The Discrete Wavelet Transform (DWT) based preprocessing technique improves the security of steganography [13] , which embeds the secret message in the frequency domain of the cover image. It hides the messages in substantial areas of the cover image to make it more robust to the attacks.

The Vector Rank M-type L (VRML) filter removes the impulsive noise and speckle noise from the color images and video sequences [14] . This filter consists of two estimators such as Median-M type (MM) and Ansari-Bradley Siegel-Tukey M-type (AM) for providing robustness in the filtering technique. The impulsive noise detectors are also included to improve the properties of noise suppression in the low and high densities of impulsive noise. It has edge preservation property and power in noise detection during the presence of impulsive noise.

The Successive Mean Quantization Transform (SMQT) extracts the structure of data in robust manner [15] . Here, the capacity of the embedding is identified from the non-zero Discrete Cosine Transform (DCT) coefficients. In Boosted Steganography Scheme (BSS), a cover image is preprocessed in two stages, once in preprocessing stage and then in embedding stage. In preprocessing stage, the image processing techniques such as contrast enhancement, SMQT and other manipulation methods are applied on the cover images.

The image saliency detection based preprocessing technique utilizes the quaternion transform. The region segmentation and Scale Invariant Feature Transform (SIFT) are considered as the preprocessing technique to extract the accurate features by removing the irrelevant features [16] .The textural features like contrast, coarseness, directionality, regularity and roughness are traced.

The Anisotropic Diffusion (AD) based pre-processing performs the image smoothing and image enhancement [17] . The matrix representing the image is flipped left to right in the vertical axis.

The combination of Chirp Z-Transform (CZT) and Goertzel algorithm are used as the preprocessing technique to normalize the image [18] . The input image is divided into specific number of same sized blocks and CZT is applied to every individual blocks. The CZT calculates the Z transform at M points in Z-plane and it transforms the image into Z-domain. Then, the Goertzel algorithm is applied to the transformed image as a reconstruction algorithm that is produced an image with the inversion of an original image. The normalized images is acquired after completion of reconstruction process.

The 2 Dimensional (2D) Wiener filter is an adaptive noise removal filter that performs low pass filtering of a gray scale image.

A Gaussian filter is a low pass filter with minimum time bandwidth product. The Gamma Intensity Correlation (GIC) enhances the local dynamic range of the image in the dark or shadowed region during compression.

Relative Auto-Decorrelation (RAD) feature extraction method extracts the intrinsic features that improve the detection of stegos from the cover [20] . The appropriate features like Local Entropies Sum (LES) and Clouds Min Sum (CMS) are acquired to obtain fast detection accuracy and improves the steganalysis results. The common part of an image is processed by a smart partioning procedure known as two-dimensional Decorrelation of the received images (2D). The presence of secret message is identified by the quadratic estimator that provides the estimation of embedding rate. The rate estimation is modified by the thresholds derived from the RAC, LES, and CMS. This type of steganalysis consists of three stages such as image clouding, RAD feature extraction and quadratic rate estimation. The clouding splits the input image into luminance aware slices. Every cloud has same luminance pixels in an edge free region of the image.

The differential Discrete Cosine Transform (DCT) coefficients extracts the absolute values of the Neighboring Joint probability density (absNJ) features [21] . This is done by applying power of DCT coefficients instead of their first power gains, which differentiates the stego images from the cover image. The inter block and intra block features of DCT coefficients are extracted, which provides different types of features. The intra block features extracts the frequency dependencies of DCT coefficients and the inter block features extracts the spatial dependencies among DCT blocks. Through the extension of original features to the extended versions, the most complicated relations between the coefficients are extracted to produce the final features.

The Shannon entropy of 2-Dimensional (2D) Gabor wavelet feature extraction model has joint localization properties in spatial domain [22] . The image texture characteristics are captured by the spatial frequency. The Shannon entropy values of image filtering coefficients are considered as steganalysis features. Initially, JPEG image is decompressed to the spatial domain without quantizing the pixel values to avoid the information loss. The image is decomposed into 8 × 8 DCT blocks and each block has 64 different DCT modes. Each filtered image can be subsampled to form a sub image. The decompressed image is filtered through 2D Gabor wavelets. Then, the entropy features are extracted from all filtered images. The entropy is the randomness measure that is used to characterize the image texture. The classifier is trained by the selected entropy features.

The spectrum based feature extraction utilize the combination of DCT and Discrete Fourier Transform (DFT) [23] . The input image contains many features and the transformation of the image in frequency domain is applied to reduce the redundancy information present in the image. The subset of transformed coefficients are required to reserve the desired important features for recognition. The DFT is applied on the pre-processed image. The low frequency components are situated in the center of the spectrum. The center portion is extracted by using centered rectangular mask. Then, the DCT is applied to the DFT spectrum to enhance the recognition rate. The DWT-Dual subband Frequency domain Feature Extraction (DDFFE) includes the combination of DWT, DFT and DCT for efficient extraction, translation and illumination of invariant features [24] . The DWT utilize the approximation coefficients along with the horizontal coefficients of 2-dimensional images. The DFT is applied to compensate the translation variance problem of DWT, which extracts the frequency characteristics of the image. The low frequency components present in the center of the DFT spectrum are extracted by the quadruple ellipse mask around spectrum center. The DFT includes the information of both magnitude and phase that is extracted separately. DFT is applied on the preprocessed image and the low frequency components at the corner of the image spectrum is shifted into the center of the spectrum.

