Steganography is the art of hiding the secret message in a cover medium and the media could be a audio or video or image. Steganography aims at hiding data as stealthy as possible in a cover medium, Steganalysis aims to detect the presence of any hidden information in the stego media here it refers to the JPEG images. Current Steganalysis aims to focus more on detecting statistical anomalies in the stego images which are based on the features extracted from typical cover images without any modifications. Most steganalysis algorithms are based on exploiting the strong interpixel dependencies which are typical of natural images. Steganalysis can be classified into two broad categories: Specific/Targeted and Blind Steganalysis. Blind steganalysis also known as universal steganalysis. Steganalysis is the modern and powerful approach to attack a stego media since this method does not depend on knowing any particular embedding technique. A pattern recognition classifier is then used to differentiate between a cover images and a stego image. A number of algorithms were used to analyse the cover media. In proposed work blind steganalysis is done using J2 technique, inorder to analyse the performance of J2 with respect to capacity and stealthiness and also to compare the detection rate of J2 with other popular algorithms using first and second order steganalysis using Support Vector Machine.

1. Mahdi Ramezani and Shahrokh Ghaemmaghami,"Adaptive Image Steganography with Mod-4 Embedding using Image Contrast", Intl. Conf. on IEEE CCNC 2010 proceedings.
2. M. Raja Sekar, Prof. P Prem Chand & Prof. I . V. Muralikrishna,"Image Authentication using Support Vector Machines", Journals of Advanced Research in Computer Engineering", vol. 3, No. 2, July-December 2009, pp. 367-374.
3. Chen, X. , Faltemier, T. , Flynn, P. , and Bowyer,K. ,"Human face modeling and face recognition through multi-view high resolution stereopsis",In Conference on computer vision and pattern Recognition (2006) pp. 50-56.
4. Bicego, M. , Grosso, E. , and Tistarelli, M. ," Face Authentication using One-Class Support Vector Machines", In International Workshop on Biometric Recognition Systems(2005) 1522.
5. Chen, Y. , Zhou, X. S. , and Huang, T. S. , " One- Class SVM for Learning in Image Retrival", In IEEE International Conference on Image Processing, (2001) 34-37.
6. Carson, C. , and Greenspan, H. , "Blobworld Image Segmentation using Expectation Maximization and its Application to Image Querying. IEEE Transaction on Pattern Analysis and Machine Intelligence, 24(8) (2002) 1026-1037.
7. Burges, C. J. , " A Tutorial on Support Vector Machines for Pattern Recognition", Data Mining and Knowledge Discovery, 2(1998) 121-167.
8. Ben-hur, A. , Horn, D. , Siegelmann, H. , and Vapnik,V. ,"Support Vector Clustering",Journal of Machine Learning Research, 2 (2001)125-137.
9. Chapelle, O. , and Haffner, p. , and Vapnik, V. N,"Support Vector Machines For Histogram based image classification", IEEE transaction on Neural Networks, 10(5) (1999) 1055-1064.
10. Burges, C. J. , " A Tutorial on Support Vector Machines for Pattern Recognition", Bell Laboratories, 2(1998) 1-43.
11. Kang Leng Chiew , Josef Pieprzyk, " Estimating Hidden Message Length in Binary Image Embedded By Using Boundary Pixels Steganography. Intl. Conf. On Availability Reliability and Security,(2010) 683-688.

Steganalysis Cover Images Stego Images Support Vector Machine Blind Steganalysis Targeted Steganalysis & Adaptive Steganography.