Digital image splicing detection based on Markov features in QDCT and QWT domain
This addresses digital forensics needs for detecting manipulated images, but it appears incremental as it builds on existing transform domains and Markov features.
The paper tackles image splicing detection by proposing a method based on Markov features in QDCT and QWT domains, and it demonstrates that the approach outperforms some state-of-the-art methods.
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.