discrete Wavelet transformation technique), (c) DFT (Discrete Fourier transformation technique).In transform domain, images are initially transformed and then the message is embedded while in spatial domain message is embedded directly inside the pixel.