Inactive Method of Noncausal 2D Image Splice Recognition Model using Markov Model - Aysha, Thofa; Manjesh, R (International Journal of Computer Sciences and Engineering, 2025)
Noncausal Markov model for a 2D signal is one of the inactive methods for spliced image. Image splicing is an image copies or merge a portion of image to same images or different images. The way Noncausal Markov model differs from traditional Markov model is the proposed methodology models a image as a 2-D noncausal signal and captures and analyzes the underlying dependencies between the current node and its neighbors in all directions. These dependencies are obtained through Discrete Cosine Transform and Discrete Wavelet Transform. These parameters give features to differentiate the natural ones with the features of spliced images. The noncausal Markov Model considers the input of block discrete cosine transformation domain, the discrete wavelet transform domain, and the cross-domain features for classification. The Expectation Maximization which is the classifier which classifies based on maximum likelihood of images. The dataset used is UCID dataset where we have uncompressed color images.