Feature Based Steganalysis Using Wavelet Decomposition and Magnitude Statistics

Steganography is broadly used to embed information in high resolution images, since it can contain adequate information within the small portion of cover image. Steganalysis is the procedure of finding the occurrence of hidden message in an image. This paper compares the efficiency of two embedding algorithms using the image features that are consistent over a wide range of cover images, but are distributed by the presence of embedded data. Image features were extracted after wavelet decomposition of the given image. These features were then given to a SVM classifier to identify the stego content.