The Perturbed Quantization (PQ) steganography analyzes the special positions of the feature extraction [25] . The changes of the global, local and dual histogram features are extracted from the DCT coefficients. The PQ is applied to the double compressed JPEG image and its DCT coefficients may be changed after completing PQ embedding. The global histogram is developed from all DCT coefficients and it is referred as the first order statistic feature to reflect the overall distribution of DCT coefficients. The local histogram reveals the distribution of all DCT blocks. The dual histogram exposes the distribution of the distinct value in some local positions of all DCT blocks. All histogram features are calculated and combined to form the enhanced histogram features to improve the detection performance.

The Logarithmic Non-Sampled Contourlet Transform(NSCT) extracts the invariant features like strong edges, weak edges and noise in the image [26] . Through the extraction and recombination of these features, two types of components are produced such as illumination component and reflectance component. The low pass subband of the image and the low frequency part of strong edges are included as the illumination component. The pathetic edges and high frequency part of strong edges are denoted as the reflectance component. The face image is polluted by the noise and the reflectance component is applied to remove such noise. The NSCT algorithm is include to effectively capture contour in the original image.

The Ensemble classifier allows the fast construction of steganography detector with improved detection accuracy [27] . To build a detector, the model for the cover source is selected for the detection of steganography. Such covers are represented in a lower dimensional feature space before training the classifier. The ensemble classifier includes many base learners that is trained on a set of cover and stego images. The concluding decision is made by aggregating the decisions of every individual base learners.

The Ensemble based Extreme Learning Machine (EN-ELM) algorithm is required, where the ensemble learning and cross validation are implanted into the training phase [28] . The ensemble is constructed with several predictors on the training set by various set of random parameters. The learning is started by partioning the entire training set into number of subsets. Each learner is trained with the reduced subset. The parameters of every predictor is updated according to particular conditions. The decision is made by the testing samples by majority voting scheme. After completion of training, all predictors present in the ensemble are stored on the basis of its norm in an increasing order. The first half of the ensemble is utilized for making the decisions through majority voting scheme. The class receives the highest vote is treated as the predicted label and the total vote received by the each class is computed.

The Extreme Learning Machine (ELM) [29] produces the best performance in multi-label classification of large dataset. It is applied for the Single hidden Layer Feed forward Neural networks (SLFN). The hidden node parameters are created randomly from the continuous sampling distribution and it is need not to be tuned. This technique reaches the smallest training error and smallest norm of output weights to produce better generalization performance. The minimized norm of output weight leads to maximize the distance of two different classes in the ELM feature space. The ELM implementation procedure includes the minimal norm least square method. The ELM provides a unified learning platform with a large type of feature mappings that can be applied to the regression and multi class classification [30] . It utilizes the single output node and the class label that is closer to the output value is considered as the predicted class label of the input data. The solution of binary classification case is became a specific case of multiclass solution. If the ELM utilizes multi output nodes, the index of the output node with highest output value is chosen as the label for the input data. The ELM involves the reduced human intervention than Support Vector Machine (SVM). If the feature mapping is known to the user, only one parameter is required to specify the user.

The Differential Evolution (DE) based ELM includes the cross validation accuracy as the performance indicator to determine the optimal ELM parameters [31] . The integration of spatial context information in the learning process produces an enhanced classification results. The hyper spectral images are categorized by high dimensional spectral features. Initially, the feature reduction is applied to reduce the dimensionality of the data. Then, the morphological operations are included for this feature. The mode selection issue associated with the ELM is addressed with the simple grid selection procedures. The required kernel matrix is calculated from the training samples.

The cognitive ensemble ELM classifier is based on the hinge loss function [32] . Here, the individual ELM classifier is established on the basis of hinge error loss function. The cognition is obtained by the weighted sum of individual classifiers through the enhancement of wining classifier. The performance of the ELM classifier is affected by the random choice of centers and widths. The Gaussian center is distributed in the entire range of input space. The cognition of the ensemble calculates the output of all classifiers and listed the correctly classified samples. The samples that are correctly classified repeatedly are denoted as winning samples. The misclassified samples by many classifier is referred as losing samples. Finally, the output is obtained by the weighted sum of all contributing classifiers for all samples.

The Support Vector Machine (SVM) classifier is a standard classifier of the machine learning algorithm for binary classification [33] . The optimal linear decision surface is found through the minimization of training feature vectors. The feature vectors are planned in the high dimensional feature space. The maximum margin hyperplane is established by the SVM that divides the different class feature vectors with maximum margin. If the margin between the vectors is high, then the classification performance is improved. The LIBSVM offers a penalty additional function for unbalanced class, which has 4 kernel functions. The kernel function helps to project the input vector in the high dimensional feature space. Generally, the kernel functions are categorized as radial basis function, polynomial, linear and sigmoid. The LIBSVM utilizes the radial basis function as the kernel function, which non-linearly moves the input feature vector into high dimensional feature space.

The difference of the detection performance and the relationship of detection results of sub-classifiers are learned by the fusing SVM classifier [34] . It has three phases: training of sub-classifiers, training of fusing classifier and testing of fusion classifier. The extracted features are divided into various groups regarding the correlation of features. The fusion classifier is trained on the basis of the detection results. The detection accuracy of steganalysis is improved by increasing the classifiers.

In Bayesian ensemble classifier, the Bayesian estimation method is incorporated with the ensemble classifier for the improvement of classification performance [35] . The extracted features are applied to train the various sub-classifiers. The training set has original images and stego images. The relevant features are extracted from each image to formulate a feature vector. A series of low dimensional classifiers are employed to avoid the classification in high dimensional space. The Fisher Linear Discriminate (FLD) classifier is trained by the sub vectors and its threshold is found by the minimal sum of false alarm and detection rate. The sub classifiers is denoted as FLD, which is trained with the feature vectors are integrated to make the optimized decisions by the Bayesian mechanism.