This paper presents two scenarios of image steganalysis, in first scenario, an alternative feature set for steganalysis based on rate-distortion characteristics of images. Here features are based on two key observations: i) Data embedding typically increases the image entropy in order to encode the hidden messages; ii) Data embedding methods are limited to the set of small, imperceptible distortions. The proposed feature set is used as the basis of a steganalysis algorithm and its performance is investigated using different data hiding methods. In second scenario, a new blind approach of image Steganalysis based on contourlet transform and nonlinear support vector machine. Properties of Contourlet transform are used to extract features of images, the important aspect of this paper is that, it uses the minimum number of features in the transform domain and gives a better accuracy than many of the existing stegananlysis methods. The efficiency of the proposed method is demonstrated through experimental results. Also its performance is compared with the Contourlet based steganalyzer (WBS). Finally, the results show that the proposed method is very efficient in terms of its detection accuracy and computational cost.