Intra Block and Inter Block Neighboring Joint Density Based Approach for Jpeg Steganalysis

Abstract

Steganalysis is the method used to detect the presence of any hidden message in a cover medium. A novel approach based on feature mining on the discrete cosine transform (DCT) domain based approach, machine learning for steganalysis of JPEG images is proposed. The neighboring joint density on both intra-block and inter-block are extracted from the DCT coefficient array. After the feature space has been constructed, it uses SVM like binary classifier for training and classification. The performance of the proposed method on different Steganographic systems named F5, Pixel Value Differencing, Model Based Steganography with and without deblocking, JPHS, Steghide etc are analyzed. Individually each feature and combined features classification accuracy is checked and concludes which provides better classification.