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\title{Steganography Images Detection using Different Steganalysis Techniques with Markov Chain Features}
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\begin{document}

             \author[1]{Rajendraprasad  K}

             \affil[1]{  Osmania University}

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\date{\small \em Received: 15 April 2015 Accepted: 30 April 2015 Published: 15 May 2015}

\maketitle


\begin{abstract}
        


Steganography is the art of covered or hidden writing. It is used for criminal activities applications environment. In this paper we focus on implementation of effective detection technique is an essential task in digital images. Previously many number of detection techniques are available for steganography images. After implementation of all approaches also again some challenges are available. This paper presents comparative study in between different steganalysis techniques. Different techniques are providing different results. Analyze of all techniques detection and embedding performance results. Finally we can decide one best steganalysis technique. It saves time and increases accuracy compare to all previous methods.

\end{abstract}


\keywords{steganography, steganalysis technique, digital images, markov chain features.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
teganography is sometimes referred to as data hiding. Steganalysis technique is opposite to steganography. Steganalysis technique focuses on detecting the presence of hiding data. Steganalysis approaches are two types. Those are 1. Target approach 2. Blind steganalysis Target approaches provide high accurate detection and reliable results compare to previous other steganalysis techniques. Previously many number of steganalysis techniques are design. Those techniques are steganography detectors, rich model detection scheme, statistics based approach, and movement based approaches. All existing approaches are not efficient under detection and reliable results generation.\par
In this paper design of new detection scheme using feature set. Feature set consist of different dimensions of input content. Combine of all dimensions of features display as a highly efficient detection results. Comparision study with other techniques also discuss in the remaining sections also. After completion of comparative study which technique is considerable that is also we can decide here. 
\section[{II.}]{II.} 
\section[{Related Work}]{Related Work}\par
Counter technique of image stenography is known as steganalysis. It's identifies the artifacts that existing in stenography images. Steganalysis provides analysis results from hidden information. Different steganalysis approaches information described below here.\par
Image stenography detectors contain two parts. Those two parts are image model and machine learning tool. Image model chosen training set of stego and cover images. Primary here we focus on detection accuracy from image model. Stenographic embedding operation image insensitive content converts into sensitive information. We can capture as many dependencies among individual image elements. Observation of all dependencies some of the disturbances are available here. Now here we can calculate the measurement features using spatial and frequency domain. These are DCT (Discrete Cosine Transform) coefficients. All domains of features we can extract with different iterations. Different iterations of features store into different matrices. Combine of all matrices display improved detection accuracy. This kind of process we can call as machine learning from all dimensions.\par
Here one richer model of JPEG images has different number of sub models. 1. Here capture the DCT coefficients using parallel channels. 2. Joint operation applies on parallel channels DCT coefficients. 3. Finally improved DCT statistical coefficients values are displayed here. Statistical coefficients are useful for construction of image models and steganalysis. Steganalysis results are improved and enhanced in the form robustness and scalability. Finally again we observed some more accuracy related issues.\par
Next other steganalysis approaches are introduced for increased statistical robustness values and scalability. New steganalysis approaches we can apply on low dimensional and high dimensional environment also. Collection of all dimensions features information and create feature set. After training operation on images using steganographic scheme next apply universal blind detection scheme. Here we can use optimal linear predictor for wavelet coefficients. All wavelet coefficients information collects and display statistical clustering results. Here we can apply threshold operation for separation of stego image from cover image. These kinds of operations are possible limited images database only. This method is not scale well and show low performance results.  
\section[{Problem Statement}]{Problem Statement}\par
Previous no single steganalysis technique is not work efficiently. Blindly all steganalysis techniques apply on stego images. All previous steganalysis techniques have take more time and resources.\par
The main goal of this paper was to the performance analysis of three different steganalysis techniques. Using three steganalysis results next we can perform detection accuracy comparison results. We test on various test images and performance calculation performs based on detected area. Comparison was made based on the number of negatives, positives and misclassified results.\par
IV. 
\section[{Implementation Details}]{Implementation Details}\par
Steganalysis techniques analyzed are listed below for detection of steganography images. Those techniques are 1. Discriminant analysis based Linear Discriminant Classification. 2. Support vector machines based classification. 
\section[{Image scanning patterns}]{Image scanning patterns}\par
In all above steganalysis techniques are using logic and features are same. Only the major difference is classification technique only. 
\section[{Detection technique Procedure}]{Detection technique Procedure}\par
We shall present the new feature set for steganalysis on historical images. Feature set have scanning patterns, markov transition probability matrix, local and global calibrations and classification techniques. 
\section[{a) Image Scanning Patterns}]{a) Image Scanning Patterns}\par
Image is divided into non overlapping blocks. Scan of each and every block of row and column and other Hibert directions also. We can observe all possible paths of correlations information also its better. 
\section[{b) Intra and Inter Block Features i. Intra Block Features}]{b) Intra and Inter Block Features i. Intra Block Features}\par
We can extract intra block features and store into matrix. Matrix contains DCT coefficients content information. All adjacent coefficients update into matrix. Next apply markov chain operations calculate transition probability features. Here we can perform different orders of markov probability matrix value. 
\section[{ii. Inter Block Features}]{ii. Inter Block Features}\par
All orders of probability matrix features are rearrange then store into new matrix. Calculate the average of all orders transition probability value finally. Finally original image features set content display here. 
\section[{c) Image Calibration}]{c) Image Calibration}\par
Image calibration is used for accurate statistics. Decompress and finally we can display original image.   
\section[{Global}]{Global} 
\section[{Experimental Results}]{Experimental Results}\par
Each approach has different features and working with different image databases, it's very complex. Following table have an aim to recapitulate the three approaches information.  
\section[{Conclusion and Future Work}]{Conclusion and Future Work}\par
We have proposed scanning patterns based approach for detection of JPEG steganography. Here we utilized feature set approaches for improving detection accuracy. Finally proposed approach compare with existing approaches. Proposed approach gives enhanced performance results, that's why proposed markov chain features approach is best\begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-2.png}
\caption{\label{fig_0}Fig. 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2}\includegraphics[]{image-3.png}
\caption{\label{fig_1}Fig. 2 :}\end{figure}
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\noindent\textbf{}\includegraphics[]{image-4.png}
\caption{\label{fig_2}}\end{figure}
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\noindent\textbf{3}\includegraphics[]{image-5.png}
\caption{\label{fig_3}Fig. 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_0}Table 1 :}\end{figure}
 			\footnote{© 2015 Global Journals Inc. (US)} 		 		\backmatter  			 
\subsection[{Global Journals Inc. (US) Guidelines Handbook 2015}]{Global Journals Inc. (US) Guidelines Handbook 2015}\par
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\end{document}
