Digital image splicing detection based on Markov features in QDCT and QWT domain

TL;DR

This paper introduces a novel color image splicing detection method using Markov transition features in QDCT and QWT domains, leveraging quaternion components to improve detection accuracy in digital forensics.

Contribution

It proposes a new approach combining Markov features from quaternion domains and ensemble classification for enhanced image splicing detection.

Findings

Outperforms several state-of-the-art methods

Effective in distinguishing spliced from authentic images

Utilizes quaternion-based features for improved forensic analysis

Abstract

Image splicing detection is of fundamental importance in digital forensics and therefore has attracted increasing attention recently. In this paper, a color image splicing detection approach is proposed based on Markov transition probability of quaternion component separation in quaternion discrete cosine transform (QDCT) domain and quaternion wavelet transform (QWT) domain. Firstly, Markov features of the intra-block and inter-block between block QDCT coefficients are obtained from the real part and three imaginary parts of QDCT coefficients respectively. Then, additional Markov features are extracted from luminance (Y) channel in quaternion wavelet transform domain to characterize the dependency of position among quaternion wavelet subband coefficients. Finally, ensemble classifier (EC) is exploited to classify the spliced and authentic color images. The experiment results demonstrate that the proposed approach can outperforms some state-of-the-art methods.