Enhanced state selection Markov model for image splicing detection

Bo Su,Shilin Wang,Chenglin Zhao,Shenghong Li,Quanqiao Yuan

doi:10.1186/1687-1499-2014-7

Abstract

Abstract 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.

Highlights

With digital imaging equipment and processing software springing up, tampering of digital image has become so easy and convenient
We focus on passive image splicing detection approach
In the work of Shi et al [9], a nature image model was proposed, which combined together the characteristic function moments of wavelet sub-bands and Markov transition probabilities on block discrete cosine transform (DCT) coefficients as splicing features and achieved an average accuracy as high as 91.87%

Summary

Introduction

With digital imaging equipment and processing software springing up, tampering of digital image has become so easy and convenient. In the work of Shi et al [9], a nature image model was proposed, which combined together the characteristic function moments of wavelet sub-bands and Markov transition probabilities on block discrete cosine transform (DCT) coefficients as splicing features and achieved an average accuracy as high as 91.87%. Though it seems natural when we take more splicing features together, we are more likely to get some higher detection rates at the cost of more algorithm complexity and time consumption. The BDCT in this paper is set to have a block size of 8 × 8 with the reasons analyzed in [9], and the transform formula (1) is given as

Discrete wavelet transform

Experiment results and analysis

Findings

Conclusions