Neighboring Joint Density and Markov Process Based Approach for JPEG Steganalysis

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, markov process based approach for modeling the difference JPEG 2-D arrays, machine learning for steganalysis of JPEG images which prevents cross validation is proposed. The neighboring joint density and absolute neighboring joint density features on both intra-block and inter-block are extracted from the DCT coefficient array. For markov process based approach, difference JPEG 2-D arrays along horizontal, vertical and diagonal directions are modeled using markov model. In addition to the utilization of difference JPEG 2-D arrays, a thresholding technique is developed to greatly reduce the dimensionality of transition probability matrices. After the feature space has been constructed, it uses SVM like binary classifier with cross validation 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.