Enhanced state selection Markov model for image splicing detection

Digital image splicing blind detection is becoming a new and important subject in information security area. Among various approaches in extracting splicing clues, Markov state transition probability feature based on transform domain (discrete cosine transform or discrete wavelet transform) seems to be most promising in the state of the arts. However, the up-to-date extraction method of Markov features has some disadvantages in not exploiting the information of transformed coefficients thoroughly. In this paper, an enhanced approach of Markov state selection is proposed, which matches coefficients to Markov states base on well-performed function model. Experiments and analysis show that the improved Markov model can employ more useful underlying information in transformed coefficients and can achieve a higher recognition rate as results.